ISSN 1580-3155 Pages 251–506 n Year 2022, Vol. 69, No. 2 ActaChimicaSlovenica ActaChimicaSlovenica SlovenicaActaChim 2 69/2022 http://acta.chem-soc.si EDITOR-IN-CHIEF KSEnIJA KoGEJ University of Ljubjana, Facuty of Chemstry and Chemical Technology, Vecna pot 113, SI-1000 Ljubljana, Slovenija E-mail: ACSi@fkkt.uni-lj.si, Telephone: (+386)-1-479-8538 Alen Albreht, National Institute of Chemistry, Slovenia Aleš Berlec, Jožef Stefan Institute, Slovenia Janez Cerkovnik, University of Ljubljana, Slovenia Mirela Dragomir, Jožef Stefan Institute, Slovenia Ksenija Kogej, University of Ljubljana, Slovenia Krištof Kranjc, University of Ljubljana, Slovenia Matjaž Kristl, University of Maribor, Slovenia Franc Perdih, University of Ljubljana, Slovenia Aleš Rucigaj, University of Ljubljana, Slovenia Helena Prosen, University of Ljubljana, Slovenia Irena Vovk, National Institute of Chemistry, Slovenia ADMINISTRATIVE ASSISTANT Marjana Gantar Albreht, National Institute of Chemistry, Slovenia Wolfgang Buchberger, Johannes Kepler University, Austria Alojz Demšar, University of Ljubljana, Slovenia Stanislav Gobec, University of Ljubljana, Slovenia Marko Golicnik, University of Ljubljana, Slovenia Günter Grampp, Graz University of Technology, Austria Wojciech Grochala, University of Warsaw, Poland Danijel Kikelj, University of Ljubljana Janez Košmrlj, University of Ljubljana, Slovenia Blaž Likozar, National Institute of Chemistry, Slovenia Mahesh K. Lakshman, The City College and The City University of New York, USA Janez Mavri, National Institute of Chemistry, Slovenia Friedrich Srienc, University of Minnesota, USA Walter Steiner, Graz University of Technology, Austria Jurij Svete, University of Ljubljana, SloveniaDavid Šarlah, University of Illinois at Urbana-Champaign, USA; Università degli Studi di Pavia, ItalyIvan Švancara, University of Pardubice, Czech Republic Jiri Pinkas, Masaryk University Brno, Czech Republic Gašper Tavcar, Jožef Stefan Institute, Slovenia Ennio Zangrando, University of Trieste, Italy Chairman Branko Stanovnik, Slovenia Members Udo A. Th. Brinkman, The Netherlands Attilio Cesaro, Italy Vida Hudnik, Slovenia Venceslav Kaucic, Slovenia Željko Knez, Slovenia Radovan Komel, Slovenia Stane Pejovnik, Slovenia Anton Perdih, Slovenia Slavko Pecar, Slovenia Andrej Petric, Slovenia Boris Pihlar, Slovenia Milan Randic, Des Moines, USA Jože Škerjanc, Slovenia Ðurda Vasic-Racki, Croatia Marjan Veber, Slovenia Gorazd Vesnaver, Slovenia Jure Zupan, Slovenia Boris Žemva, Slovenia Majda Žigon, Slovenia Acta Chimica Slovenica is indexed in: Academic Search Complete, Central & Eastern European Academic Source, Chemical Abstracts Plus, Chemical Engineering Collection (India), Chemistry Citation Index Expanded, Current Contents (Physical, Chemical and Earth Sciences), Digitalna knjižnica Slovenije (dLib.si), DOAJ, ISI Alerting Services, PubMed, Science Citation Index Expanded, SciFinder (CAS), Scopus and Web of Science. Impact factor for 2020 is IF = 1.735. 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FEATURE ARTICLE 251–260 Review articles Soft Poly(N-vinylcaprolactam) Based Aqueous Particles Joonas Siirilä and Heikki Tenhu SCIENTIFIC PAPER 261–270 Organic chemistry The Synthesis of (2R)-Aziridine-2-carboxylic Acid Containing Dipeptides Samo Kuzmic, Martina Hrast and Rok Frlan 271–280 Organic chemistry Magnetic nanoparticles Linked to Pyridinium Hydrotribromide Groups as Catalysts for Selective oxidation of Alcohols and Protection of Alcohols Sonia Tabari, Ali Reza Pourali and Ehsan Nazarzadeh Zare Graphical Contents 281–292 General chemistry (E)-1-(4-Hydroxyphenyl)-3-(substituted-phenyl) prop-2-en-1-ones: Synthesis, In Vitro Cytotoxic Activity and Molecular Docking Studies Lütfiye Sirka, Hacer Dogan, Mehmet Refik Bahar, Eray Çaliskan, Suat Tekin, Harun Uslu, Kenan Koran, Süleyman Sandal and Ahmet Orhan Görgülü 293–303 Organic chemistry Design, Synthesis, In Silico ADMET Studies and Anticancer Activity of Some new Pyrazoline and Benzodioxole Derivatives Fatih Tok, Ömer Erdogan, Özge Çevik and Bedia Koçyigit-Kaymakçioglu 304–315 Chemical, biochemical and environmental engineering Modelling and Multi-objective optimization of Continuous Indirect Electro-oxidation Process for RTB21 Dye Wastewater Using Ann-GA Approach Naresh R Vaghela and Kaushik Nath 316–321 Organic chemistry Enantioselective Henry Reaction Catalyzed by Chiral Piperazine Derivatives Leman Alkan 322–330 Analytical chemistry In-Situ Intercalating of Silica nanospheres into Polypyrrole During Its Electropolymerization to Prepare a Sorbent for Headspace Microextraction of Aldehydes in Edible oils Amin Hamdi, Kolsoum Dalvand, Alireza Ghiasvand and Nahid Heidari 331–335 Physical chemistry Effect of SDS Micelles on Actinomycin D – DnA Complexes Ana Maria Toader, Izabella Dascalu and Mirela Enache 336–348 Chemical, biochemical and environmental engineering Removal of Diazo Dye Direct Red 28 and Tetra Azo Dye Direct Black 22 Using Synthesized Magnetic Kaolin Supported Zinc Ferrite Serap Findik 349–358 Organic chemistry Efficient one-Pot Synthesis of 1,4-Dihydropyridines Catalyzed by Magnetic MnFe2o4 nanoparticles Somayeh Moradi, Mohsen Moradian and Hossein Naeimi 359–370 Chemical education opportunities for Learning: Analysis of Czech Lower-Secondary Chemistry Textbook Tasks Karel Vojír and Martin Rusek 371–377 Chemical education To What Extent do Freshmen University Chemistry Students Master Chemistry Calculations? Martin Rusek, Karel Vojír, Iveta Bártová, Marta Klecková, Vladimír Sirotek and Jitka Štrofová 378–384 Physical chemistry The Use of Yeast Saccharomyces Cerevisiae as a Biorecognition element in the Development of a Model Impedimetric Biosensor for Caffeine Detection Zala Štukovnik, Regina Fuchs Godec and Urban Bren 385–392 Organic chemistry Synthesis, Biological Evaluation, and Molecular Docking Studies of Hydrazones as novel Xanthine oxidase Inhibitors Ling-Wei Xue, Shi-Tong Li, Yong-Jun Han and Xiao-Qiang Luo 393–404 Chemical, biochemical and environmental engineering Computational Molecular Modeling Studies of Some Mycobacterium Tuberculosis Alanine Racemase Inhibitors Unni Jayaram and Mohammed Afzal Azam 405–418 Inorganic chemistry Synthesis, Characterization, Biological Activities and Ab-initio Study of Transition Metal Complexes of [Methyl 2-((4-chlorophenyl)(hydroxy)methyle) Acrylate] Shazia Ishfaq, Shazia Nisar, Saqib Ali, Sadaf Iqbal, Saima Imad, Samina Iqbal, Saeeda Bano, Syeda Kanwal Zahid, Nasreen Fatima and Muhammad Shahzaman 419–429 Organic chemistry novel Benzimidazole-Based Compounds as Antimicrobials: Synthesis, Molecular Docking, Molecular Dynamics and in silico ADME Profile Studies Elif Yesilçayir, Ismail Çelik, Hasan Tahsin Sen, Suna Sibel Gürpinar, Müjde Eryilmaz, Gülgün Ayhan-Kilcigil 430–436 Chemical, biochemical and environmental engineering Effects of Extraction Period on Bioactive Compounds Extracted from Olea Europaea (var. Domat) Leaves by Ultrasound-Assisted Extraction Dua Danjolli-Hashani and Sebnem Selen-Isbilir 437–447 Analytical chemistry Design of “Turn-off ” Fluorescent nanoprobe for Highly Sensitive Detection of Uric Acid using Green Synthesized nitrogen-Doped Graphene Quantum Dots Sopan Nangare, Shweta Baviskar, Ashwini Patil and Pravin Patil 448–457 Materials science optimisation of Amphiphilic-Polymer Coatings for Improved Chemical Stability of naYF4-based Upconverting nanoparticles Tina Cernic, Monika Koren, Boris Majaron, Maja Ponikvar-Svet and Darja Lisjak 458–465 Chemical, biochemical and environmental engineering Adsorption Properties of Low-Cost Synthesized nanozeolite L for Efficient Removal of Toxic Methylene Blue Dye from Aqueous Solution Neda Salek Gilani, Salma Ehsani Tilami and Seyed Naser Azizi 466–477 Materials science Synthesis, Characterization and Thermal Degradation Kinetics of a new Pyrazole Derived Methacrylate Polymer, Poly(1,3-Diphenyl-1H-Pyrazol-5-Yl Methacrylate) Adnan Kurt and Murat Koca 478–482 Biochemistry and molecular biology iFIT: An Automated Web Tool for Determining Enzyme-kinetic Parameters Based on the High-curvature Region of Progress Curves Boštjan Petric, Marko Golicnik and Aljoša Bavec 483–488 Biomedical applications Ibuprofen Loaded Electrospun Polymeric nanofibers: A Strategy to Improve oral Absorption Dibya Sundar Panda, Nabil K. Alruwaili, Kalpana Swain and Satyanarayan Pattnaik 489–506 Chemical, biochemical and environmental engineering Combined Pharmacophore Modeling, 3D-QSAR, Molecular Docking and Molecular Dynamics Study on Indolyl-aryl-sulfone Derivatives as new HIV1 Inhibitors Mebarka Ouassaf, Faizan Abul Qais, Salah Belaidi,Mohamed Bakhouch, Ahmed Said Mohamed and Samir Chtita DOI: 10.17344/acsi.2022.7430 Feature article Soft Poly(N-vinylcaprolactam) Based Aqueous Particles Joonas Siirilä and Heikki Tenhu Department of Chemistry, University of Helsinki, Finland * Corresponding author: E-mail: joonas.jukonen@helsinki.fi heikki.tenhu@helsinki.fi Received: 05-25-2022 Abstract Soft nanoparticles are an important class of material with potential to be used as carriers of active compounds. Swollen, penetrable particles can act as a host for the active ingredients and provide stability, stimuli-responsiveness and recy­clability for the guest. Thermoresponsive colloidal gel particles are especially attractive for such applications due to the extremely soft structure, size and responsiveness. Poly(N-vinylcaprolactam) (PNVCL) is a much studied, popular ther­moresponsive polymer. The polymer has low toxicity and the phase transition temperature is close to body temperature. During the phase transition, the polymer becomes less soluble, the particle expels a large part of water and the particle collapses to a more compact form. The diffusion of material in and from the particles is largely affected by this transition. As the solubility of the polymer changes, so do the interactions with the loaded compound. This feature article focuses on the synthetic methods, properties and applications of soft PNVCL particles. Keywords: Microgel; nanogel; synthesis; responsive; poly(N-vinyl caprolactam) 1.Introduction There is a need for aqueous carriers of active com­pounds as a large portion of drugs is insoluble in water, a solvent which is the basis of biological fluids.1 Especially for cancer drugs, there is a need to deliver the drug to tar­get site in a way that supresses the toxicity on healthy cells and also supresses side-effects.2–4 In these applications the particles are used to mask the toxicity of the drug during the transport and to release it at a target site. Water is also an important environmentally benign solvent for chemical transformations, but many catalysts and reagents need to be stabilized in water either to increase their availability and reactivity or to protect them from degradation. Soft (deformable) nanoparticles may help in compatibilization of the reagents and catalysts and act as nanoreactors. The particles may also help in separation of the catalyst from the product. Typical catalysts include both enzymes5 and metal nanoparticles.6,7 From the different options available; self-assemblies of amphiphilic polymers, mesoporous silica particles, latex particles and such, micro- and nanogel particles are unique due to their dualistic nature between a branched soluble polymer and a crosslinked dispersed insoluble polymer particle 8–12 These, 1 nm to few µm sized polymer particles are robust, swollen with water and extremely soft / deform­able. The deformability has been recognized as an impor­tant factor in determining the fate of carriers in human body as softer particles exhibit longer circulation times due to smaller response to the body immune system and due to being able to deform and pass through membranes.13–15 Especially interesting soft nanoparticles are the stim­uli responsive microgel particles as the responsiveness can be utilized to control the diffusion of material in and out from the particles16 and in separation of the particles from a dispersion. Stimuli can also be used to change the con­formation of the polymers in the particle to reveal active binding sites.17 Responsiveness is typically caused by the changed solubility of the sub-chains of the particle in re­sponse to a stimulus. Typical stimuli include pH, light and temperature. There are for example applications where the catalyt­ic activity of a loaded catalyst is controlled by the swelling degree of the polymer particle.18 On the other hand, many microgel particles have also been reported to possess sur­face active properties and been used to stabilize emul­sions.5,19–22 There is then a possibility to use these microgel particles as emulsifiers, which gives interesting possibili­ties when accompanied with a loaded catalyst for the catal­ysis at interphases.23–25 Poly(N-vinyl caprolactam), PNVCL, is a popular polymer in general and also as a basis of responsive micro­gel particles. The main reasons are the low toxicity, ther­moresponsiveness at moderate temperatures and the com­patibility with a great variety of polar and non-polar compounds. PNVCL has been used in cosmetic prod­ucts,26 protein affinity columns,27 and as a kinetic hydrate growth inhibitor in oil industry28. The polymer is made via radical polymerization of N-vinylcaprolactam (NVCL; Figure 1). The monomer is synthesized from acetylene and caprolactam, which is a monomer mainly used for the syn­thesis of Nylon-6.29 Recently, a company named Genomat­ica announced it had synthesized a precursor for caprol­actam in a 1 metric ton scale by fermentation from renewable resource.30 An excellent and comprehensive review exists about PNVCL.31 Biomedical applications of the polymer are becoming increasingly important and have been the subject of two recent review articles.32,33 Figure 1. Synthesis of poly(N-vinyl caprolactam) 1. 1. Thermoresponsivenes The phase transition temperature of PNVCL de­pends on both the concentration and the molar mass of the polymer. 34 Increasing either of these will result in low­ering the transition temperature. Similarly, added electro­lytes or cosolvents will shift the transition temperature to a degree determined by the choice of the additive. 27, 35–38 Commonly, the phase transition temperature is given as a cloud point, which is the temperature where a solution turns turbid as the polymer becomes less soluble and ag­gregates. Depending on the concentration the aggregation may result in monodisperse spherical aggregates referred to as mesoglobules or in a macroscopic phase separation.38 Even though a single temperature value, i.e. the cloud point, is often used to describe the phase transition, high sensitivity DSC measurements and IR measurements have revealed that the phase transition process is gradual and happens within a relatively broad temperature range. 39,40 According to the DSC measurements, the released heat per NVCL unit is in the range of 4–5 kJ/mol.39 The transi­tion has been studied using several methods. In a work by Spevácek et al. NMR, IR and SAXS data combined with quantum chemical calculation elucidated the molecular basis of the transition.41 Below the transition temperature the polymer is well solvated due to extensive hydrogen bonding. Every PNVCL carbonyl binds on average two water hydrogens. During the phase transition the extent of this hydrogen bonding decreases, non-directly bound wa­ter is expelled and after the transition PNVCL oxygen binds only one hydrogen. It is important to notice that the polymer is still hydrated, but to a lesser degree. For a microgel particle, the phase transition temper­ature is often referred to as a volume phase transition tem­perature. The covalently crosslinked particles exhibit simi­lar partial dehydration as the linear counterparts during heating, but instead of forming aggregates the particles shrink as water diffuses out from the gel network. Dynam­ic light scattering is the most utilized tool for analysing the transition. The degree of crosslinking is very important in determining the stability of the particles during heating. When the crosslinking degree is low, the microgels tend to aggregate similarly as the linear polymer.42 It is fascinating, how the prepacking to particles results in stability. Other tools used for analysing the transition are NMR, SANS, and DSC that directly monitor the transition. Various re­lease tests analyse the effect of the transition on the loaded compounds; fluorescence probes and model drugs have been used in release analysis. There are also examples of responsive microgels with interacting moieties which al­low temperature-switchable binding and release of pro­teins and bacteria.17 Copolymerization can be used to tailor the transi­tion temperature of the PNVCL containing copolymer.43 Statistical copolymers of PNVCL with a more hydropho­bic comonomer, such as vinyl acetate exhibit lower phase transition temperature compared to the PNVCL ho-mopolymer with similar molecular weight. Vice versa, copolymers with a more hydrophilic comonomer, such as N-methyl-N-vinylacetamide, exhibit higher phase transi­tion temperature compared to PNVCL homopolymer. Distribution of repeating units in the copolymer has also a decisive role in determining the degree to which the in­corporated comonomer affects the thermal behaviour. Therefore, it is possible to synthesize block copolymers with PNVCL block and another thermoresponsive block, which exhibit two distinct phase transition temperatures, whereas a copolymer with more randomly distributed re­peating units along the chain would exhibit only one tran­sition. In nano/microgels, the responsiveness is also often altered with comonomers, and comonomers are used to alter the stability of the dispersions. 42–52Acidic and basic comonomers affect the swelling degree depending on the pH.44,47,51,52 Crosslinking degree was already mentioned to have a huge impact on the gels. However, there are also several examples of gel particles with different sensitive cleavable crosslinkers, including pH, oxidation/reduction, enzymatically degradable and mechanoresponsive ones.42,48,55–57 The transformations induced by cleaving the crosslinks are generally not recoverable unlike the thermosensitivity derived from the responsive polymer. 1. 2. Utilization of the Thermoresponsiveness During the thermal transition, interactions between the dispersing medium and the polymer PNVCL change. This affects also the interactions between the polymer par­ticle and the loaded content.45,49,58,59 The diffusion into and out of the particle is reduced above the phase transition temperature as the polymer collapses and forms a barrier. Thermoresponsive nanoparticles, including PNVCL particles, have been widely studied as drug delivery sys­tems.45,48,49,51,58 The release from the particles is often diffu­sion controlled.16 The thermoresponsiveness is utilized to obtain a fast and efficient loading at lower temperatures and a sustained release at higher temperatures. The sustained release is important for the particle to reach its target prior to release. Specific interactions may be created by incorpo­rating comonomers, often charged ones, to the particles. Acidic or basic units can provide a means for pH specific release.53 Since the diffusion into and out of the particle de­pends on the swelling degree, accessibility and thus the ac­tivity of a loaded catalyst may be controlled with the swell­ing degree.18,60 Yang et al. have reported silver nanoparticle loaded poly(N-acryloylglycinamide) microgels with tem­perature on/off switchable catalytic activity. The reagents were able to reach the catalytic site only in the swollen state of the microgels. Similarly, functional groups in the gel structure may be available only in the collapsed state.17 Paul et al. have synthesized poly(N-isopropyl acrylamide) based microgels with a carbohydrate based comonomer. A pro­tein i.e. lectin, and E.coli bacteria were shown to bind to the microgel particles at elevated temperatures, when the mi-crogel was at the collapsed state and carbohydrate moieties were enriched on the particle outer layers. Soft microgel particles have also been studied as sta­bilizers of emulsions, and in these cases the thermal col­lapse of the polymer changes the colloidal stability of the microgel and results in destabilization of the emul­sion.5,19–21,23 There is potential in thermoresponsive soft nanoparticles in the above listed applications. However, so far the thermorepsonsiveness of poly(N-vinylcaprol­actam) has been utilized only in the loading and release of active ingredients, as far as authors are aware. 1. 3. Synthesis of Soft PNVCL-Based Particles Soft PNVCL polymer particles can be synthesized from a preformed polymer using self-assembly or from the monomer by means of polymerization, either emulsion or precipitation polymerizations. Also, “from top to bottom” approach has been reported, where microgel particles where prepared by grinding a macro hydrogel down to microgels. 1. 4. Synthesis by Self-Assembly PNVCL homopolymer may form stable self-assem­bled aggregates in aqueous solutions upon heating above the thermal transition temperature under dilute condi­tions. The particle size and size distribution depends on the molecular weight and concentration of the polymer and on the heating program.39,61,62 The self-assembled structures are stable for days, even months, and the self-as­sembly process can be used to capture material inside the particles. The particles are dynamic in their nature, stable against dilution, but disassemble by lowering the tempera­ture. However, hydrogen bonding with phenols may be used to make the particles to withstand cooling.61,63 Simi­larly, PNVCL-block copolymers can form assemblies upon heating and can be stabilized against heat induced disas­sembly.64,65 In addition to thermoprecipitation, PNVCL block copolymers form assemblies as any amphiphilic block co­polymer. In these assemblies PNVCL can be either one, the solvophobic or solvophilic block depending on the other block and on the conditions. PNVCL-PEG copolymer par­ticles have been formed for example both with thermopre­cipitation61and with solvent-exchange from DMF to H2O (37 °C).66 Additionally, PNVCL block copolymers have been self-assembled using nanoprecipitation and by film-dehydration followed by membrane extrusion.67 PNVCL has also been assembled with silk fibroin us­ing the layer-by-layer method to form multilayers on silica particles. Hydrophobic interactions and hydrogen bond­ing are responsible for the interactions between silk fibroin and PNVCL.68 The use of the self-assemblies of PNVCL copolymers in biomedical applications has recently been reviewed.69 2. Synthesis by the Means of Polymerization 2. 1. Precipitation Polymerization Precipitation polymerization is a type of free radical polymerization that is used to make particles, especially colloidal gels. In the polymerization the monomer is solu­ble in the solvent, but the formed polymer is not and as a result, the polymer will precipitate during the polymeriza­tion. When synthesising a thermoresponsive polymer, the synthesis temperature is selected such that the formed pol­ymer is insoluble. Surfactants are often used in the synthe­sis to guide the polymer to precipitate into well-defined, similar sized aggregates, which keep dispersed in the reac­tion mixture. When synthesizing colloidal gel particles, multiple polymerizable bonds containing comonomers i.e. crosslinkers, are used. Then the polymer particles/aggre­gates formed during the synthesis become permanent pol­ymer networks that do not break even upon improving the solvent quality. Synthesis of colloidal PNVCL hydrogels has been well studied and various comonomers have been incorporated to the particles during the polymerizations. 44–52,70–77 Typically, precipitation polymerizations have been performed as batch polymerizations, meaning that all monomers are present from the start. In batch polymeriza­tions, the reactivity difference between monomers can lead to a composition gradient in the particle structure as the more reactive monomer is incorporated first.52,71,72,75 For this reason, PNVCL colloidal gels usually have a more crosslinked core and dangling chains on the surface, as the crosslinker, which is the more reactive monomer, has po­lymerized first.72,75 Continuous and semi-continuous ad­dition of monomers can be used to control the spatial ar­rangement of the monomers in the gel particles. Imaz et al. and Willems et al. have reported synthesis of homogene­ously crosslinked hydrogel particles with continued feed of the crosslinking monomer during the polymerization.52,72 Temperature ramp and continuous feed have also been used to synthesize large 1 to 5 µm sized particles.78 Precip­itation polymerization has also been performed without surfactants in a inject printer, where high shear forces and pressures have resulted in small stable particles (50 nm).79 Precipitation polymerization in a continuous flow reactor has been reported as well.80 Precipitation polymerization can also be used to po­lymerize a PNVCL shell on a pre-existing particle or on a sacrificial template such as a dimethyldiethoxysilane drop­let.81 Removal of the sacrificial template will produce par­ticles with inner lumen, i.e. capsules. 2. 2. Emulsion Polymerization Emulsion polymerization is ”polymerization where­by monomer(s), initiator, dispersion medium, and possi­bly colloid stabilizer constitute initially an inhomogeneous system resulting in particles of colloidal dimensions con­taining the formed polymer”, according to the IUPAC defi­nition.82 The typical precipitation polymerization of NVCL in water is sometimes referred to as an emulsion polymeriza­tion. However, in this text the term precipitation polymer­ization is used for the aqueous polymerizations of NVCL, which are performed above the phase transition tempera­ture of PNVCL, and where the starting NVCL concentra­tion (0.5–3 wt% monomer with respect to H2O) is close to the solubility limit of NVCL. Most of the PNVCL particle synthesis are precipitation polymerizations. The use of larger concentrations of NVCL has been reported to lead to colloidal instability and to the formation of coagulum during the polymerization.72 In addition to precipitation polymerizations in wa­ter, PNVCL particles have also been synthesized with miniemulsion83,84 and inverse miniemulsion polymeriza­tions.85,86 In miniemulsion and in inverse miniemulsion polymerizations, the initial polymerization mixture con­sists of evenly sized droplets dispersed in a continuous phase, and these droplets act as the loci of the polymeriza­tion and in the end turn in to polymer particles (Figure 2).87,88 In contrast, in (macro)emulsion polymerization the picture is more complex. The starting mixture typically contains small surfactant micelles and large monomers droplets. There is a need for mass transportation from the monomer droplet to the growing particles during the po­lymerization. This can cause problems, including macro phase separation and formation of coagulum, when the formulation contains very solvophobic components with limited ability to migrate in the continuous phase. With these formulations, to avoid phase separation and instabil­ity, it is beneficial to choose the miniemulsion polymeriza­tion method, where transportation form monomer drop­lets to growing particles does not happen. Suitable conditions for miniemulsion are achieved by choosing correct surfactant and by using an intensive premixing process to create evenly sized small droplets before the po­lymerization. Additionally, the dispersed droplets often need to be stabilized against Ostwald ripening with a costabilizer, which is a compound that is very insoluble in the continuous phase, but soluble in the monomer phase. Figure 2. In emulsion polymerization (a) the polymer chains start to grow either in the surfactant micelles or in the continuous phase, whereas in miniemulsions (b) the chains grow in surfactant stabilized monomer droplets. Figure 3. Polymerization induced self-assembly in water (drawn by Vikram Baddam) In inverse miniemulsion polymerizations, the dis­persed droplets consist of a polar solvent (usually water) and the monomer, and the continuous phase is non-polar. This method is suitable for the synthesis of water-soluble polymers. When the polymerization takes place in the droplet phase instead of the continuous water phase, poly­mer particles are formed instead of a macrogel. Effective incorporation of water-soluble compounds to the formed PNVCL colloidal gel particles could be a reason to use this syntheses method. In miniemulsion polymerization, the dispersed phase is organic and contains the monomer, and the con­tinuous phase is aqueous. The method has been used for the synthesis of PNVCL particles with high monomer con­centrations (up to 16 wt% with respect to H2O)83,84 and to synthesize PNVCL particles with a water insoluble co-monomer,89 which can be difficult with precipitation po­lymerization. The miniemulsion polymerization often de­mands the use of an additional hydrophobe (costabilizer) such as hexadecane and possibly the use of a cosolvent for the formation of the dispersed phase. The additives can re­sult in a need of extensive purification steps. There are however, reports of miniemulsion-like polymerization of NVCL without any cosolvent or costabilizers,89,90 with CTAB as the stabilizer and with starting NVLC concentra­tion <1.4 wt% performed after homonogenization with a microfluidizer (at least 1100 bars and 8 cycles). The polym­erization conditions are almost identical to those used in the precipitation polymerizations, except for the homoge­nization process. NVCL is soluble in water at the used con­centration, however relatively stable (at least for 250 min) monomer/surfactant droplets were observed with dynam­ic light scattering prior to addition of the initiator. This was because of the slowness of the dissolution of NVCL in wa­ter. This raises a question about the homogeneity of the starting situation in the precipitation polymerizations and on the correctness of the use of the term. In studies on the precipitation polymerization of NVCL, the starting mix­ture has seldom been investigated to verify the homogene­ity. 2. 3. Polymerization Induced Self-Assembly Polymerization induced self-assembly (PISA) is a type of controlled polymerization, where a solvophilic pol­ymer is chain extended with a solvophobic block (Figure 3).91,92 During the polymerization the growth of the solvo-phobic block causes the polymer to self-assemble. The po­lymerizations are most often reversible addition-fragmen­tation chain transfer (RAFT) polymerizations, where a solvophilic macromolecule with a chain transfer agent (CTA) as an end group, i.e. macromolecular CTA (mac­roCTA), is used to control the polymerization and as the soluble block in the forming copolymer. This is also a syn­thetic route to obtain polymer particles. These particles are not polymer networks but amphiphilic self-assemblies consisting of block copolymers with narrow molecular weight distributions. Usually, no surfactant is needed in addition to the soluble polymer whose chain is to be ex­tended. Another appealing aspect of the polymerization is the possibility to obtain different well-defined morpholo­gies by changing the block length ratio and concentration of the polymerization. Typically, high monomer concen­trations (10 to 30 wt%) can and are used in the PISA po­lymerizations. Recently, the first example of PISA of NVCL as the sole monomer was reported.65 Prior work has shown how a partial utilization of the PISA concept can also be beneficial compared to the precipitation/emulsion free radical polym­erizations. Etchenausia et al. have synthesized cationic hy­drogel particles by polymerizing NVCL in water above the phase transition temperature of PNVCL with crosslinker and a cationic macroCTA.93 Using a crosslinker in a batch polymerization makes it unlikely to obtain a controlled po­lymerization and hence the polymerization induced self-as­sembly. However, the synthetic approach proved to be suit­able for the production of colloidal hydrogel particles at high monomer concentrations (up to 10 wt% in respect to mass of H2O) compared to a free radical precipitation po­lymerization counterpart (no CTA, just surfactants). The macroCTA group reacted during polymerization and con­ferred unanticipated stability to the system against coagula­tion. The same group also performed aqueous PISA copoly­merization of NVCL and vinylacetate with a PEG based macroCTA.94 Control over molecular mass was limited and only few polymerizations were made, but the resultant ma­terial was interesting as the formed polymers resembled by composition Soluplus95 (a commercial PNVCL containing polymer in clinical trials). No crosslinker was used as the hydrophobic comonomer prevented the dissolution of the formed assembly upon cooling if used in sufficiently high amounts (47 mol% of monomers in feed). 3. Comparison of the Synthesis Methods interfacial reactions. Current trend is to add functionality to the microgels for more specific tasks, and for more accurate delivery and release of the active ingredient. Similarly, more sophisti­cated architectures such as vesicular particles with drug loaded to the inner lumen surrounded by a membrane with temperature dependent permeability,67 degradable crosslinks for triggered release,64,81 and core-shell particles with pH dependent accelerated release of proteins53 are promising. There is also a report of PNVCL gel particles show­ing antiviral activity against HIV-virus.77 Additionally, soft particles may be found in future in various scaveng­ing applications including temperature dependent inter­actions with bacteria17 or removing small amounts of oil from water 22. New innovative applications keep appear­ing. As the synthesis methods develop and are getting industrially applicable, we see a great potential for soft PNVCL particles. Acknowledgement We thank Vikram Baddam for Figure 3. Tabela 1. Various selected aspects of synthesis methods of PNVCL particles Method Concentrationa Suitable for Surfactantb REF. 37,59,60,62,65,66 Self-assembly = 0.2 wt% homo- and copolymers not used 42–44, 46–50, Precipitation polymerization 0.5–3 wt% homo- and copolymers 0–8 wt%, or surfactant-type 57,68–78,94 comonomer (2 – 50 wt%) 81,82,87,88 Miniemulsion polymerization = 16 wt% homo- and copolymers 0.4-2 wt% + possibly an additional costabilizer 84 Inverse miniemulsion polymerization = 5 wt% homo- and copolymers 100 wt% 63,91,92 PISA 1–30 wt% copolymers macroCTA a) monomers with respect to the weight of solvents plus continuous phase b) wt% given in respect to monomer weight Various aspects of the different synthesis methods to obtain soft PNVCL nanoparticles are presented in Table 1, to allow convenient comparison between them. 4. Conclusions and Future The PNVCL particles are widely utilized as active in­gredient carriers and emulsion stabilizers. 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BASF Soluplus. https://pharmaceutical.basf.com/en/Drug-For­ mulation/Soluplus.html (accessed 27.11., 2019). 96. Siirilä, J.; Hietala, S.; Ekholm, F. S.; Tenhu, H. Glucose and Maltose Surface-Functionalized Thermoresponsive Poly(N -Vinylcaprolactam) Nanogels. Biomacromolecules 2020, 21, 955–965. DOI:10.1021/acs.biomac.9b01596 Povzetek Mehki nanodelci so pomembna vrsta materiala in se lahko uporabljajo kot nosilci aktivnih spojin. Nabrekli in prehodni delci lahko delujejo kot gostitelj za aktivne sestavine in zagotavljajo stabilnost, odzivnost na dražljaje in reciklabilnost za gosta. Termoodzivni koloidni gelski delci so še posebej privlac.ni za takšne aplikacije zaradi njihove izjemno meh­ke strukture, velikosti in odzivnosti. Poli(N-vinilkaprolaktam) (PNVCL) je veliko raziskan in priljubljen termoodzivni polimer. Ni (oz. je zelo malo ) strupen, temperatura faznega prehoda pa je blizu telesne temperature. Med faznim pre­hodom postane polimer manj topen, PNVCL nanodelec izloc.i velik del vode in se skrc.i v bolj kompaktno obliko. Ta pre-hod v veliki meri vpliva na difuzijo materiala v delce in iz njih. Hkrati s topnostjo polimera se spreminjajo tudi interakcije z vgrajeno spojino. Ta c.lanek se osredotoc.a na sintetic.ne metode, lastnosti in aplikacije mehkih PNVCL delcev. Except when otherwise noted, articles in this journal are published under the terms and conditions of the Creative Commons Attribution 4.0 International License DOI: 10.17344/acsi.2021.6673 Scientific paper The Synthesis of (2R)-Aziridine-2-carboxylic Acid Containing Dipeptides Samo Kuzmic, Martina Hrast and Rok Frlan* University of Ljubljana, Faculty of Pharmacy, The Chair of Pharmaceutical Chemistry, Aškerceva 7, 1000 Ljubljana, Slovenia * Corresponding author: E-mail: rok.frlan@ffa.uni-lj.si; Tel.: +386 1 4769 674; Fax: +386-1-4258031 Received: 03-05-2021 Abstract Optimized conditions for the synthesis of fully deprotected (2R)-aziridine containing dipeptides are described. Prepa­ration of fully protected N- and C- terminal aziridine containing dipeptides was found to be straightforward and high yielding for the majority of compounds, whereas their full deprotection was possible only for C-terminal analogs. De-protection of N-terminal derivatives using standard procedures of peptide chemistry was found difficult providing only mixtures of unidentifiable products. The described molecules have potential as building blocks in synthetic chemistry, in the chemical biology arena, as covalent modifiers, and as biomarkers. Keywords: Dipeptides; aziridines; biomarkers; warheads; antibacterial agents 1.Introduction Small molecules that mimic the structures of bioac­tive peptides are an important synthetic tool in organic chemistry. A special interest has been given to the synthe­sis of conformationally restricted ß-turn dipeptide mimet­ics, peptide bond isosteres and nonproteinogenic deriva­tives to obtain drug-like target molecules.1–3 A common strategy, that has been also widely applied, especially in the design of several protease inhibitors is to incorporate an electrophilic warhead that forms a covalent bond between amino acids in the active site and the inhibitor.4,5 The importance of aziridine-2-carboxylic acid, as well as aziridine-containing peptides as useful interme­diates in the synthesis of various amino acid and peptide derivatives, has been extensively recognized, both from a medicinal and a synthetic point of view.6–10 Further­more, despite their reactivity aziridine-containing pep­tides and related compounds may be found in numerous bioactive compounds of natural origin, such as madura­statin A111 and miraziridine A12 (Figure 1). In addition, this heterocyclic fragment has also been incorporated in numerous synthetic peptides as reactive electrophilic warheads to obtain promising irreversible inhibitors of different proteases, such as cathepsins,13,14 papain,15 as-partate proteases,16 HIV proteases17 and SARS-CoV main protease.18 Their unusual reactivity due to ring strain renders aziridine-2-carboxylic acid derivatives important for the preparation of amino acids,19–21 amino alcohols,22,23 pep­tides and other peptide-like compounds.17,24–33 In addi­tion, they have also been used for labelling biomolecules with fluorine-18.34,35 Consequently, the preparation of aziridine-containing dipeptides, which is a subject of this communication, is of great importance for medicinal as well as organic chemistry. There have been many reports in the literature describing their synthesis.36,37 Nonetheless, up to now, literature re­ports on the preparation of partially or fully deprotected di-peptide derivatives have been rare. To the best of our knowl­edge, there has been only one report by Korn36 describing the synthesis of fully deprotected H-Azy-Leu-OH and only a couple of papers describing the synthesis of N-deprotect­ed21,27,38,39 and C-deprotected dipeptides.34,36 Most of them suffer from low isolated yields following work-up and chro­matography which reflects the inherent instability of these compounds. At the same time, it is known that aziri­dine-containing peptides possess the potential antifun-gal,16,40 antiviral18 antiprotozoal13,14 and cytostatic activi­ty.13–15,41 Clearly, a more versatile approach to deprotected aziridine-containing dipeptides is desired, given their po­tential to serve as electrophiles in reactions with appropriate nucleophiles, as biomarkers and bioactive compounds. Figure 1. Natural products with aziridine ring and D-Ala:D-Ala ligase (Ddl) inhibitor 1. During our ongoing study concerning the dipeptide D-Ala-D-Ala, we were interested in preparing its electro-philic aziridine-containing analogs with potential biologi­cal activity. D-Ala-D-Ala sequence is found in the stem termini of peptidoglycan side-chain pentapeptide and is recognized by multiple essential bacterial enzymes. Any molecule that could mimic its structure thus offers an at­tractive potential toward the development of new inhibi­tors or as a false substrate for any of those enzymes.42 In­deed, it was reported that dipeptides with D-Ala as a first amino acid inhibit bacterial enzyme D-Alanine:D-Alanine ligase (Ddl) in micromolar range (1, Figure 1).43 (R)-aziri­dine-2-carboxylic acid was therefore chosen as a rigid sur­rogate of D-Ala because of its electrophilic character and close resemblance to D-Ala. Herein, we report details of our efforts to develop a new approach to the preparation of a series of fully depro­tected N-terminal aziridine containing dipeptides with general structure 2. In addition, an attempt on the synthe­sis of C- terminal aziridine containing dipeptides with general structure 3 is also reported (Figure 2). The designed molecules are very attractive not only because of their biological potential but also because aziri-dines have become important building blocks in synthetic chemistry. Such small dipeptides have a potential as covalent modifiers and are therefore useful in the chemical biology arena with different applications including bioconjugation, activity-based protein profiling and target identification.6,8 2. Experimental 2. 1. General Methods The reactions were monitored by TLC carried out on Merck silica gel (60 F254) by using UV light as a visualizing agent, KMnO4 in water, phosphomolybdic acid in ethanol and ninhydrin in ethanol and heat as developing agents. Column chromatography was performed using Merck Sili­ca Gel 60. Proton nuclear magnetic resonance spectra (1H NMR) were obtained at 400 MHz on Bruker Avance III 400 spectrometers. Spectra were recorded in CDCl3, MeOD and DMSO-d6 solutions. Chemical shifts are reported in ppm, referenced to tetramethylsilane (TMS) as the external refer­ence. Carbon-13 nuclear magnetic resonance spectra (13C NMR) were obtained at 100 MHz on Bruker 400 spectrom­eter. Chemical shifts are reported in ppm, referenced to the solvent peak of CDCl3. Low-resolution mass spectra were obtained with a Shimadzu GC-MS-QP2010 mass spec­trometer. High resolution mass spectra (HRMS) were re­corded on Q Executive Plus LC-MS/MS system (Thermo Scientific). Melting points were found on the Cambridge instruments melting point apparatus and are corrected. Op­tical rotation was found with Perkin Elmer 1241MC po­larimeter at wavelength 589,3 nm (l = 10 cm). Ethyl acetate and methanol were used as solvents. Infrared spectra were recorded on a Perkin-Elmer FTIR 1600 spectrometer. Melt­ing points were determined using a Reichert hot-stage mi­croscope and are corrected. HPLC analyses were performed on an HPLC Dionex UltiMate 3000 instrument with a UV­VIS detector. Three different analytical methods were used. Method 1: Column: Phenomenex Luna® 5 µm C18 100 Å; Injection volume: 10 µL; Flow rate: 1,5 mL/min; Detection wavelengths: . = 210 nm, 220 nm, 254 nm and 280 nm; Column temperature: 25 °C; Mobile phase: 40% ACN in water to 100% ACN in 15 min. Method 2: Column: Supelco SUPELCOSIL™ LC-1 HPLC; Injection volume: 1 µL; Flow rate: 1 mL/min; Detection Wavelengths: . = 195 nm, 210 nm, 220 nm and 254 nm; Column temperature: 25 °C; Mo­bile phase: 5% ACN in 20 mM phosphate buffer (pH = 2.10) to 60% ACN in 20 min. Method 3: Column: Agilent ZOR­BAX Extend-C18; Injection volume: 5 µL; Flow rate: 1 mL/ min; Detection wavelengths . = 210 nm, 220 nm, 254 nm and 280 nm; Column temperature25 °C; Mobile phase: 40% ACN in water to 100% ACN in 20 min. 2. 2. Synthesis and Characterization (R)-3-hydroxy-1-methoxy-1-oxopropan-2-aminium chloride (4). To a solution of D-serine (20.00 g, 190.3 mmol, 1.00 equiv.) in 500 mL MeOH, SOCl2 (24.90 mL, 342.6 mmol, 1.80 equiv.) was added at 0 °C. The reaction mixture was stirred at 80 °C for 2 h and stirred at room temperature for an additional 20 h. The solution was concentrated under reduced pressure and diethyl ether (250 mL) was added to remove the excess of HCl. The solvent was evaporated un­der reduced pressure yielding 28.95 g (97.8%) of com­pound 2 as white crystals. mp 178.1–179.0 °C (lit. 175­176 °C44). Rf 0.11 (CH2Cl2:MeOH = 7:1 + 3% Et3N). Methyl trityl-D-serinate (5) To a cooled solution of 4 (29.27 g, 188.1 mmol, 1.00 equiv.) and Et3N (55.10 mL, 396.1 mmol, 2.11 equiv.) in CH2Cl2 (500 mL), trityl chloride (52.45 g, 188.1 mmol, 1.00 equiv.) was added and the mixture was stirred for 2 hours at 0 °C. The reaction mixture was washed with 10% citric acid solution (3 × 150 mL) and brine (150 mL). The organic phase was dried over anhydrous Na2SO4, the dry­ing agent was filtered off and the solvent was evaporated under reduced pressure. The product was purified via flash column chromatography (hexane:EtOAc = 2:1) to obtain 55.70 g (81.9%) of 5 as white solid. mp 158.8-161.5 °C (lit. 152–154 °C45). Rf: 0.27 (hexane:EtOAc = 2:1). 1H NMR (400 MHz, CDCl3): d 2.29 (dd, J 6.2 Hz, 6.0 Hz, 1H, OH), 2.97 (bs, 1H, NH), 3.30 (s, 3H, CH3), 3.50-3.61 (m, 2H, CH2 and CH), 3.65-3.75 (m, 1H, CH2), 7.16-7.32 (m, 9H, CPh3), 7.43-7.51 (m, 6H, CPh3) ppm. Methyl (R)-1-tritylaziridine-2-carboxylate (6) To a cooled solution of 5 (5.01 g, 13.9 mmol, 1.00 equiv.) and Et3N (4.25 mL, 30.5 mmol, 2.20 equiv.) in anhy­drous THF (40 mL), methanesulfonyl chloride (1.08 mL, 14.0 mmol, 1.01 equiv.) was added dropwise. Reaction mix­ture was stirred for 30 minutes at room temperature. The temperature was then raised to 65 °C (reflux temperature) and reaction mixture was refluxed for 48 hours. The solvent was evaporated under reduced pressure, the solid residue was dissolved in EtOAc (40 mL) and washed with 10% cit­ric acid solution (3 × 10 mL), saturated NaHCO3 solution (3 × 10 mL) and brine (1 × 10 mL). The organic phase was dried over anhydrous Na2SO4, the drying agent was filtered off and the solvent was evaporated under reduced pressure. The product was purified via flash column chromatography (hexane:EtOAc = 4:1) to yield 3.42 g (71.9%) 6 as white crystals. mp 122.0–123.0 °C (lit. 122-123 °C46). Rf: 0.36 (Hexane:EtOAc = 4:1). 1H NMR (400 MHz, CDCl3): d1.41 (dd, J 6.3 Hz, 1.7 Hz, 1H, CH2), 1.89 (dd, J 6.3 Hz, 2.7 Hz, 1H, CH), 2.26 (dd, J 2.7 Hz, 1.7 Hz, 1H, CH2), 3.76 (s, 3H, CH3), 7.19-7.31 (m, 9H, CPh3), 7.47-7.52 (m, 6H, CPh3) ppm. IR (ATR): . 3705, 3467, 2973, 1742, 1596, 1489, 1445, 1394, 1328, 1234, 1181, 1011, 893, 843, 756, 697 cm–1. Potassium (R)-1-tritylaziridine-2-carboxylate (7, C22H18 KNO2) To a cooled solution of 6 (5.78 g, 16.8 mmol, 1.00 equiv.) in THF (17 mL), 1M solution of KOH was added (16.84 mL, 16.84 mmol, 1.00 equiv.). Reaction mixture was stirred at room temperature for 24 hours. The solvent was removed under reduces pressure to obtain 6.15 g (99.4%) of 7 as lightly yellow solid. Reaction product was stable in salt form, but unstable in acid form. Yield: 99.4. mp Ther­mal Tdec > 250.0 °C. Rf: 0.30 (hexane:EtOAc = 1:1 + 0.3% CH3COOH). 1H NMR (400 MHz, CDCl3): d 1.80-1.88 (m, 2H, CH2), 3.71-3.78 (m, 1H, CH), 7.18-7.33 (m, 15H, CPh3) ppm. 13C NMR (100 MHz, CDCl3): d 28.82, 34.95, 74.28, 126.67-128.36 (signals overlap), 129.51, 144.32, 178.15 ppm. MS m/z (relative intensity): 327.7 ([M-H]–, 100%). HRMS-ESI: [M-H]– calcd for C22H18NO2, 328.1343. found, 328.1341. IR (ATR): . 3366, 3060, 2972, 2167, 1960, 1580, 1488, 1423, 1325, 1217, 1155, 1057, 1013, 900, 847, 750, 699 cm–1. [a]D20 +184.0 (c 0.31, EtOAc). 2. 3. General Procedure for Coupling reactions (Procedure A, Compounds 8a-8e) To a cooled solution of compound 7 (1.00 equiv.) and protected D-amino acid (1.00–1.10 equiv.) in 50 mL dichloromethane, HOBt (1.10 equiv.), NMM (3.00 equiv.) and EDC (1.10 equiv.) were added at 0 °C. The reaction mixture was stirred at room temperature for 24 h. The mixture was washed with 10% citric acid solution (3 × 50 mL), saturated NaHCO3 solution (3 × 50 mL) and brine (1 × 50 mL). The organic phase was dried over anhydrous Na2SO4, the drying agent was filtered off and the solvent was evaporated under reduced pressure. Methyl ((R)-1-tritylaziridine-2-carbonyl)-D-alaninate (8a) The compound was synthesized according to the general procedure A. The product was purified via flash column chromatography. Initial mobile phase was CH2 Cl2:hexane = 2:1, which was gradually replaced by hex-ane:EtOAc = 1:1. Yield: 1.198 g (53.1%) of 8a as white crys­tals. Mp 161.0–165.2 °C. Rf: 0.48 (hexane:EtOAc = 1:1). 1H NMR (400 MHz, CDCl3): d 1.44-1.51 (m, 4H, CHCH3 and aziridine CH2), 1.98-2.02 (m, 2H, aziridine CH and aziri-dine CH2), 3.81 (s, 3H, OCH3), 4.69 (qd, J 7.5 Hz, 7.2 Hz, 1H, NHCHCH3), 7.20-7.34 (m, 10H, CPh3 and CONH), 7.41-7.48 (m, 6H, CPh3) ppm. 13C NMR (100 MHz, CDCl3): d 18.79, 30.12, 34.17, 47.61, 52.79, 74.82, 127.34, 128.00, 129.60, 143.45, 170.74, 173.46 ppm. MS (ESI+): m/z = 436.85 ([M+Na]+, 100%). HRMS-ESI (m/z): [M+H]+ calcd for C26H27N2O3, 415,2016. found, 415.2017. IR (ATR): . 3705, 3293, 2972, 2869, 1746, 1645, 1532, 1491, 1448, 1359, 1276, 1206, 1165, 1056, 1009, 956, 905, 857, 771, 747, 703 cm–1. HPLC: Method 1: tr: 6.25 min (95.1% at 220 nm). [a]D20 +132.1 (c 0.33, EtOAc). Methyl (R)-3-(1-tritylaziridine-2-carboxamido)propa­noate (8b, C26H26N2O3) The compound was synthesized according to the general procedure A. The product was purified via flash column chromatography (hexane:EtOAc = 2:1), which was gradually replaced by hexane:EtOAc = 1:1. Yield: 1.24 g (62.9%) of 8b as white crystals. mp 65.0-67.0 °C. Rf: 0.32 (hexane:EtOAc = 1:1). 1H NMR (400 MHz, CDCl3): d 1.45 (dd, J 6.6 Hz, 0.7 Hz, 1H, aziridine CH2), 1.96 (dd, J 2.7 Hz, 0.7 Hz, 1H, aziridine CH2), 1.99 (dd, J 6.6 Hz, 2.7 Hz, 1H, aziridine CH), 2.57-2.71 (m, 2H, CH2COO), 3.44-3.55 (m, 1H, NHCH2), 3.65-3.79 (m, 4H, NHCH2 and OCH3), 7.20-7.30 (m, 9H, CPh3), 7.33 (dd, J 6.2 Hz, 6.2 Hz, 1H, CONH), 7.38-7.44 (m, 6H, CPh3) ppm. 13C NMR (100 MHz, CDCl3): d 29.94, 34.15, 34.29, 34.61, 52.10, 74.73, 127.30, 127.91, 129.48, 143.47, 171.12, 172.99 ppm. MS m/z (relative intensity): 436.7 ([M+Na]+, 100%). m/z = 412.5 ([M-H]–, 100%). HRMS-ESI (m/z): [M+H]+ calcd for C26H27N2O3, 415.2016. found, 415.2014. IR (ATR): . 3706, 3333, 2949, 1733, 1663, 1522, 1442, 1365, 1256, 1180, 1059, 1009, 901, 858, 749, 702 cm–1. HPLC: Method 1: tr: 5.69 min (99.4% at 220 nm). [a]D20 +109.4 (c 0.33, EtOAc). Methyl ((R)-1-tritylaziridine-2-carbonyl)-D-phenylala­ninate (8c) The compound was synthesized according to the general procedure A. The product was purified via flash column chromatography (hexane:EtOAc = 2:1), yielding 1.480 g (62.8%) of 8c as white crystals. mp 120.5–122.0 °C. Rf: 0.23 (hexane:EtOAc = 2:1). 1H NMR (400 MHz, CDCl3): d 1.37 (dd, J 6.7 Hz, 0.7 Hz, 1H, aziridine CH2), 1.69 (dd, J 2.7 Hz, 0.7 Hz, 1H, aziridine CH2), 1.95 (dd, J 6.7 Hz, 2.7 Hz, 1H, aziridine CH), 3.18 (dd, J 13.9 Hz, 6.2 Hz, 1H, CH2Ph), 3.21 (dd, J 13.9 Hz, 6.0 Hz, 1H, CH2Ph), 3.80 (s, 3H, OCH3), 4.93 (ddd, J 8.7 Hz, 6.2 Hz, 6.0 Hz, 1H, CH2Ph), 7.15-7.39 (m, 21H, CPh3, Ph and CONH) ppm. 13C NMR (100 MHz, CDCl3): d 30.04, 33.99, 38.06, 52.08, 52.66, 74.64, 127.25, 127.50, 127.90 (signals overlap), 128.91, 129.46, 135.98, 143.35, 170.54, 171.99 ppm. MS m/z (relative intensity): 488.6 ([M-H]–, 100%). HRMS-ESI (m/z): [M+H]+ calcd for C32H31N2O3, 491.2329. found, 491.2326. IR (ATR): . 3709, 3360, 3281, 2974, 2037, 1743, 1679, 1596, 1501, 1446, 1356, 1277, 1197, 1164, 1129, 1057, 1009, 903, 858, 808, 748, 703 cm–1. HPLC: Method 1: tr: 7.69 min (99.5% at 220 nm). [a]D20 +74.9 (c 0.31, EtOAc). Methyl ((R)-1-tritylaziridine-2-carbonyl)-D-valinate (8d) The compound was synthesized according to the general procedure B. The product was purified via flash column chromatography (hexane:EtOAc = 4:1), yielding 1.17 g (64.7%) of 8f as white crystals. mp 111.0–113.0 °C. Rf: 0.34 (hexane:EtOAc = 3:1). 1H NMR (400 MHz, CDCl3): d0.97 (d, J 6.8 Hz, 3H, CH(CH3)2), 0.98 (d, J 6.8 Hz, 3H, CH(CH3)2), 1.51 (dd, J 6.2 Hz, 1.0 Hz, 1H, aziri-dine CH2), 1.99-2.03 (m, 2H, aziridine CH2 and aziridine CH), 2.25 (qqd, J 6.8 Hz, 6.8 Hz, 5.0 Hz, 1H, CH(CH3)2), 3.81 (s, 3H, COOCH3), 4.59 (dd, J 9.3 Hz, 5.0 Hz, 1H, CH­COO), 7.21-7.32 (m, 9H, CPh3), 7.34 (d, J 9.3 Hz, 1H, CONH), 7.42-7.47 (m, 6H, CPh3) ppm. 13C NMR (100 MHz, CDCl3): d 17.95, 19.39, 30.33, 31.50, 34.28, 52.45, 56.53, 74.77, 127.32, 127.99, 129.54, 143.44, 170.93, 172.39 ppm. MS m/z (relative intensity):440.6 ([M-H]–, 100%). HRMS-ESI (m/z): [M+H]+ calcd for C29H33N2O3, 457.2486. found, 457.2486. IR (ATR): . 3273, 2965, 1742, 1649, 1554, 1490, 1444, 1314, 1264, 1210, 1150, 1011, 902, 749, 701, 634 cm–1. HPLC: Method 1: tr 7.32 min (100.0% at 220 nm). [a]D20 +145.0 (c 0.32, EtOAc). Dimethyl ((R)-1-tritylaziridine-2-carbonyl)-D-glutamate (8e) The compound was synthesized according to the general procedure A. The product was purified via flash column chromatography (hexane:EtOAc = 2:1), yielding 0.555 g (23.9%) of 8e as white crystals. mp 151.5–152.5 °C. Rf: 0.23 (hexane:EtOAc = 2:1). 1H NMR (400 MHz, CDCl3): d 1.49 (dd, J 6.0 Hz, 1.5 Hz, 1H, aziridine CH2), 1.96-2.00 (m, 2H, aziridine CH2 and aziridine CH), 2.05-2.16 (m, 1H, CH2COO), 2.23-2.34 (m, 1H, CH­2COO), 2.36-2.54 (m, 2H, CH2), 3.67 (s, 3H, OCH3), 3.82 (s, 3H, OCH3), 4.68 (ddd, J 8.3 Hz, 8.3 Hz, 5.1 Hz, 1H, CHCOO), 7.21-7.32 (m, 9H, CPh3), 7.44-7.50 (m, 7H, CPh3 and CONH) ppm. 13C NMR (100 MHz, CDCl3): d 27.30, 30.05, 30.37, 34.13, 51.44, 52.13, 52.82, 74.84, 127.29, 127.95, 129.59, 143.44, 171.33, 172.23, 173.59 ppm. MS m/z (relative intensity):508.7 ([M+Na]+, 80%). m/z = 484.5 ([M-H]–, 70%). HRMS-ESI (m/z): [M+H]+ calcd for C29H31N2O5, 487.2227, found, 487.2228. IR (ATR): . 3696, 3362, 3059, 2956, 2068, 1977, 1738, 1670, 1596, 1509, 1441, 1373, 1344, 1312, 1205, 1181, 1129, 1098, 1065, 1013, 904, 869, 820, 768, 747, 705 cm–1. HPLC: Method 1: tr: 6.33 min (99.9% at 220 nm). [a]D20 +119.4 (c 0.32, EtOAc). 2. 4. General Procedure for Removal of Trityl Protection Group (Procedure B, Compounds 9a-9e) To a cooled solution of the starting compound (8a­ 8e, 1.00 equiv.) and triethylsilane (1.75 equiv.) in 30 mL dichloromethane, trifluoroacetic acid (3.50 equiv.) was added drop-wise at 0 °C and stirred for another 30 min on ice bath. After the completion of the reaction (monitored by TLC), the solvent was evaporated under reduced pres­sure and the solid residue was washed with 25 mL diethyl ether and water (25 mL). NaHCO3 was added to the aque­ous phase to give a solution with a pH = 10. To further re­duce solubility of the product, NaCl was added and the aqueous phase was washed with ethyl acetate (6 × 25 mL). The collected organic phases were washed with brine (1×50 mL) and dried over anhydrous Na2SO4. The drying agent was filtered off and the solvent was evaporated under reduced pressure. Methyl ((R)-aziridine-2-carbonyl)-D-alaninate (9a, C7H12 N2O3) The compound was synthesized according to the general procedure B. Yield: 0.140 mg (52.7%) as white crystals. mp 99.5-102.3 °C. Rf: 0.45 (CH2Cl2 +:MeOH = 9:1 + 3% Et3N). 1H NMR (400 MHz, MeOD): d 1.39 (d, I = 7.3 Hz, 3H, CHCH3), 1.79-1.86 (m, 2H, aziridine CH2), 2.55 (dd, J 5.7 Hz, 3.2 Hz, 1H, aziridine CH), 3.70 (s, 3H, OCH3), 4.44 (q, J 7.3 Hz, 1H, CHCH3) ppm. 13C NMR (100 MHz, MeOD): d 17.65, 26.28, 30.42, 52.95, 172.73, 174.55 ppm, one signal covered by solvent. MS m/z (rela­tive intensity):171,3 ([M-H]–, 50%). HRMS-ESI (m/z): [M+H]+ calcd for C7H12N2O3, 172.0848. found, 173.0920. IR (ATR): . 3701, 3286, 3196, 2973, 1732, 1665, 1560, 1451, 1405, 1374, 1344, 1211, 1137, 1058, 1011, 932, 826, 713 cm–1. HPLC: Method 2: tr: 4.29 min (94.6% at 195 nm). [a]D20 +32.9 (c 0.33, MeOH). Methyl (R)-3-(aziridine-2-carboxamido)propanoate (9b) The compound was synthesized according to the general procedure B. The product was purified via flash column chromatography using Al2O3 (CH2Cl2:MeOH = 20:1), yielding 0.173 g (43.6%) of 9b as white crystals. mp 93.8-95.8 °C. Rf: 0.26 (CH2Cl2:MeOH = 9:1 + 3% Et3N). 1H NMR (400 MHz, CDCl3): d 1.70-2.00 (m, 2H, aziridine CHAHB), 2.46 (bs, 1H, aziridine CH), 2.56 (t, J 6.1 Hz, 2H, CH2COO), 3.49-3.60 (m, 2H, NHCH2CH2), 3.71 (s, 3H, OCH3), 7.09 (bs, 1H, CONH) ppm. 13C NMR (100 MHz, CDCl3): d 26.33, 30.34, 33.80, 34.98, 51.87, 171.11, 172.77 ppm. HRMS-ESI (m/z): [M+H]+ calcd for C7H­12N2O3, 172.0848. found, 173.0924. IR (ATR): . 3704, 3272, 3212, 2958, 2841, 1723, 1657, 1577, 1440, 1407, 1371, 1295, 1266, 1227, 1195, 1172, 1112, 1056, 1017, 978, 909, 884, 841, 740 cm–1. HPLC: Method 2. tr: 4.23 min (97.6% at 210 nm). [a]D20 +32,9 (c 0,33, MeOH). Methyl ((R)-aziridine-2-carbonyl)-D-phenylalaninate (9c, C13H16N2O3) The compound was synthesized according to the general procedure B. The product was purified via flash column chromatography (CH2Cl2:MeOH = 20:1), yielding 0.450 g (67.9%) of 9c as white crystals. mp 95.7–98.2 °C. Rf: 0.24 (CH2Cl2:MeOH = 20:1). 1H NMR (400 MHz, MeOD): d 1.82 (bs, 2H, airidine CH2), 2.53 (dd, J 5.5 Hz, 3.2 Hz, 1H, aziridine CH), 3.01 (dd, J 13.8 Hz, 8.8 Hz, 1H, CH2Ph), 3.20 (dd, J 13.8 Hz, 5.6 Hz, 1H, CH2Ph), 3.71 (s, 3H, OCH3), 4.73 (dd, J 8.8 Hz, 5.6 Hz, 1H, CHCH2Ph), 7.19-7.34 (m, 5H, Ph) ppm. 13C NMR (100 MHz, MeOD): d 26.18, 30.24, 38.45, 52.77, 55.31, 127.98, 129.54, 130.23, 137.98, 172.72, 173.17 ppm. MS m/z (relative intensity): 271,24 ([M+Na]+, 100%). HRMS-ESI (m/z): [M+H]+ cal-cd for C13H16N2O3, 248.1161. found, 249.1230. IR (ATR): . = 3703, 3208, 2945, 1736, 1661, 1543, 1446, 1401, 1362, 1219, 1164, 1110, 1055, 1012, 954, 914, 827, 699 cm–1. 20 HPLC: Method 2: tr: 14.84 min (99.3% at 210 nm). [a]D +18.7 (c 0.31, MeOH). Methyl ((R)-aziridine-2-carbonyl)-D-valinate (9d) The compound was synthesized according to the general procedure C, yielding 0.365 mg (75.5%) of 9d. mp 93.5-94.8 °C. Rf: 0.42 (CH2Cl2:MeOH = 9:1 + 3% Et3N). 1H NMR (400 MHz, MeOD): d 0.96 (d, J 6.9 Hz, 3H, CH(CH3)2), 0.96 (d, J 6.9 Hz, 3H, CH(CH3)2), 1.85 (bs, 2H, aziridine CH2), 2.16 (qqd, J 6.9 Hz, 6.9 Hz, 5.9 Hz, 1H, CH(CH3)2, 2.66 (dd, J 5.4 Hz, 3.1 Hz, 1H, aziridine CH), 3.72 (s, 3H, COOCH3), 4.37 (d, J 5.9 Hz, 1H, CH­COOCH3) ppm. 13C NMR (100 MHz, MeOD): d 18.53, 19.57, 26.37, 30.33, 32.00, 52.67, 59.53, 173.17, 173.44 ppm. HRMS-ESI (m/z): [M-H]– calcd for C9H16N2O3, 200.1161. found, 199.1081. IR (ATR): . 3704, 3278, 3202, 2967, 1731, 1666, 1558, 1463, 1439, 1391, 1346, 1318, 1285, 1235, 1203, 1160, 1069, 1005, 940, 891, 840, 822, 722 cm–1. HPLC: Method 2. tr: 6.98 min (95.0% at 195 nm). [a]D20 +114.7 (c = 0.34, MeOH). Dimethyl ((R)-aziridine-2-carbonyl)-D-glutamate (9e, C10H16N2O5) The compound was synthesized according to the general procedure B. The product was purified via flash column chromatography using Al2O3 (CH2Cl2:MeOH = 50:1), which was gradually replaced by CH2Cl2:MeOH = 20:1, yielding 0.124 g (48.9%) of 9d as colourless viscous oil. Rf: 0.38 (CH2Cl2:MeOH = 9:1). 1H NMR (400 MHz, CDCl3): d 1.84 (bs, 1H, aziridine CH2), 1.88-2.07 (m, 2H, aziridine CH2 and CH2COO), 2.16-2.28 (m, 1H, CH2 COO), 2.31-2.48 (m, 2H, CH2), 2.54 (bs, 1H, aziridine CH), 3.69 (s, 3H, OCH3), 3.76 (s, 3H, OCH3), 4.58-4.68 (m, 1H, CHCO), 7.16 (d, J 8.0 Hz, 1H, CONH) ppm. 13C NMR (100 MHz, CDCl3): d 26.73, 27.35, 30.13, 30.38, 51.64, 52.00, 52.69, 171.27, 172.13, 173.25 ppm. HRMS­ESI (m/z): [M+H]+ calcd for C10H16N2O5, 244.1059. found, 245.1131. IR (ATR): . 3704, 3668, 2970, 2868, 1736, 1657, 1535, 1437, 1341, 1215, 1170, 1056, 1011, 833 cm–1. 20 HPLC: Method 2: tr: 5.66 min (96.6% at 210 nm). [a]D+54.0 (c 0.25, MeOH). 2. 5. General Procedure for Removal of Methyl protection group (procedure C, compounds 2a-2e) To a cooled solution of 8a-8e (1.00 equiv.) in MeOH (20 mL), 0.1 M LiOH solution (5.00 equiv.) was added drop-wise at 0 °C. After the completion of the reaction (monitored by TLC), the pH of the reaction mixture was adjusted to pH=7 with a 0.1 M HCl solution and the sol­vent was evaporated under reduced pressure. ((R)-aziridine-2-carbonyl)-D-alanine (2a) The compound was synthesized according to the general procedure C. The product was purified via flash column chromatography (EtOAc:MeOH:H2O = 4:2:1), yielding 0.080 g (98%) of 2a as lightly yellow crystals. mp thermal Tdec > 250 °C. Rf: 0.26 (EtOAc:MeOH:H2O = 4:2:1). 1H NMR (400 MHz, MeOD): d 1.36 (d, J 7.1 Hz, 3H, CHCH3), 1.78-1.87 (m, 2H, aziridine CH2), 2.58 (dd, J 5.8 Hz, 3.2 Hz, 1H, aziridine CH), 4.24 (q, J 7.1 Hz, 1H, CHCH3) ppm. 13C NMR (100 MHz, MeOD): d 19.48, 26.17, 30.85, 52.26, 171.82, 179.66 ppm. HRMS-ESI (m/z): [M+H]+ calcd for C6H10N2O3:158.0691. found, 159.0764. IR (ATR): . 3659, 3324, 3100, 2970, 2872, 1643, 1606, 1562, 1457, 1405, 1365, 1319, 1269, 1236, 1167, 1104, 1054, 1017, 981, 942, 919, 868, 837, 756, 685 cm–1. HPLC: Meth­od 2. tr: 3.46 min (95.1% at 195 nm). [a]D20 +66.7 (c 0.33, MeOH). (R)-3-(aziridine-2-carboxamido)propanoic acid (2b, C6H10N2O3) The compound was synthesized according to the general procedure C. The product was purified via flash column chromatography (EtOAc:MeOH:H2O = 4:2:1), yielding 0.096 g (65%) 2b as white crystals. mp thermal Tdec > 250 °C. Rf: 0.24 (EtOAc:MeOH:H2O = 4:2:1). 1H NMR (400 MHz, D2O): d 1.83 (d, J 3.1 Hz, 1H, aziri-dine CH2), 1.87 (d, J 6.0 Hz, 1H, aziridine CH2), 2.36 (t, J 6.9 Hz, 2H, CH2COO), 2.56 (dd, J 6.0 Hz, 3.1 Hz, 1H, aziridine CH), 3.39 (t, J 6.9 Hz, 2H, NHCH2) ppm. 13C NMR (100 MHz, D2O): d 24.82, 29.59, 33.32, 53.91, 172.50, 180.15 ppm. HRMS-ESI (m/z): [M+H]+ calcd for C6H10N2O3, 158.0691. found, 159.0764. IR (ATR): . 3702, 3659, 3258, 3086, 2975, 2871, 1646, 1556, 1404, 1312, 1259, 1162, 1126, 1061, 1016, 908, 832, 622 cm–1. [a]D20 +143.6 (c 0.30, MeOH). ((R)-aziridine-2-carbonyl)-D-phenylalanine (2c) The compound was synthesized according to the general procedure C. The product was purified via flash column chromatography (EtOAc:MeOH:H2O = 4:1:1), yielding 0.163 g (71.4%) of 2c as white crystals. mp ther­mal Tdec > 180 °C. Rf: 0.37 (EtOAc:MeOH:H2O = 4:2:1). 1H NMR (400 MHz, D2O): d 1.75 (bs, 2H, aziridine CHAHB), 2.52 (bs, 1H, aziridine CH), 2.91 (dd, J 14.0 Hz, 8.8 Hz, 1H, CH2Ph), 3.18 (dd, J 14.0 Hz, 4.9 Hz, 1H, CH2Ph), 4.45 (dd, J 8.8 Hz, 4.9 Hz, 1H, CHCOO), 7.21-7.36 (m, 5H, Ph) ppm. 13C NMR (100 MHz, MeOH): d 25.05, 29.48, 38.63, 54.38, 126.89, 128.61, 129.30, 137.59, 172.08, 178.00 ppm. HRMS-ESI: [M-H]- calc for C12H14N2O3, 234.1004. found, 233.0928. IR (ATR): . 3371, 2976, 2117, 2005, 1595, 1418, 1273, 1162, 1106, 1056, 923, 696 cm–1. 20 HPLC: Method 2. tr: 7.22 min (86.5% at 210 nm). [a]D+25.7 (c 0.30, MeOH). ((R)-aziridine-2-carbonyl)-D-valine (2d, C9H16N2O3) The compound was synthesized according to the gen­eral procedure C. The product was purified via flash column chromatography (EtOAc:MeOH:H2O = 4:2:1), yielding 0.109 g (63.7%) of 2e. mp thermal Tdec > 220 °C. Rf: 0.31 (EtOAc:MeOH:H2O = 4:2:1). 1H NMR (400 MHz, D2O): d 0.87 (d, J 7.0 Hz, 3H, CH(CH3)2), 0.90 (d, J 7.0 Hz, 3H, CH(CH3)2), 1.84 (d, J 3.4 Hz, 1H, aziridine CH2), 1.90 (d, J 5.9 Hz, 1H, aziridine CH2), 2.10 (qqd, J 7.0 Hz, 7.0 Hz, 5.6 Hz, 1H, CH(CH3)2, 2.68 (dd, J 5.9 Hz, 3.4 Hz, 1H, aziri-dine CH), 4.06 (d, J 5.6 Hz, 1H, CHCOO) ppm. 13C NMR (100 MHz, MeOD): d18.36, 20.33, 26.30, 30.89, 32.67, 61.76, 172.45, 178.40 ppm. HRMS-ESI: [M-H]– calc for C8H13N2O3, 185.0932. found, 185.0933. IR (ATR): ): . 3707, 3666, 3288, 2970, 2871, 1644, 1590, 1546, 1424, 1235, 1162, 1057, 1011, 939, 915, 835, 752, 669 cm–1. HPLC: Method 2. tr: 4.11 min (85.4% at 210 nm). [a]D20 +52.6 (c 0.33, MeOH). Benzyl aziridine-2-carboxylate (10) To a cooled solution of 7 (15.01 g, 40.84 mmol, 1.00 equiv.) in acetonitrile (400 mL) benzyl bromide (4.85 mL, 40.8 mmol, 1equiv.) was added. After stirring at room tem­perature for 3 h, solvent was evaporated under reduced pressure. The oily residue was dissolved in CH2Cl2 (200 mL), and washed with water (200 mL), and brine (150 mL). The organic phase was dried over anhydrous Na2SO4, the drying agent was filtered off and the solvent was evap­orated under reduced pressure to obtain 14.34 g (83.4%) viscous oil, which was used for further reaction without additional purification. The trityl group was removed fol­lowing the standard procedure B, yielding 0.697 g (83.9%) of 10 as colourless oil. Rf: 0.32 (hexane:EtOAc = 1:2 + 3% Et3N). 1H NMR (400 MHz, CDCl3): d 1.89 (dd, J 5.5 Hz, 1.4 Hz, 1H, CH2), 2.04 (dd, J 2.9 Hz, 1.4 Hz, 1H, CH2), 2.58 (dd, J 5.5 Hz, 2.9 Hz, 1H, CH), 5.18 (d, J 12.3 Hz, 1H, CH2Ph), 5.22 (d, J 12.3 Hz, 1H, CH2Ph), 7.32-7.42 (m, 5H, Ph) ppm. IR (ATR): . 3285, 3229, 3065, 3037, 1725, 1564, 1456, 1402, 1360, 1187, 1113, 1007, 970, 871, 825, 744, 697 cm–1. 1H NMR spectra was found to be identical to the ones described in ref.19 Benzyl (R)-1-((tert-butoxycarbonyl)-D-alanyl)aziridine -2- carboxylate (11) The compound was synthesized according to the general procedure A. The product was purified via flash column chromatography (hexane:EtOAc = 3:1), yielding 0.471 g (25.7%) of 11 as viscous oil. Rf: 0.23 (MF: hex-ane:EtOAc = 3:1). 1H NMR (400 MHz, CDCl3): d 1.42 (s, 9H, C(CH3)3), 1.45 (d, J 7.1 Hz, 3H, CHCH3), 2.63 (dd, J 3.1 Hz, 1.9 Hz, 1H, aziridine CH2), 2.72 (dd, J 5.8 Hz, 1.9 Hz, 1H, aziridine CH2), 3.28 (dd, J 5.8 Hz, 3.1 Hz, 1H, aziridine CH), 4.30 (qd, J 7.1 Hz, 7.1 Hz, 1H, CHCH3), 5.01 (d, J 7.1 Hz, 1H, CONH), 5.20 (s, 2H, CH2Ph), 7.32-7.42 (m, 5H, Ph) ppm. 13C NMR (100 MHz, MeOD): d18.98, 28.42, 30.96, 34.45, 51.30, 67.71, 80.02, 128.65, 128.73, 128.78 (overlapping of signals), 135.06, 155.33, 168.31, 184.23 ppm. HRMS-ESI (m/z): [M+H]+ calcd for C18H24N2O5, 348.1685. found, 349.1753. IR (ATR): . 3354, 3177, 3036, 1972, 2935, 2878, 1681, 1497, 1452, 1367, 1324, 1273, 1248, 1168, 1063, 1018, 944, 913, 854, 750, 699 cm–1. 20 HPLC: Method 3. tr: 6.64 min (95.1% at 220 nm). [a]D+84,4 (c = 0,27, EtOAc). Benzyl (R)-1-(((benzyloxy)carbonyl)-D-phenylalanyl) aziridine-2-carboxylate (13) The compound was synthesized according to the general procedure B. The product was purified via flash column chromatography (hexane:EtOAc = 3:1), yielding 1.67 g (92.4%) 11 as viscous oil. Rf: 0.62 (hexane:EtOAc = 1:1). 1H NMR (400 MHz, CDCl3): d 2.57-2.63 (m, 2H, aziridine CH2), 3.05-3.15 (m, 2H, CH2Ph and aziridine CH), 3.25 (dd, J 14.0 Hz, 5.8 Hz, 1H, CH2Ph), 4.55-4.63 (m, 1H, CH), 5.0 (d, J 12.3 Hz, 1H, OCH2Ph), 5.06 (d, J 12.3 Hz, 1H, OCH2Ph), 5.17 (s, 2H, OCH2Ph), 5.23 (d, J 8.1 Hz, 1H, OCONH), 7.12-7.42 (m, 15H, 3 × Ph) ppm.13C NMR (100 MHz, MeOD): d 31.68, 35.63, 38.98, 58.87, 67.65, 68.65, 127.87, 128.75, 129.04, 129.57, 129.61, 129,76 (overlapping of 3 signals), 130.52, 136.95, 138.32, 138.70, 158.37, 169.74, 183.98 ppm. MS m/z (relative intensity): 480.6 ([M+Na]+, 100%). HRMS-ESI: [M-H]+ calcd for C27H26N2O5, 458.1842. found, 457.1773. IR (ATR): . 3328, 3062, 3033, 2952, 1699, 1505, 1452, 1375, 1248, 1191, 1077, 1047, 1027, 910, 747, 696 cm–1. HPLC: Method 3. tr: 9.24 min (70.5% at 210 nm). [a]D20 +45,6 (c = 0.43, EtOAc). 3. Results and Discussion There are two common strategies concerning the synthesis of aziridine containing peptides. The first strate­gy starts from a partially protected serine dipeptides fol­lowed by cyclization. This approach is reportedly less effi­cient because several various by-products are formed in the cyclization step.27,28,37,39,47 The second more efficient strategy, which starts from the aziridine-2-carboxylic acid, was therefore used.34,39,48 The synthesis of N-terminal aziridine containing di-peptides 2a-e was straightforward and high yielding. It started with the introduction of a trityl protective group onto D-Ser-OMe (4)49 in 82% yield followed by cyclization using methanesulfonyl chloride50,51 to obtain the aziridine 6 in 72% yield. Next, the screening of different bases (NaOH, LiOH, KOH) and solvents (THF, CH3CN and EtOH) for the subsequent saponification was performed. 1M KOH in THF was found to be the most optimal in terms of yield (99%) and purity of the final carboxylate 7. Our attempts to neutralize 7 with diluted HCl or acetic acid were not successful because of its decomposition upon neutralization. Potassium salt was therefore used in the coupling reactions between (R)-aziridine-2-carboxyl-ate 7 and different amino acids to obtain the aziridine con­taining dipeptide products 8a-e in 24–65% yield.39 Acido­lytic treatment of 8 following the literature reported procedures37,39 using CF3COOH in CH2Cl2/MeOH or CH2Cl2 generated a mixture of degradation products and was therefore unsatisfactory for the synthesis of larger amounts of final products. The analysis of reaction mix­tures by NMR confirmed that the products were present in very small quantities, which we were unable to purify. Dif­ficulties in removing trityl as well as Boc protective group from aziridine peptides using CF3COOH, formic acid or HCl have been previously reported.7,36 Instead, optimiza­tion of reaction conditions following different reaction procedures demonstrated that the addition of Et3SiH to the reaction mixture was essential for the successful reac­tion.52 Hence, reductive deprotection of N-tritylaziridines 8 and basification with triethylamine prior to isolation was successfully applied to obtain deprotected aziridines 9a-e in 31–98% yield. Finally, basic hydrolysis with LiOH in a mixture of MeOH and H2O yielded compounds 2a–d in 31–98% yield. A reaction with Glu derivative 9e yielded 2e as confirmed by MS and NMR analysis. However, a prod­uct was unstable on silica or Al2O3 and purification was therefore not possible. The majority of compounds were stable for up to 3 weeks if stored under argon and in the fridge. However, after prolonged storage, significant de­composition was observed, which is in agreement with literature data. It is not uncommon that many aziridine derivatives exhibit sequence-dependent instability in both reaction and purification steps, as well as on storage. This is caused by self-protonation to generate a reactive aziri­dinium species that subsequently decompose (Figure 3).31,53,54 The synthesis of compounds with general structure 3 was started from benzyl protected (R)-aziridine-2-carbox­ylic acid 10 which was then coupled with Boc-D-Ala and Figure 3. Synthesis of aziridine containing dipeptides. Cbz-D-Phe to obtain tert-butoxycarbonyl and benzyloxy-carbonyl protected dipeptides 11 and 13 in 26% and 32% yields, respectively. N-acylated aziridine derivatives have major issues with stability due to acylation and subsequent lower electronic density of the aziridine ring.25 It was therefore expected that the removal of a protective group could potentially yield unstable compounds if free NH2 group was present. Boc and Cbz protective groups were therefore chosen because they are easy to remove to hypo­thetical products 12 and 14 that have NH2 groups in the form of a HCl salt or as a zwitterion, respectively. However, the deprotection of Boc to obtain 12 using CF3COOH or HCl in anhydrous THF or diethyl ether did not provide us with the final product and only a mixture of unidentifiable compounds could be isolated. NMR analysis indicated that the opening of the aziridine ring could be the cause. Interestingly, NMR analysis also indicated that the open­ing of the aziridine ring is favoured compared to the re­moval of Boc protective group at concentrations of HCl below 0.1 M. Deprotection under neutral conditions using catalytic hydrogenation was next applied to remove both benzyl protective groups from 13 and to yield 14 in .zwit­terionic. form. However, a mixture of unidentifiable prod­ucts was obtained, again (Figure 4). Figure 4. Study on the preparation of 12 and 14. 4. Conclusions In conclusion, fully deprotected aziridine-containing dipeptides can be easily synthesized from trityl protected aziridine-carboxylic acid. In most cases, the deprotection of the trityl group from the aziridine ring and final hydrolysis proceeds smoothly in moderate to high yields. However, significant decomposition of deprotected dipeptides was observed after 3 weeks even if stored under argon in the fridge. The synthesis of derivatives with aziridine-2-carbox­ylic acid in place of a second amino acid in the dipeptide sequence was not possible due to the instability of the acy-lated aziridine ring in the presence of a free amino group. Acknowledgement This research was funded by Slovenian Research Agency (ARRS, research core funding no. P1-566 0208). Author Contribution All authors contributed to the study conception and design. Material preparation, data collection and analysis were performed by Samo Kuzmic and Rok Frlan. The first draft of the manuscript was written by Rok Frlan and all authors commented on previous versions of the manu­script. All authors read and approved the final manuscript. The authors declare no competing financial interest. 5. References 1. A. Burger, in: E. Jucker (Ed.): Isosterism and bioisosterism in drug design, in Progress in Drug Research, Birkhäuser, Basel, 1991, pp. 287–371. DOI:10.1007/978-3-0348-7139-6_7 2. Peptidomimetic, in: G. Detlev, R. Klaus (Eds.): Encyclopedic Reference of Genomics and Proteomics in Molecular Medi­cine, Springer, Berlin, Heidelberg, 2006, pp. 1377–1378. DOI:10.1007/3-540-29623-9_8175 3. J. Vagner, H. Qu, V. J. Hruby, Curr. Opin. Chem. 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Odstranjevanje zašcite z N-terminalnih derivatov z uporabo standardnih postopkov peptidne kemije se je izkazalo kot težko, saj vodi do mešanice nedolocljivih produktov. Opisane molekule imajo velik potencial kot gradniki v sintezni kemiji, na podrocju kemijske biologije, kot kovalentni modifikatorji in kot biomarkerji. Except when otherwise noted, articles in this journal are published under the terms and conditions of the Creative Commons Attribution 4.0 International License DOI: 10.17344/acsi.2021.6761 Scientific paper Magnetic Nanoparticles Linked to Pyridinium Hydrotribromide Groups as Catalysts for Selective Oxidation of Alcohols and Protection of Alcohols Sonia Tabari, Ali Reza Pourali* and Ehsan Nazarzadeh Zare School of Chemistry, Damghan University, Damghan 36715-364, Iran * Corresponding author: E-mail: pourali@du.ac.ir +982335220095; Fax: +982335220095. ORCID: 0000-0002-8918-1818 Received: 02-18-2021 Abstract In this research, a novel magnetic nanocatalyst based on iron oxide nanoparticles linked with pyridinium hydrotribro­mide (Fe3O4@PyHBr3) was synthesized in three steps. In the first step, 3-(aminopropyl)triethoxysilane (APTES) was reacted with 4-(bromomethyl)pyridine hydrobromide. In the second step, the product obtained in the first step was re­acted with iron oxide nanoparticles. In the last step, a grinding reaction was carried out with KBr and HIO4 in a mortar. The Fe3O4@PyHBr3 nanocatalyst was characterized by FT-IR, CHN, XRD, SEM, TGA and VSM analysis. The magnetic nanocatalyst was used as a catalyst for the selective oxidation of alcohols to aldehydes and ketones using 30% H2O2 as oxidant in a short time and with high yields. Moreover, no overoxidation of the alcohols was observed. The nanocatalyst was efficiently recycled in five consecutive cycles without significant loss of its catalytic activity. Moreover, trimethylsilyl­ation and tetrahydropyranylation of alcohols were carried out in the presence of this nanocatalyst. Keywords: Magnetic nanoparticles; pyridinium hydrotribromide; oxidation of alcohols; tetrahydropyranylation; tri­methylsilylation 1.Introduction Organic tribromides are widely used as selective bro-mination reagents for various organic functionalities.1–6 In addition, they are also used as efficient catalysts for the ox­idation of some organic substrates such as alcohols,7 alde­hydes,8 and sulfides,9,10 trimethylsilylation11,12 or tetrahy­dropyranylation13 of alcohols, a-thiocyanation14 or thioacetalization15 of ketones, and the synthesis of hetero-cyclic rings.16 Typical organic tribromides include tetrabu­tylammonium tribromide (TBATB),2,9 pyridinium hydro-tribromide (PyHTB),4–6,14 benzyltrimethylammonium tribromide (BTMATB),7 2-carboxyethyltriphenylphos­phonium tribromide (CTPTB)11 and benzyltriphenyl­phosphonium tribromide.13,15 These reagents have attract­ed considerable attention in organic synthesis and have proven to be leaders over liquid bromine such as N-bro­mosuccinimide (NBS), Br2/HBr and other conventional bromination reagents due to their ease of use, mildness, efficiency and selectivity. They are stable and crystalline solids that can release a stoichiometric amount of bro­mine. Other advantages include their recoverability and reusability when used as catalysts. - In recent years, much attention has been paid to Br3 immobilized on polymers,1,17 magnetic nanoparticles (MNP)16,18–23 and other solid supports.24 The organic tribromide immobilized on magnetic nanoparticles can be easily separated from the reaction mixture using an exter­nal magnet without the need for filtration or other workup processes. The use of magnetic nanoparticles (MNP) as a catalyst support enables efficient recovery of the catalyst. This can solve the problems associated with nanoparticle separation.25,26 Although several immobilized tribromides have been synthesized, in most cases their preparation in­volves the use of liquid Br2 and/or HBr,16,21–24 which leads to an environmental problem. Numerous improved methods for the synthesis of organic tribromides have been described in the literature and are considered to be environmentally friendly. These improved methods use an oxidation system for the oxida­tion of organic bromide to organic tribromides, such as 7101112 (NH4)2S2O8/H+, NaOCl/H+, KMnO4 /H+, H5IO6,1527, oxone®, MCPBA,3 H2MoO4 or V2O5/ H2O2, and CAN.28 Despite the introduction of these oxidation meth­ods in the literature, many of the recently reported meth­ods for the synthesis of magnetic nanoparticles (MNP)16,21– 23 or polymer-assisted1,17 bromine sources involve liquid Br2 and/or HBr, which continues to cause environmental problems. Therefore, the development of newer strategies that do not require the use of molecular bromine or metals and strong mineral acids remains a challenge for the syn­thesis of immobilized organic tribromides, especially nanoparticle-based magnetic catalysts. In this work, we reported a novel method for the syn­thesis of pyridinium hydrotribromide on magnetic Fe3O4 nanoparticles (MNPs) by oxidation of immobilized organic bromide to tribromide using periodic acid (H5IO6) as a cheap, nontoxic, efficient, and easy-to-use oxidant. Fe3O4@ PyHBr3 was used as a catalyst for the selective oxidation of alcohols to aldehydes and ketones using 30% H2O2. In addi­tion, trimethylsilylation and tetrahydropyranylation of al­cohols were carried out in the presence of this catalyst.. 2. Experimental Section 2. 1. Instrumentation, Analysis and Raw Materials All products were known and characterized by com­paring their physical data with those of known samples or by their spectral data. Infrared spectra (IR) were recorded on the KBr matrix using a Perkin Elmer RXI spectrometer. Nuclear magnetic resonance (NMR) spectra were record­ed in DMSO-d6 or CDCl3 using tetramethylsilane (TMS) as an internal standard with a Bruker Avance DRX spec­trometer. Thermogravimetric analysis (TGA) was per­formed using a BAHR Thermo analyze STA 503 instru­ment. XRD patterns were recorded using a Bruker D8-Advance diffractometer. Elemental analysis was per­formed using a Costech Elemental Combustion System CHNS-O (ECS 4010). Field emission scanning electron microscopy (FESEM) was performed using a MIRA3 TES­CAN instrument. A Lakeshore7400 vibratory magnetom­eter (VSM) was used to characterize magnetic properties at room temperature. 2. 2. Preparation of the Fe3O4 Magnetic Nanoparticles (Fe3O4 MNP) Fe3O4-MNP were prepared according to the meth­od described in the literature.29 Typically, FeCl3 · 6H2O (5.406 g, 20 mmol) and FeCl2 · 4H2O (1.988 g, 10 mmol) were dissolved in distilled water (75 mL) in a three-neck flask (250 mL) under an inert atmosphere. Then NaOH solution (10 M, 10 mL) was added with vigorous mechan­ical stirring until the pH of the solution was increased to 11. The temperature of the mixture was raised to 80 °C. The mixture was kept in an ultrasonic bath for 1 hour. The black precipitate was separated with a magnet, washed with double-distilled water to neutrality, then washed with ethanol (2 × 20 mL) and dried at 60 °C in a vacuum oven. 2. 3. Synthesis of Fe3O4@PyHBr3 MNP (3-Aminopropyl)triethoxysilane (APTES) (1.771 g, 8 mmol) was dissolved in CHCl3 (10 mL) and then 4-(bro­momethyl) pyridine hydrobromide (2.024 g, 8 mmol) was added to the solution. The mixture was heated under re­flux for 24 hours. Then the solvent was evaporated and the product (APTES-MPyHBr) was dried. Then magnetic Fe3O4 nanoparticles (1.2 g) were added to APTES-MPyHBr (2 g) in toluene (10 mL) and the mixture was heated for 12 hours under N2 atmosphere under reflux conditions. The magnetic Fe3O4@PyHBr nanoparticles were isolated with a magnet, washed with CH2Cl2 (3 × 20 mL), and dried at 60 °C in a vacuum oven. Analytical data for Fe3O4@PyHBr: C, 18.65; H, 3.02; N, 4.86%. A mixture of Fe3O4@PyHBr (3 g), KBr (1.27 g, 10.67 mmol), and HIO4 (2.70 g, 14.07 mmol) were ground in a mortar for 30 min at room temperature. The color of the mixture was changed from orange to dark brown. The Fe3O4@PyHBr3 MNPs were washed with H2O (3 × 15 mL) and then with ethanol (3 × 15 mL) and dried at 60 °C in a vacuum oven. Analytical data for Fe3O4@PyHBr3: C, 12.96; H, 1.67; N, 3.65%. 2. 4. Typical Procedures 2. 4. 1. Oxidation of 4-nitrobenzyl Alcohol Magnetic nanocatalyst Fe3O4@PyHBr3 (0.070 g, 0.091 mmol) was added to a mixture of 4-nitrobenzyl alcohol (0.153 g, 1 mmol) and H2O (1 mL), followed by the addition of hydrogen peroxide (30%, 0.45 mL, 4 mmol), and the re­sulting mixture was heated to 50 °C. The progress of the re­action was monitored by TLC using n-hexane-ethyl acetate (7:3) as eluent. After completion of the reaction (75 min), the catalyst was separated using a magnet. Then the reaction mixture was extracted with diethyl ether (3 × 5 mL). The organic layer was dried over anhydrous magnesium sulfate and evaporated under reduced pressure to give 4-nitro­benzaldehyde (0.128 g, 0.85 mmol); mp: 104–106 °C. FT-IR (KBr, cm–1): . 3107 (w), 3066 (w), 2956(s), 2926 (s), 2854 (s), 1706 (s), 1608 (s), 1544 (s), 1454 (s), 1378 (s), 1360 (s), 1346 (s), 1197 (s), 852 (s), 818 (s), 740 (s). 1HNMR (DMSO-d6, ppm): d 8.12–8.17 (m, 2H), 8.39–8.42 (m, 2H), 10.15 (s, 1H). 2. 4. 2. Trimethylsilylation of Benzyl Alcohol The magnetic Fe3O4@PyHBr3 nanocatalyst (0.070 g, 0.091 mmol) was added to a mixture of benzyl alcohol (0.108 g, 1 mmol) and HMDS (0.113 g, 0.7 mmol) in CH2Cl2 (5 mL), and the resulting mixture was mechani­cally stirred at room temperature. The progress of the reac­tion was monitored by TLC using n-hexane-ethyl acetate (7:3) as eluent. After completion of the reaction (10 min), the catalyst was separated using a magnet. Then the reac­tion mixture was extracted with diethyl ether (3 × 5 mL). The organic layer was dried over anhydrous magnesium sulfate and evaporated under reduced pressure to give benzyl trimethylsilyl ether (0.166 g, 0.92 mmol). Colorless liquid; b.p. 93 °C. FT-IR (KBr, cm–1): . 2957, 1250, 1094, 727. 1HNMR (CDCl3, ppm): d 0.18 (s, 9H), 4.29 (s, 2H), 7.26–7.40 (m, 5H); 13C NMR (CDCl3) d: 0.09, 70.7, 127.1, 127.3, 129.0, 145.7. 2. 4. 3. Tetrahydropyranylation of 4-chlorobenzyl Alcohol The magnetic nanocatalyst Fe3O4@PyHBr3 (0.070 g, 0.091 mmol) was added to a mixture of 4-chlorobenzyl alcohol (0.143 g, 1 mmol) and DHP (0.126 g, 1.5 mmol) in CH3CN (5 mL), and the resulting mixture was me­chanically stirred at room temperature. The progress of the reaction was monitored by TLC using n-hexane-ethyl acetate (7:3) as eluent. After completion of the reaction (3 h), the catalyst was separated using a magnet. Then the reaction mixture was extracted with diethyl ether (3 × 5 mL). The organic layer was dried over anhydrous magne­sium sulfate and evaporated under reduced pressure to afford 4-chlorobenzyl tetrahydropyranyl ether (0.213 g, 0.94 mmol). FT-IR (KBr, cm–1): . 3070 (w), 2944(s), 2871 (s), 1442 (m), 1343 (m), 1128 (s), 1029 (s), 754 (s).1HNMR (CDCl3, ppm): d 1.57–1.60 (m, 2H), 1.64– 1.65 (m, 2H), 1.66–1.78 (m, 2H), 3.56–3.60 (m, 1H), 3.91– 3.96 (m, 1H), 4.50–4.52 (d, 1H), 4.72–4.74 (t, 1H), 4.77– 4.80 (d, 1H), 7.30–7.36 (m, 4H). 3. Results and Discussion The synthesis of Fe3O4 MNP linked to pyridinium hydrotribromide (Fe3O4@ PyHBr3) was carried out in three steps. In the first step, APTES-MPyHBr was synthe­sized by the reaction of 3-(aminopropyl)triethoxysilane (APTES) with 4-(bromomethyl)pyridine hydrobromide (MPyHBr) in CHCl3 under reflux conditions. In the sec­ond step, APTES-MPyHBr was reacted with Fe3O4 mag­netic nanoparticles to obtain the Fe3O4@PyHBr magnetic nanoparticles. In the last step, the mixture of Fe3O4@PyH-Br, KBr and HIO4 was ground in a mortar to obtain the magnetic Fe3O4@PyHBr3 nanoparticles (Scheme 1). Fe3O4@PyHBr3 was characterized by FT-IR, CHN, XRD, SEM, TGA and VSM analysis. The FT-IR spectra confirmed the preparation of the magnetic nanocatalyst at each step (Figure 1). The FTIR spectrum of Fe3O4@ PyH­Br3 nanoparticles (Figure 2) showed two sharp absorption Scheme 1. Synthesis of magnetic Fe3O4@PyHBr3 nanoparticles. bands at 2927 cm–1 and 1222 cm–1, which are related to stretching and bending vibrations of CH2 groups, respec­tively. The broad absorption bands at 1045 cm–1 and 1115 cm–1 are related to the stretching vibrations of the Si-O bonds. The absorption band at 3421 cm–1 is due to the N-H group overlaid by the hydroxyl group (Fe3O4 has a OH -rich surface). In addition, the absorption band at 585 cm–1 is due to the stretching vibration of the Fe-O bond in Fe3O4. The elemental analysis results (CHN) showed that the carbon, hydrogen and nitrogen contents of Fe3O4@ PyHBr3 were 12.96, 1.67 and 3.65 (wt%), respectively, corresponding to a loading of 1.3 mmol of nitrogen groups (Br3- content) per gram of catalyst. The analytical results of CHN showed that the carbon, hydrogen and ni­trogen contents of Fe3O4@PyHBr (monobromide) were 18.65, 3.02 and 4.86 (wt%), respectively. Comparing the total weight percentages of carbon, hydrogen and nitro­gen in Fe3O4@PyHBr and Fe3O4@PyHBr3, the ratio is 1.45, which is very close to 1.36, calculated from the chemical formula of the organic segment of these com­pounds. X-ray powder diffraction (XRD) analysis was per­formed to characterize the crystalline structure of Fe3O4@ PyHBr3. The XRD pattern of Fe3O4@PyHBr3 showed a semi-crystalline nature compared to the XRD pattern of Fe3O4 (not shown here).26 The image of the magnetic Fe3O4@PyHBr3 nanopar­ticles in the field emission scanning electron microscope (FESEM) shows a spherical structure with a particle size in the range of 35–40 nm (Figure 4). The magnetic properties of Fe3O4 and Fe3O4@PyH­Br3 were studied using a vibrating sample magnetometer (VSM) at room temperature. The saturation magnetization of Fe3O4@PyHBr3-MNP was about 50 emu g–1, which was Figure 2. FT-IR spectrum of Fe3O4@PyHBr3 MNP (KBr). lower than that of pure Fe3O4-MNP (74 emu g–1) (not shown here). The decrease in the measured saturation magnetization was due to the presence of organics around Fe3O4. The thermal stability of the Fe3O4 MNPs and Fe3O4@ PyHBr3 MNPs was also investigated. Comparison of the respective thermograms showed that no significant weight loss was observed for the Fe3O4 thermogram (Figure 5, black thermogram). On the other hand, the thermograms of Fe3O4@PyHBr3 showed three weight loss steps of about 15 % in the range of 200–600 °C, which could be attributed to the evaporation of residues trapped in the samples and the removal of the organic content. The oxidation of alcohol is one of the most import­ant reactions in the synthesis of organic compounds.30–38 Some of these reactions occur under difficult conditions, such as high temperatures, the use of unfavorable sol­vents, and the use of toxic oxidizing agents, including transition metal compounds that produce harmful by­products. Usually, compounds such as chromic acid, di- Figure 5. Thermograms of pure Fe3O4 MNPs (black) and Fe3O4 @ PyHBr3 MNPs (red). chromate/H+, PCC, PDC are used to oxidize alcohols.34 Due to the limitations of existing methods, efforts are be­ing made to perform environmentally friendly reactions. The goal of these efforts is to catalytically oxidize alcohols using low-cost green oxidants such as air, oxygen, and hy­drogen peroxide, and using heterogeneous (solid) cata­lysts and harmless and environmentally friendly solvents. Hydrogen peroxide (H2O2) is an attractive oxidant for liquid-phase reactions.34–38 This water-soluble oxidant is safe, inexpensive, and has a high oxygen content that does not require a buffer. In addition, it is considered a green and environmentally friendly oxidant because it re­leases water as a byproduct. After the synthesis of pyri­dinium hydrotribromide on Fe3O4 MNP, we investigated the effectiveness of Fe3O4@PyHBr3 MNP as a catalyst in the oxidation of benzyl alcohols with 30% hydrogen per­oxide. To optimize the reaction conditions, the oxidation of 4-bromobenzyl alcohol with different amounts of 30% hydrogen peroxide, different amounts of Fe3O4@PyHBr3 catalyst and different reaction temperatures were investi­gated (Scheme 2). (Table 1, entries 1–4). According to the results, it was found that the reaction had a higher conversion (60 %) and a shorter time (60 minutes) when 4 mmol of H2O2 was used, but this time additional amounts of catalyst were used to increase the conversion (Table 1, entries 5–6 and 9). The results show that increasing the amount of catalyst leads to an increase in conversion. The effects of reaction temperature were also studied (Table 1, entries 6–8). At 25 °C and 70 °C, the conversions were 65 % and 90 %, respectively. The reaction was also carried out with Fe3O4 (0.23 g), and it was found that the reaction time was longer than that of the Fe3O4@PyHBr3 catalyst (Table 1, entry 11). When the reaction was carried out without catalyst, the desired product was produced after 4 hours with a very low conversion (Table 1, entry 12). According to the results in Table 1, the optimum conditions for this reaction are 4 mmol 30% hydrogen per­oxide as oxidant, 0.070 g (9.1 mol%) Fe3O4@PyHBr3 as catalyst at 50 °C (Table 1, entry 6). Under these optimum conditions, 4-bromobenzaldehyde was obtained in 20 min Scheme 2. Oxidation of 4-bromobenzyl alcohol by hydrogen peroxide in the presence of Fe3O4@ PyHBr3. To determine the optimum amount of hydrogen peroxide, the reaction of 4-bromobenzyl alcohol (1 mmol) in various molar ratios of H2O2 to alcohol was studied in the presence of 0.020 g catalyst at 50 °C in H2O (1 mL) Table1. Optimization of the oxidation of 4-bromobenzyl alcohol by a 30% H2O2. Entry H2O2 Catalyst T Reaction Conversion alcohol (g) (°C) time (min) (%) Molar ratio 1 1 0.02 50 120 30 2 2 0.02 50 120 45 3 3 0.02 50 90 50 4 4 0.02 50 60 60 5 4 0.04 50 45 80 6 4 0.07 50 20 100 7 4 0.07 25 60 65 8 4 0.07 70 20 90 9 4 0.08 50 18 100 10 3 0.07 50 40 80 11 4 0.23b 50 40 96 12 4 – 50 240 5 a The reaction was performed using 4-bromobenzyl alcohol (1 mmol) in H2O (1 mL). b Fe3O4 MNP were used as a catalyst. with an excellent conversion rate of 100 %. The reaction of benzyl alcohol and other benzyl alcohols with electron do­nor and electron acceptor substituents was carried out un­der these optimum conditions, and the results are shown in Table 2. Benzyl alcohols with electron donor group (Ta­ble 2, entries 2, 6, 7, 9) were oxidized in shorter time than alcohols with strong electron acceptor group (Table 2, en­tries 8, 10, 11). The secondary benzyl alcohols (entries 13 and 14) were also oxidized to the corresponding ketones. The study showed that the oxidation time of the non-ben­zyl alcohol 2-phenyl-1-propanol was longer than that of the benzyl alcohols (Table 2, entry 12). Moreover, no addi­tional oxidation to the carboxylic acid was observed for the primary alcohols, which is one reason for the mildness and selectivity of the present method. After the reaction was completed, the catalyst was separated with a magnet. To evaluate the performance of Fe3O4@PyHBr3 catalyst after recovery, the catalyst used in each reaction was separated with a magnet and reused in the next reaction after rinsing with CH2Cl2 (3 × 5 ml). The catalyst recovery results for the oxidation of 4-bromoben­zyl alcohol showed that the catalyst could be used for up to 5 cycles without significantly reducing the reaction yield (Table 3). Table 2. Fe3O4@ PyHBr3 MNP catalyze the oxidation of alcohols to carbonyl compounds.a Entry Substrate Product Reaction Time (min.) yieldb (%) 20 88 10 95 20 95 30 93 35 90 25 98 15 90 75 85 18 86 60 97 60 88 80 91 35 94 10 95 a Reaction conditions: substrate (1 mmol), Fe3O4@ PyHBr3 MNP (0.07 g, 9.1 molar%), aqueous hydrogen peroxide (4 mmol, 30%), H2O (1 mL), at 50 °C. b Isolated yields. Table 3. Recyclability of Fe3O4@PyHBr3 catalyst. The efficiency of this catalyst can also be illustrated by Run 1 2 3 4 5 comparing the results obtained in this study for the oxida- Yield (%) Time (min) 95 20 90 20 88 20 85 20 80 20 tion of benzyl alcohol with those published in the literature. For this purpose, several recent methods are given in Table 4 along with the temperature, reaction time, and yield. Table 4. Comparison of the present method with other methods reported for the oxidation of benzyl alcohol. Reaction time Temperature Reagents Yield% (min) (°C) PVP-Br3 70 r.t. 9417 Silica-supported *DABCO-Br3 60 80 9524 H2O2 (1.1 eq), {[K.18-Crown-6]Br3}n 30 80 8136 H2O2 (3 eq), **DBDMH (0/5 eq) 120 60 8537 H2O2 (1.3eq), MgFe2 O4 MNPs 55 60 8838 H2O2, K8[BW11O39H]13H2O 360 90 9834 H2O2,VHPW/MCM-41/NH2 480 80 9735 88this work H2O2, Fe3O4@PyHBr3 20 50 *DABCO =1,4-diazabicyclo[2.2.2]octane **DBDMH= 1,3-Dibromo-5,5-dimethylhydatoin Scheme 3. Trimethylsilylation and tetrahydropyranylation of benzyl alcohols in the presence of Fe3O4@PyHBr3 Trimethylsilylation and tetrahydropyranylation of benzyl alcohols were studied in the presence of Fe3O4@ PyHBr3 (Scheme 3). According to the optimum conditions obtained for 4-bromobenzyl alcohol, trimethylsilylation of various alcohols was carried out with Fe3O4@PyHBr3 (0.07 g, 9.1 mol%), HMDS (0.7 mmol) in CH2Cl2 (5 mL) at room temperature (Scheme 3, Table 5). Tetrahydropyra­nylation of alcohols was also carried out with Fe3O4@PyH­Br3 (0.07 g, 9.1 mol%), DHP (1.5 mmol) in CH3CN (5 mL) at room temperature (Scheme 3, Table 5). These results show that for all types of benzyl alco­hols with electron donor and electron acceptor substitu­ents, the corresponding trimethylsilyl ethers were obtained in short time and in excellent yield. The reaction times are shorter for benzyl alcohols with electron donor substitu­ents than for benzyl alcohols with strong electron acceptor substituents. These results showed that the corresponding tetra-hydropyranyl ethers were obtained in high yields for pri­mary benzyl alcohols with electron acceptor or electron donor substitutions. However, the reaction times were shorter for benzyl alcohols with electron donor substitu­tions. 4. Conclusions In summary, the magnetic Fe3O4@PyHBr3 nanocat­alyst was prepared and characterized by FTIR, CHN, TGA, XRD, FE-SEM and VSM analyzes. The efficiency of this magnetic nanocatalyst in the oxidation reaction of alco­hols to aldehydes and ketones was investigated. The results showed that Fe3O4@PyHBr3 was very effective as a catalyst in the oxidation of alcohols using H2O2 as a green oxidant. This method has some advantages, such as very mild reac­tion conditions, easy separation and reusability of the cat­alyst, very short reaction times, high yields, and the use of H2O as a green solvent. No additional oxidation to the car-boxylic acid was observed for the primary alcohols, which is one reason for the mildness and selectivity of the present method. The use of this magnetic catalyst in the reactions of trimethylsilylation and tetrahydropyranylation of alco­hols was also studied, and it was found to have many ad­vantages, such as easy separation of the catalyst, good yields, and mild reaction conditions. Acknowledgment The authors gratefully acknowledge financial sup­port for this work from Damghan University. 5. 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V zadnjem koraku so izvedli reakcijo s pomocjo mletja s KBr in HIO4 v mlinu, in nanokatalizator Fe3O4@PyHBr3 okarakterizirali s FT-IR, CHN, XRD, SEM, TGA in VSM analizo. Magnetni nanokatalizator je bil upora­bljen kot katalizator za selektivno oksidacijo alkoholov v aldehide in ketone z uporabo 30 % H2O2 kot oksidanta. Reakcija je potekla v kratkem casu in z visokimi izkoristki, poleg tega pa niso opazili nadaljnje oksidacije alkoholov. Magnetni nanokatalizator so ucinkovito reciklirali v petih zaporednih ciklih brez pomembnejše izgube kataliticne aktivnosti. Prav tako so z njim uspešno izvedli trimetilsililiranje in tetrahidropiranilizacijo alkoholov. Except when otherwise noted, articles in this journal are published under the terms and conditions of the Creative Commons Attribution 4.0 International License DOI: 10.17344/acsi.2021.7080 Scientific paper (E)-1-(4-Hydroxyphenyl)-3-(substituted-phenyl)prop-2-en-1-ones: Synthesis, In Vitro Cytotoxic Activity and Molecular Docking Studies Lütfiye Sirka,1 Hacer Dogan,1 Mehmet Refik Bahar,2 Eray Çaliskan,3 Suat Tekin,4 Harun Uslu,5 Kenan Koran,1,* Süleyman Sandal4 and Ahmet Orhan Görgülü6 1 Firat University, Faculty of Science, Department of Chemistry, 23119, Elazig, Turkey 2 Osmaniye Korkut Ata University, Faculty of Health Sciences, Department of Nutrition and Dietetics, 80010, Osmaniye, Turkey 3 Bingol University, Faculty of Science, Department of Chemistry, 12000, Bingol, Turkey 4 Inonu University, Faculty of Medicine, Department of Physiology, 44280, Malatya, Turkey 5 Firat University, Vocational School of Health Services, Department of Anesthesiology, 23600, Elazig, Turkey 6 Marmara University, Faculty of Arts and Sciences, Department of Chemistry, 34722, Istanbul, Turkey * Corresponding author: E-mail: kkoran@firat.edu.tr Received: 07-29-2021 Abstract A series of chalcone compounds (2–11) were designed and synthesized to determine their cytotoxic effects. The struc­tures of 2–11 were fully characterized by their physical and spectral data. The in vitro cytotoxic effects of 2–11 were eval­uated against human ovarian cancer (A2780), breast cancer (MCF-7) and prostate cancer (PC-3 and LNCaP) cell lines. The activity potentials of compounds were further evaluated through molecular docking studies with AutoDock4 and Vina softwares. All the compounds (except compound 5) showed significant cytotoxic effects at high doses in all cancer cell lines. Among all the compounds studied, one compound i.e. compound 2 demonstrated dose-dependent activity, particularly against A2780/LNCaP cancer cell lines. The most effective compounds 8, 9, 10 and 11 reduced the cell via­bility of A2780, MCF-7, PC-3 and LNCaP cells by 50–98%, while other compounds 2, 4 and 7 reduced the cell viability of A2780 cells by 70–90% at concentrations of 50 and 100 µM. Keywords: Chalcone; cytotoxic; A2780; MTT assay; molecular docking erative,13 antiplatelet,14,15 and antimalarial.16,17 Numerous 1.Introduction studies have attempted to elucidate the mechanisms of ac- The chalcones or phenyl styryl ketones are unsatu-tion and target interactions responsible for these biological rated ketones, containing the reactive keto-ethylenic activities.18–20 Additionally, these compounds have appli-group. Chalcone is an important chemo type that has at-cations in a variety of areas including nonlinear optical tracted great research interest for decades due to the abun-materials,21,22 dye sensitized solar cell,23,24 and optoelec­dant natural chalcone-based compounds, the easy synthe-tronic and fluorescence materials.25–27 sis and derivatization, and most importantly, the diverse There are several methods available for the synthesis biological activities of various chalcone-based com-of chalcones. The Claisen–Schmidt condensation reaction, pounds.1–6 The well documented biological activities of one of the most widely used chalcone synthesis methods, chalcones include anti-HIV,7 antibacterial,8 anti-cancer was used in the synthesis of compounds in this article by and antioxidant,9–11 antituberculosis agents,12 anti-prolif-using sodium/potassium hydroxide.28–36 The base-cata­lysed other method of chalcone synthesis include Claisen– Schmidt reaction.37 The acid catalyzed methods that have been used to synthesize chalcones includes Friedel–Crafts acylation,38 silica-sulfuric acid,39 dry HCl,40, 41 boron tri­fluoride-diethyletherate (BF3-Et2O),42 Aldol condensa-tion,43 Suzuki coupling reaction,44 Julia–Kocienski olefi­nation45 and Wittig reaction.46 Among these the direct aldol condensation and Claisen–Schimdt condensation still occupy prominent position. In addition to the wide potential applications of chalcone derivatives, their ability to be easily isolated from natural plants as well as their synthetic accessibility with many synthesis procedures, as mentioned above, increased the studies on these compounds. Herein, we have synthesized a series of chalcone de­rivatives in order to evaluate their cytotoxic effects against four human cancer cell lines namely ovarian (A2780), prostate (PC-3 and LNCaP), and breast (MCF-7). For this reason, the synthesis of target compounds was achieved by the condensation of para-hydroxyacetophenone with benzaldehyde using sodium hydroxide as a condensing agent. These compounds 2–11 were confirmed by using mass (MALDI-TOF-MS), FT-IR, elemental analysis, 1H, 13C-APT NMR spectroscopy. Disruption of the microtubules can lead to apoptosis induction as it has vital importance to the continuation of the cell cycle. Therefore, the literature emphasizes that tu­bulin inhibitors can be used as effective anticancer drugs.47,48 Binding site for colchicine is well determined in the tubule.49 Potential tubulin inhibition effects with syn­thesized molecules were determined by tubulin-colchicine complex. The interactions of compounds with the tubu­lin-colchicine complex were put forthed via molecular docking studies. And their possible anti-cancer properties were investigated against A2780, MCF-7, PC-3 and LN-CaP cell lines by using MTT assay method.50–53 Our re­sults indicate that these compounds displayed strong cyto­toxic activity on these cell lines. 2. Experimental 2. 1. Synthesis All aldehydes and solvents used in the present study were provided by Sigma-Aldrich and Merck. 1H and 13C-APT NMR spectra, infrared analysis and microanaly­sis were acquired using a Bruker DPX-400 MHz spectrom­eter, a Perkin Elmer FT-IR spectrometer and a LECO 932 CHNS-O apparatus, respectively. A Bruker microflex LT MALDI-TOF MS spectrometer was used to obtain mass spectra. In cell culture studies, the human prostate (PC-3 and LNCaP), breast (MCF-7), and ovarian cancer cell lines were provided by ATCC (the American Type Culture Col­lection), new-born calf serum and Dulbecco’s modified Eagle’s medium (DMEM) were provided by Hyclone (Waltham, MA, USA); and penicillin, trypsin, streptomy­cin. Nuve MN-120 as biological safety cabinet, Panasonic as CO2 Incubator, BioTEK spectrophotometer as micro-plate reader, Inverted Microscope SOIF-XDS for mainte­nance and control of cells and Nuve for Sterilization were used. 2. 1. 1. General Synthesis Method of (E)-1-(4­Hydroxyphenyl)-3-(substituted-phenyl)prop-2-en-1-one Compounds Appropriate para-hydroxyacetophenone (1.00 equiv.) was placed in a round bottom flask with ethanol (50 mL) and 60% sodium hydroxide (25 mL). After 30 min, aldehyde (1.015 equiv.) was added to the reaction mixture. The solution was let to stir overnight. The mix­ture was then poured into an ice/water mixture. Concen­trated HCl was added until the solution reached an acidic pH (until about pH 6). Upon addition of the acid a precip­itate formed. The precipitate was filtered and washed with plenty of water to pH neutral. The product was recrystal­lized in ethanol. (E)-1-(4-Hydroxyphenyl)-3-(3-methoxyphenyl)prop-2­en-1-one (2) Yield: 92%; yellow solid; MALDI-MS: m/z calcd: 254.29; found: 255.58 [M+H]+. FT-IR (KBr) .max (cm–1): 3294 .OH, 3000 and 3018 .Ar-CH, 2829, 2903 and 2965 .Aliphatic-CH, 1652 .C=O, 1512, 1573, 1594 and 1606 .C=C. 1H NMR (DMSO-d6) d 3.84 (3H, s, H17 (-OCH3)), 6.91– 6.93 (2H, d, J = 8.8 Hz, Ar-H3,5), 7.01–7.04 (1H, d, Ar­H14), 7.35–7.39 (1H, t, Ar-H15), 7.42–7.43 (1H, d, Ar-H12), 7.47–7.48 (1H, d, Ar-H16), 7.65–7.69 (1H, d, J = 15.6 Hz, H9 (-CH=)), 7.91–7.95 (1H, d, J = 15.6 Hz, H10 =CH-)), 8.09–8.11 (2H, d, J = 8.8 Hz, Ar-H2,6), 10.43 (1H, s, H7 (Ph-OH)). 13C-APT NMR (DMSO-d6) d 55.75 (C17 (-OCH3)), 113.64 (Ar-C14), 115.85 (Ar-C3,5), 116.90 (Ar­C12), 122.01 (Ar-C16), 122.80 (C9 (-CH=)), 129.55 (Ar­C1), 130.37 (Ar-C15), 131.73 (Ar-C2,6), 136.77 (Ar-C11), 143.21 (C10 (=CH-)), 160.11 (Ar-C13), 162.71 (Ar-C4), 187.59 (C8 (-C=O)). Anal. Calcd for C16H14O3: C, 75.58; H, 5.55. Found: C, 75.69; H, 5.49%. (E)-1-(4-Hydroxyphenyl)-3-(2,4-dimethoxyphenyl) prop-2-en-1-one (3) Yield: 83%; yellow solid; MALDI-MS: m/z calcd: 284.31; found: 284.48. FT-IR (KBr) .max (cm–1): 3144 .OH, 3005 and 3013 .Ar-CH, 2835 and 2934 .Aliphatic-CH, 1645 .C=O, 1506, 1541, 1569, 1590 and 1600 .C=C. 1H NMR (DMSO-d6) d 3.85 (3H, s, H18 (-OCH3)), 3.91 (3H, s, H17 (-OCH3)), 6.64–6.65 (2H, m, Ar-H13, H15), 6.89–6.91 (2H, d, J = 8.4 Hz, Ar-H3,5), 7.72–7.76 (1H, d, J = 15.6 Hz, H9 (-CH=)), 7.89–7.91 (1H, d, J = 8.8 Hz, Ar-H16), 7.93–7.97 (1H, d, J = 15.6 Hz, H10 (=CH-)), 8.02–8.05 (2H, d, J = 8.4 Hz, Ar-H2,6), 10.36 (1H, s, H7 (Ph-OH)). 13C-APT NMR (DMSO-d6) d 55.98 (C18 (-OCH3)), 56.26 (C17 (-OCH3)), 98.74 (Ar-C13), 106.70 (Ar-C15), 115.77 (Ar-C3,5), 116.55 (Ar-C11), 119.55 (C9 (-CH=)), 129.96 (Ar-C1), 130.35 (Ar­C16), 131.37 (Ar-C2,6), 137.93 (C10 (=CH-)), 160.22 (Ar­C14), 162.35 (Ar-C12), 163.28 (Ar-C4), 187.65 (C8 (-C=O)). Anal. Calcd for C17H16O4: C, 71.82; H, 5.67. Found: C, 71.89; H, 5.72%. (E)-1-(4-Hydroxyphenyl)-3-(3,4-dimethoxyphenyl) prop-2-en-1-one (4) Yield: 77%; yellow solid; MALDI-MS: m/z calcd: 284.31; found: 285.38 [M+H]+1. FT-IR (KBr) .max (cm–1): 3112 .OH, 3026 and 3071 .Ar-CH, 2835 and 2956 .Aliphat-ic-CH, 1641 .C=O, 1506, 1541, 1581, 1591 and 1608 .C=C. 1H NMR (DMSO-d6) d 3.83 (3H, s, H18 (-OCH3)), 3.88 (3H, s, H17 (-OCH3)), 6.91–6.93 (2H, d, J = 8.8 Hz, Ar-H3,5), 7.01– 7.03 (1H, d, J = 8.4 Hz, Ar-H16), 7.35–7.38 (1H, d, J = 9.6 Hz, Ar-H15), 7.53 (1H, s, Ar-H12), 7.64–7.68 (1H, d, J = 15.6 Hz, H9 (-CH=)), 7.79–7.83 (1H, d, J = 15.6 Hz, H10 (=CH-)), 8.08–8.10 (2H, d, J = 8.4 Hz, Ar-H2,6), 10.39 (1H, s, H7 (Ph-OH)). 13C-APT NMR (DMSO-d6) d 56.05 (C17 (-OCH3)), 56.20 (C18 (-OCH3)), 111.09 (Ar-C12), 112.02 (Ar-C15), 120.13 (C9 (-CH=)), 124.10 (Ar-C16), 128.20 (Ar-C11), 129.83 (Ar-C1), 131.54 (Ar-C2,6), 143.68 (C10 (=CH-)), 149.49 (Ar-C13), 151.50 (Ar-C14), 162.49 (Ar­C4), 187.52 (C8 (-C=O)). Anal. Calcd for C17H16O4: C, 71.82; H, 5.67. Found: C, 71.89; H, 5.72%. (E)-1-(4-Hydroxyphenyl)-3-(2,3,4-trimethoxyphenyl) prop-2-en-1-one (5) Yield: 79%; yellow solid; MALDI-MS: m/z calcd: 314.34; found: 315.42 [M+H]+1. FT-IR (KBr) .max (cm–1): 3392 .OH, 3005 and 3050 .Ar-CH, 2830 and 2938 .Aliphat-ic-CH, 1657 .C=O, 1530, 1580, and 1606 .C=C. 1H NMR (DMSO-d6) d 3.79 (3H, s, H18 (-OCH3)), 3.87 (3H, s, H19 (-OCH3)), 3.89 (3H, s, H17 (-OCH3)), 6.90–6.94 (3H, m, Ar-H3, H15), 7.75–7.80 (2H, m, H9 (-CH=), Ar-H16), 7.75– 7.88 (1H, d, J = 15.6 Hz, H10 (=CH-)), 8.03–8.05 (2H, d, J = 8 Hz, Ar-H2,6), 10.37 (1H, s, H7 (Ph-OH)). 13C-APT NMR (DMSO-d6) d 56.51 (C19 (-OCH3)), 60.94 (C18 (-OCH3)), 61.98 (C17 (-OCH3)), 108.90 (Ar-C15), 115.82 (Ar-C3,5), 120.91 (C9 (-CH=)), 121.65 (Ar-C11), 123.73 (Ar-C16), 129.76 (Ar-C1), 131.50 (Ar-C2,6), 137.58 (C10 (=CH-)), 142.21 (Ar-C13), 153.42 (Ar-C12), 155.96 (Ar-C14), 162.52 (Ar-C4), 187.57 (C8 (-C=O)). Anal. Calcd for C18H18O5: C, 68.78; H, 5.77. Found: C, 68.71; H, 5.71%. (E)-1-(4-Hydroxyphenyl)-3-(2,4,5-trimethoxyphenyl) prop-2-en-1-one (6) Yield: 72%; yellow solid; MALDI-MS: m/z calcd: 314.34; found: 314.48. FT-IR (KBr) .max (cm–1): 3307 .OH, 3028 and 3067 .Ar-CH, 2827 and 2929 .Aliphatic-CH, 1639 .C=O, 1505, 1544, 1583, and 1613 .C=C. 1H NMR (DM­SO-d6) d 3.83 (3H, s, H18 (-OCH3)), 3.88 (3H, s, H19 (-OCH3)), 3.91 (3H, s, H17 (-OCH3)), 6.76 (1H, s, H13), 6.90–6.92 (2H, d, J = 8.4 Hz, Ar-H3,5), 7.50 (1H, s, H16), 7.71–7.75 (1H, d, J = 15.6 Hz, H9 (-CH=)), 7.98–8.02 (1H, d, J = 15.6 Hz, H10 (=CH-)), 8.04–8.06 (2H, d, J = 8.4 Hz, Ar-H2,6), 10.29 (1H, s, H7 (Ph-OH)). 13C-APT NMR (DM­SO-d6) d 56.33 (C17 (-OCH3)), 56.94 (C18 (-OCH3)), 56.96 (C19 (-OCH3)), 98.28 (Ar-C13), 111.73 (Ar-C16), 115.16 (Ar-C11), 115.74 (Ar-C3,5), 119.49 (C9 (-CH=)), 130.09 (Ar-C1), 131.38 (Ar-C2,6), 137.73 (C10 (=CH-)), 143.65 (Ar-C12), 153.07 (Ar-C15), 154.53 (Ar-C14), 162.32 (Ar­C4), 187.69 (C8 (-C=O)). Anal. Calcd for C18H18O5: C, 68.78; H, 5.77. Found: C, 68.83; H, 5.82%. (E)-1-(4-Hydroxyphenyl)-3-(1,1.-biphenyl)prop-2-en-1-one (7) Yield: 65%; yellow solid; MALDI-MS: m/z calcd: 300.36; found: 300.54. FT-IR (KBr) .max (cm–1): 3162 .OH, 3026 and 3059 .Ar-CH, 2824 and 2960 .Aliphatic-CH, 1652 .C=O, 1515, 1553, 1595, and 1609 .C=C. 1H NMR (DM­SO-d6) d 6.91–6.94 (2H, d, J = 8.8 Hz, Ar-H3,5), 7.41–7.43 (1H, t, H20), 7.49–7.51 (2H, t, H19,21), 7.72–7.79 (5H, m, H9 (-CH=), Ar-H12,16, Ar-H18,22), 7.94–7.98 (3H, m, H10 (=CH-), Ar-H13,15), 8.09–8.11 (2H, d, J = 8.4 Hz, Ar-H2,6), 10.41 (1H, s, H7 (Ph-OH)). 13C-APT NMR (DMSO-d6) d 115.88 (Ar-C3,5), 122.54 (C9 (-CH=)), 127.20 (C20), 127.53 (Ar-C12,16), 128.43 (Ar-C13,15), 129.43 (Ar-C18,22), 129.61 (Ar-C1), 129.87 (Ar-C19,21), 131.69 (Ar-C2,6), 134.53 (Ar­C11), 139.75 (Ar-C14), 142.27 (Ar-C17), 142.70 (C10 (=CH­)), 162.75 (Ar-C4), 187.55 (C8 (-C=O)). Anal. Calcd for C21H16O2: C, 83.98; H, 5.37. Found: C, 84.05; H, 5.42%. (E)-1-(4-Hydroxyphenyl)-3-(3,5-difluorophenyl)prop-2-en-1-one (8) Yield: 66%; yellow solid; MALDI-MS: m/z calcd: 260.24; found: 261.31 [M+H]+. FT-IR (KBr) .max (cm–1): 3223 .OH, 3026 and 3100 .Ar-CH, 2829 and 2972 .Aliphat-ic-CH, 1657 .C=O, 1516, 1593, and 1613 .C=C. 1H NMR (DMSO-d6) d 6.91–6.93 (2H, d, J = 8.8 Hz, Ar-H3,5), 7.29– 7.34 (1H, Ar-H14), 7.63–7.67 (1H, d, J = 15.6 Hz, H9 (-CH=)), 7.70–7.72 (2H, m, Ar-H12, Ar-H16), 8.03–8.06 (1H, d, J = 15.6 Hz, H10 (=CH-)), 8.11–8.13 (2H, d, J = 8.4 Hz, Ar-H2,6), 10.49 (1H, s, H7 (Ph-OH)). 13C-APT NMR (DMSO-d6) d 105.80 (Ar-C14), 112.02 (Ar-C12), 112.21 (Ar-C16), 115.86 (Ar-C3,5), 125.25 (C9 (-CH=)), 129.25 (Ar-C1), 131.93 (Ar-C2,6), 139.22 (Ar-C11), 140.58 (C10 (=CH-)), 161.96 (Ar-C15), 162.96 (Ar-C4), 164.40 (Ar­C13), 187.30 (C8 (-C=O)). Anal. Calcd for C15H10F2O2: C, 69.23; H, 3.87. Found: C, 69.29; H, 3.92%. (E)-1-(4-Hydroxyphenyl)-3-(2-fluoro-4-chlorophenyl) prop-2-en-1-one (9) Yield: 58%; yellow solid; MALDI-MS: m/z calcd: 276.69; found: 276.98. FT-IR (KBr) .max (cm–1): 3270 .OH, 3026 and 3067 .Ar-CH, 2865 and 2943 .Aliphatic-CH, 1647 .C=O, 1514, 1572, 1595 and 1605 .C=C. 1H NMR (DM­SO-d6) d 6.91–6.93 (2H, d, J = 8.8 Hz, Ar-H3,5), 7.40–7.42 (1H, d, J = 9.6 Hz, Ar-H15), 7.55–7.57 (1H, d, J = 10.4 Hz, Ar-H16) 7.70–7.74 (1H, d, J = 15.6 Hz, H9 (-CH=)), 7.95– 7.99 (1H, d, J = 15.6 Hz, H10 (=CH-)), 8.06–8.08 (2H, d, J = 8 Hz, Ar-H2,6), 8.15 (1H, s, Ar-H13), 10.46 (1H, s, H7 (Ph­OH)). 13C-APT NMR (DMSO-d6) d 115.95 (Ar-C3,5), 117.32 (Ar-C13), 122.11 (Ar-C11), 125.02 (Ar-C15), 125.80 (C9 (-CH=)), 129.19 (Ar-C1), 130.65 (Ar-C16), 131.83 (Ar­C2,6), 133.10 (C10 (=CH-)), 136.03 (Ar-C14), 159.78 (Ar­C12), 162.98 (Ar-C4), 187.16 (C8 (-C=O)). Anal. Calcd for C15H10ClFO2: C, 65.11; H, 3.64. Found: C, 65.17; H, 3.69%. (E)-1-(4-Hydroxyphenyl)-3-(3-(trifluoromethyl)phe-nyl)prop-2-en-1-one (10) Yield: 69%; yellow solid; MALDI-MS: m/z calcd: 292.26; found: 293.19 [M+H]+. FT-IR (KBr) .max (cm–1): 3330 .OH, 3013 and 3067 .Ar-CH, 2882 and 2981 .Aliphat-ic-CH, 1646 .C=O, 1514, 1557, 1597 and 1606 .C=C. 1H NMR (DMSO-d6) d 6.92–6.94 (2H, d, J = 8.8 Hz, Ar-H3,5), 7.67– 7.71 (1H, t, Ar-H15), 7.75–7.79 (1H, d, J = 15.6 Hz, H9 (-CH=)), 7.77 (1H, s, Ar-H16), 8.09–8.18 (4H, m, Ar-H2,6, H10 (=CH-)), Ar-H14), 8.33 (1H, s, Ar-H12), 10.46 (1H, s, H7 (Ph-OH)). 13C-APT NMR (DMSO-d6) d = 115.88 (Ar­C3,5), 124.51 (Ar-C12), 125.40 (C9 (-CH=)), 126.90 (Ar­C14), 129.35 (Ar-C1), 130.10 (Ar-C17), 130.38 (Ar-C15), 130.42 (Ar-C13), 131.91 (Ar-C2,6), 133.28 (Ar-C16), 136.55 (Ar-C11), 141.39 (C10 (=CH-)), 162.90 (Ar-C4), 187.43 (C8 (-C=O)). Anal. Calcd for C16H11F3O2: C, 65.76; H, 3.79. Found: C, 65.83; H, 3.75%. (E)-1-(4-Hydroxyphenyl)-3-(3-(trifluorometh­yl)-4-chlorophenyl)prop-2-en-1-one (11) Yield: 73%; yellow solid; MALDI-MS: m/z calcd: 326.70; found: 327.73 [M+H]+. FT-IR (KBr) .max (cm–1): 3315 .OH, 3029, 3060 and 3076 .Ar-CH, 2837 and 2961 .Aliphatic-CH, 1647 .C=O, 1515, 1553, 1595 and 1603 .C=C. 1H NMR (DMSO-d6) d 6.91–6.93 (2H, d, J = 8.8 Hz, Ar-H3,5), 7.74–7.78 (1H, d, J = 15.6 Hz, H9 (-CH=)), 7.81–7.83 (1H, d, J = 8.4 Hz, Ar-H15), 8.10–8.14 (1H, d, J = 15.6 Hz, H10 (=CH-)), 8.12–8.14 (2H, d, J = 8.4 Hz, Ar-H2,6), 8.21–8.23 (2H, d, J = 8.4 Hz, Ar-H16), 8.39 (1H, s, Ar-H12), 10.52 (1H, s, H7 (Ph-OH)). 13C-APT NMR (DMSO-d6) d 115.88 (Ar­C3,5), 124.17 (Ar-C17), 125.10 (C9 (-CH=)), 127.53 (Ar­C13), 128.33 (Ar-C15), 129.29 (Ar-C1), 131.93 (Ar-C2,6), 132.17 (Ar-C14), 132.58 (Ar-C16), 134.30 (Ar-C12), 135.24 (Ar-C11), 140.30 (C10 (=CH-)), 162.93 (Ar-C4), 187.35 (C8 (-C=O)). Anal. Calcd for C16H11F3O2: C, 58.82; H, 3.09. Found: C, 58.93; H, 3.14%. 2. 2. In Vitro Cytotoxic Activity Changes in cell viability of compounds at concentra­tions such as 1, 5, 25, 50 and 100 µM were determined by MTT analysis. This method is based on the principle of MTT dye breaking down the tetrazolium ring, and in the first stage, MTT is actively absorbed into living cells and the reaction is catalyzed by mitochondrial succinate dehy­drogenase. Finally, it is reduced to the blue-violet water-in­soluble form. Formazan formation, a marker of cell viabil­ity, occurs only in living cells with active mitochondria. The value determined spectrophotometrically is related to the number of live cells. 0.5 mg/mL MTT working solution in sterile PBS was prepared from the stock MTT solution and added to 96 well plates. The optical densities of the cells in the plates were kept in the incubator for 3 hours and then scanned at the 550 nm wavelength with ELISA device (Synergy HT USA). The average of the absorbance values obtained from the control wells was accepted as 100% live cells. The absorbance values obtained from the solvent and agent and wells were proportional to the con­trol absorbance value and were considered as percent via­bility.50–53 In this study, human prostate cancer (PC-3, LNCaP), human breast cancer (MCF-7) and human ovarian cancer cell lines (A2780) were selected as cell types. Feeding of all cells was done using RPMI-1640 medium (prepared by adding 10% FCS, 100 U/mL penicillin and 0.1 mg/mL streptomycin) in 25 cm2 culture flasks. The cells of the me­dium were kept twice at 37 °C in the carbon dioxide (5% CO2) incubator and the medium was changed twice a week. When cells were combined, they were removed from the flasks using trypsin-EDTA solution and transferred to 96-well plates and used for 3-(4,5-dimethylthiazole-2-yl) diphenyltetrazolium bromide (MTT) analysis. Relative cell viability (%) was expressed as a percentage relative to untreated control cells. Each value represents an average of 10 measurements. All cellular results were obtained against negative control cells.51–53 In cell culture experiments, solutions of the com­pounds in dimethyl sulfoxide (DMSO) were used. In the comparison of the obtained results for this reason, the effects of the substances against DMSO were determined by statistical analysis. The same amounts of solvent (DMSO) were added to the wells containing the concen­trations of the tested compounds at 1, 5, 25, 50 and 100 µM and were incubated in a CO2 incubator (Panasonic, Japan) for 24 hours at 37 oC. After incubations, viability of the cells was determined using 0.4% tryphan blue in a hemocytometer. IBM SPSS Statistics 22.0 (Windows) package pro­gram was used for statistical analysis. Conformity to nor­mal distribution was evaluated with the Shapiro Wilk test. Comparison of quantitative variables between groups was measured by Kruskal Wallis H test. When significant sta­tistical differences between the groups were determined, multiple comparisons between the groups were made with the Bonferroni-corrected Mann Whitney U test. Data were presented as mean ± standard deviation. p < 0.05 value was considered statistically significant. LogIC50 val­ues were calculated by using Graphpad prism 6 program in computer environment according to the obtained MTT results. 2. 3. Molecular Docking Studies Ligands were energy-minimized using ChemOffice on Windows 10 operating system. Grid box points as size of 60·60·60 Å3 and a regular space of 0.375 Å were deter­mined by centering on colchicine. “Tubulin-colchicine complex” pdb file (PDB ID: 4O2B) was get (https://www. rcsb.org/) and was modified using the Maestro.54 La-marckian Genetic Algorithm was preferred and standard settings used for all compounds. Docking scores were ob­tained using both AutoDock 4.2 software,55 and AutoDock Vina software.56 To validate the Autodock program, the co-crystallized ligand (PDB ID: LOC) was redocked on the target and RMSD value of 0.42 was found for tubu­lin-colchicine complex. 3. Results and Discussion 3. 1. Synthesis In this work, 1-(4-hydroxyphenyl)-3-(3-substitut­ed-phenyl)prop-2-en-1-one compounds 2–11 were pre­pared by the interaction of para-hydroxyacetophenone (1) with substitute aldehydes (3-methoxy, 2,4-dimethoxy, 3,4-dimethoxy, 2,3,4-trimethoxy, 2,4,5-trimethoxy, 4-phe­nyl, 3,5-difluoro, 4-chloro-2-fluoro, 3-(trifluoromethyl) phenyl, 4-chloro-3-(trifluoromethyl) benzaldeyhe) in the presence of ethanol and aqueous NaOH at room tempera­ture.28 The structures of compounds 2–11 were deter­mined by MS, FT-IR, microanalysis, and 1D (1H and 13C-APT) NMR spectroscopic methods. The synthetic pathway of 2–11 and their numbering for 1H and 13C-APT NMR characterizations is shown in Scheme 1. The compounds have only primary, tertiary and qua­ternary (–CH, –CH3 and –C) carbon atoms in their struc­ture. Therefore, 13C-APT NMR analyzes were performed. These carbons provided easier assignment of NMR spectra. When the proton and carbon NMR spectra of 2–11 are investigated, the methoxy proton and carbon peaks (H/C) of compounds containing methoxy as side groups have been observed at 3.84 / 55.75 ppm for 2 (number 17 proton and carbon peaks); 3.91 / 56.26 and 3.85 / 55.98 for 3; 3.88 / 56.05 and 3.83 / 56.20 for 4 (number 17 and 18 proton and carbon peaks, respectively); 3.89 / 61.98, 3.79 / 60.94 and 3.87 / 56.51 for 5; and 3.91 / 56.33, 3.83 / 56.94 and 3.88 / 56.94 for 6 (number 17, 18 and 19 proton and carbon peaks, respectively). The carbonyl stretching vibrations, carbonyl protons and carbonyl carbon peaks of the starting compound alde­hydes were not observed in the FT-IR, 1H and 13C-APT NMR spectra of the synthesized compounds 2–11, respec­tively. The carbon numbers and the integration of proton signals in the NMR spectra of 2–11 support the structures of synthesized compounds. The 1H and 13C-APT NMR spectra of compound 2 are given as examples in Figure 1. The spectra of other compounds are given in Figures S1– 30 in the Supplementary Information file. 3. 2. In Vitro Cytotoxic Activity The chalcone compounds contain two phenyl rings shown as A and B in the scaffold (see Scheme 1). By chang- Scheme 1. Synthetic pathway of 2–11 and their numbering for 1H and 13C-APT NMR characterizations Figure 1. (A) 1H NMR spectrum and (B) 13C-APT NMR spectrum of 2 (DMSO-d6) ing the side groups on the phenyl rings of these com-show various biological activities, including anti-ulcer/ pounds, biologically active molecules with different effects mucoprotective activity (sofalkonin), vascular protection are formed. Numerous pure chalcones isolated from dif-(hesperidin methyl-chalcone), anti-microbial activity (iso­ferent plants have been approved for clinical studies in the bavachalcone), anti-cancer effect (flavokawain A), inflam-treatment of cancer, viral and cardiovascular disorders. matory action (cardamonin) and anti-diabetes-acting For example, the methochalcone compound containing compound (4-hydroxyderricin) can be given as exam-methoxy group in the 2 and 4 positions of ring A and in ples.18–20 Each of these compounds has different side the 4 positions of ring B has been approved for clinical use groups in rings A and B. This causes different biological as a choleretic and diuretic agent. Similarly, some chalcone activity. Therefore, in this study, a series of chalcone com-derivatives have been clinically tested and approved to pounds with various groups in the side chains were syn­thesized and their cytotoxic effects on different cancer cells were investigated. For this purpose, compounds having the -OH functional group in ring A and different side groups in ring B were synthesized. Changes in cell viability rates of compounds 2–11 against A2780, LNCaP, PC-3 and MCF-7 cell lines are pre­sented in Tables 1 and 2. When the cytotoxic effects of the compounds 2–6 with the methoxy group(s) at different positions of the ring B against A2780 cells were examined, it was found that the compound 2 caused a significant decrease in cell viability in a dose-dependent manner (p < 0.05). In other com­pounds, cytotoxic effects were observed only for 25, 50 and 100 µM concentrations (p < 0.05). When the structure-ac­tivity relationship between the compound 2 with one me-thoxy group and the compounds 3, 4, 5 and 6 with two or three methoxy groups was examined, different cytotoxic effects were observed at different concentrations. Com­pound 2 exhibited cytotoxic effect at all concentrations. However, other compounds with more than one methoxy group did not show this effect. Despite these results, com­pounds carrying more than one methoxy group caused sig­nificant reductions at high concentrations. In particular, the effect of the compound 4 on cell viability is noteworthy. In terms of IC50 values, it is seen that the compound 2 has an IC50 value of 10 µM, and the compound 4 has an IC50 value of 12.61 µM. When the cytotoxic effects of these compounds in LNCaP cancer cell lines were examined, it was found that compound 2 decreased cell viability in the cell line de­pending on the dose (p < 0.05). Other compounds con­taining the methoxy group also had an effect at only 25, 50 and 100 µM concentrations (p < 0.05). Compound 4 caused decreases in cell viability at only 100 µM concen­trations (p < 0.05). Although it causes a significant de­crease at this concentration, its cytotoxic effect does not seem to be as strong as in A2780 cells. Especially for com­pound 2, which acts on dose-dependent reduction of cell viability against A2780 and LNCaP cell lines, the IC50 val­ue is 10.9 µM against A2780 cell lines, while this value is 1.823 µM against LNCaP cell lines. Considering the re­sults, compound 2 seems to have a better effect against A2780 cell lines than LNCaP cell lines. When the cytotoxic effects of these compounds on PC-3 and MCF-7 cancer cell lines were examined, it was observed that especially the compound 2 did not produce a cytotoxic effect in both cell lines. A similar situation was observed for the compound 5. Compound 6 showed an effect at only 100 µM concentrations in both cells (p < 0.05). These compounds, which contain the methoxy group, appear to have very different effects in different cells. Overall, the results show that methoxy-containing compounds 2–6 are more effective against A2780 and LN-CaP cells (p < 0.05). Table 1. Cell viability (in %) of compounds 2–11 against human cancer cell lines (p* < 0.05). A2780 Human Cancer Cell Lines Control Solvent 1 µM 5 µM 25 µM 50 µM 100 µM 2 100 ± 9.67 91.25 ± 8.16 61.82 ± 8.12* 51.25 ± 5.58* 44.80 ± 9.21* 30.18 ± 6.94* 23.74 ± 4.11* 3 100 ± 9.67 91.25 ± 8.16 109.5 ± 12.31 100.9 ± 13.09 66.66 ± 6.29* 64.64 ± 7.02* 47.49 ± 5.16* 4 100 ± 9.67 91.25 ± 8.16 92.18 ± 8.06 77.25 ± 10.05 37.64 ± 6.68* 10.77 ± 2.06* 6.54 ± 2.19* 5 100 ± 9.67 91.25 ± 8.16 97.22 ± 8.65 85.48 ± 7.77 68.95 ± 6.09* 68.88 ± 8.01* 14.42 ± 2.26* 6 100 ± 9.67 91.25 ± 8.16 199.5 ± 9.31 90.58 ± 8.1 65.33 ± 5.27* 66.34 ± 6.3* 30.26 ± 5.36* 7 100 ± 9.67 91.25 ± 8.16 81.56 ± 10.17 66.07 ± 8.72* 11.35 ± 2.36* 13.98 ± 3.24* 11.64 ± 4.41* 8 100 ± 9.67 91.25 ± 8.16 85.62 ± 7.16 96.10 ± 11.29 6.78 ± 2.03* 4.74 ± 1.41* 4.60 ± 1.12* 9 100 ± 9.67 91.25 ± 8.16 102.6 ± 13.28 60.41 ± 10.25* 4.75 ± 2.02* 3.23 ± 1.26* 2.45 ± 0.96* 10 100 ± 9.67 91.25 ± 8.16 103.8 ± 14.06 101.0 ± 11.64 26.38 ± 4.11* 6.91 ± 3.09* 4.67 ± 1.01* 11 100 ± 9.67 91.25 ± 8.16 104.1 ± 10.21 97.87 ± 12.17 5.70 ± 3.06* 4.51 ± 2.41* 4.14 ± 2.13* MCF-7 Human Breast Cancer Cell Lines Control Solvent 1 µM 5 µM 25 µM 50 µM 100 µM 2 100 ± 8.76 94.22 ± 7.87 106.6 ± 9.75 105.1 ± 14.21 97.2 ± 11.12 105.2 ± 10.11 93.97 ± 13.91 3 100 ± 8.76 94.22 ± 7.87 103.5 ± 9.51 84.89 ± 8.74 84.76 ± 11.41 81.64 ± 6.43* 67.24 ± 7.79* 4 100 ± 8.76 94.22 ± 7.87 103.3 ± 11.5 98.59 ± 13.54 86.02 ± 13.41 28.45 ± 6.97* 29.17 ± 9.98* 5 100 ± 8.76 94.22 ± 7.87 118.3 ± 17.7 109.0 ± 15.62 105.8 ± 14.5 106.7 ± 17.9 107.3 ± 12.4 6 100 ± 8.76 94.22 ± 7.87 10.75 ± 11.37 99.77 ± 10.23 78.81 ± 9.40 83.88 ± 12.84 29.04 ± 5.42* 7 100 ± 8.76 94.22 ± 7.87 99.66 ± 13.68 102.9 ± 14.81 102.0 ± 17.9 93.00 ± 9.72 37.22 ± 6.38* 8 100 ± 8.76 94.22 ± 7.87 93.04 ± 12.31 66.94 ± 10.23* 16.26 ± 4.81* 11.15 ± 2.37* 11.34 ± 3.46* 9 100 ± 8.76 94.22 ± 7.87 96.83 ± 12.56 103.4 ± 16.66 21.15 ± 5.67* 11.37 ± 3.06* 12.11 ± 4.21* 10 100 ± 8.76 94.22 ± 7.87 92.46 ± 9.99 70.47 ± 5.25* 21.93 ± 3.30* 23.42 ± 4.81* 23.69 ± 2.46* 11 100 ± 8.76 94.22 ± 7.87 105.6 ± 14.5 106.2 ± 15.88 31.97 ± 5.64* 21.74 ± 3.77* 21.25 ± 4.27* µM: Micromolar. Table 2. Cell Viability (in %) of compounds 2–11 against cancer cell lines (p* < 0.05). LNCaP Human Prostate Cancer Cell Lines Control Solvent 1 µM 5 µM 25 µM 50 µM 100 µM 2 100 ± 8.99 93.17 ± 10.12 42.86 ± 5.21* 38.13 ± 3.26* 34.88 ± 6.78* 24.44 ± 4.12* 20.21 ± 3.96* 3 100 ± 8.99 93.17 ± 10.12 103.8 ± 14.4 92.66 ± 13.29 59.60 ± 9.87* 47.28 ± 8.86* 38.22 ± 9.16* 4 100 ± 8.99 93.17 ± 10.12 96.94 ± 9.26 99.02 ± 11.23 78.82 ± 10.87 70.03 ± 14.69 58.95 ± 8.26* 5 100 ± 8.99 93.17 ± 10.12 77.79 ± 10.86 74.92 ± 12.22 51.56 ± 8.26* 45.94 ± 9.13* 39.18 ± 7.75* 6 100 ± 8.99 93.17 ± 10.12 81.98 ± 10.34 86.62 ± 11.2 54.5 ± 8.7* 46.21 ± 8.6* 38.12 ± 9.6* 7 100 ± 8.99 93.17 ± 10.12 95.72 ± 14.26 90.69 ± 12.25 90.82 ± 13.23 99.67 ± 10.51 61.55 ± 8.29* 8 100 ± 8.99 93.17 ± 10.12 86.25 ± 9.12 85.55 ± 8.26 55.74 ± 9.41* 48.52 ± 7.26* 39.05 ± 7.29* 9 100 ± 8.99 93.17 ± 10.12 96.00 ± 9.26 97.42 ± 13.41 52.40 ± 8.24* 51.00 ± 9.12* 41.92 ± 7.66* 10 100 ± 8.99 93.17 ± 10.12 91.28 ± 8.21 83.96 ± 9.22 51.78 ± 7.42* 10.14 ± 2.45* 11.52 ± 3.11* 11 100 ± 8.99 93.17 ± 10.12 88.21 ± 9.77 80.90 ± 9.29 62.67 ± 7.39* 14.78 ± 2.88* 10.78 ± 3.21* PC-3 Human Prostate Cancer Cell Lines Control Solvent 1 µM 5 µM 25 µM 50 µM 100 µM 2 100 ± 9.13 92.36 ± 8.13 110.2 ± 13.97 103.3 ± 13.2 96.57 ± 11.2 108.8 ± 15.2 90.97 ± 9.96 3 100 ± 9.13 92.36 ± 8.13 101.26 ± 7.34 110.7 ± 12.7 76.93 ± 8.96 70.95 ± 9.34* 5.67 ± 1.22* 4 100 ± 9.13 92.36 ± 8.13 99.53 ± 10.41 98.96 ± 8.41 67.72 ± 7.34* 62.73 ± 6.13* 23.98 ± 3.81* 5 100 ± 9.13 92.36 ± 8.13 103.5 ± 12.32 95.55 ± 13.21 99.24 ± 8.99 93.53 ± 14.22 92.23 ± 7.49 6 100 ± 9.13 92.36 ± 8.13 102.6 ± 7.14 109.6 ± 11.5 79.94 ± 8.86 85.96 ± 8.33 5.67 ± 1.22* 7 100 ± 9.13 92.36 ± 8.13 112.4 ± 10.85 85.97 ± 9.34 89.10 ± 8.45 80.97 ± 7.78 11.14 ± 2.39* 8 100 ± 9.13 92.36 ± 8.13 88.59 ± 9.29 91.12 ± 11.29 55.90 ± 8.21* 7.98 ± 1.14* 3.61 ± 1.22* 9 100 ± 9.13 92.36 ± 8.13 70.11 ± 8.28* 65.34 ± 7.92* 64.46 ± 9.12* 2.68 ± 0.78* 1.81 ± 0.84* 10 100 ± 9.13 92.36 ± 8.13 82.71 ± 9.21 80.33 ± 8.93 38.16 ± 6.23* 1.90 ± 0.37* 1.73 ± 0.22* 11 100 ± 9.13 92.36 ± 8.13 92.08 ± 10.12 87.54 ± 11.98 72.65 ± 9.93 1.85 ± 0.42* 1.65 ± 0.34* µM: Micromolar. Although compound 7 significantly reduced cell via-position caused a significant decrease in cell viability. The bility at 5, 25, 50 and 100 µM concentrations in A2780 cell logIC50/IC50 values of the compounds are given in Table 3. lines, it caused significant reductions in cell viability at When the cytotoxic activities of the compounds 8, 9, 10 only 100 µM concentration in other cell lines (p < 0.05). and 11, which contain -F, -Cl, -CF3 groups separately or The IC50 value of the compound 7 against A2780 cell lines together, were observed against four cell lines, there was a is 6.898 µM. In terms of structure-activity relationship, the significant decrease in cell viability, especially at concen-fact that the compound contains a phenyl ring at the para trations of 50 and 100 µM (p < 0.05). In particular, these Table 3. The logIC50/IC50 (50% inhibition-causing concentration) values (µM) of compounds 2–11 against A2780, LNCaP, PC-3 and MCF-7 cancer cell lines A2780 LNCaP PC-3 MCF-7 Comp. LogIC50 / IC50 (µM) LogIC50 / IC50 (µM) LogIC50 / IC50 (µM) LogIC50 / IC50 (µM) 2 1.037 / 10.9 0.2607 / 1.823 – 3.537 / 3445 3 1.911 / 81.4 1.674 / 47.24 1.763 / 57.88 2.285 / 192.9 4 1.101 / 12.61 2.097 / 125.1 1.747 / 55.91 1.67 / 46.78 5 1.712 / 51.53 1.548 +/ 35.35 2.1066 / 27.616 – 6 1.818 / 65.71 1.605 / 40.29 1.853 / 71.36 1.935 / 86.12 7 0.8387 / 6.898 2.403 / 253 1.866 / 73.52 2.18 / 151.2 8 0.9888 / 9.746 1.635 / 43.19 1.259 / 18.16 0.9071 / 8.075 9 0.7853 / 6.1 1.684 / 48.34 1.112 / 12.93 1.164 / 14.57 10 1.153 / 14.24 1.242 / 17.47 1.049 / 11.18 1.062 / 11.54 11 1.027 / 10.64 1.3198 / 20.84 1.312 / 20.49 1.32 / 20.9 Paclitaxel 0.7516 / 5.645 – – – Tamoxifen – – – 1.308 / 20.33 Docetaxel – 0.7792 / 6.014 1.229 / 16.95 – µM: Micromolar. Table 4. Molecular docking binding scores of some compounds, within the tubulin-colchicine complex (PDB ID: 4O2B) active site. Residues partic­ipating in H-bonds with the compounds are shown. Comp. Auto dock Result Vina Result Residues participating Docking Score Docking Score H-bonds Receptor Estimated Inhibition Estimated Free Energy A Chain B Chain Constant, Ki of Binding (kcal/mol) 2 Gln11, Asn101 Gln247, Lys254 1.91 µM –7.80 –8.4 3 Gln11 Gln247, Lys254 1.96 µM –7.79 –8.2 4 Gln11, Asn101 Gln247, Lys254 1.77 µM –7.85 –8.2 5 Gln11, Asn101 Gln247, Lys254 1.25 µM –8.05 –8.4 6 Gln11, Asn101 Gln247, Lys254 1.07 uM –8.15 –8.2 7 Gln11, Asn101 Gln247, Lys254 22.01 µM –10.45 –8.9 8 Gln11, Asn101 Gln247, Lys254 3.48 µM –7.45 –8.5 9 Gln11, Asn101 Gln247, Lys254 1.16 µM –8.10 –8.3 10 Gln11, Asn101 Gln247, Lys254 2.25 µM –7.70 –9.3 11 Gln11 Gln247, Lys254 1.25 µM –8.05 –9.7 nM: nanomolar, µM: micromolar Figure 2. 2D interaction diagram for compounds 7–11 at the tubulin binding cavity. Figure 3. Compounds 7 (blue), 8 (yellow), 9 (orange), 10 (green), and 11 (black), colchicine (pink) and guanosine-5’-triphosphate (red) are present­ed in the tubulin-colchicine complex binding cavity. compounds were found to have strong cytotoxic effects (p < 0.05) against A2780 and PC-3 cell lines. According to the results, especially against the A2780 and LNCaP cell lines, the compounds 2–7 with electron releasing groups (methoxy / phenyl) in the phenyl ring showed strong cytotoxic effect (p < 0.05). Compounds 8–11 containing electron withdrawing groups in the phe­nyl ring were found to have strong cytotoxic effects against four cell lines at different doses (p < 0.05). 3. 3. Molecular Docking Studies According to the X-ray crystallographic structure of tubulin-colchicine complex (PDB ID: 4O2B), main binding site has been determined around small molecules such as colchicine (ligand ID: LOC) and guanosine-5’-tri-phosphate (ligand ID: GTP) in receptor (https://www. rcsb.org/). It has been declared that colchicine interacts with active site in tubulin as the binding site. It has been previously established that colchicine interacts with Se­r178A, Thr179A, Ala180A, Val181A, Cys241B, Leu242B, Leu248B, Ala250B, Asp251B, Lys254B, Leu255B, Asn258B, Met259B, Thr314B, Val315B, Ala316B, Ile318B, Asn350B, Lys352B, Ile378B residues (https://www.ebi. ac.uk/pdbe/). The formation of hydrogen bonds between the hydroxyl group in the phenol ring of all compounds by Gln11A and Gln247B showed binding to the gorge. The Lys254B has a position in the active site to interact with carbonyl of compounds by establishing a hydrogen bond (Table 4). Docking studies were performed for all compounds and interaction modes for compounds 7, 8, 9, 10 and 11 with enzyme active sites were determined (Figure 2). The binding types and residues were produced showed by Maestro software (Maestro, Schrödinger, LLC, New York, NY, 2020). These compounds binding modes were similar as with colchicine (Figure 3). The results of molecular docking studies were exhibited to be relevant for the re­sults of the in vitro activity studies. 4. Conclusion All the compounds (except compound 5) showed significant cytotoxic effects at high doses in all cancer cell lines. Among all the compounds studied, one compound i.e. compound 2 demonstrated dose-dependent activity, particularly against A2780/LNCaP cancer cell lines. The most effective compounds 8, 9, 10 and 11 reduced the cell viability of A2780, MCF-7, PC-3 and LNCaP cells by 50– 98%, while other compounds 2, 4 and 7 reduced the cell viability of A2780 cells by 70–90% at concentrations of 50 and 100 µM. The results indicate that these compounds have high cytotoxic effect against these human cancer cell lines. Molecular docking studies exhibited the interaction mode of all compounds with tubulin including hydropho­bic interactions and hydrogen bonds. 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Potencialne aktivnosti spojin smo še dodatno raziskali s pomocjo študij molekulskega sidranja s pro-gramoma AutoDock4 in Vina. Vse spojine (z izjemo spojine 5) so pri visokih odmerkih pokazale opazne citotoksicne ucinke proti vsem rakastim celicnim linijam. Izmed vseh preiskovanih spojin, je spojina 2 izkazala aktivnost, odvisno od koncentracije, še posebej proti A2780/LNCaP rakastim celicnim linijam. Najbolj ucinkovite spojine 8, 9, 10 in 11 so zmanjšale sposobnost preživetja celic pri A2780, MCF-7, PC-3 ter LNCaP celicnih linijah za 50–98%; spojine 2, 4 in 7 pa so zmanjšale sposobnost preživetja celic pri A2780 celicni liniji za 70–90% pri koncentracijah 50 in 100 µM. Except when otherwise noted, articles in this journal are published under the terms and conditions of the Creative Commons Attribution 4.0 International License DOI: 10.17344/acsi.2021.7119 Scientific paper Design, Synthesis, In Silico ADMET Studies and Anticancer Activity of Some New Pyrazoline and Benzodioxole Derivatives Fatih Tok,1,* Ömer Erdogan,2 Özge Çevik2 and Bedia Koçyigit-Kaymakçioglu1 1 Department of Pharmaceutical Chemistry, Faculty of Pharmacy, Marmara University, Istanbul, Turkey 2 Department of Biochemistry, School of Medicine, Aydin Adnan Menderes University, * Corresponding author: E-mail: fatih.tok@marmara.edu.tr Tel: +90-216-7775200. Received: 09-02-2021 Abstract A new series of 2-pyrazoline derivatives starting from substituted benzodioxole chalcones were designed and synthe­sized. IR and 1H NMR spectral data and elemental analysis were used to characterize the structures of the synthesized compounds. The cytotoxic activities on HeLa, MCF-7 cancer cell lines and NIH-3T3 for these compounds were tested by using MTT assay. Among the synthesized compounds 2d, 2j, 3j and 3n against MCF-7 cells, and 3c against HeLa exhibited significant cytotoxic activity with IC50 between 10.08 and 27.63 µM. Compound 3f showed the most potent anticancer activity against both cancer cells with good selectivity (IC50 = 11.53 µM on HeLa with SI = 81.75 and IC50 = 11.37 µM on MCF-7 with SI = 82.90). Furthermore, in silico ADMET analyses were performed and the drug-likeness properties of the compounds were investigated. Keywords: Pyrazoline; carboxamide; anticancer activity; breast cancer; cervical cancer. 1.Introduction Pyrazolines are electron rich nitrogen heterocycles that are appropriate for the discovery of bioactive mole­cules.1 Pyrazolines occur naturally in animal and plant cells, in the form of vitamins, pigments and alkaloids.2 These heterocyclic compounds are known to exhibit good pharmacological and biological acitivities such as antimi­crobial, antiinflammatory, analgesic, anti-depressant and anticancer.3–7 Heterocyclic anticancer agents bearing nitrogen-ni­trogen (N–N) bonds, such as crizotinib, ruxolitinib, ax-itinib, encorafinib and ibrutinib, have been approved by the FDA in the last decades.8 Podophyllotoxin and stega­nacin bearing benzo[d][1,3]dioxol moiety are natural bio-active molecules with very strong cytotoxic activity. Some adverse effects related to complex pharmacokinetics in­cluding unpredictable drug-drug interactions can appear because of the therapeutics of combining multiple drugs. It is an important strategy for drug discovery that construct­ing a single molecule modulates multiple targets simulta­neously.9,10 Therefore, we designed two series of pyrazo-line derivatives containing benzo[d][1,3]dioxol moiety by the molecular assembly principle (Figure 1). Herein we report the synthesis of 2-pyrazolines starting from 1-(benzo[d][1,3]dioxol-5-yl)ethanone and further evaluated their anticancer activities on HeLa (hu­man cervical adenocarcinoma), MCF7 (human breast ad-enocarcinoma) cancer cells. NIH-3T3 mouse embryonic fibroblast cell lines were also used to determine the tumor selectivity of the synthesized compounds. 2. Experimental 2. 1. Chemistry All chemicals were obtained from Sigma-Aldrich or Merck Chemical Company and used without further puri­fication. The reaction processes and the purity of com­pounds were monitored by thin layer chromatography us­ing a UV lamp. Melting points were determined on a Kleinfield SMP II apparatus and are uncorrected. The IR Figure 1. Design strategy of the target compounds spectra were recorded using a Schimadzu FTIR 8400S spectrometry. 1H NMR spectra were taken on a Bruker (400 MHz) spectrometer. Elemental analysis was per­formed on Leco CHNS-932 analyzer. General Procedure for the Preparation of Chalcone De­rivatives 1a–1o 1-(Benzo[d][1,3]dioxol-5-yl)ethanone (1 mmol, 0.164 g) and corresponding aromatic aldehyde (1 mmol) were dissolved in 20 mL methanol. A solution of sodium hydroxide (50%, 3 mL) was added to the reaction mixture. The mixture was stirred on magnetic stirrer for 8 h. The precipitated product was filtered, washed with water and recrystallized from methanol.11 General Procedure for Preparation of Pyrazoline Deriv­atives 2a–2o Chalcone derivatives (1 mmol) and phenylhydrazine hydrochloride (1 mmol, 0.145 g) was dissolved in 25 mL ethanol. Catalytic quantity of acetic acid (1.5 mL) was add­ed to the reaction mixture. The mixture was refluxed for 8 h. The mixture was cooled, the precipitated product was fil­tered, washed with water and recrystallized from ethanol.12 3-(Benzo[d][1,3]dioxol-5-yl)-1,5-diphenyl-4,5-dihy-dro-1H-pyrazole (2a) Yield: 280 mg (82%) of white powder; m.p. 172.8– 173.2 °C. IR (.max, cm–1): 3028 (=C-H), 2914 (C-H), 1593 (C=N), 1249 (C-O), 1033 (C-N); 1H NMR (400 MHz, DMSO-d6): d 3.03 (dd, Jax = 6.5 Hz, Jab = 17.4 Hz, 1H, Ha), 3.84 (dd, Jbx = 12.2 Hz, Jab = 17.5 Hz, 1H, Hb), 5.40 (dd, Jax = 6.4 Hz, Jbx = 12.1 Hz, 1H, Hx), 6.04 (s, 2H, O-CH2-O), 6.65–7.36 (m, 13H, Ar-H). Anal. Calcd for C22H18N2O2: C, 77.17; H, 5.30; N, 8.18. Found: C, 77.07; H, 5.32; N, 8.15%. 3-(Benzo[d][1,3]dioxol-5-yl)-5-(4-fluorophenyl)-1-phe-nyl-4,5-dihydro-1H-pyrazole (2b) Yield: 288 mg (80%) of white powder; m.p. 138.1– 138.3 °C. IR (.max, cm–1): 3066 (=C-H), 2891 (C-H), 1591 (C=N), 1211 (C-O), 1037 (C-N); 1H NMR (400 MHz, DMSO-d6): d 3.03 (dd, Jax = 6.3 Hz, Jab = 17.5 Hz, 1H, Ha), 3.83 (dd, Jbx = 12.1 Hz, Jab = 17.5 Hz, 1H, Hb), 5.44 (dd, Jax = 6.3 Hz, Jbx = 12.0 Hz, 1H, Hx), 6.05 (s, 2H, O-CH2-O), 6.68–7.31 (m, 12H, Ar-H). Anal. Calcd for C22H17FN2O2: C, 73.32; H, 4.75; N, 7.77. Found: C, 73.20; H, 4.77; N, 7.80%. 3-(Benzo[d][1,3]dioxol-5-yl)-5-(4-bromophenyl)-1­phenyl-4,5-dihydro-1H-pyrazole (2c) Yield: 341 mg (81%) of white crystals; m.p. 164.5– 164.8 °C. IR (.max, cm–1): 3082 (=C-H), 2885 (C-H), 1595 (C=N), 1211 (C-O), 1033 (C-N); 1H NMR (400 MHz, DMSO-d6): d 3.04 (dd, Jax = 6.3 Hz, Jab = 17.4 Hz, 1H, Ha), 3.83 (dd, Jbx = 12.2 Hz, Jab = 17.5 Hz, 1H, Hb), 5.42 (dd, Jax = 6.3 Hz, Jbx = 12.1 Hz, 1H, Hx), 6.05 (s, 2H, O-CH2-O), 6.68–7.51 (m, 12H, Ar-H). Anal. Calcd for C22H17BrN2O2: C, 62.72; H, 4.07; N, 6.65. Found: C, 62.81; H, 4.08; N, 6.61%. 3-(Benzo[d][1,3]dioxol-5-yl)-5-(4-nitrophenyl)-1-phe-nyl-4,5-dihydro-1H-pyrazole (2d) Yield: 325 mg (84%) of yellow powder; m.p. 110.2– 110.5 °C. IR (.max, cm–1): 3078 (=C-H), 2881 (C-H), 1595 (C=N), 1213 (C-O), 1033 (C-N); 1H NMR (400 MHz, DMSO-d6): d 3.10 (dd, Jax = 6.3 Hz, Jab = 17.5 Hz, 1H, Ha), 3.90 (dd, Jbx = 12.3 Hz, Jab = 17.5 Hz, 1H, Hb), 5.61 (dd, Jax = 6.3 Hz, Jbx = 12.3 Hz, 1H, Hx), 6.05 (s, 2H, O-CH2-O), 6.70–8.20 (m, 12H, Ar-H). Anal. Calcd for C22H17N3O4: C, 68.21; H, 4.42; N, 10.85. Found: C, 68.04; H, 4.41; N, 10.89%. 3-(Benzo[d][1,3]dioxol-5-yl)-1-phenyl-5-para-tolyl-4,5-dihydro-1H-pyrazole (2e) Yield: 278 mg (78%) of white powder; m.p. 146.4– 146.6 °C. IR (.max, cm–1): 3022 (=C-H), 2901 (C-H), 1595 (C=N), 1215 (C-O), 1035 (C-N); 1H NMR (400 MHz, DMSO-d6): d 2.23 (s, 3H, CH3), 3.00 (dd, Jax = 6.4 Hz, Jab = 17.4 Hz, 1H, Ha), 3.81 (dd, Jbx = 12.2 Hz, Jab = 17.4 Hz, 1H, Hb), 5.35 (dd, Jax = 6.4 Hz, Jbx = 12.1 Hz, 1H, Hx), 6.04 (s, 2H, O-CH2-O), 6.66–7.35 (m, 12H, Ar-H). Anal. Calcd for C23H20N2O2: C, 77.51; H, 5.66; N, 7.86. Found: C, 77.39; H, 5.68; N, 7.85%. 4-(3-(Benzo[d][1,3]dioxol-5-yl)-1-phenyl-4,5-dihydro­1H-pyrazol-5-yl)benzonitrile (2f) Yield: 290 mg (79%) of white crystals; m.p. 135.9– 136.3 °C. IR (.max, cm–1): 3053 (=C-H), 2901 (C-H), 1593 (C=N), 1217 (C-O), 1033 (C-N); 1H NMR (400 MHz, DMSO-d6): d 3.08 (dd, Jax = 6.3 Hz, Jab = 17.5 Hz, 1H, Ha), 3.87 (dd, Jbx = 12.3 Hz, Jab = 17.5 Hz, 1H, Hb), 5.55 (dd, Jax = 6.2 Hz, Jbx = 12.1 Hz, 1H, Hx), 6.05 (s, 2H, O-CH2-O), 6.70–7.80 (m, 12H, Ar-H). Anal. Calcd for C23H17N3O2: C, 75.19; H, 4.66; N, 11.44. Found: C, 75.11; H, 4.66; N, 11.46%. 3-(Benzo[d][1,3]dioxol-5-yl)-5-(2,6-dimethylphenyl)­1-phenyl-4,5-dihydro-1H-pyrazole (2g) Yield: 296 mg (80%) of white powder; m.p. 117.8– 118.0 °C. IR (.max, cm–1): 3037 (=C-H), 2881 (C-H), 1597 (C=N), 1213 (C-O), 1037 (C-N); 1H NMR (400 MHz, DMSO-d6): d 2.03 (s, 3H, CH3), 2.24 (s, 3H, CH3), 3.01 (dd, Jax = 6.3 Hz, Jab = 17.4 Hz, 1H, Ha), 3.91 (dd, Jbx = 12.1 Hz, Jab = 17.4 Hz, 1H, Hb), 5.56 (dd, Jax = 6.3 Hz, Jbx = 12.0 Hz, 1H, Hx), 6.05 (s, 2H, O-CH2-O), 6.66–7.36 (m, 11H, Ar-H). Anal. Calcd for C24H22N2O2: C, 77.81; H, 5.99; N, 7.56. Found: C, 77.90; H, 5.96; N, 7.59%. 3-(Benzo[d][1,3]dioxol-5-yl)-1-phenyl-5-ortho-tolyl-4,5-dihydro-1H-pyrazole (2h) Yield: 303 mg (85%) of white powder; m.p. 163.9– 164.2 °C. IR (.max, cm–1): 3066 (=C-H), 2881 (C-H), 1597 (C=N), 1203 (C-O), 1033 (C-N); 1H NMR (400 MHz, DMSO-d6): d 2.24 (s, 3H, CH3), 2.92 (dd, Jax = 6.6 Hz, Jab = 17.4 Hz, 1H, Ha), 3.96 (dd, Jbx = 12.3 Hz, Jab = 17.4 Hz, 1H, Hb), 5.50 (dd, Jax = 6.6 Hz, Jbx = 12.3 Hz, 1H, Hx), 6.05 (s, 2H, O-CH2-O), 6.68–7.36 (m, 12H, Ar-H). Anal. Calcd for C23H20N2O2: C, 77.51; H, 5.66; N, 7.86. Found: C, 77.59; H, 5.66; N, 7.88%. 3-(Benzo[d][1,3]dioxol-5-yl)-1-phenyl-5-meta-tolyl-4,5-dihydro-1H-pyrazole (2i) Yield: 310 mg (87%) of white powder; m.p. 148.8– 149.2 °C. IR (.max, cm–1): 3061 (=C-H), 2893 (C-H), 1595 (C=N), 1211 (C-O), 1033 (C-N); 1H NMR (400 MHz, DMSO-d6): d 2.24 (s, 3H, CH3), 3.01 (dd, Jax = 6.3 Hz, Jab = 17.5 Hz, 1H, Ha), 3.83 (dd, Jbx = 12.1 Hz, Jab = 17.5 Hz, 1H, Hb), 5.33 (dd, Jax = 6.3 Hz, Jbx = 12.0 Hz, 1H, Hx), 6.05 (s, 2H, O-CH2-O), 6.67–7.31 (m, 12H, Ar-H). Anal. Calcd for C23H20N2O2: C, 77.51; H, 5.66; N, 7.86. Found: C, 77.61; H, 5.64; N, 7.89%. 4-(3-(Benzo[d][1,3]dioxol-5-yl)-1-phenyl-4,5-dihydro­1H-pyrazol-5-yl)pyridine (2j) Yield: 268 mg (78%) of gray powder; m.p. 108.2– 108.3 °C. IR (.max, cm–1): 3030 (=C-H), 2881 (C-H), 1597 (C=N), 1242 (C-O), 1033 (C-N); 1H NMR (400 MHz, DMSO-d6): d 3.06 (dd, Jax = 6.4 Hz, Jab = 17.4 Hz, 1H, Ha), 3.82 (dd, Jbx = 12.1 Hz, Jab = 17.4 Hz, 1H, Hb), 5.43 (dd, Jax = 6.4 Hz, Jbx = 12.1 Hz, 1H, Hx), 6.08 (s, 2H, O-CH2-O), 6.52–7.62 (m, 12H, Ar-H). Anal. Calcd for C21H17N3O2: C, 73.45; H, 4.99; N, 12.24. Found: C, 73.32; H, 5.01; N, 12.23%. 3-(Benzo[d][1,3]dioxol-5-yl)-1-phenyl-5-(thiophen-2­yl)-4,5-dihydro-1H-pyrazole (2k) Yield: 271 mg (78%) of red powder; m.p. 152.7–153.0 °C. IR (.max, cm–1): 3074 (=C-H), 2893 (C-H), 1595 (C=N), 1201 (C-O), 1031 (C-N); 1H NMR (400 MHz, DMSO-d6): d 3.21 (dd, Jax = 5.9 Hz, Jab = 17.3 Hz, 1H, Ha), 3.81 (dd, Jbx = 11.6 Hz, Jab = 17.3 Hz, 1H, Hb), 5.75 (dd, Jax = 5.9 Hz, Jbx = 11.6 Hz, 1H, Hx), 6.06 (s, 2H, O-CH2-O), 6.72– 7.36 (m, 11H, Ar-H). Anal. Calcd for C20H16N2O2S: C, 68.94; H, 4.63; N, 8.04. Found: C, 68.85; H, 4.65; N, 8.08%. 3-(Benzo[d][1,3]dioxol-5-yl)-5-(furan-2-yl)-1-phenyl-4,5-dihydro-1H-pyrazole (2l) Yield: 272 mg (82%) of brown powder; m.p. 120.1– 120.5 °C. IR (.max, cm–1): 3078 (=C-H), 2908 (C-H), 1595 (C=N), 1211 (C-O), 1033 (C-N); 1H NMR (400 MHz, DMSO-d6): d 3.28 (dd, Jax = 6.0 Hz, Jab = 17.3 Hz, 1H, Ha), 3.70 (dd, Jbx = 12.1 Hz, Jab = 17.3 Hz, 1H, Hb), 5.53 (dd, Jax = 6.0 Hz, Jbx = 12.2 Hz, 1H, Hx), 6.05 (s, 2H, O-CH2-O), 6.38–7.54 (m, 11H, Ar-H). Anal. Calcd for C20H16N2O3: C, 72.28; H, 4.85; N, 8.43. Found: C, 72.41; H, 4.88; N, 8.38%. 3-(Benzo[d][1,3]dioxol-5-yl)-5-(5-bromothiophen-2­yl)-1-phenyl-4,5-dihydro-1H-pyrazole (2m) Yield: 376 mg (88%) of gray powder; m.p. 143.0– 143.4 °C. IR (.max, cm–1): 3039 (=C-H), 2908 (C-H), 1595 (C=N), 1215 (C-O), 1035 (C-N); 1H NMR (400 MHz, DMSO-d6): d 3.01 (dd, Jax = 6.4 Hz, Jab = 17.4 Hz, 1H, Ha), 3.82 (dd, Jbx = 12.1 Hz, Jab = 17.4 Hz, 1H, Hb), 5.36 (dd, Jax = 6.4 Hz, Jbx = 12.1 Hz, 1H, Hx), 6.06 (s, 2H, O-CH2-O), 6.67–7.36 (m, 10H, Ar-H). Anal. Calcd for C20H15BrN2O2S: C, 56.21; H, 3.54; N, 6.56. Found: C, 56.08; H, 3.53; N, 6.59%. 3-(Benzo[d][1,3]dioxol-5-yl)-1-phenyl-5-(4-(trifluoro­methyl)phenyl)-4,5-dihydro-1H-pyrazole (2n) Yield: 328 mg (80%) of white powder; m.p. 146.4– 146.6 °C. IR (.max, cm–1): 3078 (=C-H), 2887 (C-H), 1597 (C=N), 1213 (C-O), 1033 (C-N); 1H NMR (400 MHz, DMSO-d6): d 3.10 (dd, Jax = 5.9 Hz, Jab = 17.3 Hz, 1H, Ha), 3.88 (dd, Jbx = 11.6 Hz, Jab = 17.3 Hz, 1H, Hb), 5.57 (dd, Jax = 5.9 Hz, Jbx = 11.6 Hz, 1H, Hx), 6.06 (s, 2H, O-CH2-O), 6.70–7.71 (m, 12H, Ar-H). Anal. Calcd for C23H17F3N2O2: C, 67.31; H, 4.18; N, 6.83. Found: C, 67.15; H, 4.17; N, 6.79%. 3-(Benzo[d][1,3]dioxol-5-yl)-5-(3-chlorophenyl)-1­phenyl-4,5-dihydro-1H-pyrazole (2o) Yield: 320 mg (85%) of white crystals; m.p. 131.0– 131.3 °C. IR (.max, cm–1): 3072 (=C-H), 2908 (C-H), 1593 (C=N), 1213 (C-O), 1035 (C-N); 1H NMR (400 MHz, DMSO-d6): d 3.11 (dd, Jax = 6.3 Hz, Jab = 17.5 Hz, 1H, Ha), 3.92 (dd, Jbx = 12.1 Hz, Jab = 17.5 Hz, 1H, Hb), 5.48 (dd, Jax = 6.2 Hz, Jbx = 12.1 Hz, 1H, Hx), 6.08 (s, 2H, O-CH2-O), 6.58–7.68 (m, 12H, Ar-H). Anal. Calcd for C22H17ClN2O2: C, 70.12; H, 4.55; N, 7.43. Found: C, 70.21; H, 4.57; N, 7.42%. General Procedure for Preparation of Pyrazoline Deriv­atives 3a–3o Firstly, the synthesis of N-(4-chlorophenyl)hydra­zinecarboxamide was carried out. Hydrazine monohy­drate (1 mmol, 48.5 µL) was added dropwise to 4-chloro­phenylisocyanate (1 mmol, 128.0 µL) in 15 mL diethyl ether. The mixture was stirred for 1 h. The white precipitat­ed product was filtered, dried.13 Chalcone derivatives (1 mmol) and N-(4-chlorophe­nyl)hydrazinecarboxamide (1 mmol) was dissolved in 20 mL ethanol, and then a solution of sodium hydroxide (20%, 1 mL) was added to the reaction mixture. The mix­ture was refluxed for 10 h. The mixture was cooled, the precipitated product was filtered, washed with water and recrystallized from ethanol.13 3-(Benzo[d][1,3]dioxol-5-yl)-N-(4-chlorophenyl)-5­phenyl-4,5-dihydro-1H-pyrazole-1-carboxamide (3a) Yield: 328 mg (78%) of white powder; m.p. 139.1– 139.3 °C. IR (.max, cm–1): 3290 (N-H), 3078 (=C-H), 2989, 2901 (C-H), 1666 (C=O), 1631 (C=N), 1222 (C-O), 1033 (C-N); 1H NMR (400 MHz, DMSO-d6): d 3.13 (dd, Jax = 5.2 Hz, Jab = 17.8 Hz, 1H, Ha), 3.81 (dd, Jbx = 11.7 Hz, Jab = 17.8 Hz, 1H, Hb), 5.51 (dd, Jax = 5.2 Hz, Jbx = 11.7 Hz, 1H, Hx), 6.08 (s, 2H, O-CH2-O), 6.90–7.97 (m, 12H, Ar-H), 9.14 (s, 1H, NH). Anal. Calcd for C23H18ClN3O3: C, 65.79; H, 4.32; N, 10.01. Found: C, 65.89; H, 4.35; N, 9.95%. 3-(Benzo[d][1,3]dioxol-5-yl)-N-(4-chlorophenyl)-5-(4­fluorophenyl)-4,5-dihydro-1H-pyrazole-1-carboxam­ide (3b) Yield: 350 mg (80%) of white powder; m.p. 134.4– 134.5 °C. IR (.max, cm–1): 3292 (N-H), 3066 (=C-H), 2987, 2885 (C-H), 1666 (C=O), 1633 (C=N), 1228 (C-O), 1033 (C-N); 1H NMR (400 MHz, DMSO-d6): d 3.15 (dd, Jax = 5.3 Hz, Jab = 17.9 Hz, 1H, Ha), 3.85 (dd, Jbx = 11.6 Hz, Jab = 17.9 Hz, 1H, Hb), 5.52 (dd, Jax = 5.2 Hz, Jbx = 11.7 Hz, 1H, Hx), 6.14 (s, 2H, O-CH2-O), 7.04–7.94 (m, 11H, Ar-H), 9.16 (s, 1H, NH). Anal. Calcd for C23H17ClFN3O3: C, 63.09; H, 3.91; N, 9.60. Found: C, 63.00; H, 3.90; N, 9.65%. 3-(Benzo[d][1,3]dioxol-5-yl)-5-(4-bromophenyl)-N-(4­chlorophenyl)-4,5-dihydro-1H-pyrazole-1-carboxam­ide (3c) Yield: 424 mg (85%) of white powder; m.p. 123.7– 123.9 °C. IR (.max, cm–1): 3290 (N-H), 3072 (=C-H), 2974, 2989 (C-H), 1666 (C=O), 1633 (C=N), 1222 (C-O), 1033 (C-N); 1H NMR (400 MHz, DMSO-d6): d 3.14 (dd, Jax = 5.5 Hz, Jab = 17.9 Hz, 1H, Ha), 3.80 (dd, Jbx = 11.6 Hz, Jab = 17.9 Hz, 1H, Hb), 5.49 (dd, Jax = 5.3 Hz, Jbx = 11.7 Hz, 1H, Hx), 6.02 (s, 2H, O-CH2-O), 7.07–7.85 (m, 11H, Ar-H), 9.06 (s, 1H, NH). Anal. Calcd for C23H17BrClN3O3: C, 55.39; H, 3.44; N, 8.42. Found: C, 55.55; H, 3.41; N, 8.45%. 3-(Benzo[d][1,3]dioxol-5-yl)-N-(4-chlorophenyl)-5-(4­nitrophenyl)-4,5-dihydro-1H-pyrazole-1-carboxamide (3d) Yield: 400 mg (86%) of yellow powder; m.p. 128.8– 129.0 °C. IR (.max, cm–1): 3290 (N-H), 3078 (=C-H), 2989, 2912 (C-H), 1666 (C=O), 1631 (C=N), 1230 (C-O), 1033 (C-N); 1H NMR (400 MHz, DMSO-d6): d 3.14 (dd, Jax = 5.2 Hz, Jab = 18.0 Hz, 1H, Ha), 3.81 (dd, Jbx = 11.9 Hz, Jab = 18.0 Hz, 1H, Hb), 5.51 (dd, Jax = 5.2 Hz, Jbx = 11.9 Hz, 1H, Hx), 6.10 (s, 2H, O-CH2-O), 7.10–7.77 (m, 11H, Ar-H), 9.06 (s, 1H, NH). Anal. Calcd for C23H17ClN4O5: C, 59.43; H, 3.69; N, 12.05. Found: C, 59.60; H, 3.73; N, 12.12%. 3-(Benzo[d][1,3]dioxol-5-yl)-N-(4-chlorophenyl)-5-pa­ra-tolyl-4,5-dihydro-1H-pyrazole-1-carboxamide (3e) Yield: 356 mg (82%) of white powder; m.p. 161.9– 162.3 °C. IR (.max, cm–1): 3292 (N-H), 3078 (=C-H), 2987, 2887 (C-H), 1666 (C=O), 1631 (C=N), 1228 (C-O), 1033 (C-N); 1H NMR (400 MHz, DMSO-d6): d 2.30 (s, 3H, CH3), 3.07 (dd, Jax = 5.3 Hz, Jab = 17.9 Hz, 1H, Ha), 3.78 (dd, Jbx = 11.9 Hz, Jab = 17.9 Hz, 1H, Hb), 5.43 (dd, Jax = 5.3 Hz, Jbx = 11.9 Hz, 1H, Hx), 6.07 (s, 2H, O-CH2-O), 7.05–7.65 (m, 11H, Ar-H), 9.00 (s, 1H, NH). Anal. Calcd for C24H20ClN3O3: C, 66.44; H, 4.65; N, 9.68. Found: C, 66.29; H, 4.62; N, 9.73%. 3-(Benzo[d][1,3]dioxol-5-yl)-N-(4-chlorophenyl)-5-(4­cyanophenyl)-4,5-dihydro-1H-pyrazole-1-carboxamide (3f) Yield: 312 mg (70%) of gray powder; m.p. 188.1–188.5 °C. IR (.max, cm–1): 3292 (N-H), 3084 (=C-H), 2918, 2848 (C-H), 1666 (C=O), 1631 (C=N), 1230 (C-O), 1033 (C-N);1H NMR (400 MHz, DMSO-d6): d 3.11 (dd, Jax = 5.6 Hz, Jab = 17.9 Hz, 1H, Ha), 3.78 (dd, Jbx = 12.0 Hz, Jab = 17.9 Hz, 1H, Hb), 5.48 (dd, Jax = 5.5 Hz, Jbx = 12.0 Hz, 1H, Hx), 6.09 (s, 2H, O-CH2-O), 6.98–7.85 (m, 11H, Ar-H), 9.02 (s, 1H, NH). Anal. Calcd for C24H17ClN4O3: C, 64.80; H, 3.85; N, 12.59. Found: C, 64.65; H, 3.88; N, 12.56%. 3-(Benzo[d][1,3]dioxol-5-yl)-N-(4-chlorophenyl)-5­(2,6-dimethylphenyl)-4,5-dihydro-1H-pyrazole-1-car­boxamide (3g) Yield: 323 mg (72%) of white powder; m.p. 155.1– 155.5 °C. IR (.max, cm–1): 3290 (N-H), 3066 (=C-H), 2987, 2918, 2848 (C-H), 1672 (C=O), 1631 (C=N), 1220 (C-O), 1033 (C-N); 1H NMR (400 MHz, DMSO-d6): d 2.08 (s, 3H, CH3), 2.34 (s, 3H, CH3), 3.04 (dd, Jax = 5.9 Hz, Jab = 17.3 Hz, 1H, Ha), 3.81 (dd, Jbx = 11.6 Hz, Jab = 17.3 Hz, 1H, Hb), 5.85 (dd, Jax = 5.9 Hz, Jbx = 11.6 Hz, 1H, Hx), 6.10 (s, 2H, O-CH2-O), 7.00–7.70 (m, 10H, Ar-H), 9.12 (s, 1H, NH). Anal. Calcd for C25H22ClN3O3: C, 67.04; H, 4.95; N, 9.38. Found: C, 66.91; H, 4.99; N, 9.45%. 3-(Benzo[d][1,3]dioxol-5-yl)-N-(4-chlorophenyl)-5-or­tho-tolyl-4,5-dihydro-1H-pyrazole-1-carboxamide (3h) Yield: 343 mg (79%) of white powder; m.p. 129.8– 130.1 °C. IR (.max, cm–1): 3367 (N-H), 3095 (=C-H), 2982, 2881 (C-H), 1667 (C=O), 1631 (C=N), 1220 (C-O), 1033 (C-N); 1H NMR (400 MHz, DMSO-d6): d 2.34 (s, 3H, CH3), 2.99 (dd, Jax = 5.5 Hz, Jab = 17.8 Hz, 1H, Ha), 3.87 (dd, Jbx = 12.0 Hz, Jab = 17.8 Hz, 1H, Hb), 5.63 (dd, Jax = 5.5 Hz, Jbx = 12.0 Hz, 1H, Hx), 6.12 (s, 2H, O-CH2-O), 7.00–7.67 (m, 11H, Ar-H), 9.20 (s, 1H, NH). Anal. Calcd for C24H20ClN3O3: C, 66.44; H, 4.65; N, 9.68. Found: C, 66.57; H, 4.64; N, 9.72%. 3-(Benzo[d][1,3]dioxol-5-yl)-N-(4-chlorophenyl)-5-me­ta-tolyl-4,5-dihydro-1H-pyrazole-1-carboxamide (3i) Yield: 339 mg (78%) of white powder; m.p. 159.8– 160.2 °C. IR (.max, cm–1): 3292 (N-H), 3093 (=C-H), 2989, 2901 (C-H), 1689 (C=O), 1633 (C=N), 1220 (C-O), 1033 (C-N); 1H NMR (400 MHz, DMSO-d6): d 2.24 (s, 3H, CH3), 3.10 (dd, Jax = 5.9 Hz, Jab = 17.6 Hz, 1H, Ha), 3.80 (dd, Jbx = 11.6 Hz, Jab = 17.6 Hz, 1H, Hb), 5.45 (dd, Jax = 5.9 Hz, Jbx = 11.6 Hz, 1H, Hx), 6.08 (s, 2H, O-CH2-O), 6.76–7.96 (m, 11H, Ar-H), 9.18 (s, 1H, NH). Anal. Calcd for C24H20ClN3O3: C, 66.44; H, 4.65; N, 9.68. Found: C, 66.31; H, 4.61; N, 9.73%. 3-(Benzo[d][1,3]dioxol-5-yl)-N-(4-chlorophenyl)-5-(pyr­idin-4-yl)-4,5-dihydro-1H-pyrazole-1-carboxamide (3j) Yield: 337 mg (80%) of gray powder; m.p. 134.4–134.6 °C. IR (.max, cm–1): 3290 (N-H), 3005 (=C-H), 2918, 2848 (C-H), 1672 (C=O), 1631 (C=N), 1220 (C-O), 1033 (C-N);1H NMR (400 MHz, DMSO-d6): d 3.11 (dd, Jax = 5.6 Hz, Jab = 17.9 Hz, 1H, Ha), 3.81 (dd, Jbx = 12.0 Hz, Jab = 17.9 Hz, 1H, Hb), 5.48 (dd, Jax = 5.5 Hz, Jbx = 11.9 Hz, 1H, Hx), 6.08 (s, 2H, O-CH2-O), 6.96–7.87 (m, 11H, Ar-H), 9.16 (s, 1H, NH). Anal. Calcd for C22H17ClN4O3: C, 62.79; H, 4.07; N, 13.31. Found: C, 62.93; H, 4.11; N, 13.37%. 3-(Benzo[d][1,3]dioxol-5-yl)-N-(4-chlorophenyl)-5-(thio­phen-2-yl)-4,5-dihydro-1H-pyrazole-1-carboxamide (3k) Yield: 320 mg (75%) of yellow powder; m.p. 158.7– 159.0 °C. IR (.max, cm–1): 3296 (N-H), 3093 (=C-H), 2974, 2901 (C-H), 1689 (C=O), 1631 (C=N), 1228 (C-O), 1033 (C-N); 1H NMR (400 MHz, DMSO-d6): d 3.30 (dd, Jax = 4.7 Hz, Jab = 18.0 Hz, 1H, Ha), 3.79 (dd, Jbx = 11.6 Hz, Jab = 18.0 Hz, 1H, Hb), 5.84 (dd, Jax = 4.7 Hz, Jbx = 11.6 Hz, 1H, Hx), 6.08 (s, 2H, O-CH2-O), 7.06–7.65 (m, 10H, Ar-H), 9.08 (s, 1H, NH). Anal. Calcd for C21H16ClN3O3S: C, 59.22; H, 3.79; N, 9.87. Found: C, 59.05; H, 3.75; N, 9.93%. 3-(Benzo[d][1,3]dioxol-5-yl)-N-(4-chlorophenyl)-5-(fu­ran-2-yl)-4,5-dihydro-1H-pyrazole-1-carboxamide (3l) Yield: 332 mg (81%) of brown powder; m.p. 175.5– 175.9 °C. IR (.max, cm–1): 3294 (N-H), 3080 (=C-H), 2980, 2901 (C-H), 1680 (C=O), 1631 (C=N), 1219 (C-O), 1033 (C-N); 1H NMR (400 MHz, DMSO-d6): d 3.30 (dd, Jax = 5.2 Hz, Jab = 17.3 Hz, 1H, Ha), 3.68 (dd, Jbx = 11.9 Hz, Jab = 17.3 Hz, 1H, Hb), 5.60 (dd, Jax = 5.2 Hz, Jbx = 11.9 Hz, 1H, Hx), 6.11 (s, 2H, O-CH2-O), 7.01–7.83 (m, 10H, Ar-H), 9.10 (s, 1H, NH). Anal. Calcd for C21H16ClN3O4: C, 61.54; H, 3.94; N, 10.25. Found: C, 61.68; H, 3.96; N, 10.28%. 3-(Benzo[d][1,3]dioxol-5-yl)-5-(5-bromothiophen-2­yl)-N-(4-chlorophenyl)-4,5-dihydro-1H-pyra­zole-1-carboxamide (3m) Yield: 404 mg (80%) of gray powder; m.p. 108.9– 109.1 °C. IR (.max, cm–1): 3288 (N-H), 3086 (=C-H), 2989, 2918 (C-H), 1681 (C=O), 1631 (C=N), 1220 (C-O), 1033 (C-N); 1H NMR (400 MHz, DMSO-d6): d 3.26 (dd, Jax = 5.0 Hz, Jab = 17.9 Hz, 1H, Ha), 3.77 (dd, Jbx = 11.5 Hz, Jab = 17.9 Hz, 1H, Hb), 5.73 (dd, Jax = 5.0 Hz, Jbx = 11.4 Hz, 1H, Hx), 6.09 (s, 2H, O-CH2-O), 6.99–7.78 (m, 9H, Ar-H), 9.09 (s, 1H, NH). Anal. Calcd for C21H15BrClN3O3S: C, 49.97; H, 3.00; N, 8.32. Found: C, 50.11; H, 3.02; N, 8.35%. 3-(Benzo[d][1,3]dioxol-5-yl)-N-(4-chlorophenyl)-5­(4-(trifluoromethyl)phenyl)-4,5-dihydro-1H-pyra­zole-1-carboxamide (3n) Yield: 366 mg (75%) of white powder; m.p. 110.6– 110.9 °C. IR (.max, cm–1): 3290 (N-H), 3093 (=C-H), 2980, 2866 (C-H), 1670 (C=O), 1631 (C=N), 1222 (C-O), 1033 (C-N); 1H NMR (400 MHz, DMSO-d6): d 3.16 (dd, Jax = 5.7 Hz, Jab = 17.7 Hz, 1H, Ha), 3.84 (dd, Jbx = 11.6 Hz, Jab = 17.7 Hz, 1H, Hb), 5.64 (dd, Jax = 5.7 Hz, Jbx = 11.6 Hz, 1H, Hx), 6.11 (s, 2H, O-CH2-O), 7.02–7.85 (m, 11H, Ar-H), 9.17 (s, 1H, NH). Anal. Calcd for C24H17ClF3N3O3: C, 59.09; H, 3.51; N, 8.61. Found: C, 59.27; H, 3.54; N, 8.65%. 3-(Benzo[d][1,3]dioxol-5-yl)-5-(3-chlorophenyl)-N-(4­chlorophenyl)-4,5-dihydro-1H-pyrazole-1-carboxam­ide (3o) Yield: 327 mg (72%) of white powder; m.p. 168.3– 168.5 °C. IR (.max, cm–1): 3292 (N-H), 3093 (=C-H), 2914, 2947 (C-H), 1687 (C=O), 1631 (C=N), 1228 (C-O), 1033 (C-N); 1H NMR (400 MHz, DMSO-d6): d 3.12 (dd, Jax = 5.5 Hz, Jab = 17.9 Hz, 1H, Ha), 3.78 (dd, Jbx = 11.8 Hz, Jab = 17.9 Hz, 1H, Hb), 5.49 (dd, Jax = 5.4 Hz, Jbx = 11.8 Hz, 1H, Hx), 6.06 (s, 2H, O-CH2-O), 6.99–7.86 (m, 11H, Ar-H), 9.10 (s, 1H, NH). Anal. Calcd for C23H17Cl­2N3O3: C, 60.81; H, 3.77; N, 9.25. Found: C, 60.93; H, 3.75; N, 9.28%. 2. 2. Anticancer Activity HeLa (human cervical adenocarcinoma), MCF-7 (human breast adenocarcinoma) and NIH-3T3 (mouse embryonic fibroblast) cell lines were used for MTT assay.14 Cells were cultured in DMEM medium supplemented with 10% fetal bovine serum (FBS), 100 U/mL penicillin and 100 µg/mL streptomycin and 2 mM L-glutamine. The cultures were incubated at 37 °C in a humidified atmo­sphere with 5% CO2. Briefly, cells were seeded into a 96­well plate at a density of 1 × 104 cells/well in the 100 µL medium. After incubating the cells for 24 h, the dilutions of compounds at different doses (0.1–1000 µM) were add­ed and incubated for 24h. After that, the culture medium was discarded and the wells were washed with PBS twice, followed by the addition of 10 µL MTT dye (0.5 mg/mL) into each well in the 100 µL medium. The cells were incu­bated for another 4 h at 37 °C. After removing all the cul­ture medium, 100 µL DMSO was added per well. The per­centage of cell viability was measured on ELISA reader (Biotek Co., USA) at wavelength of 570 nm. The IC50 val­ues of compounds on the HeLa, MCF-7 and NIH-3T3 cells were calculated using GraphPad Prism 7. The images of cells treatment of compound were also assessed by invert­ed microscope (Zeiss Axiovert). All experiments were re­peated multiple times. 2. 3. Colony Formation Assay MCF-7 and HeLa cells were seeded in to 12-well plates at a density of 1000 cells/well in the 800 µL medium. After incubating the cells for 24 h, the at IC50 values of compound (µM) were added and incubated for 24 h. After treatment, the medium containing compounds was re­moved and replaced with pure medium. Medium was changed every 3 days for 10 days until visible colonies were formed. The medium was aspirated from wells and cells were washed three times with PBS. Then, colonies were si­multaneously fixed with 500 µL of fixing reagent (metha-nol/acetic acid = 3/1) and incubated at room temperature for 5 min. Then, cells were stained with 0.5% crystal violet solution and incubated at room temperature. The plate was washed with ddH2O. The stained cells were examined with invert microscope (Zeiss Axiovert, Germany) and imaging system.15 2. 4. In Silico ADMET Analysis The ADMET parameters, pharmacokinetic proper­ties and violations of drug-likeness of all synthesized com­pounds were investigated with the SwissADME webserver (http://www.swiss adme.ch/). 2. 5. Statistical Analysis In this study, data were collected from 3 different bi­ological replicates and the results were plotted as mean ± Scheme 1. The synthesis pathway of the target compounds: (i) methanol, NaOH; (ii) ethanol, acetic acid; (iii) hydrazine monohydrate, ether; (iv) ethanol, NaOH. SD. One-way ANOVA was used as statistical analysis by GraphPad Prism 7. P-value <0.05 was considered statisti­cally significant. 3. Result and Discussion 3. 1. Chemistry The synthesis of new pyrazoline derivatives was car­ried out by the methods outlined in Scheme 1. The cycliza­tion of chalcones with phenylhydrazine hydrochloride in acidic media gave the 2-pyrazoline compounds (2a–2o). The cyclization of chalcones with semicarbazide in basic media gave the 2-pyrazoline-1-carboxamide compounds (3a–3o). The structures of these pyrazolines were elucidat­ed by using IR and 1H NMR spectral methods besides ele­mental analysis. The IR spectra of compounds 2a–2o and 3a–3o showed C=N intense absorption bands in the range of 1591–1597 cm–1 and 1631–1633 cm–1, respectively. The IR spectra of compounds 3a–3o demonstrated intense ab- Table 1. The IC50 values of synthesized pyrazoline derivatives sorption bands in the range of 3288–3367 cm–1 due to the N–H bond and 1666–1689 cm–1 due to the C=O bond. In the 1H NMR spectrum, the finding of three double doublet peaks belonging to the Ha, Hb and Hx protons indicated the synthesis of the pyrazoline ring successfully. The Ha and Hb methylene protons of the pyrazoline ring in posi­tion 4 appeared as double doublet signals at 2.92–3.30 ppm and 3.68–3.96 ppm, respectively. The methine proton of Hx resonated as double doublet signals at 5.33–5.85 ppm. Furthermore, compound 3a–3o revealed the singlet signal assigned to NH proton of carboxamide moiety at 9.00– 9.20 ppm. The methylene protons belonging to 1,3-benzo­dioxole ring gave a singlet peak at 6.02–6.14 ppm. The ele­mental analysis also supported these structures. 3. 2. Biological Activity The anticancer activity of synthesized pyrazoline de­rivatives was evaluated on HeLa (human cervical adeno-carcinoma), MCF-7 (human breast adenocarcinoma) and NIH-3T3 (mouse embryonic fibroblast) cell lines using Compounds IC50 (µM) Selectivity Index (SI)* HeLa MCF-7 NIH-3T3 NIH-3T3/HeLa NIH-3T3/MCF-7 2a 128.24 ± 20.16 109.65 ± 6.68 ND 9.87 11.54 2b 93.88 ± 14.02 ND 684.13 ± 14.20 7.29 ND 2c 103.91 ± 2.65 113.19 ± 11.02 594.89 ± 31.08 5.73 5.26 2d 289.24 ± 24.28 27.63 ± 3.51 899.52 ± 3.65 3.11 32.56 2e 75.15 ± 22.14 269.19 ± 2.69 212.12 ± 16.21 2.82 0.79 2f 82.35 ± 14.62 186.39 ± 12.66 724.93 ± 5.22 8.80 3.89 2g 168.42 ± 6.31 ND 533.436 ± 3.68 3.17 ND 2h 81.02 ± 2.44 74.75 ± 12.55 290.13 ± 14.03 3.58 3.88 2i 158.31 ± 13.70 255.61 ± 22.08 152.08 ± 8.25 0.96 0.59 2j 108.84 ± 3.11 14.30 ± 1.10 528.71 ± 6.69 4.86 36.97 2k 97.54 ± 6.63 83.07 ± 3.08 495.38 ± 18.09 5.08 5.96 2l 111.58 ± 4.12 181.76 ± 6.24 985.04 ± 10.85 8.83 5.42 2m 188.75 ± 2.25 221.12 ± 19.30 181.74 ± 14.03 0.96 0.82 2n 79.18 ± 12.37 54.07 ± 6.36 ND ND ND 2o 50.02 ± 3.48 340.56 ± 17.10 908.74 ± 7.49 18.17 2.67 3a 75.26 ± 13.50 ND 232.78 ± 21.01 3.09 ND 3b 93.49 ± 2.84 114.05 ± 6.66 476.97 ± 13.14 5.10 4.18 3c 13.74 ± 1.33 24.49 ± 3.81 381.21 ± 3.86 27.74 15.57 3d 120.22 ± 10.07 78.85 ± 5.64 665.01 ± 18.29 5.53 8.43 3e 81.15 ± 2.66 37.75 ± 3.37 615.61 ± 11.94 7.59 16.31 3f 11.53 ± 3.09 11.37 ± 1.08 942.61 ± 18.38 81.75 82.90 3g 533.14 ± 18.13 117.45 ± 6.54 233.74 ± 10.93 0.44 1.99 3h 123.09 ± 8.07 ND 556.01 ± 14.22 4.52 ND 3i 111.84 ± 6.53 108.14 ± 12.07 279.01 ± 19.38 2.49 2.58 3j 422.80 ± 2.49 10.08 ± 1.04 693.45 ± 9.21 1.64 68.79 3k 137.21 ± 15.08 516.56 ± 3.98 130.76 ± 3.45 0.95 0.25 3l ND 122.29 ± 11.82 560.43 ± 3.70 ND 4.58 3m 115.53 ± 5.74 369.25 ± 22.47 605.59 ± 17.93 5.24 1.64 3n ND 13.67 ± 2.65 968.15 ± 21.80 ND 70.82 3o ND 41.53 ± 6.12 252.15 ± 15.54 ND 6.07 * ND: not determined MTT assay. The IC50 values of all compounds are given in Table 1. Compound 2d bearing 4-nitrophenyl and com­pound 2j bearing pyridine ring from phenylpyrazoline de­rivatives (2a–2o) and compound 3j bearing pyridine and 3n bearing 4-trifluoromethylphenyl ring showed the high­est cytotoxicity activity the MCF-7 cancer cell lines. On the other hand, compound 3c bearing 4-bromophenyl ring from pyrazoline carboxamide derivatives (3a–3o) demonstrated the highest cytotoxicity activity on HeLa cancer cell lines. Among all pyrazoline derivatives, com­pound 3f exhibited the most potent cytotoxic activity on both two cancer cells. Additionally, selectivity index (SI) IC50 for NIH-3T3 cell line/IC50 for HeLa and MCF-7 cell line was calculated for the evaluation of cytotoxic effects of molecules against both normal and cancer cells. Most of the compunds dis­played low cytotoxicity against normal cells and especially, it was determined that the selectivity indices of com­pounds 2d, 2j, 3c, 3f, 3j, 3n showing the highest cytotoxic activity were quite high. The most selective compound 3f had SI 81.75 for HeLa, and 82.90 for MCF-7 cell line. After the MTT results, the images taken under the microscope of the compounds with antiproliferative effect are presented in Figure 2. When the synthesized com­pounds were compared with the control group in MCF-7 cells, their interaction with each other decreased and their growth was found to be slowed down when treated with compounds 2d, 2j, 3f, 3j and 3n. Although cancer cells are cells that grow close to each other and proliferate rapidly, the synthesized compounds at IC50 reduced high-growing cancer cells such as MCF7 and HeLa. In HeLa cells, it was shown that reduced cell interactions and growth was ob­served when treated with compounds 3c and 3f in com­parison to the control group. Cancer cells can survive when they migrate from their region to other regions by forming a colony on their own. Colony formation assay measurement is an import­ant method to measure the effect of cancer cells on growth alone and is used to determine the effects of drugs on cir­culating cancer cells. The effect of newly synthesized com­pounds on the colony formation potential is shown in Fig­ure 3 with MCF-7 and Hela cells. Compared with the control group MCF-7 cells, it was shown that compounds 2d, 2j, 3f, 3j and 3n reduced colony formation in cells and reduced the potential for colony formation. In HeLa cells, when compared to the control group, it was observed with the microscope that compounds 3c and 3f reduced the col­ony formation of the cells. 3. 3. In Silico ADMET Results Oral bioavailability is an important factor in order to develop new drug candidates targeted for oral use. It is possible for drug candidates to succeed in phase studies with good lipophilicity, water solubility, physicochemical and pharmacokinetics properties.16 Therefore, we evaluat- Figure 2. The images of cells treatment with IC50 values of synthesized pyrazoline derivatives in MCF-7 cells and HeLa cells Figure 3. The colony formation images of MCF-7 cells and HeLa cells. Table 2. Lipinski and Veber Parameters of the synthesized compounds. Comp Physicochemical Properties Lipophilicity Druglikeness Water Solubility PharmacokineticsMW Fsp3 RB HBA HBD MR tPSA clogP Lipinski Ghose Veber Egan Muegge ESOL Class GI absorption F 2a 342.39 0.14 3 3 0 108.32 34.06 4.07 v v v v v –5.33 Moderately High 0.55 2b 360.38 0.14 3 4 0 108.28 34.06 4.38 v v v v v –5.48 Moderately High 0.55 2c 421.29 0.14 3 3 0 116.02 34.06 4.70 v v v v 1 –6.23 Poorly High 0.55 2d 387.39 0.14 4 5 0 117.15 79.88 3.65 v v v v v –5.37 Moderately High 0.55 2e 356.42 0.17 3 3 0 113.29 34.06 4.39 v v v v 1 –5.62 Moderately High 0.55 2f 367.40 0.13 3 4 0 113.04 57.85 3.85 v v v v v –5.26 Moderately High 0.55 2g 370.44 0.21 3 3 0 118.26 34.06 4.70 v v v v 1 –5.92 Moderately High 0.55 2h 356.42 0.17 3 3 0 113.29 34.06 4.39 v v v v 1 –5.62 Moderately High 0.55 2i 356.42 0.17 3 3 0 113.29 34.06 4.41 v v v v 1 –5.62 Moderately High 0.55 2j 343.38 0.14 3 4 0 106.12 46.95 3.32 v v v v v –4.66 Moderately High 0.55 2k 348.42 0.15 3 3 0 106.20 62.30 4.06 v v v v v –5.17 Moderately High 0.55 2l 332.35 0.15 3 4 0 100.59 47.20 3.39 v v v v v –4.69 Moderately High 0.55 2m 427.31 0.15 3 3 0 113.90 62.30 4.75 v v v v 1 –6.29 Poorly High 0.55 2n 410.39 0.17 4 6 0 113.33 34.06 5.09 v v v v 1 –6.17 Moderately High 0.55 2o 376.84 0.14 3 3 0 113.33 34.06 4.59 v v v v 1 –5.91 Moderately High 0.55 3a 419.86 0.13 5 4 1 122.51 63.16 4.07 v v v v v –5.41 Moderately High 0.55 3b 437.85 0.13 5 5 1 122.47 63.16 4.51 v v v v v –5.57 Moderately High 0.55 3c 498.76 0.13 5 4 1 130.21 63.16 4.81 v 1 v v 1 –6.32 Poorly High 0.55 3d 464.86 0.13 6 6 1 131.33 108.98 3.52 v v v v v –5.48 Moderately High 0.55 3e 433.89 0.17 5 4 1 127.48 63.16 4.53 v v v v v –5.71 Moderately High 0.55 3f 444.87 0.12 5 5 1 127.23 86.95 3.99 v v v v v –5.36 Moderately High 0.55 3g 447.91 0.20 5 4 1 132.44 63.16 4.73 v 1 v v 1 –6.02 Poorly High 0.55 3h 433.89 0.17 5 4 1 127.48 63.16 4.41 v v v v v –5.71 Moderately High 0.55 3i 433.89 0.17 5 4 1 127.48 63.16 4.53 v v v v v –5.71 Moderately High 0.55 3j 420.85 0.14 5 5 1 120.31 76.05 3.33 v v v v v –4.74 Moderately High 0.55 3k 425.89 0.14 5 4 1 120.39 91.40 4.10 v v v v v –5.26 Moderately High 0.55 3l 409.82 0.14 5 5 1 114.78 76.30 3.42 v v v v v –4.77 Moderately High 0.55 3m 504.78 0.14 5 4 1 128.09 91.40 4.86 2 1 v v 1 –6.38 Poorly High 0.17 3n 487.86 0.17 6 7 1 127.51 63.16 5.12 v 2 v 1 1 –6.27 Poorly High 0.55 3o 454.31 0.13 5 4 1 127.52 63.16 4.73 v v v v 1 –6.01 Poorly High 0.55 Molecular weight: MW, topological polar surface area: tPSA, Molar Refractivity: MR, fraction of sp3 carbon atoms: Fsp3, HBD: hydrogen bonds donor, HBA: hydrogen bond acceptor, RB: rotatable bonds, LogP values: indicator of Lipophilicity, ESOL: aqueous solubility parameter, Log Kp: skin permeation, F: Bioavailability Score. ed the druglikeness properties such as Lipinski, Ghose, Veber, Egan and Muegge filters of all synthesized com­pounds. The results showed that the majority of all com­pounds did not violate these filters. Aqueous solubility plays a critical role for drug’s bioavailability. Therefore, ESOL (Log S) values of all compounds were calculated and the solubility of all compounds was determined to be moderate, except for compounds 2c, 2m, 3c, 3g, 3m, 3n and 3o. In addition, the GI absorptions of all compounds was determined to be high. The bioavailability score of compounds was found to be ideal.17 Among these com­pounds 3f, showing the best anticancer activity, possesses good drug score values. Compound 3f did not appear to violate any of the rules described here (Table 2). The BOILED-Egg server getting from swissadme program gives pharmacokinetic properties (such as gas­trointestinal absorption, brain access and P-glycoprotein) of molecules by calculating with partition constant (log P) and topological polar surface area (TPSA). The yellow area (BBB) shows molecules that easily permeate through the blood-brain barrier, the white region (HIA) demostrates molecules that easily permeate through the gastrointesti­nal membranes, while the gray area presents molecules having low absorption. Based on this BOILED-Egg server, all synthesized molecules permeate through either the blood-brain barrier or the gastrointestinal membranes. None of the molecules are in the low absorption region (Figure 4). In addition, PGP+ identifies the molecules to be effluated from the central nervous system by the P-glyco-protein. PGP– also identifies the molecules that are non-substrate of the P-glycoprotein. Among these com­pounds 3f, showing the best anticancer activity, permeates through the gastrointestinal membranes easily and is non-substrate of the P-glycoprotein according to Figure 4. 4. Conclusion In the present study, a number of new molecules contained two pharmacodynamic groups (2-pyrazoline and benzo[d][1,3]dioxol) inducing significant synergistic lethality of cancer cells were designed and synthesized. These pyrazolines were assessed for anticancer activity on HeLa, MCF-7 cancer cell lines and NIH-3T3 normal cell lines. The results demonstrated that compounds 2d, 2j, 3c, 3j and 3n exhibited higher activity than the other sub­stituted pyrazoline derivatives. Notably, compound 3f was identified as the most potent cytotoxic molecule in this study with IC50 values of 11.53 µM on HeLa and 11.37 µM on MCF-7. In silico ADMET analysis and drug-likeness displayed that compound 3f has good pharmacokinetic characteristics and absorption-solubili­ty properties. Therefore, compound 3f can be a lead com­pound for the development of novel anticancer agents in drug discovery. Acknowledgements This research was partly presented at the 13th Inter­national Symposium on Pharmaceutical Sciences, June 22–25, 2021, Ankara, Turkey. Figure 4. The BOILED-Egg predictive models of all compounds Supporting Information Contains copies of IR and 1H NMR spectra of syn­thesized compounds (2a–2o and 3a–3o). 5. References 1. S. C. Karad, V. B. Purohit, P. Thakor, V. R. Thakkar, D. K. Raval, Eur. J. Med Chem. 2016, 112, 270–279. DOI:10.1016/j.ejmech.2016.02.016 2. M. Yusuf, P. Jain, Arab. J. Chem. 2014, 7, 553–596. DOI:10.1016/j.arabjc.2011.09.013 3. F. Hayat, A. Salahuddin, S. Umar, A. Azam, Eur. J. Med Chem. 2010, 45, 4669–4675. DOI:10.1016/j.ejmech.2010.07.028 4. A. Özdemir, M. D. Altintop, Z. A. Kaplancikli, Ö. D. Can, Ü. Demir Özkay, G. Turan-Zitouni, Molecules, 2015, 20, 2668– 2684. DOI:10.3390/molecules20022668 5. S. Y. Jadhav, S. P. Shirame, S. D. Kulkarni, S. B. Patil, S. K. Pa-sale, R. B. Bhosale, Bioorg. Med. Chem. Lett. 2013, 23, 2575– 2578. 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Except when otherwise noted, articles in this journal are published under the terms and conditions of the Creative Commons Attribution 4.0 International License DOI: 10.17344/acsi.2021.7146 Scientific paper Modelling and Multi-Objective Optimization of Continuous Indirect Electro-Oxidation Process for RTB21 Dye Wastewater Using ANN-GA Approach Naresh R Vaghela1 and Kaushik Nath2 1 Department of Chemical Engineering, Government Engineering College (Affiliated t Gujarat Technological University, Ahmedabad), Bharuch- 392002, Gujarat, India 2 Department of Chemical Engineering, G H Patel College of Engineering & Technology, Vallabh Vidyanagar- 388120, Gujarat, India * Corresponding author: E-mail: email:drkaushiknath2013@gmail.com Received: 09-14-2021 Abstract A continuous indirect electro-oxidation (EO) process was developed using graphite electrode to investigate the treata­bility of reactive turquoise blue RTB21 dye wastewater under specific operating conditions of initial pH, current density, hydraulic retention time (HRT), and electrolyte (NaCl) concentration. The experiments were performed in accordance with the central composite design (CCD), and the findings were used to create a model utilizing artificial neural net­works (ANNs). According to the predicted findings of the ANN model, the MSE values for colour and COD removal ef­ficiencies were estimated to be 0.748 and 0.870, respectively, while the R2 values were 0.9999 and 0.9998, respectively. The Multi-objective optimization using genetic algorithm (MOGA) over the ANN model maximizes the multiple responses: colour and COD removal efficiency (%). The MOGA generates a non-dominated Pareto front, which provides an insight into the process’s optimum operating conditions. Keywords: Multi-objective optimization, Artificial neural network, Genetic algorithm, wastewater, reactive turquoise blue 21 1.Introduction Clean water and sanitation is one of the major agenda of the United Nation’s sustainable development initiative. According to the UN’s world water development report 2021, around 2 billion individuals live under water stress conditions, and by 2030, the world would face a water defi­cit of 40%.1 The quantity of wastewater generated world­wide has increased exponentially due to the rise in popu­lation and rapid industrial and technological development over the last few decades. India generates approximately 64,000 tonnes of dyes annually, of which 7,040 tonnes are dumped directly into the environment. These dyestuffs are widely used by textiles, paper and pulp, leather, and many other industries, but unfortunately, their impact on health and the environment are poorly evaluated.2 Despite nu­merous Physico-chemical techniques for dye wastewater treatment, most of these systems appear to be marred by low practical efficiency or an inadequate benefit-cost ratio. Several traditional, as well as novel strategies, have been proposed in the literature to treat dye wastewater. These include adsorption by activated carbon,3 chemical coag­ulation,4 photodegradation under UV light irradiation,5 hydrodynamic cavitation,6 sonochemical degradations,7 ozonation,8 electrocoagulation,9 Fenton like processes,10 membrane filtration,11 electrochemical methods,12–14 and many more. A primary drawback with many of these dye remov­al techniques is that they cannot remove all types of dyes from the wastewater. Adsorption is a highly successful technique for treating dye-containing effluent, but ad­sorbent regeneration is an expensive process. Prolonged treatment time and post-treatment solid disposal are lim­itations of the adsorption process.15 Chemical coagula­tion can bring high removal efficiency and a high quan­tity of wastewater but relatively high processing costs.16 Fenton-like techniques generate a substantial amount of sludge and are thus impractical for completely degrading dye molecules. Almost all types of dyes may be separat­ed from wastewater using the membrane process. There is no sludge development, and footprint requirement is also low. However, the cost of the membrane and associ­ated equipment and the fouling issue during operation are drawbacks of the membrane process.17 While biological treatment could be cost-competitive, it is less effective to deal with refractory organic wastes.16 As a result, finding an effective and environmentally acceptable treatment technique with high removal efficiency and low cost is essential for completely removing dye molecules from in­dustrial wastewater. Electrochemical techniques, such as anodic oxi­dation or indirect electrochemical oxidation (EO), have garnered significant interest in industrial wastewater treatment.18Electrochemical techniques offer enormous promise for wastewater treatment due to their wide range of environmental compatibility, increased process effi­ciency, and cost-effectiveness. However, several process factors, such as initial pH, current density, electrolyte concentration, process time, etc., impact electrochemical processes. Thus, optimizing process parameters is crucial from a process performance, economic, and scale-up per­spective.19 Typically, modelling and optimization of electro­chemical processes have been described using the conven­tional one-variable-at-a-time approach (OVAT). However, OVAT ideas suffer from several drawbacks, including the inability to show the interactive effect of process factors, being time demanding, and economically costly.20 Arti­ficial intelligence (AI) has gained immense attention to overcome such limitations and emerged as an encouraging tool for modelling and process optimization. AI tools, such as ANN, GA, fuzzy logic, and machine learning have been widely considered for modeling and optimizing waste­water treatment processes.21 ANN, inspired by biological neuron phenomena of the human brain, was a computa­tional modelling technique used for non-linear problems and to predict the output values for given input parameters from their training values.22 GA optimization tool can be used to more precisely optimized the ANN model. GA is a search heuristic algorithm inspired by Charles Darwin’s principle of natural evolution on the concept of “surviv­al of the fittest”. The GA approach is used to search for a global optimum solution with the ANN model as a fitness function.23 Single-objective optimization of the electrochemical process using the ANN-GA approach has been fairly re­ported in the literature. However, optimization problems involve multiple objectives, which require simultaneously to optimize: maximize or minimize. Many attempts have so far been made for multi-objective optimization of the electrocoagulation process for wastewater. However, to the best of our knowledge, the literature has not reported ANN modeling accompanied by multi-objective optimi­zation using GA of continuous EO process for dye waste­water. In this work, our primary objective of research is to develop two distinctive ANN models for the prediction of colour removal efficiency and COD removal efficiency for continuous EO process to degrade reactive turquoise blue 21 (RTB21) dye wastewater. Finally, ANN models are simultaneously optimized using the GA approach to max­imize both the objectives: colour removal efficiency and COD removal efficiency. 2. Materials and Method 2. 1. Chemicals and Materials M/s Snehal Dye Chem Ltd, Ankleshwar, Gujarat, In­dia, supplied the model RTB21 dye. All other chemicals used during experiments were of analytical grade. De­ionized water (DI) with 1 µS/cm conductivity was used to perform all experiments. Graphite electrode plates (10×10×0.2 cm) used in the EO process were procured from M/s Prime Industries, Maharashtra, India. 2. 2. Electrochemical experiments The continuous flow EO rector was constructed from an acrylic sheet with three sections, as shown in Fig.1, with Fig. 1. (a) A conceptual schematic diagram of Continuous EO experimental setup, (b) Actual Experimental setup of continuous EO process: (1) RTB21 Wastewater Reservoir, (2) Peristaltic Pump, (3) DC Power Supply, (4) Electrochemical Cell, (5) Graphite Electrodes, and (6) Receiving cell a working volume of 1.5 L. The two graphite electrode plates were connected vertically with a gap of 10 mm, con­nected in monopolar mode to a DC power supply (Make: SIGMA, 0–30 V 0-5 A). A mini peristaltic pump (GOSO Technology, Model: AB 11, 7.5 W) was used to pump the simulated wastewater into the top of the first section of the EO rector from the reservoir. The wastewater from the first section passed to the second section through holes provided at the bottom of the first section, flowed upward through the layers of the electrodes, and drained out at the third section from the top of the reactor. After experienc­ing the specified EO process time, the effluent samples of the continuous EO process were collected. To change the pH of the solution, dilute solutions of H2SO4 and NaOH were used. NaCl was added to the solution as an electrolyte to adjust the conductivity. The colour removal efficiency of the sample collected at various time intervals was calculated using Eq. 1.24 (1) Where A0 and A are the light absorbance of a sample before and after the electrochemical process, respectively, measured using a UV/VIS spectrophotometer (Model CL 335). (2) Where C0 and C are the COD of a sample before and after the electrochemical process, respectively, measured using the open reflux method following standard meth­odologies (APHA. American Public Health Association 2005). 2. 3. Experimental design Central composite design (CCD) was used for the design of experiments (DOE). Four process variables: ini- Table 1. Experiment range and levels of Independent variable used as per CCD. Variables Levels –2(a) –1 0 +1 +2(a) Initial pH CD (A/m2) 3 100 5 150 7 200 9 250 11 300 HRT (min) 50 75 100 125 150 NaCl Conc. (g/L) 1 1.5 2 2.5 3 tial pH (3-11), Current density (100-300 A/m2), Hydraulic retention time (HRT) (50-150 min), Electrolyte NaCl con­centration (1-3 g/L), with five levels used for the design of experiments as shown in Table 1. A total of 31 experiments as per Table 2 design were performed, and colour removal efficiency and COD re­moval efficiency were calculated. To avoid systemic bias, the runs were carried out in a randomized manner. Colour removal and COD removal efficiencies were considered to be response 1 and response 2, respectively. These were ob­tained for the experiments performed and are presented in Table 2. Fig. 2. Optimized architecture of ANN network for EO process. Table 2. CCD design with observed and predicted responses. Exp. Parameters Response 1 Response 2 Run (Actual value) Colour removal (%) COD removal (%) pH Current density HRT NaCl Conc. Expt. ANN Predicted Expt. ANN Predicted 1 3 200 100 2.0 21.12 21.12 12.78 11.99 w`2 5 150 75 1.5 14.20 14.25334 9.60 9.69 3 5 150 75 2.5 45.02 45.02 21.21 21.53 4 5 150 125 1.5 33.89 33.89 25.55 26.33 5 5 150 125 2.5 65.32 65.31208 40.22 39.71 6 5 250 75 1.5 24.79 24.79 15.50 16.24 7 5 250 75 2.5 48.22 48.22 32.30 32.41 8 5 250 125 1.5 77.01 76.86996 44.50 44.24 9 5 250 125 2.5 99.20 99.2 60.12 61.95 10 7 100 100 2.0 44.32 44.32 21.30 20.75 11 7 200 50 2.0 8.26 8.26 6.40 5.75 12 7 200 100 1.0 43.43 43.43 32.23 31.58 13 7 200 100 2.0 96.11 95.64333 58.23 58.65 14 7 200 100 2.0 95.15 95.64333 59.03 58.65 15 7 200 100 2.0 96.28 95.64333 59.34 58.65 16 7 200 100 2.0 95.92 95.64333 58.51 58.65 17 7 200 100 2.0 95.10 95.64333 58.11 58.65 18 7 200 100 2.0 95.50 95.64333 59.20 58.65 19 7 200 100 2.0 95.00 95.64333 58.11 58.65 20 7 200 100 3.0 97.00 97 62.00 61.13 21 7 200 150 2.0 78.12 78.12 52.80 51.93 22 7 300 100 2.0 89.22 89.22 50.50 49.54 23 9 150 75 1.5 9.40 9.4 5.90 5.97 24 9 150 75 2.5 38.88 38.88 17.10 17.82 25 9 150 125 1.5 28.23 28.23 22.34 22.61 26 9 150 125 2.5 56.20 56.2 35.11 36.00 27 9 250 75 1.5 19.99 19.25079 12.12 12.52 28 9 250 75 2.5 40.87 40.64716 28.33 28.69 29 9 250 125 1.5 70.05 70.05 39.80 40.53 30 9 250 125 2.5 91.10 90.93601 58.70 58.23 31 11 200 100 2.0 8.10 8.1 5.30 4.57 2. 4. ANN Model ANN models were created for both the responses to measure the performance of the EO reactor to treat the RTB21 model dye wastewater. The artificial neural net­work (ANN) is a soft computation tool inspired by bio­logical neurons in the brain.25 In recent years, the ANN has been employed as an efficient and versatile approach in various applications.26 In ANN, there are three layers: the input layer, one or more hidden layers, and the output layer shown in Fig. 2. An ANN’s structure is made of processing compo­nents referred to as neurons (nodes). Weights and biases are used to connect each layer of neurons.27 The weighted sum of each neuron’s inputs are passed through the activa­tion function to produce the output.28 Feed-forward Lev-enberg-Marquardt Back-Propagation (LM-BP) algorithms were used for learning. ANN models were developed for both responses to evaluate the EO reactor’s performance in treating the RTB21 model dye wastewater. The mean square error (MSE) was utilized for training the ANN. 70% of the data were used to train the neural network. Af­ter training, the remaining data were used in an equiva­lent proportion for validation and testing. More data were allocated for the training, which resulted in an improved model with a shorter processing time. The testing offered an unbiased evaluation of the network’s performance, whereas the validation evaluated the network’s generalization, which was terminated when no further progress was detected.29 The ANN models can be used as a fitness function for a GA used for multi-objec­tive EO process optimization. 2. .5 GA Model GA is a well-known robust AI technique for solv­ing global search optimization problems.30 The GA al­gorithm is based on Darwin’s evolutionary theory. The ‘gamultiobj’ function in MATLAB (R2020b) was used to generate the Pareto front of colour removal efficien­cy and COD removal efficiency using GA and the direct search toolbox. The ‘gamultiobj’ function in MATLAB employs a controlled elitist GA, which is a variation of NSGA-II.31 Initially, a random population of individuals called chromosomes is randomly generated within the lower and upper limits of decision variables, and the optimization process starts. Three genetic algorithm rules are used to produce the populations of the future generation: selec­tion, crossover, and mutation. To generate the next gen­eration, the best population was chosen based on fitness level function. The fitness value indicates an individual’s merits for evaluation.28 Crossover, also known as recombi­nation, is the process through which two populations’ ge­netic information is combined to generate new offspring. Then, random changes in the individual population are performed during the mutation process to preserve and add diversity. When the fitness value of a population does not improve over subsequent generations, the population eventually achieves the optimal solution.32 2. 6. ANN-GA Typically, when we deal with more than one objec­tive, they are often conflicting with each other. Therefore, objectives are simultaneously optimized using a multi-ob­jective optimization algorithm; mathematically equally good solutions known as non-dominated or Pareto fron­tier are selected as the optimum designs.33 ANN-MOGA has been applied for predicting optimum conditions of co-lour removal efficiency and COD removal efficiency. The ANN models were used as a fitness function for MOGA. An ANN function named myANN1 and myANN2 was trained using the experimental data for Colour remov­al efficiency and COD removal efficiency, respectively. Then, using MATLAB (R2020b) and the myANN1 and myANN2 function, a multi-objective function code was developed. Finally, the MOGA program was run to gener­ate Pareto optimal solutions by setting all input variables’ upper and lower limits. 3. Results and Discussion 3. 1. Experimental Results In a batch electrochemical cell, the indirect electro­chemical oxidation method was carried out to treat the RTB21 wastewater. The experimental results obtained from the batch process were then used to build a con­tinuous process, and colour removal efficiency and COD removal efficiency were measured. Based on the batch process results, the initial pH, current density, and NaCl concentration range were fixed for the continuous process. It was possible to determine the influence of hydraulic re­tention time (HRT) on the colour removal and COD re­moval efficiency by altering the HRT values from 25 to 200 min, as shown in Fig. 3. The other parameters, such as pH, current density, and NaCl concentration, were kept constant at 7, 200 A/ m2, and 2 g/L, respectively. HRT was found to have a ben­eficial influence on the EO process by increasing the re­sponse time. As per the DOE set, continuous EO process experiments were performed, and colour removal efficien­cy and COD removal efficiency were calculated, as shown in Table 2. By raising the HRT, adequate time for process reaction could be provided; hence, the colour removal effi­ciency (%) and the COD removal efficiency (%) improved. The synergistic effect of increased HRT can be attributed Fig. 3. Effect of HRT (min) on Colour removal efficiency (%) and COD removal efficiency (%) at 7 pH, 200 A/m2CD, and 2 g/L NaCl concentration. to the generation of more oxidants for pollutant degrada­tion.34 With the enhancement of HRT more than 100 min, degradation efficiency declines because of unsuitable side reactions.35 The highest colour removal efficiency (%) and COD removal efficiency (%) achieved were 96.11% and 58.23% at 100 HRT (min), pH of 7, 200 A/m2, and 2 g/L NaCl concentration. 3. 2. Prediction with ANN The primary objective of the ANN is to predict the colour removal efficiency and COD removal efficiency for DOE data sets. The present research used a feed-for- Fig. 4. Performance of ANN for colour removal efficiency ward LM-BP ANN with a tangent sigmoid transfer func­tion (tansig) at a hidden layer. At each iteration, the ANN learns by testing and validating the predictability against the remaining data and deciding its absolute accuracy based on the overall correlation coefficient. It is always critical to pick a sufficient number of neurons in the hid­den layer to properly well train the network. However, the larger the hidden layer’s number of neurons, the longer it takes to process the data and learn the noise.36 Therefore, a solid network is required to determine an accurate ANN architecture to obtain accurate predictions, and this step was developed through trial and error.37 This was done to achieve the minimum possible deviation between predic- Fig. 5. Error histogram with 20 bins for the training, validation, and testing of ANN for colour removal efficiency prediction. tions and experimental results and limit the potential of over-fitting the model to the data. Numerous topologies were tested to obtain the optimal ANNs network based on the most miniature Mean Square Error (MSE) and largest Correlation coefficient (R) values. The best ANN network was a back-propagation with 4-9-6-3-1 neurons. The inputs consist of pH, CD, HRT, NaCl concentration, and output consists of colour removal efficiency and COD removal efficiency. MSE is a statistic that represents the average of the squares of the errors, the magnitude by which the value indicated by the model varies from the quantity to be ob­served; when MSE reaches zero, it indicates that our mod­el’s error reduces. The R is calculated by dividing the coef­ficient of determination (R2) by its square root function, which determines the relationship between outputs and targets. The R-value of 0 and 1 indicates a random rela­tionship and close relationship, respectively. For the colour removal efficiency, the optimum net­work performance was reached at an epoch of 3. Fig. 4 shows the performance of the ANN for colour removal ef­ficiency prediction during training, validation, and testing and the mean squared error (MSE) of the network. As shown in Fig. 4, the error started at a high val­ue during training, validation, and testing but gradually reduced as the number of epochs increased. The training was terminated at epoch 3 to prevent overfitting the data sets, and the best validation performance was achieved at epoch 3, with a mean square error of 0.025191. As the error histogram of the colour removal efficien­cy (Fig. 5) shows, most of the errors fall between –0.6066 and 0.7046. The zero error is in a vertical line parallel to the ordinate with 19 instances during training. The values of regression coefficient of correlation be­tween the experimentally obtained colour removal and the ANN predicted colour removal during training, validation, and testing are 0.99998, 1, and 0.999993 (Fig. 6 (a,b,c)), re­spectively. The regression coefficient of the network (train­ing, testing, and validation) was 0.99996 (Fig. 6 (d)). For the COD removal efficiency, the optimum net­work performance was reached at an epoch of 2. Fig. 7 shows the performance of the ANN for COD removal ef­ficiency prediction during training, validation, and testing and the mean squared error (MSE) of the network. As Fig. 7 shows, the best validation performance occurred at ep­och 2, with a 0.01971 MSE value. Fig. 6. ANN predictions of colour removal efficiency versus experimental data Fig. 7. Performance of ANN for COD removal efficiency. Fig. 8. Error histogram with 20 bins for the training, validation, and testing of ANN for COD removal efficiency prediction. Fig. 8 shows the error histogram of the ANN for COD removal efficiency. As the error histogram shows, most of the errors fall between -0.8647 and 0.6529. The zero error is in a vertical line parallel to the ordinate with 19 instances during training. The regression coefficient of correlation between the experimentally obtained COD removal efficiency and the ANN predicted COD removal efficiency during training, validation, and testing is shown in Fig. 9 (a,b,c) with values of 0.9999, 0.99987, and 1, respectively. The regression co­efficient of the network (training, testing, and validation) was 0.99989, as shown in Fig. 9 (d). 3. 3. ANN-GA Process Result The well-trained ANNs MATLAB functions were used as the fitness function, and the MOGA function is used to optimize all responses simultaneously using the “gamultiobj” algorithm. Fig. 10 depicts the conceptual model of the technique designed for multi-objective opti­mization of a continuous EO process for RTB21 dye waste­water treatment using ANN and MOGA. The upper and lower bounds are set in accordance with DOE data. To generate the Pareto front, the popu­lation size, and scattered crossover rate was set to 50 and Fig. 9. ANN predictions of colour removal efficiency versus experimental data. Table 3. Pareto front results. Sr No. pH CD HRT NaCl Conc. Colour removal COD removal efficiency efficiency 1 8.787827 299.5076 138.5276 2.996047 99.61891 73.9798 2 6.363786 298.0398 140.3197 2.081875 85.10173 79.17201 3 6.449391 298.1739 140.1821 2.235604 91.86767 78.88044 4 6.974005 299.1398 138.9455 2.56925 99.39556 77.10913 5 9.185247 299.9831 138.2816 2.999956 99.62148 71.86423 6 6.363786 298.0085 140.3197 2.107265 86.02775 79.16041 7 6.428636 299.3274 140.3134 2.171365 88.94894 79.08443 8 6.711324 298.9897 139.8307 2.345993 96.60149 78.53708 9 7.927858 298.8051 138.4173 2.989545 99.61496 74.53391 10 8.10627 299.6478 139.1243 2.908942 99.60828 75.46253 11 6.847544 298.2432 140.1611 2.494421 99.04663 77.75342 12 6.579091 298.9179 139.9584 2.312797 95.42957 78.61332 13 6.45257 298.1872 140.203 2.146821 87.71312 79.09912 14 6.79187 298.932 139.2222 2.43517 98.55288 77.99996 15 6.92752 298.8918 139.8317 2.372651 97.32729 78.46639 16 6.593751 298.7015 140.2943 2.202841 90.35593 79.00198 17 9.091421 299.5083 138.4265 2.996635 99.62033 72.40382 18 6.534363 298.0117 140.3169 2.274754 93.64972 78.79096 0.8, respectively. The Pareto front, as a result of MOO, is depicted in Fig. 11. Table 3 contains the input values for both responses in accordance with Pareto solutions. The multi-objective optimization solution of co-lour removal efficiency and COD removal efficiency has no unique solution but a mathematically equally good solution known as non-dominated or Pareto optimal solutions. As can be observed colour removal efficiency increases from 85.10% to 99.62% at the cost of decreas­ing COD removal efficiency from 79.17% to 72.40%. As shown from this Fig. 10, the Pareto front provides all possible Colour removal efficiency and COD removal ef­ficiency choices. The utopia point C (99.05, 77.75) is se­lected such that it has the minimum Euclidean distance from the reference point R. The reference point is a point corresponding to maximum values of colour removal effi­ciency point B (99.62, 72.40) and COD removal efficiency point A (85.10, 79.17). Table 4. Statistical Parameters of well-trained ANN models for colour removal efficiency and COD removal efficiency. Statistical Parameters MSE RMSE MAE MPE (%) Chi Square statistics (.2) R2 Colour removal efficiency 0.0748 0.2735 0.1462 0.2737 0.0478 0.9999 COD removal efficiency 0.0870 0.2949 0.1569 0.1398 0.0919 0.9998 3. 4. Confirmatory Experiments The results of the well-trained ANNs model were checked with their predicted capability in terms of stan­dard statistical performance parameters such as mean square error (MSE), root mean square error (RMSE), mean absolute error (MAE), model predictive error (MPE) (%), Chi-Square statistics (.2), and R2. A chi-square test is a statistical test used to compare experimental results with predicted results. The value of .2 is close to zero, and the R2 value close to one displays the well-trained ANN models. Statistical parameters of well-trained ANN models for col­or and COD removal efficiencies are presented in Table 4. Optimized parameters (pH: 6.85, CD: 298.24 A/m2, HRT: 140.16 min, NaCl concentration: 2.49 g/L) generat­ed by the ANN-MOGA model for achieving maximizing Colour removal efficiency (%) and COD removal efficiency (%) simultaneously were validated by performing confirma­tory experiments. The confirmatory experiment was carried out at the predicted optimal condition for model validation in triplicate, and average values are shown in Table 5. Table 5. Comparative results of optimized and confirmatory exper­iments for model validation. Process Parameters ANN-MOGA Optimized Experimental Value Value pH 6.85 6.9 CD 298.24 299 HRT 140.16 141 NaCl Conc. 2.49 2.5 Colour removal efficiency (%) 99.05 98.82 COD removal efficiency (%) 77.75 77.60 A perusal of Table 5 indicates close agreements be­tween optimized and experimental values. The percentage variation between experimental and simulated results was determined in the ~±1% error range. This suggested that the models’ prediction capacity was satisfactory. Hence, the adequacy of ANN-MOGA models on predicting the Colour removal efficiency (%) and COD removal efficien­cy (%) was validated. 4. Conclusion The ANN approach was successfully used to forecast all responses for continuous EO processes in accordance with an input parameter such as pH, current density, hy­draulic retention time, and NaCl concentration. To build ANN models, the feed-forward LM-BP training algorithm was used. The well-trained ANN models had three hidden layers with 9-6-3 neurons in the hidden layers. For colour removal efficiency and COD removal efficiency, the MSE values were 0.748, 0.870, and the R2 values were 0.9999, 0.9998, respectively. The ANN modelling technique could offer several advantages, including speed, dependability, fault tolerance, resilience, universal application, and us­ability, making it an intriguing choice for modelling com­plicated systems such as wastewater treatment. The use of a genetic algorithm to perform multi-ob­jective optimization of the ANN model resulted in a set of Pareto optimum points. The solutions to the Pareto front points could be utilised as a guideline for designing a con­tinuous EO for the RTB21 dye wastewater process. The ANN-MOGA approach was utilised to estimate the best process variable values for maximum colour removal effi­ciency and COD removal efficiency, which resulted in the generation of the Convex nature Pareto front. The utopia point (99.05, 77.75) was selected such that it had the min­imum euclidean distance from the reference point R. In essence, data obtained in the present study could be useful for possible scale-up of the electro-oxidation process for similar types of reactive dyes. 5.Reference 1. United Nations, VALUING WATER (2021): The United Na­tions World Water Development Report 2021. Vol 9781849773.; 2021. DOI:10.4324/9781849773355 2. P. V. Nidheesh, M. Zhou, M. A. 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Srivastav, J Food Process Preserv, 2021, 45(1), 1-14. DOI:10.1111/jfpp.15078 37. A. Picos, J. M. Peralta-Hernández, Water Sci Technol, 2018, 78(4), 925–935. DOI:10.2166/wst.2018.370 Povzetek Razvili smo kontinuirni posredni proces elektrooksidacije (EO) z uporabo grafitne electrode, s katerim smo preucili možnosti odstranjevanja barvila turkizno modro RTB21 in odpadnih voda: Pri tem smo optimirali zaceno pH vrednost, gostoto elektricnega toka, zadrževalni cas (HRT) in koncentracijo elektrolita (NaCl). Poskusi so bili izvedeni v skladu s središcnim sestavljenim nacrtom (ang. CCD), rezultati pa so bili uporabljeni za ucenje modela na osnovi umetnih nevronskih mrež (ang. ANN). Glede na predvidene ugotovitve modela ANN so bile MSE vrednosti za ucinkovitost od­stranjevanja barve in KPK ocenjene na 0.748 oziroma 0.870, vrednosti R2 pa na 0.9999 oziroma 0.9998. Veckriterijska optimizacija z genetskimi algoritmi (MOGA) uporabljena po ANN modelu je odatno optimizirala ucinkovitost odstran­jevanja barvo in zmanjševanja KPK. MOGA poda nedominantno (Pareto) fronto, ki omogocajo vpogled v optimalne pogoje delovanja procesa. Except when otherwise noted, articles in this journal are published under the terms and conditions of the Creative Commons Attribution 4.0 International License DOI: 10.17344/acsi.2021.7150 Scientific paper Enantioselective Henry Reaction Catalyzed by Chiral Piperazine Derivatives Leman Alkan* Department of Chemistry, Faculty of Science, Ege University, Izmir, 35100 Turkey * Corresponding author: E-mail: alkanleman@gmail.com Tel: +90 232 388 82 64 Received: 09-15-2021 Abstract A series of novel tridentate Schiff bases is synthesized from piperazine-amine and substituted salicylaldehydes, and char­acterized by spectroscopic methods. These chiral ligands were used to catalyze the addition of nitromethane to various aldehydes in the presence of Cu(II) ions under ambient conditions in good yields (98%) and high enantioselectivities (9:91 er). Keywords: Piperazine; Schiff base; chiral catalyst; enantioselective Henry reaction 1.Introduction The catalytic asymmetric Henry (nitroaldol) reac­tion is a very efficient and atom economic method for ste­reoselective C–C bond formation. The resulting chiral products of this reaction, ß-hydroxynitroalkanes, contain two functional groups (hydroxyl and nitro groups) and can be easily transformed into many valuable building blocks in asymmetric organic synthesis. The asymmetric Henry reaction has also been applied to the synthesis of bioactive natural products and pharmaceutical agents.1–7 Since the Shibasaki group reported the first catalytic asym­metric Henry reaction,8 a great deal of effort has been de­voted to the development of various metal-containing cat­alysts.9–13 In particular, Cu(II) complexes of various tridentate ligands have recently been used as chiral cata­lysts with good enantioselectivity.14–18 The piperazine skeleton is a unique structure often found in biologically active compounds. Piperazine based compounds are found in antihistamines, antibiotics, anti­depressant and anticancer drugs. Their metal complexes are also successfully used as catalyst in different organic reactions.19 Transition metal complexes of amine ligands containing piperazine moiety are very stabile form. How­ever, there isn’t any report that tridentate Schiff base lig­ands derived from piperazine are used as catalyst in the asymmetric Henry reaction. In our previous publications we reported the synthe­sis and catalytic activity of copper(II) complexes of triden­tate Schiff base ligands derived from amino acids.20–21 Herein, we report novel chiral tridentate Schiff bases which can be readily synthesized from piperazine-amine and can be used as catalysts in enantioselective Henry re­actions under mild conditions. We thought that the N group in the ring, which is not coordinated to the metal, will increase the enantioselectivity due to the bifunctional capability of the piperazine derivatives. 2. Results and Discussion For the purpose of creating structurally different chi­ral tridentate Schiff bases, (S)-1-(4-methylpiperazin-1-yl)­3-phenylpropan-2-amine 4 was reacted with a variety of 2-hydroxybenzaldehydes. Our preparative routes to the piperazine-amine involved protection of amino alcohol derivative followed by addition of piperazine ring and de-protection of Boc group as can be seen in Scheme 1. Sub­sequent reaction with different aldehydes afforded the de­sired chiral Schiff base ligands 5–8 in high yields. Initial studies were focused on the effect of these lig­ands 5–8 on the asymmetric Henry reaction by carrying out reactions between 2-chlorobenzaldehyde as a model substrate and nitromethane in the presence of Cu(OAc)2·n­H2O. The results are summarized in Table 1. The first experimental results showed that substitu­ents in aromatic ring of the ligand had a significant effect on the enantioselectivity. Ligand 5 was expressly superior Scheme 1. Synthesis of chiral Schiff base ligands. Table 1. Reaction of 2-chlorobenzaldehyde: screening of ligands and solvent effect erb,c Entry Ligand Solvent Time (h) Yielda (%) 1 5 TBME 48 98 9:91 2 5 i-PrOH 48 73 20:80 3 5 THF 48 49 41:59 4 6 TBME 48 84 10:90 5 6 i-PrOH 48 67 14:86 6 6 THF 48 38 39:61 7 7 TBME 48 60 22:78 8 7 i-PrOH 48 49 24:76 9 7 THF 48 45 24:76 10 8 TBME 48 n.d. – 11 8 i-PrOH 48 50 15:75 12 8 THF 48 42 28:72 a Isolated yieds after column chromatography; b enantiomeric ratio (er) determined by HPLC analysis using a Chiralcel OD-H column; c the absolute configuration of the major product was assigned as S by comparison with the literature values;22–24 n.d.: not deter­mined. Table 2. Optimization of the reaction conditions erb,c Entry Metal Salt Temp. (°C) Time (h) Yielda (%) 1 Copper(II) benzoate RT 48 75 9:91 2 Copper(II) formate RT 48 n. d. – 3 Copper(II) acetate RT 48 98 9:91 4 5d Copper(II) acetate Copper(II) acetate 0 RT 48 48 52 74 27:73 42:58 a Isolated yieds after column chromatography; b enantiomeric ratio (er) determined by HPLC analysis using a Chiralcel OD-H column; c the absolute configuration of the major product was assigned as S by comparison with the literature values;22–24 d with 10 mol% ligand and Cu(OAc)2 loading; n.d.: not determined. Table 3. Substrate scope Entry ArCHO Product Time (h) Yielda erb Config.c 1 2-Chlorobenzaldehyde 9a 48 98 9:91 S 2 2-Bromobenzaldehyde 9b 48 83 15:85 S 3 2-Methoxybenzaldehyde 9c 48 89 10:90 S 4 3-Nitrobenzaldehyde 9d 48 71 51:49 S 5 Benzaldehyde 9e 96 92 21:79 S 6 4-Methylbenzaldehyde 9f 120 85 35:65 S 7 4-Ethylbenzaldehyde 9g 120 60 24:76 n. d. 8 4-Methoxybenzaldehyde 9h 120 68 64:36 R 9 4-Chlorobenzaldehyde 9i 48 88 13:87 S a Isolated yieds after column chromatography; b enantiomeric ratio (er) determined by HPLC analysis using a Chiralcel OD-H column; c the absolute configuration of the major product was assigned by comparison with the literature values;22–24 n.d.: not determined. among the other ligands 6–8 in terms of er values. Next, tert-butyl methyl ether was found to be the best solvent for enantioselective nitroaldol reaction. The best results were observed when substrate was stirred for 48 h in the pres­ence of 5 mol% 5 and Cu(OAc)2·nH2O at room tempera­ture in TBME (Table 1, entry 1). After the selection of the ligand for the catalyst sys­tem and the solvent, the reaction parameters, including copper salt, temperature and catalyst loading, were opti­mised. As can be seen from Table 2, surprisingly, when the reaction was performed using copper(II) formate, product formation was not observed (entry 2). However, when copper(II) benzoate and copper(II) acetate were used in the reaction, high er values were observed (entries 1, 3). These results show that carboxylate counter ion in copper salt is very effective in the copper(II) catalysed enantiose­lective Henry reaction. Afterward, the catalyst loadings were tested (entries 3, 5) and 5 mol% was found to afford the best results. In addition, room temperature is an opti­mal reaction temperature (entries 3, 4). Finally, with the optimised conditions, the scope of the substrate was investigated using a variety of aromatic aldehydes (Table 3). Moderate to good enantiomeric ratio and yield of the desired ß-hydroxynitroalkanes were ob­tained. The data clearly showed that the substrates with ortho-substituent (entries 1–3) gave seriously higher enan­tioselectivities than other. These increases in enantioselec­tivity are possibly due to the geometry of the substrate hav­ing a great and important role in the mechanism. 3. Experimental All chemicals were purchased from Merck, Sig-ma-Aldirch, Alfa Aesar, or Fluka and were used without any purification. Silica gel F254 (Merck 5554) precoated plates were used for thin layer chromatography. For col­umn chromatography, silica gel 60 (Merck 7743) was used. IR spectra were recorded using a Perkin Elmer 100 FTIR spectrometer. 1H NMR and 13C NMR spectra were ob­tained by using a 400 MHz Varian NMR spectrometer at ambient temperature. Optical rotations were determined using a Rudolph Research Analytical AUTOPOL I auto­matic polarimeter. HPLC analysis was carried out on Agi-lent Technologies 1200 series with chiral stationary phase column (Chiralcel OD-H). Elemental analyses were per­formed on a Leco CHNS-932 elemental analyzer. High resolution mass spectra (HRMS) were measured on a Wa­ters Synapt G1 mass spectrometer using ESI-TOF ioniza­tion. Compound 2 was prepared according to the litera­ture method.25 Preparation of (S)-tert-butyl (1-benzyl-2-chloroethyl) carbamate (2). SOCl2 (0.3 mL, 4 mmol) was added drop-wise to a solution of (S)-2-amino-3-phenyl-1-propanol (150 mg, 1 mmol) in THF (10 mL) at 0 °C and the result­ing solution was refluxed for 12 h. Then the solvent was evaporated in vacuo and the residue 1 was used without purification. To a solution of 1 and NaOH (88 mg, 2.2 mmol) in THF (10 mL) was added di-tert-butyl dicarbo­nate (Boc2O) (240 mg, 1.1 mmol). The reaction mixture was stirred at room temperature for 24 h. Evaporation of the solvent gave a residue that was purified with column chromatography (1:8 ethyl acetate:hexane) to give the ti­tle compound as light yellow crystals. Yield 94%, mp 83–84 °C. IR (KBr) . 3339, 2978, 1691, 1528, 1169 cm–1. 1H NMR (400 MHz, CDCl3) d 7.33–7.24 (m, 5H), 4.80 (br s, 1H), 4.12 (br s, 1H), 3.63–3.47 (dd, J = 3.6, 11.2 Hz, 2H), 2.93–2.85 (m, 2H), 1.43 (s, 9H). 13C NMR (400 MHz, CDCl3) d 154.98, 137.01, 129.16, 128.58, 126.82, 79.76, 51.90, 46.88, 37.77, 28.32. Anal. Calcd for C14H20ClNO2: C, 62.33; H, 7.47; N, 5.19. Found: C, 61.40; H, 7.17; N, 5.13. Preparation of (S)-tert-butyl [1-(4-methylpiperaz­in-1-yl)-3-phenylpropan-2-ylcarbamate] (3). To a solu­tion of 2 (50 mg, 0.185 mmol) in acetonitrile under nitro­gen atmosphere was added triethylamine (0.078 mL, 0.555 mmol) and 1-methylpiperazine (185.3 mg, 1.85 mmol). The reaction mixture was refluxed for 12 h. After comple­tion of the reaction (as monitored by TLC), the solvent was evaporated in vacuo. The residue was purified with col­umn chromatography (1:1 ethyl acetate:hexane) to give the title compound as an off-white solid. Yield 86%, mp 125– 127 °C. IR (KBr) . 3385, 2944, 1681, 1508, 1166 cm–1. 1H NMR (400 MHz, CDCl3) d 7.29–7.16 (m, 5H), 4.58 (br s, 1H), 3.92 (br s, 1H), 2.85 (d, J = 6 Hz, 2H), 2.41–2.26 (m, 10H), 2.24 (s, 3H), 1.42 (s, 9H). 13C NMR (400 MHz, CDCl3) d 155.54, 137.77, 129.65, 128.21, 126.22, 79.06, 60.44, 55.13, 53.28, 48.54, 45.99, 28.40. Anal. Calcd for C19H31N3O2: C, 68.43; H, 9.37; N, 12.60. Found: C, 66.42; H, 8.92; N, 12.74. Preparation of (S)-1-(4-methylpiperazin-1-yl)-3-phe­nylpropan-2-amine (4). The compound 3 (100 mg, 0.3 mmol) was dissolved in 3 mL THF and 3 mL 85% H3PO4 was added dropwise to the solution. The reaction was stirred overnight at room temperature. To terminate the reaction, 5 mL distilled water was added and the reaction mixture was neutralized with saturated NaOH. The solu­tion was extracted with ethyl acetate. The organic phase was dried using Na2SO4 and evaporated in vacuo. The res­idue was purified with column chromatography (1:1 meth-anol:ethyl acetate) to give the title compound as a yellow oil. Yield 95%. IR (NaCl) . 3362, 2800, 1601 cm–1. 1H NMR (400 MHz, CDCl3) d 7.31–7.19 (m, 5H), 3.22–3.15 (m, 1H), 2.74–2.71 (dd, J = 4.8, 13.6 Hz, 1H), 2.50–2.29 (m, 11H), 2.27 (s, 3H). 13C NMR (400 MHz, CDCl3) d 139.18, 129.22, 128.38, 126.23, 64.71, 55.21, 53.42, 49.22, 45.91, 42.07. General Procedure for The Preparation of Chiral Schiff Bases 5–8 A solution of 4 (1 mmol) and the appropriate aldehyde (1 mmol) in 10 mL ethanol was refluxed until the aldehyde has been consumed. The solvent was evaporated in vacuo and crude product was purified with flash chromatogra­phy. 2-((E)-{[(1S)-1-Benzyl-2-(4-methylpiperazin-1-yl) ethyl]imino}methyl)phenol (5). Yellow crystals, yield 96%, mp 88–90 °C, = [a]19 D –120 (c 0.25, CH3OH). IR (KBr) . 3213, 2796, 1630, 1581 cm–1. 1H NMR (400 MHz, CDCl3) d 8.04 (s, 1H), 7.28–7.13 (m, 7H), 6.82 (d, J = 8 Hz, 1H), 6.77 (t, J = 7.6 Hz, 1H), 3.71–3.64 (m, 1H), 3.07–3.02 (dd, J = 4, 16 Hz, 1H), 2.84–2.79 (dd, J = 8, 12 Hz, 1H), 2.65–2.62 (dd, J = 4, 8 Hz, 2H), 2.55–2.34 (m, 8H) 2.23 (s, 3H). 13C NMR (400 MHz, CDCl3) d 165.22, 162.38, 138.17, 132.29, 131.42, 129.30, 128.85, 127.90, 125.95, 118.32, 117.79, 116.74, 67.67, 62.61, 54.20, 52.54, 44.76, 40.41. HRMS ESI-TOF (m/z): [M + H]+ calcd for C21H28N3O: 338.2232. Found: 338.2229. HPLC (hexane:i-PrOH (95:15), wavelength: 267 nm, flow rate: 1.0 mL/min): tr = 10.627 min. 3-((E)-{[(1S)-1-Benzyl-2-(4-methylpiperazin-1-yl) ethyl]imino}methyl)benzene-1,2-diol (6). Yellow solid, yield 87%, mp 101–102 °C, = [a]19 D –102 (c 0.26, CH3OH). IR (KBr) . 3439, 2808, 1632, 1540 cm–1. 1H NMR (400 MHz, CDCl3) d 8.00 (s, 1H), 7.24–7.14 (m, 5H), 6.83– 6.81 (dd, J = 1.6, 7.6 Hz, 1H), 6.65–6.63 (dd, J = 1.6, 8 Hz, 1H), 6.57 (t, J = 8 Hz, 1H), 3.75–3.68 (m, 1H), 3.07–3.03 (dd, J = 4, 12 Hz, 1H), 2.86–2.80 (dd, J = 8, 12 Hz, 1H), 2.65–2.62 (dd, J = 2.8, 5.2 Hz, 2H), 2.56–2.37 (m, 8H), 2.23 (s, 3H). 13C NMR (400 MHz, CDCl3) d 165.43, 153.26, 146.04, 138.02, 129.32, 128.00, 126.06, 122.14, 117.55, 117.04, 116.94, 66.87, 62.53, 54.45, 52.69, 44.54, 40.24. HRMS ESI-TOF (m/z): [M + H]+ calcd for C21H28N3O2: 354.2182. Found: 354.2177. HPLC (hex­ane:i-PrOH (95:15), wavelength: 267 nm, flow rate: 1.0 mL/min): tr = 15.218 min. 4-((E)-{[(1S)-1-Benzyl-2-(4-methylpiperazin-1-yl)ethyl] imino}methyl)benzene-1,3-diol (7). Pale yellow solid, yield 92%, mp 112–114 °C, = [a]19 D –104 (c 0.25, CH3OH). IR (KBr) . 3384, 2811, 1624, 1601 cm–1. 1H NMR (400 MHz, CDCl3) d 7.80 (s, 1H), 7.29–7.15 (m, 6H), 6.92 (d, J = 12 Hz, 1H), 6.11 (d, J = 8 Hz, 1H), 3.80–3.75 (m, 1H), 3.06–3.01 (dd, J = 8, 16 Hz, 1H), 2.88–2.82 (dd, J = 8, 12 Hz, 1H), 2.64–2.54 (m, 10H), 2.31 (s, 3H). 13C NMR (400 MHz, CDCl3) d 174.16, 162.85, 137.45, 134.61, 129.20, 128.11, 126.23, 109.91, 107.82, 62.62, 61.82, 54.53, 52.64, 44.54, 39.53. HRMS ESI-TOF (m/z): [M + H]+ calcd for C21H28N3O2: 354.2182. Found: 354.2165. HPLC (hex­ane:i-PrOH (95:15), wavelength: 267 nm, flow rate: 1.0 mL/min): tr = 15.302 min. 2-((E)-{[(1S)-1-Benzyl-2-(4-methylpiperazin-1-yl) ethyl]imino}methyl)benzene-1,4-diol (8). Light brown solid, yield 79%, mp 142–144 °C, = D –112 (c 0.25, [a]19CH3OH). IR (KBr) .: 3193, 2810, 1636, 1591 cm–1. 1H NMR (400 MHz, CDCl3) d 7.94 (s, 1H), 7.24–7.13 (m, 5H), 6.78–6.75 (dd, J = 4, 8 Hz, 1H), 6.69 (d, J = 8 Hz, 1H), 6.58 (d, J = 4 Hz, 1H), 3.66–3.60 (m, 1H), 3.05–3.01 (dd, J = 4, 12 Hz, 1H), 2.82–2.76 (dd, J = 8, 12 Hz, 1H), 2.65–2.62 (dd, J = 2.8, 5.2 Hz, 2H), 2.60–2.44 (m, 8H), 2.30 s (3H). 13C NMR (400 MHz, CDCl3) d 164.91, 154.20, 149.03, 138.35, 129.37, 128.99, 127.90, 125.93, 119.65, 118.49, 116.74, 116.23, 68.52, 62.88, 54.41, 54.13, 52.74, 44.54, 40.60, 40.31. HRMS ESI-TOF (m/z): [M + H]+ calcd for C21H28N3O2: 354.2182. Found: 354.2180. HPLC (hex­ane:i-PrOH (95:15), wavelength: 267 nm, flow rate: 1.0 mL/min): tr = 17.391 min. General Procedure for the Asymmetric Henry Reaction The chiral Schiff base ligand 5–8 (0.01 mmol) and Cu(OAc)2 · nH2O (0.01 mmol) were allowed to stir in 2 mL of solvent at room temperature for 2 h. A color of solution change from light green to dark green was observed during this time. Subsequently, the appropriate aldehyde (0.2 mmol) and nitromethane (2 mmol) were added into the solution. The reaction mixture was stirred until most of the aldehyde has been consumed. The volatile components were evapo­rated in vacuo and the crude product was purified with col­umn chromatography (1:5 ethyl acetate:hexane) to give the desired ß-hydroxynitroalkane. Enantiomeric ratio (er) was determined by using HPLC with Chiracel OD-H column. (S)-1-(2-Chlorophenyl)-2-nitroethanol (9a).22 Colorless oil, yield 98%. HPLC conditions: hexane:i-PrOH (93:7), wavelength: 267 nm, flow rate: 0.8 mL/min, tminor = 14.3 min, tmajor = 14.8 min, 9:91 er. (S)-1-(2-Bromophenyl)-2-nitroethanol (9b).22 Colorless oil, yield 83%. HPLC conditions: hexane:i-PrOH (85:15), wavelength: 267 nm, flow rate: 1.0 mL/min, tminor = 9.2 min, tmajor = 9.7 min, 15:85 er. (S)-1-(2-Methoxyphenyl)-2-nitroethanol (9c).22 Yellow oil, yield 89%. HPLC conditions: hexane:i-PrOH (90:10), wavelength: 267 nm, flow rate: 1.0 mL/min, tminor = 11.8 min, tmajor = 12.7 min, 10:90 er. (S)-1-(3-Nitrophenyl)-2-nitroethanol (9d).24 Yellow oil, yield 71%. HPLC conditions: hexane:i-PrOH (90:10), wavelength: 267 nm, flow rate: 1.0 mL/min, tmajor = 28.2 min, tminor = 31.0 min, 51:49 er. (S)-1-Phenyl-2-nitroethanol (9e).23 Yellow oil, yield 92%. HPLC conditions: hexane:i-PrOH (90:10), wavelength: 267 nm, flow rate: 1.0 mL/min, tminor = 14.1 min, tmajor = 15.1 min, 21:79 er. (S)-1-(4-Methylphenyl)-2-nitroethanol (9f).22 Yellow crystals, yield 85%. HPLC conditions: hexane:i-PrOH (90:10), wavelength: 267 nm, flow rate: 1.0 mL/min, tminor = 12.9 min, tmajor = 15.4 min, 35:65 er. (S)-1-(4-Ethylphenyl)-2-nitroethanol (9g).22 Yellow oil, yield 60%. HPLC conditions: hexane:i-PrOH (90:10), wavelength: 267 nm, flow rate: 1.0 mL/min, tminor = 11.6 min, tmajor = 14.2 min, 24:76 er. (R)-1-(4-Methoxyphenyl)-2-nitroethanol (9h).22 Yellow oil, yield 68%. HPLC conditions: hexane:i-PrOH (90:10), wavelength: 267 nm, flow rate: 1.0 mL/min, tmajor = 19.9 min, tminor = 24.3 min, 64:36 er. (S)-1-(4-Chlorophenyl)-2-nitroethanol (9i).22 Colorless oil, yield 88%. HPLC conditions: hexane:i-PrOH (90:10), wavelength: 267 nm, flow rate: 1.0 mL/min, tminor = 14.0 min, tmajor = 16.7 min, 13:87 er. 4. Conclusions Four novel tridentate Schiff bases have been pre­pared from a chiral (S)-2-amino-3-phenyl-1-propanol, which can be used in the asymmetric Henry reaction. Compared with other catalytic systems, the advantages of our method are mild reaction conditions, low loading of the catalyst (5 mol %) necessary, no need for additives and without any precautions against the presence of air and moisture needed. Acknowledgments This study was financially supported by Ege Univer­sity Scientific Research Projects Coordination Unit (Grant number: 18 FEN 045). I thank Prof. Stephen T. Astley for helpful discussions concernig about this manuscript. 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DOI: 10.1016/j.tetasy.2006.12.005 25. A. Roy, L. A. Reddy, N. Dwivedi, J. Naram, R. Swapna, G. C. Malakondaiah, M. Ravikumar, D. Bhalerao, T. B. Pratap, P. P. Reddy, A. Bhattacharya, R. Bandichhor, Tetrahedron Lett., 2011, 52, 6968–6970. DOI: 10.1016/j.tetlet.2011.10.087 Povzetek Iz piperazin-aminov in substituiranih salicilaldehidov smo sintetizirali novo serijo tridentatnih Schiffovih baz ter jih karakterizirali s spektroskopskimi metodami. Tako pripravljene kiralne ligande smo uporabili kot katalizatorje za adici­jo nitrometana na razlicne aldehide, ki smo jo izvedli ob dodatku Cu(II) ionov pri sobnih pogojih, produkte pa smo pripravili z dobrimi izkoristki (98 %) in visokimi enantioselektivnostmi (9:91 er). Except when otherwise noted, articles in this journal are published under the terms and conditions of the Creative Commons Attribution 4.0 International License DOI: 10.17344/acsi.2021.7178 Scientific paper In-Situ Intercalating of Silica Nanospheres into Polypyrrole During Its Electropolymerization to Prepare a Sorbent for Headspace Microextraction of Aldehydes in Edible Oils Amin Hamdi, Kolsoum Dalvand*, Alireza Ghiasvand* and Nahid Heidari Department of Chemistry, Lorestan University, Khoramabad, Iran * Corresponding author: E-mail: kolsoomdalvand@gmail.com, a_ghiasvand@yahoo.com Tel-Fax: +98 66 33120612,+98 66 33120612 Received: 09-29-2021 Abstract The quality of edible oils is significantly affected by autoxidation of lipids, which alters their flavor and nutritional quality through production of toxic materials like aldehydes (an important class of oil deterioration markers). Herein, an ami-no-silica nanospheres/polypyrrole (ASNS/PPy) nanocomposite sorbent was synthesized and used as the fiber coating for headspace solid-phase microextraction (HS-SPME) of aldehydes in edible oils, followed by gas chromatography (GC) separation and determination. Amino-silica nanoparticles were prepared by an amended Stöber method and compos­ited with polypyrrole during its electropolymerization on the surface of a platinized stainless-steel fiber. The synergy between in-situ electropolymerization and rough surface of the platinized metal substrate created a durable fiber coating with unique uniformity, cohesiveness, and adsorption properties. The synthesized nanocomposite was characterized using Fourier transform infrared spectroscopy and scanning electron microscopy techniques. The performance of the prepared fiber was optimized by investigating the affecting variables including extraction temperature and time, stirring rate, and desorption conditions. The obtained limits of detection for hexanal and heptanal in sunflower oil were 0.005­ 0.009 µg mL-1. The prepared fiber exhibited excellent repeatability and reproducibility with the intra-fiber and inter-fiber relative standard deviations in the ranges of 3.9–8.8% and 7.3–15.1%, respectively. The proposed HS-SPME-GC strategy was successfully applied for the analysis of aldehydes in commercial edible oil samples. Keywords: Amino-silica nanospheres; polypyrrole nanocomposite; headspace solid-phase microextraction; edible oil; aldehydes. 1.Introduction Researchers have recently paid more attention to ed­ible oils because of their importance in human health and nutrition. The quality of edible oils depends on their ori­gin, processing, and oxidation state. The main edible oil components are saturated and unsaturated fatty acids.1 Oxidation is the main reason for the instability of edible oils and causes a change in chemical and nutritional prop­erties of oils. Oxygen in the air causes unsaturated fatty acids of edible oil to undergo non-enzymatic self-oxida­tion, leading to the production of peroxides.2,3 Aldehydes are an important class of lipid oxidation products, well-known for their health risks. They are a major cause of un­pleasant odor of rancid oil. Therefore, quantification of al­dehydes (as chemical markers) in edible oils is of great importance for quality control purpose. There is a wide range of saturated and unsaturated aldehydes and ketones in oxidized edible oils, however, short chain aldehydes like hexanal and heptanal are more abundant. Therefore, these compounds are the most used biomarkers for the quality control of edible oils, due to their higher concentrations in oxidized edible oils. Despite huge advances in chemical analysis, sample pretreatment of edible oils for oxidation stability studies is still mostly performed using classical ex­traction methods like solvent extraction and Soxhlet which are tedious, time-consuming, and expensive. More­over, these methods usually use toxic organic solvents and reactive chemical compounds which can expose operators and/or samples to hazardous materials.4 Accordingly, de­velopment and application of novel and green methods for chemical analysis of edible oils is now a necessity. Solid-phase microextraction (SPME) is a green sol-vent-free sample pretreatment technique, demonstrated to be a promising method for the sampling of organic com­pounds in food, biological, and environmental samples.5 SPME can be easily coupled with different chromato­graphic techniques, automated, and portabilized. The most widely used mode of SPME is headspace (HS), which ex­tracts analytes from the upper atmosphere of the sample, without direct contact with the sample matrix. HS-SPME is the best choice for the sampling of volatile and semi-vol­atile organic compounds. That is why HS-SPME has been widely used for the sampling of odors, flavors, and fra­grances in food samples.1,6 Extensive efforts have been de­voted to expanding applicability and improve performance of SPME, particularly by preparation of new sorbents us­ing nanomaterials. Compared to the commercial sorbents, nanomaterial-based sorbents benefit from higher porosity, larger surface-to-volume ratio, better thermal and chemi­cal stability, more selectivity, and higher extraction effi­ciency.5 Among these, silica nanoparticles have received more attention due to their abundance, cheapness, various available surface modification methods, high porosity and adsorption power, and non-toxicity.7–9 To improve the ex­traction efficiency and selectivity, silica nanoparticles have 10 been composited with different materials like TiO2, 12 graphene oxide (GO),11 Fe3O4, and conductive poly­mers.13 Among conductive polymers, polypyrrole (PPy) and its derivatives have attracted much consideration in SPME techniques due to their significant advantages in­cluding non-toxicity, low-cost, thermal stability, and par­ticularly simple synthesis routes.14 As a unique and prom­ising feature in the preparation of SPME sorbents, thin layers of PPy with controllable properties can be coated on conductive surfaces like stainless-steel through in-situ electropolymerization during electrophoretic deposition (EPD) from different aqueous or organic solutions.15 For this reason, different composites of PPy like graphene/ polypyrrole,16,17 magnetite nanoparticles/polypyrrole,18 multiwalled carbon nanotubes/ polypyrrole,19 functional-ized graphene and ionic liquid co-doped polypyrrole,20 and iron oxide/silica/polypyrrole21 have been successfully employed for the preparation of SPME fibers. The efficien­cy of PPy composites (e.g., polypyrrole/graphene oxide) for the extraction of aldehydes in edible oil has been previ­ously demonstrated through a reduced-pressure fi­ber-in-needle technique.22 In this research, amino-silica nanospheres (ASNS) were synthesized and composited with PPy during its in-situ electropolymerization on the surface of a stain-less-steel fiber. The ASNS/PPy coated fiber was applied for the HS-SPME sampling of aldehydes (hexanal and heptan­al) in edible oils. The analytes were quantified using gas chromatography-flame ionization detection (GC-FID), without derivatization. To the best of our knowledge ASNS/PPy has not been previously reported as a SPME fiber coating. 2. Materials and Methods 2. 1. Chemicals Analytical reagent grade aldehydes were obtained from Merck (Darmstadt, Germany). Stock standard solu­tions (1000 mg L–1) of hexanal (Hex) and heptanal (Hep) were prepared in methanol and stored at 4 °C. Working standard solutions were prepared by dilution of the stock solution in double distilled water, on a daily basis. Pyrrole (> 99%) was provided by Fluka (Buchs, Switzerland). Pyr-role was redistilled and stored in a dark bottle under nitro­gen atmosphere in a refrigerator before each use. Tetraeth­yl orthosilicate (TEOS, 98%), succinic anhydride, N,N-dimethylformamide (DMF) and, N,N-dicyclohexyl­carbodiimide (DCC) were purchased from Merck. Analyt­ical grade potassium hexachloroplatinate(IV) (K2PtCl6), ethanol, methanol, NaNO3, NaOH, LiClO4, concentrated hydrochloric acid (HCl), and aqueous ammonia (NH3,aq) solutions were purchased from Merck. 2. 2. Gas Chromatographic Analysis A Shimadzu GC-2010 Plus AF gas chromatograph (Shimadzu, Kyoto, Japan), equipped with a split/splitless in­jector (SPL-2010 Plus) and a flame ionization detector (FID-2010 Plus), was used for the gas chromatographic sep­arations and determinations. It used GC solution software (version 2.4) and a BP-5 fused-silica capillary column (30 m × 0.25 mm × 0.25 µm). Ultra-high purity nitrogen was used as the carrier gas with a constant flow rate of 1 mL min-1. All SPME injections were performed in splitless mode. The ini­tial column temperature was set at 60 °C and then increased to 100 °C at a rate of 4 °C min–1. Finally, it was ramped to 280 °C at a rate of 70 °C min–1. The GC detector and injector were maintained at 300 and 280 °C, respectively. Hydrogen and zero-air were used as the FID gases at flow rates of 30 and 300 mL min–1. Nitrogen was used as the make-up gas with a flow rate of 30 mL min–1. Fourier transform infrared (FT-IR) spectra were obtained in the transmittance mode, by a Shimadzu FT-IR 8400 spectrometer. Scanning electron microscopy (SEM) micrographs were recorded using a CM120 Vega field-emission scanning electron microscope (TESCAN, Brno, Czech Republic), to investigate the surface morphology of the nanocomposite. 2. 3. Preparation of ASNS/PPy Coated SPME Fiber Silica nanospheres was prepared through hydrolysis of TEOS according to an amended version of Stober’s method.9 For synthesis of ASNS, 20 mg of silica nano-spheres was dispersed in 20 mL of DMF in an ultrasonic bath. Then, 20 mL of succinic anhydride solution (0.1 M) was added dropwise, and the suspension stirred for 24 h. Finally, the precipitate was centrifuged, rinsed with DMF, and dried in a vacuum oven at 60 °C for 4 h. To create a porous, high surface area and robust fiber substrate, the surface of stainless-steel fiber (4 cm length and 0.075 mm i.d.) was platinized based on an EPD meth­od.23 Then, the fiber was coated by ASNS/PPy nanocom­posite via intercalating of ASNS in PPy during electropo­lymerization of pyrrole, using a two-electrode electro­chemical system. To this end, 0.08 g ASNS was dispersed in 10 mL pyrrole solution (containing 0.1 M LiClO4). The platinized fiber was connected to the anode and a normal stainless-steel wire was connected to the cathode of a DC power supply and immersed into the suspension. The dis­tance between the electrodes was 2.0 cm and a 1.0 V DC voltage applied to the electrodes for 20 min. Then, the fiber was removed from the electrochemical cell and rinsed with water and ethanol and dried. Before the first use the fiber was preconditioned under nitrogen atmosphere for 30 min at 280 °C. 2. 4. HS-SPME Sampling of Aldehydes in Edible Oils Using a blank sample matrix (model matrix) is es­sential to optimize an extraction method in a reliable way. This fact is more serious for the analysis of complex sample matrices like edible oils. In this research, a liquid oil sam­ple was used as the model matrix, after being processed to remove its aldehyde content. For this purpose, 500 mL of sample oil was poured into a 1000-mL vacuum container and subjected to vacuum for 20 min at 25 °C along with continuous stirring. Under the vacuum, aldehyde contents were removed from the oil matrix, resulting in a model matrix without aldehydes. It was stored at 4 °C in a fridge under nitrogen atmosphere to prevent further oxidation and production of aldehydes. The absence of aldehydes in the model sample was checked by using GC analysis. To optimize the parameters affecting the extraction efficiency, 3 mL of model sample was transferred to a 10 mL SPME vial and spiked with known concentrations of the analytes. Then, the vial was capped, and the sample was homoge­nized by sonication for 2 min. After that, the extraction vial was placed into a water bath on a hotplate-magnetic stirrer at 50 ° and stirred at 750 rpm. The ASNS/PPy fiber was mounted on a handmade fiber holder and exposed to the headspace of the sample for 15 min. Finally, the fiber was retracked and immediately injected into the GC injec­tion port for thermal desorption of the extracted analytes at 280 ° for 1 min. 3. Results and Discussion Regardless of the physicochemical stability of the coating and fiber substrate, a serious problem in commer­cial and homemade SPME fibers is weak adhesion between the coating and the fiber substrate. Hence, the coating is easily stripped or exfoliated from the substrate after expo­sure to high temperature, organic solvents, and/or me­chanical tension. Two complementing strategies have been suggested to address this problem, the first chemical bond­ing of the coating to the substrate24 and the second rough­ening the surface of the substrate.25 Fortunately, to prepare industrial protective and anticorrosion coatings for stain-less-steel extensive studies have been done and deep knowledge developed.26 For example, Vasconcelos et al.27 prepared a very highly thermally stable and substantially corrosion resistant coating for stainless-steel using silica thin-films through a sol-gel technique. Accordingly, in this research, the surface of the stainless-steel fiber was plati­nized to create a rough and increased surface area sub­strate. The effectiveness of this method has been well prov­en in our previous studies.9,23,28 This research tried to fabricate a very durable SPME fiber by electrodeposition of ASNS/PPy on the rough surface of platinized stain-less-steel fiber. 3. 1. Characterization of ASNS/PPy Nanocomposite To characterize the chemical bonds in the structure of SNS, ASNS, and ASNS/PPy, their FT-IR spectra were recorded (Fig. 1). The absorption peak at 1099 cm–1 (indi­cating O-Si-O bond) and the peaks appearing at 802.33 and 470.60 cm–1 (correspond to the symmetric stretching and rocking vibrations of Si-O-Si, respectively) show the polymeric structure of silica. The presence of N-H bond in the ASNS structure can be clearly inferred from the vibra­tional peak 1635 cm–1. While ASNS/PPy nanocomposite characteristic peaks can be seen at 1099 cm–1 (O-Si-O bond), 1554 cm–1 (N-H and C-N bonds), 1635 cm–1 (N-H bond), 1658 cm–1 (C=O bond), 2927 cm–1 (C-H bond), and 3417 cm–1 (O-H bond).29 SEM micrographs of ASNS and ASNS/PPy nano-composite were recorded to evaluate their morphological surface features, as depicted in Fig. 2. The spherical con­figuration of the silica nanospheres and their particle size uniformity are obvious in Fig. 2a. Additionally, conical pillars of PPy can be clearly observed in Fig. 2b and 2c. These conical pillar structures can be considered as the grown (thickened) forms of PPy nanowires, as has been shown previously.30 Mechanical strength of the coating was tested by scraping the fiber coating using a knife to simulate its possible damage by the edge of the protecting needle during the injection/withdraw cycles.31 It was found that the ASNS/PPy coating was difficult to be scraped using a knife. The thermal stability of the fiber was demonstrated by performing up to 90 extractions, each followed by a thermal desorption step in the GC in­jector at 280 °C. To evaluate the stability of the ASNS/PPy coated fiber against organic solvents, it was directly im­mersed in ethanol, acetone, acetonitrile, hexane, and methanol for 30 min. Then, the fiber was rinsed with wa­ter and conditioned in GC injector for 10 min at 250 °C to Fig. 1. FT-IR spectra of SNS (a), ASNS (b), and ASNS/PPy nanocomposite (c). Fig. 2. SEM micrographs of (a) SNS, (b and c) ASNS/PPy nanocomposite at different magnifications, and (d) the ASNS/PPy coated stainless-steel fiber. remove any possible solvent residues, before extracting the analytes. The results demonstrated that the fiber was resistant to organic solvents and the extraction efficiency didn’t change significantly (< 5%). 3. 2. Optimization of the Extraction Conditions To achieve the highest extraction efficiency, the im­portant influential variables including desorption time, desorption temperature, sampling temperature, sampling time, and stirring rate were optimized. To ensure the com­plete transfer of the extracted analytes from the fiber to the chromatographic column and acquire accurate results, de­sorption conditions must be optimized first. Accordingly, desorption temperature and desorption time were investi­gated in the ranges of 200–300 °C and 1–4 min, respective­ly. The highest peak areas were observed at desorption temperatures more than 280 °C and at desorption time of 1 min, with the minimum carryover effect (< 5%). There­fore, 280 °C and 1 min were selected as the optimal de­sorption conditions for further investigations. 3. 2. 1. Effect of Sampling Temperature Extraction temperature is one of the main influential parameters affecting the equilibration of analytes between the headspace and fiber coating, and consequently on the extraction efficiency, particularly for volatile analytes like short-chain aldehydes. Higher temperatures improve the release of analyte from the sample matrix and increase their concentrations in the headspace, leading to an in­crease in the extraction amounts. Simultaneously, tem­perature of the fiber coating also rises and reduces the an-alyte trapping, due to the exothermic effect of adsorption process. For this reason, HS-SPME sampling temperature needs to be optimized carefully.32 Therefore, sampling temperature was evaluated over the range of 40–90 °C. As can be seen from the results (Fig. 3a), the peak areas of the analytes increased by raising sample temperature. This fact can be explained by the formation of more aldehydes by raising the sample temperature (due to increased lipid ox­idation), as substantiated in previous investigations.22 Therefore, it was better to select a temperature from the most stable part of the graph (Fig. 3a), with minimal fluc­tuations of response with temperature change. Therefore, Fig. 3. Dependence of the extraction efficiency of the developed HS-SPME method on sample temperature (a), stirring speed (b), and extraction time (c). Comparison of the ASNS/PPy coated fiber with commercial polyacrylate fiber and a homemade PPy fiber (d), under the optimized condi­tions (extraction time, 20 min; stirring rate, 600 rpm; desorption temperature: 280 °C; and desorption time, 1 min; repeated three times). 50 °C was chosen as the optimal HS-SPME sampling tem­perature. 3. 2. 2. Effect of Sample Stirring Speed Increasing the stirring speed in HS-SPME improves the mass transfer and increases concentration of analytes in the sample headspace. Furthermore, the time required to achieve thermodynamic equilibrium is reduced. To evaluate this effect, stirring speed was varied over the range of 200– 1000 rpm and GC-FID peak areas of the analytes were re­corded. The results depicted in Fig. 3b show that the peak areas for both analytes increase with raising the stirring speed up to 750 rpm and then remain nearly constant. So, 750 rpm was chosen as the optimal stirring speed for further studies. 3. 2. 3. Effect of Sampling Time To achieve the highest extraction efficiency, the ex­traction time was examined in the range of 5–40 min. As shown in Fig. 3c, peak areas of both analytes increased by prolonging the extraction time up to 15 min and then re­mained constant. Thus, 15 min was chosen as the ex­traction time for further experiments. 3. 3. Analytical Performance To evaluate the quantitative analytical figures of merit of the developed method, its linear dynamic ranges (LDRs), limits of detection (LODs), and relative standard deviations (RSDs) for the extraction of Hex and Hep from edible oil samples were investigated, under the optimized conditions. The calibration graphs were linear over the range of 0.03–20 µg mL–1 for hexane and 0.05–25 µg mL–1 for heptanal, with linear regression coefficients greater than 0.998. The LODs correspond to the analyte amounts for which the signal-to-noise ratio was equal to 3, and were found to be 0.005 for hexanal and 0.009 µg mL–1 for heptanal. The precision was assessed by obtaining the RSDs including repeatability (intra-fiber) and reproduc- Table 1. Analytical figures of merit of the HS-SPME-GC procedure for the sampling and quantification of hexanal and heptanal in edible oil. RSD% (n = 6) Analyte Equation R2 LDR (µg mL–1) LOD (µg mL–1) Intra-fiber Inter-fiber Hexanal y = 1139.4x + 5807.1 0.9989 0.03-20 0.005 3.9 7.3 Heptanal y = 209.09x + 1586.2 0.9982 0.05-25 0.009 8.8 15.1 ibility (inter-fiber), which varied between 3.9–8.8% and 7.3–15.1%, respectively (Table 1). A single fiber was used for more than 90 extractions (extraction/desorption cy­cles) without significant change (< 5%) in its extraction efficiency. To evaluate reliability and applicability of the ASNS/ PPy coated fiber, it was compared with a commercial PA coated fiber and a homemade PPy coated fiber, under the optimized conditions (Fig. 3d). The results demonstrated that the ASNS/PPy fiber was remarkably more efficient than the tested fibers for the extraction of the aldehydes in edible oils. 3. 4. Comparison of the Proposed Method with Similar Reported Procedures To further indicate the supremacy of the proposed microextraction strategy, its significant features were com­pared with some of the similar reported microextraction methods.22,33–36 As can be seen in Table 2, the proposed procedure shows wider linear ranges and acceptable RSDs, compared to most of the listed procedures. Additionally, the LODs are lower than most of the reported methods, even compared to the method that used a MS detector.36 However, its LODs and LDRs are not comparable with the reduced-pressure fiber-in-needle method (RP-FIN) tech­nique, which has used a low-pressure dynamic microex-traction strategy.22 Anyhow, the proposed method uses a low-cost and durable fiber and instrumentation (GC-FID), without any derivatization methods. 3. 5. Determination of Hexanal and Heptanal in Edible Oils To test the applicability of the developed method, it was applied for the determination of hexanal and heptanal in three commercial sunflower oil samples of different brands, produced at different dates. Samples 1, 2, and 3 had been produced 35, 36, and 4 months before the analy­sis time, respectively. The samples had been stored un­opened at the same conditions. To ensure the reliability of the HS-SPME-GC-FID strategy, each sample was also spiked with 1 µg mL–1 of the analytes and subjected to the analysis, three times. As shown by the results in Table 3, acceptable recoveries and RSDs were obtained, indicating that the developed method can be successfully applied for the quantification of aldehydes and study of oxidative sta­bility of edible oils. Fig. 4 shows a typical GC-FID chro­matogram of a sunflower oil, analyzed by the developed HS-SPME-GC-FID method. Table 2. Comparison of the developed HS-SPME-GC procedure with some similar microextraction procedures for the analysis of aldehydes in edi­ble oils. LDR LOD Intra-fiber Inter-fiber Method Extraction phase Matrix Ref. (µg mL–1) (ng mL–1) RSD (%) RSD (%) USABE-GC-FIDa Triton X-114 in CHCl3 0.05–20 20–150 2–4 – Heated soybean, sunflower, and olive oils 35 HS-SPME-GC-FID PDMS/CAR 0.05–10 5–10 8.9 – Peanut, soybean, and olive oils 36 HS-SPME-GC-FID DVB/CAR/PDMS – 160–350 4.02-8.53 – Sunflower and rapeseed oils 37 SPME-GC-MS PDMS/PEG 0.02–1 6–15 8.9-11.2 9.3–16.2 Sunflower oil 38 RP-FIN-GC-FIDb PPY/GO 0.0007–50 1–8 5.9–9.0 – Corn, sunflower, soybean, olive, canola, and sesame oils 24 HS-SPME-GC-FID ASNS/PPy 0.03–25 5–9 3.9–8.8 7.3–15.1 Sunflower oil This work a Ultrasound-assisted back extraction reverse micelles method coupled with GC-FID. b Reduced-pressure fiber-in-needle coupled with GC-FID. Table 3. HS-SPME-GC-FID sampling and determination of hexanal and heptanal in commercial edible oil samples using the ASNS/PPy coated fiber. Found (µg mL–1) Recovery (%) Edible oil Added (µg mL–1) Hexanal Heptanal Hexanal Heptanal Sample# 1 0 0.67 1.79 – – 1 1.58 2.73 90 93 Sample# 2 0 9.34 8.99 – – 1 10.28 9.87 94.5 88.5 Sample# 3 0 0.56 0.09 – – 1 1.43 0.93 86.8 84.2 Fig. 4. A GC-FID chromatogram of a sunflower edible oil sample after HS-SPME sampling using an ASNS/PPy coated fiber. 4. Conclusions Amino-functionalized silica nanospheres/polypyr-role (ASNS/PPy) composite was synthesized, character­ized, and employed to prepare a SPME fiber. First ASNS was synthesized via a green sol-gel procedure. They were then dispersed into a pyrrole solution, in a tow-electrode electrochemical cell, and coated on the surface of a plati­nized stainless-steel fiber, during electropolymerization of polypyrrole. 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DOI:10.1016/j.chroma.2017.11.016 32. K. Dalvand and A. Ghiasvand, Simultaneous analysis of PAHs and BTEX in soil by a needle trap device coupled with GC-FID and using response surface methodology involving Box-Behnken design, Anal. Chim. Acta 2019, 1083, 119–129. DOI:10.1016/j.aca.2019.07.063 33. Z. Ramezani, R. Mirzajani and F. Kardani, A novel ultra-sound-assisted back extraction reverse micelles method cou­pled with gas chromatography–flame ionization detection for determination of aldehydes in heated edibles oils, Food Chem. 2015, 188, 30–36. DOI:10.1016/j.foodchem.2015.04.112 34. C. Ma, J. Ji, C. Tan, D. Chen, F. Luo, Y. Wang and X. Chen, Headspace solid-phase microextraction coupled to gas chro­matography for the analysis of aldehydes in edible oils, Ta-lanta 2014, 120, 94–99. DOI:10.1016/j.talanta.2013.11.021 35. J. Gromadzka and W. Wardencki, Static headspace sampling 36. F. Hasanli, S. Mohammadiazar, M. Bahmaei and A. A. M. and solid-phase microextraction for assessment of edible oils Sharif, Coating of sol–gel film on silver nanodendrite as a stability, Chromatographia 2010, 71, 81–86. novel solid-phase microextraction fiber for determination of DOI:10.1365/s10337-010-1516-y volatile aldehydes in edible oils, Food Anal. Methods 2018, 11, 2149–2157. DOI:10.1007/s12161-018-1189-y Povzetek Na kakovost jedilnih olj znatno vpliva avtooksidacija lipidov, ki spremeni njihov okus in prehransko kakovost zaradi nastanka strupenih snovi, kot so aldehidi (pomemben razred oznacevalcev razkroja olja). V našem primeru smo sin-tetizirali nanokompozitni sorbent: nanokroglice iz amino-silicijevega dioksida/polipirola (ASNS/PPy), ki smo ga upora­bili kot prevleko za vlakno za mikroekstrakcijo na trdno fazo iz nadprostora (HS-SPME) aldehidov iz jedilnih olj, sledila pa je locba in dolocitev s plinsko kromatografijo (GC). Nanodelci iz amino-silicijevega dioksida so bili pripravljeni po prirejeni Stöberjevi metodi in skupaj s polipirolom z elektropolimerizacijo deponirani na površino platiniziranega vlakna iz nerjavecega jekla. Sinergija med in-situ elektropolimerizacijo ter grobo površino platiniziranega kovinskega substrata je ustvarila odporno prevleko vlakna z edinstveno enakomernim nanosom, kohezivnostjo in adsorpcijskimi lastnostmi. Sintetizirani nanokompozit smo okarakterizirali z infrardeco spektroskopijo s Fourierjevo transformacijo ter z vrsticno elektronsko mikroskopijo. Ucinkovitost pripravljenega vlakna smo optimizirali s spreminjanjem pogojev, kot so temperatura in cas ekstrakcije, hitrost mešanja in pogoji desorpcije. Dosežene meje zaznave za heksanal in heptanal v soncnicnem olju so bile 0,005–0,009 µg mL–1. Pripravljeno vlakno je imelo odlicno ponovljivost in obnovljivost z rel-ativnimi standardnimi odmiki 3,9–8,8 % za eno vlakno ter 7,3–15,1 % med vlakni. Predlagano HS-SPME-GC metodo smo uspešno uporabili za analizo aldehidov v komercialnih vzorcih jedilnih olj. Except when otherwise noted, articles in this journal are published under the terms and conditions of the Creative Commons Attribution 4.0 International License DOI: 10.17344/acsi.2021.7189 Scientific paper Effect of SDS Micelles on Actinomycin D – DNA Complexes Ana Maria Toader, Izabella Dascalu and Mirela Enache* Institute of Physical Chemistry Ilie Murgulescu, Romanian Academy, Splaiul Independentei 202, Bucharest 060021, Romania * Corresponding author: E-mail: enachemir@yahoo.com Received: 10-06-2021 Abstract DNA thermal denaturation was evaluated as a measure of the effect of antitumor drug actinomycin D on the stability of the double helix and also the effect of SDS micelles on actinomycin D – DNA complexes. The results indicated that the melting temperature of DNA was dependent on drug concentration, increasing with actinomycin D concentration. High thermal stabilization (about 10 °C) of the DNA helix after the association with actinomycin D clearly demonstrates the intercalative binding mode. The presence of SDS micelles leads to the release of intercalated actinomcyin D molecules from DNA double helix and their further relocation in surfactant micelles. These results highlighted that the drug release can be controlled in time and by varying the concentration and nature of surfactant. Keywords: Actinomycin D, DNA, micelles, thermal denaturation 1.Introduction Actinomycin D (ActD) is a chromopeptide anti­cancer drug used for the treatment of pediatric rhabdo­myosarcoma and Wilms’ tumor as a component of VAC (vincristine, actinomycin D, cyclophosphamide) therapy.1 Structurally, actinomycin D contains a 2-aminophenoxaz­in-3-one chromophore and two identical cyclic pentapep-tide lactones (Fig. 1). Actinomycin D exerts the anticancer activity by tight binding to DNA which determines the in­hibition of transcription elongation by blocking RNA pol­ymerase.2,3 The interaction of actinomycin D with DNA in terms of binding mode and sequence specificity was extensively investigated using different experimental tech­niques.4–8 The actinomycin D-DNA complex is made by intercalation of planar phenoxazone ring between 5’GpC3’ sequence and strong hydrogen bonds are formed in the minor groove between the guanine 2-amino groups and the carbonyl oxygen atoms of the L-threonine residues of the pentapeptides chains.4,5 Additional stabilization of this complex are acquired from hydrophobic interactions be­tween groups on the pentapeptides and sugar residues and from other specific hydrogen bonding and atom–atom intermolecular interactions.6,9 Actinomycin D also binds to some DNA sequences that do not contain G-C sites10,11 and to single stranded DNA.12,13 Stopped-flow kinetic studies indicate that the bind­ing of actinomycin D to DNA is characterized by five rate constants with three slow processes.14,15 As these slow processes are absent in the binding of actinomine (a pep-tide-lacking analog of actinomycin D) to DNA, the slow kinetics of interaction of actinomycin D with DNA was ex­plained by conformational changes in the peptide chains of actinomycin D during the accommodation of molecules into double helix of DNA.14 Also, SDS induced actinomy­cin D dissociation from DNA presents multiexponential decay, with the slowest rate of 1500 seconds.16 In the present work, the thermal denaturation profile of DNA is used to evaluate the effect of anticancer drug actinomycin D on the stability of the DNA double helix. These results together with our previous research can pro­vide information about the nature and the strength of in­teraction between actinomycin D and DNA. Moreover, the effect of SDS micelles on actinomycin D – DNA complex­es is investigated taking into account that micelle-induced sequestration can be applied as a simple method for the removal of drug molecules from DNA.17 2. Materials and Methods 2. 1. Materials Actinomycin D (ActD), deoxyribonucleic acid (DNA) sodium salt from calf thymus and sodium dodecyl sulphate (SDS) were purchased from Sigma-Aldrich. The concentration of the prepared DNA stock solutions was determined by measuring the absorbance at 260 nm using the molar absorbance coefficient, e = 6600 M–1 cm–1 (in nucleotide concentration). All the sample solutions were prepared in 0.1 M phosphate buffer, pH 7.4. All chemi­cals used were of analytical grade or higher and were used without further purification. The concentration of actin­omycin D was determined spectrophotometrically using the molar absorption coefficient at 440 nm, e = 24400 M–1 cm–1. 2. 2. Thermal Denaturation Experiments Thermal denaturation experiments were performed in stoppered quartz cuvettes on a Jasco V-550 UV-VIS spectrophotometer equipped with a Jasco ETC-505T cell-temperature controller. The temperature of DNA solutions alone and in the presence of actinomycin D at ratios (R) of [ActD]/[DNA]: 0.05, 0.1, 0.2, 0.5 and 1.00 was increased gradually from 25 to 103 °C at a speed of 1 °C/ min and the absorbance at 260 nm was read automatically. The melting temperature (Tm) of DNA in the absence and the presence of actinomycin D was determined using the second derivative method in the frame of Melting Tem­perature Calculation Program provided by the spectro­photometer. All measurements of Tm were repeated three times and the data presented are the average values. The hyperchromicity (%H) of DNA was calculated in the absence and presence of the different concentrations of actinomycin D at 260 nm using the equation (1):18 (1) where AU and AL are the absorbance of the upper baseline and the absorbance of the lower baseline respectively. 3. Results and Discussion The DNA helix is a fairly stable structure due to hy­drogen bonds between base-pairs and base stacking in­teractions. Thermal DNA denaturation or DNA melting is the process of the dissociation of double helix of DNA into two single strands by the breaking of hydrogen bonds between the bases when temperature is raised. The ther­mal behavior of DNA in the presence of different ligand molecules can give information about the nature and the strength of the interaction of ligands with DNA. In gener­al, the intercalation binding stabilize the DNA double helix leading to a significant rise in Tm by about 5–8 °C, while in the case of non-intercalative interaction (groove bind­ing or electrostatic binding at the DNA surface), a small change in the Tm is observed.19,20 The experimental melting curves of DNA upon ad­dition of actinomycin D within the concentration range 0 < R < 1, where R = [ActD]/[DNA] ratios are presented in Fig. 2. The parameters of the helix-to-coil transition (the melting temperature (Tm), the melting shift (.Tm – the difference between the melting temperatures of actinomy­cin D – DNA complexes and DNA alone) and the hyper-chromicity (%H)) are reported in Table 1 and Fig. 2. In the present work, the melting temperature of DNA is about 86.51 °C under our experimental conditions. The addition of actinomycin D to DNA solution results in an increase in the Tm of DNA with the increasing R ratio. The high posi­tive .Tm values (Fig.3) indicate the stabilization of double helix of DNA by intercalation mode of binding of actin­omycin D drug. The value of .Tm ~ 10 °C is in the range of the values corresponding to other intercalative antican­cer drugs, such as mitoxantrone, doxorubicin, daunomy- Figure 2. Thermal melting profile of DNA alone and in the presence of actinomycin D at different molar ratio (R = [ActD]/[DNA]). cin.21,22 In addition, it can also see that the maximum val­ue of .Tm is reached at R = 1. However, the magnitude of .Tm in the range of R = 0.2 – 1 does not differ significantly. Table 1. DNA melting temperature (Tm) and the hyperchromicity (%H) at different molar ratios (R = [ActD]/[DNA]). R = [ActD]/[DNA] Tm (°C)a %H DNA alone 86.5±0.3 53.3 0.05 90.4±0.4 42.4 0.1 92.3±0.2 24.3 0.2 95.1±0.2 18.6 0.5 95.6±0.1 13.0 1 96.1±0.3 7.87 a All values are the average ± standard deviation of three experi­ments. Also, in Fig. 2 a decrease of the hyperchromicity (%H) is observed. In the absence of the drug, the hyper-chromicity of DNA calculated using the equation (1) is about 53.3% (Table 1). As the concentration of actinomy­cin D increases (R ratio increases), the hyperchromicity of DNA significantly decreases. These results suggest that the dissociation of double helix in the DNA solutions contain­ing actinomycin D is obstructed to take place completely. The reduction of hyperchromicity and high increase of Tm clearly indicate that the interaction of actinomycin D with DNA determine a high stabilization of double helix of DNA. These results are in agreement with the intercalation mode of binding of actinomycin D molecule between the base pairs of DNA. Also, specific hydrogen bonding and other atom-atom intermolecular interactions contribute to the stabilization of the actinomycin D-DNA complexes.9 Further, the influence of SDS concentration on (ActD-DNA) complexes was investigated. The use of sur­factant micelles to sequestrate the drug molecules disso­ciated from DNA is a well-established method to study the kinetics of dissociation of drug – DNA complexes.15,23 Also, the interaction of drug molecules with surfactant micelles is important to understand the nature of drug-bi­omembrane interactions but can also help in the case of drug overdoses to remove the excess of the drug and the removal of mutagens.24,25 Previous studies (absorption, thermal denaturation and circular dichroism) indicate that the native B-form of DNA is not altered by the presence of SDS micelles.21 Also, DNA hold its native B-form even in the presence of the highest SDS concentration (6.98 × 10–3 M) used in the present study, as is indicated by the melting curve (Fig. 4) and the value of melting temperature (85.87 °C) which is very close to the value of melting temperature of DNA alone. The critical micellar concentration (CMC) of pure SDS in 0.1 M phosphate buffer (pH 7.4) was previously determined from conductivity measurements and it is 9.28 × 10–4 M.26 This value is smaller than the CMC of SDS in pure water (8.08 × 10–3 M) and it is an agreement with lit­erature data which indicate that the CMC value decreases in phosphate buffer as the concentration of electrolyte in­creases.27 Submicellar (6.09 × 10–4 M) and micellar (2.24 × 10–3 M, 4.38 × 10–3 M, 6.98 × 10–3 M) SDS concentra­tions were used to assess the influence of SDS surfactant on ActD – DNA complexes. The presence of submicellar SDS concentration does not change significantly the melting temperature and the hyperchromicity of actinomycin D – DNA complexes (Fig. 4). Instead, the presence of increasing micellar SDS concentrations leads to the continuous decrease of melt­ing temperature of actinomycin D-DNA complexes up to almost the melting temperature value of DNA alone. This decrease of Tm after SDS micelles addition signifies that actinomycin D – DNA complex is disrupted and actino­mycin D molecules are relocated from DNA into SDS mi­celles. In other words, the deintercalation of actinomycin D molecules from DNA takes place in the presence of the micelles and not in the presence of monomeric surfactant molecules. Also, in the presence of increasing micellar SDS con­centrations, the hyperchromicity enhances but it does not reach the value obtained for DNA in the absence of drug. Similar with actinomycin D, the intercalative binding of mitoxantrone to DNA induces the increase of melting temperature with about 10 °C.21 The addition of SDS mi­celles conducts to the exclusion of intercalated actinomy­cin D and mitoxantrone from DNA helix, the melting tem­perature decreases and reaches the value corresponding to DNA alone. In the case of mitoxantrone – DNA complex, the initial value of DNA hyperchromicity is restored in the presence of SDS micelles,21 as against actinomycin D – DNA complex when the hyperchromicity enhances in the presence of micelles but is not recovered completely. The most likely explanation for the lack of recovery of the hyperchromicity could be the fact that some actinomycin D molecules are still associated with DNA. Also, this dif­ferent behavior between the two drugs could be due to the more complex structure of actinomycin D and hence prob­ably a more complicated mode of interaction with DNA. Figure 4. Thermal melting profile of DNA alone, in the presence of 6.98 × 10–3 M SDS, actinomycin D, and actinomycin D and different SDS concentrations. Table 2. The DNA melting temperature (Tm), the melting shift (.Tm) and the hyperchromicity (%H) of actinomycin D-DNA com­plexes (R = 0.5) in the presence of different SDS concentrations. System Tm (°C) .Tm (°C) %H (ActD-DNA) 95.56 9.05 12.96 (ActD-DNA) + 6.09 × 10–4 M SDS 94.92 8.41 12.37 (ActD-DNA) + 2.24 × 10–3 M SDS 92.07 5.56 20.43 (ActD-DNA) + 4.38 × 10–3 M SDS 89.12 2.61 21.72 (ActD-DNA) + 6.98 × 10–3 M SDS 87.12 0.61 23.68 The deintercalation of actinomycin D molecules from DNA helix is also observed in the absorption spectra when SDS micelles are added to intercalated (ActD-DNA) complex. The absorption spectrum of actinomycin D is char­acterized by a broad absorption band around 440 nm. The formation of actinomycin D – DNA complex results in a hypochromic effect and a shift of the maximum towards longer wavelength. In our previous study, these changes in absorption spectra accompanying the titration of ac-tinomycin D with DNA were used to calculate binding constant and the size of binding site.28 The addition of mi-cellar concentrations of SDS leads to an increase in the ab-sorbance and the splitting of the absorption maximum in two peaks. The spectral modulation in Fig. 5 suggests that upon addition of SDS micelles, actinomycin D molecules experiences a different environment than that in DNA, the absorption spectrum being similar with the spectrum of actinomycin D in SDS micelles.29 These results are in agreement with melting experiments and lead to the con­clusion that the presence of SDS micelles induces the dein­tercalation of actinomcyin D molecules from DNA double helix and their further relocation in surfactant micelles. 4. Conclusions The consequences of actinomycin D – DNA complex formation on the stability of the double helix of DNA as well as the influence of anionic surfactant SDS on drug-DNA complexes were evaluated by recording the DNA melting profiles. The melting temperature values indicate an increasing stabilization of DNA as the concentration of added actinomycin D increases. Also, the large values of melting shift (~10 °C) point out for the intercalation mode of binding of actinomycin D to DNA. Addition of SDS micelles to actinomycin D – DNA complexes leads to the deintercalation of drug molecules from DNA helix and their further relocation into surfactant micelles. Be­sides providing an insight into the stability of actinomycin D – DNA complexes, the present work also demonstrates the excretion of the drug molecules from the biomacro-molecular assembly using surfactant micelles. 5. References 1. H. Hosoi, Pediatr. Int. 2016, 58, 81–87. DOI:10.1111/ped.12867 2. T. Bunte, U. Novak, R. Friedrich, K. Moelling, Biochim. Bio-phys. Acta 1980, 610, 241–247. DOI:10.1016/0005-2787(80)90006-4 3. H. M. Sobell, Proc. Natl. Acad. Sci. U.S.A. 1985, 82, 5328– 5331. DOI:10.1073/pnas.82.16.5328 4. H. M. Sobell, S. C. Jain, T. D. Sakore,C. E. Nordman, Nat. New Biol. 1971, 231, 200–205. DOI:10.1038/newbio231200a0 5. H. M. Sobell, S. C. Jain, J. Mol. Biol. 1972, 68, 21–34. DOI:10.1016/0022-2836(72)90259-8 6. S. Kamitori, F. Takusagawa, J. Am. Chem. Soc. 1994, 116, 10, 4154–4165. DOI:10.1021/ja00089a002 6. T. R. Krugh, E. S. Mooberry, Y.-C. C. Chiao, Biochemistry 1977, 16, 740–747. DOI:10.1021/bi00623a028 7. F. M. Chen, Biochemistry 1998, 37, 3955–3964. DOI:10.1021/bi972110x 8. F. Sha, F. M. Chen, Biophys. J. 2000, 79, 2095–2104. DOI:10.1016/S0006-3495(00)76457-5 9. C. Bendic, M. Enache, E. Volanschi, J. Mol. Graph. Model. 2005, 24, 10–16. DOI:10.1016/j.jmgm.2005.03.004 10. J. G. Snyder, N. G. Hartman, B. L. D’Estantoit, O. Kennard, D. P. Remeta, K. J. Breslauer, Proc. Natl. Acad. Sci. U.S.A. 1989, 86, 3968–3972. DOI:10.1073/pnas.86.11.3968 11. S. A. Bailey, D. E. Graves, R. Rill, Biochemistry 1994, 33, 11493–11500. DOI:10.1021/bi00204a011 12. R. L. Rill, K. H. Hecker, Biochemistry 1996, 35, 3525–3533. DOI:10.1021/bi9530797 13. R. M. Wadkins, E. A. Jares-Erijman, R. Klement, A. Rüdiger, T. M. Jovin, J. Mol. Biol. 1996, 262, 53–68. DOI:10.1006/jmbi.1996.0498 14. R. Bittman, L. Blau, Biochemistry 1975, 14, 2138–2145. DOI:10.1021/bi00681a015 15. W. Muller, D. M. Crothers, J. Mol. Biol. 1968, 35, 251–290. DOI:10.1016/S0022-2836(68)80024-5 16. K. R. Fox, M. J. Waring, Eur. J. Biochem. 1984, 145, 579–586. DOI:10.1111/j.1432-1033.1984.tb08596.x 17. P. Kundu, S. Das, N. Chattopadhyay, Int. J. Pharm. 2019, 565, 378–390. DOI:10.1016/j.ijpharm.2019.04.058 18. H. K. S. Souza, Thermochim. Acta 2010, 501, 1–7. DOI:10.1016/j.tca.2009.12.012 19. S. U. Rehman, T. Sarwar, H. M. Ishqi, M. A. Husain, Z. Hasan, M. Tabish, Arch. Biochem. Biophys. 2015, 566, 7–14. DOI:10.1016/j.abb.2014.12.013 20. R. Anwer, N. Ahmad, K. I. Al Qumaizi, O. A. Al Khamees, W. M. Al Shaqha, T. Fatma, J. Mol. Recognit. 2016, 1–6. DOI:10.1002/jmr.2599 21. M. Enache, S. Ionescu, E. Volanschi, J. Mol. Liq. 2015, 208, 333–341. DOI:10.1016/j.molliq.2015.05.006 22. G. P. Sartiano, W. E. Lynch, W. D. Bullington, J. Antibiot. 1979, 32, 1038–1045. DOI:10.7164/antibiotics.32.1038 23. F. Westerlund, L. M. Wilhelmsson, B. Norden, P. Lincoln, J. Am. Chem. Soc. 2003, 125, 3773–3779. DOI:10.1021/ja029243c 24. A. K. Mora, P. K. Singh, S. Nath, J. Phys. Chem. B 2016, 120, 4143-4151. DOI:10.1021/acs.jpcb.5b12689 25. M. Varshney, T. E. Morey, D. O. Shah, J. A. Flint, B. M. Moudg­il, C. N. Seubert, D. M. Dennis, J. Am. Chem. Soc. 2004, 126, 5108–5112. DOI:10.1021/ja0394479 26. A. M. Toader, P. Oancea, M. Enache, Acta Chim. Slov. 2020, 67, 629–637. DOI:10.17344/acsi.2019.5641 27. E. Fuguet, C. Rafols, M. Roses, E. Bosch, Anal. Chim. Acta 2005, 548, 95–100. DOI:10.1016/j.aca.2005.05.069 28. M. Enache, M. Hillebrand, E. Volanschi, Romanian J. Biophys. 2001, 11, 93–105. 29. M. Enache, D. Bulcu, I. Serbanescu, E. Volanschi, Rev. Rou­maine Chim. 2007, 52, 725-731. Povzetek Termicno denaturacijo DNA smo ovrednotili kot merilo ucinka protitumorske ucinkovine aktinomicina D na stabilnost dvojne vijacnice in tudi ucinka micel SDS na komplekse aktinomicin D – DNA. Rezultati so pokazali, da je bila temper-atura tališca DNK odvisna od koncentracije zdravila, ki se povecuje s koncentracijo aktinomicina D. Visoka toplotna stabilizacija (približno 10 °C) vijacnice DNA po vezavi z aktinomicinom D jasno kaže interkalativni nacin vezave. Prisot­nost micel SDS vodi do sprošcanja interkaliranih molekul aktinomciina D iz dvojne vijacnice DNA in njihove nadaljnje premestitve v micele površinsko aktivne snovi. Ti rezultati so pokazali, da je sprošcanje zdravila mogoce nadzorovati pravocasno in s spreminjanjem koncentracije in narave površinsko aktivne snovi. DOI: 10.17344/acsi.2021.7289 Scientific paper Removal of Diazo Dye Direct Red 28 and Tetra Azo Dye Direct Black 22 Using Synthesized Magnetic Kaolin Supported Zinc Ferrite Serap Findik* Chemical Engineering Department, Engineering Faculty, Hitit University, Kuzey Yerleskesi, Cevre Yolu Bulvari, 19030, Çorum, Turkey * Corresponding author: E-mail: serapfindik@hitit.edu.tr Received: 11-21-2021 Abstract The presence of dye molecules in water resources has harmful effects on environment. Therefore, it is important to remove dyes from wastewater using eco-friendly materials. In this study, adsorptive removal of diazo dye Direct Red 28 (DR-28) and tetra azo dye Direct Black 22 (DB-22) was investigated by using a new composite, kaolin supported zinc ferrite (KZF) as adsorbent. Characteristics of the composite, KZF were determined using various techniques such as FTIR, SEM, XRD, VSM, and EDS/Mapping. The adsorption of DR-28 and DB-22 on KZF was studied as a function of contact time, initial dye concentration, adsorbent amount, temperature, initial pH of the solution as well as the heat treatment of the composite. The removal of DR-28 was found to be 92.4% for the KZF concentration of 1g/200mL, initial dye concentration of 20 mg/L, contact time of 120 min at original pH and 21 °C. On the other hand the removal of DB­22 was found to be 91.7% under the same conditions at 40 °C. The results showed that Langmuir isotherm is suitable for the adsorption of DR-28 and DB-22 on KZF under given conditions. It was found that adsorption of DR-28 on KZF was exothermic while that of DB-22 was endothermic. The enthalpy changes (.H0) for the adsorption of DR-28 and DB-22 dyes on KZF were found to be –24.59 kJ/mol and 61.95 kJ/mol respectively. .S0 for the adsorption of DR-28 dye was found to be negative while .S0 for the adsorption of DB-22 was positive. The kinetic data .tted well to the pseudo second order model for the adsorption of DR-28 and DB-22 on KZF composite. These results showed that the prepared adsorbent KZF could be used as an adsorbent for the efficient removal of anionic dyes. Keywords: Adsorption, direct black 22, direct red 28, magnetic kaolin, zinc ferrite 1.Introduction Dyes are chemical compounds used to color prod­ucts in many fields such as textile, leather, paper, rubber, printing and plastic.1 Dyes in wastewater are dangerous and negatively affect the water quality. The discharge of wastewater containing dyes into water resources causes a decrease in gas solubility, light and oxygen permeability, and therefore a decrease in photosynthesis. It also affects aquatic life and human life due to its carcinogenic and mu-tagenic effects.1–4 Dyes entering the human body through the food chain cause diseases such as dizziness, jaundice, diarrhea, and nausea. They also damage to organs such as kidney, brain and liver.1,3 Dyes can be divided into two categories: natural and synthetic dyes. With the increase in dye consumption, nat­ural dyes have been replaced by synthetic ones. Synthetic dyes consist of three parts: chromophoric groups, auxo-chromes and conjugated aromatic structures. Chromo­phoric groups are unsaturated and they consist of atoms or groups of atoms that allow the light to be absorbed.5 Dyes can also be classi.ed according to the chromophoric groups: Azo, Anthraquinone, Indigoid, Nitroso, Nitro, and Triarylmethane.1,6 Azo dyes are characterized by the num­ber of azo bond groups such as monoazo, diazo, triazo, polyazo and azoic. Polyazo dyes are complex structures and contain three or more azo groups in the same mole­cule.7 Direct red 28 (DR-28, known as Congo red) and Di­rect black 22 (DB-22) are widely used in textile, paper and plastic industries.8,9 DR-28 contains two azo groups and DB-22 contains tetra azo groups. Both DR-28 and DB-22 are anionic dyes.7 DR-28 and DB-22 in wastewater dam­age the environment due to their carcinogenic and toxic properties. Therefore, DR-28 and DB-22 containing wastewater should be treated before being released into environment. Different treatment methods such as coagula­tion-flocculation10, Fenton11, ozonation12, UV13, photo-catalytic process14, and adsorption15 are applied to remove dyes from aqueous solutions. Among these methods, ad­sorption has gained importance in recent years since it be­comes more economical with cheap, simple, and efficient adsorbents. Natural adsorbents such as clinoptilolites, montmorillonites, esmectites, kaolinites, and bentonites have been successfully used for adsorption.16 Kaolin has a great potential in the removal of dye­stuffs from wastewater thanks to its chemical and mechan­ical stability, high cation exchange capacity, adsorption ability, unique structural properties as well as its low cost, high efficiency, and abundance.17,18 On the other hand, the surface properties such as surface area and surface ex­change sites of kaolin should be improved to increase the adsorption capacity. For this purpose, physical and chem­ical processes such as milling, heat treatment, acid treat­ment, and alkaline treatment can be applied. While alka­line treated kaolin has a small surface area, kaolin with a large surface area can be obtained by acid treatment. How­ever, acid treatment is difficult due to the inert nature of kaolin. Metakaolinite is obtained by the calcination of ka­olinite.17,19 Magnetic adsorbents are new generation adsorbents with high adsorption capacity and speed. Compared to other adsorbents, magnetic adsorbents have some advan­tages such as less production cost, easy separation from solution, low diffusion resistance, and large surface area.4 Among the magnetic nanoparticles, spinel ferrites with the chemical composition of MFe2O4 (M=Cu, Mn, Zn, Mg, Co, Ni and other metals) have been extensively studied so far due to their special properties, where oxygen ions (O2–) make a cubic structure, Fe3+ cations occupy half of the oc­tahedral holes and M2+ ion is a divalent cation and place in the eight holes of the tetrahedral.2 Zinc, cobalt, and nickel ferrites are widely used as a magnetic support thanks to their high saturation magnetization intensity and excellent mechanical strength.20 The addition of inorganic particles such as spinel fer­rite to the matrix of kaolinites can improve the adsorption capacity and facilitate its separation from the aqueous solution by an external magnet. This process improves both the physical and chemical properties of kaolinite and provides high adsorption capacity by preventing agglom­eration of inorganic nanoparticles.21 The aim of this study was to investigate the perfor­mance of magnetic kaolin supported zinc ferrite as an ad­sorbent for the removal of diazo dye DR-28 and tetra azo dye DB-22 from aqueous solutions. The prepared adsor­bent was characterized using FTIR, SEM-EDS, XRD and VSM techniques. In order to determine the optimum ad­sorption parameters, the effect of process variables such as heat treatment of the composite KZF, adsorbent amount, initial dye concentration, contact time, initial pH of the solution, and temperature have been investigated. After optimization of the effective factors, the kinetics and iso­therm of the adsorption were investigated. A thermody­namic study was realized to understand the behavior of the adsorption process. 2. Materials and Methods 2. 1. Materials and Equipments The kaolin clay with a particle size of 28 µ was pro­cured from a company in Balikesir, Turkey. It was a com­mercial product and used without purification. DR-28 was supplied from Isolab, DB-22 (commercial name Direct Black 22 VSF 1600) from a company named “HNY” in Turkey, and FeSO47H2O and ZnSO47H2O from Merck. A magnetic stirrer (HSD-180), pH meter (C561, Consort), centrifuge (Nuve, NF 200) and oven (Proterm, PLF 120/5) were used in the study. UV- spectrofotomoter (Hach, DR-2400) was used to measure the absorbance of the dye samples. 2. 2. Preparation of the Adsorbent The composite kaolin/Zn/Fe was prepared by chem­ical coprecipitation method. First, iron II sulphate heptahydrate (FeSO4.7H2O) and zinc sulphate heptahy­drate (ZnSO4.7H2O) with a molar ratio of 2:1 were dis­solved in 200 ml distilled water. Then, kaolin clay was add­ed to the solution and heated to 65–70 °C while stirring with a magnetic stirrer. The mixture was stirred for 30 min. 3M NaOH solution was added dropwise to the solu­tion, and pH of the solution was adjusted to 12. After addi­tion of NaOH solution, stirring was continued for one hour at 100 °C. The prepared composite was left for one day at room conditions and then placed in water bath for 4 h at 95 °C. After that, it was dried at 95 °C for 90 h. Finally, the dried composite was heat treated at 200 °C for 3 h. The composite with and without heat treatment were coded as KZF-200 and KZF, respectively, and the raw kaolin as K. 2. 3. Characterization of Adsorbent The adsorbents used in the study were characterized by XRD, FTIR, SEM, EDS, and VSM. Powder X-ray dif­fraction (XRD) patterns of the adsorbent were recorded using Rigaku Smart Lab with Cu-Ka radiation at 40 kV and 30 mA to determine the crystalline structure of the samples. The samples were scanned from 5°–90° at a rate of 2°/min, and with a step size of 0.01. Fourier transform infrared (FTIR) (PerkinElmer, Spectrum Two) spectrosco­py analysis was performed in the range of 400–4000 cm–1 to identify the functional groups of the adsorbents before and after adsorption. SEM and EDS analysis were per­formed using Jeol, JSM 7001F. Magnetic saturation was measured using a vibrating sample magnetometer (VSM Lake Shore 7407). 2. 4. Adsorption Experiments A stock solution of dye was prepared using distilled water. Standart dye solutions with known concentration were prepared using stock solution, then absorbance val­ues of the standard dye solutions were recorded using UV- spectrophotometer (Hach, DR-2400). The absorbance of DR-28 and DB-22 solutions was recorded at 497 nm and 481 nm, respectively. The calibration graph was drawn us­ing the absorbance values of the standard solutions. For the adsorption experiments, the desired amount of adsorbent was put in 200 ml of dye solutions and the sample was magnetically stirred continuously at 600 rpm. The stirring rate was constant in all experiments. The dye solutions were kept at room temperature (21 °C) in all ex­periments except those in which the effect of temperature was investigated. pH was adjusted with HCl and NaOH solutions to 6.5–9. The original pH of the dye solution was ˜7.4. In the experiments, the amount of adsorbent and the initial dye concentration were changed in the range of 0.6– 1.2 g/200mL and 20–50 mg/L, respectively. All the experi­ments were repeated twice. Process variables such as the heat treatment of the composite KZF, contact time, pH, the initial concentration of dye solution, amount of adsorbent, and temperature were investigated in the study. The sam­ples were withdrawn from the reaction mixture and cen­trifuged at 5000 rpm for 10 minutes to remove the adsor­bent. The absorbance of the samples was measured to find the concentration of dye. Efficiency of the dye removal, (R) was calculated us­ing Eq. (1). R, % = [Co-Ct)/Co]*100 (1) Equation 2 gives the adsorption capacity of the ad­sorbent: qe= ((Co-Ce)*V)/W (2) where qe is the adsorption capacity of the adsorbent at equilibrium (mg/g), Co is the initial concentration of dye, Ct is the concentration of dye at any time (mg/L), Ce is the concentration of dye at equilibrium (mg/L), V is the vol­ume of the dye solution (L), and W is the weight of the adsorbent (g). 3. Results and Discussion 3. 1. Adsorbent Characterization The SEM and EDS/Map analyses were used to exam­ine the structure and distribution of elements at the sur­face of the adsorbent. SEM, EDS spectra and EDS map­ping images of the composite KZF are given in Fig. 1. As shown in Fig. 1-a, KZF has an irregular and porous surface structure. The EDS/Map analyses were used to determine the Zn and Fe particles in the kaolin layers, and the results confirm that there are Fe and Zn ions in the structure of KZF. KZF includes iron (7.1%), zinc (4.8%), silisium (6.2%), oxygen (45%), sodium (16.7%), aluminum (4.1%), carbon (9.6%) and sulphur (5.3%). According to the re­sults of EDS analysis, kaolin was successfully loaded with Fe and Zn, and the elements were observed to distribute uniformly. As shown in Fig. 2, the bands found at ~3690 and 3620 cm–1 are the typical bands for the ordered structure of kaolin. The bands at ~3690 cm–1 and 3620 cm–1 are re­lated to the OH and H-O-H stretchings, respectively.19 The peaks at 1117, 1030, and 1001 cm–1 may be due to Si-O stretching.2,19 The peak at 910 cm–1 can be attributed to Al-OH-Al stretching vibration.22 The peaks at 796, 752 and 691 cm–1 may be due to the tensile vibrations of Si-O-Si, Si-O-Al and Si-O-Mg and the bending vibrations of SiO in the structure of kaolin.2 The peaks detected at 910, 940, 1001, 1030, and 1117 cm–1 in the spectrum of kaolin disappeared in the FTIR spectra of KZF and KZF-200. The bands of KZF and KZF­200 (752, 796, 3620 and 3690 cm–1) were similar to those of the raw kaolin but transmittance of these bands was higher than that of the kaolin. The weak and disappeared peaks showed the change in the structure of kaolin. This can be interpreted as the Al3+ crystals in the kaolin struc­ture were replaced by Fe3+.19 There were new bands at 1440, 1100 and 980 cm–1 in KZF and KZF-200. The peaks at 1440, 1100 and 980 cm–1 may be due to O-H bending, C-O stretching and C=C bending, respectively. When the FTIR spectra of KZF were compared with the FTIR spectra taken after the adsorption of DR-28 or DB-22 on KZF, it was seen that both had the same bands except the peaks at 1440 and 1100 cm–1. After the adsorp­tion of DR-28 and DB-22 by KZF, the peaks at 1440 and 1100 cm–1 disappeared. The transmittance of KZF after the adsorption of dye was lower than that of KZF. This indi­cates that the functional groups of KZF were involved in the dye adsorption.23 After the dyes were adsorbed by KZF, some of the peaks disappeared and the range of some of the absorption peaks changed. This may be due to the in­teraction between the functional groups in the KZF and the dye molecules.2 Fig. 3 shows XRD patterns of K, KZF, and KZF-200. As seen in Fig. 3, kaolin has three intense diffraction peaks at 2. at 12.34°, 24.87° and 26.61°. These peaks are associat­ed with the presence of kaolinite. Similar diffraction peaks were reported by Meroufel et al.8 and Niu et al.24 The dif­fraction peak at 20.86° is associated with quartz.8 In addi­tion to the typical peaks of kaolin, new peaks at 19°, 28.9°, 32.1°, 33.8°, 35°, 48.7°, 54.6°, 59.5° and 62° were observed in XRD spectra of KZF and KZF-200. According to the PDF card, peaks appear at 19°, 28.9°, 35°, 59.5° and 62° Fig. 1. The SEM (a), EDS spectra (b), EDS Mapping analyses (c) for KZF composite The functional groups of the kaolin, KZF, KZF-200 and DR-28 or DB-22 adsorbed KZF were determined using FTIR analysis. The related FTIR spectra are shown in Fig. 2. Fig. 2. FTIR spectra of K, KZF, KZF-200, and dye adsorbed KZF match well with PDF card 00-010-0467 (Franklinite, Zn­Fe2O4). The peaks at 28.9° and 33.8° indicate that these com­posites contain typical zinc oxide, magnetite, and zinc fer­rite structures. The diffraction peak at 28.9° indicates the successful introduction of Zn to Fe spinel after synthesis. The peak at 35° shows the development of the spinel phase indicating a zinc ferrite formation in the composite.25 The magnetic behavior of KZF at 298K was investi­gated by VSM analysis. The result is shown in Fig. 4. As can be seen in the Fig. 4, the amount of magnetic satura­tion for the KZF was determined as 3.3 emu/g. The low value of magnetic saturation for KZF is due to the fact that the magnetite was covered by the kaolin.26 However, KZF can be separated from the solution using a permanent magnet. The magnetization curve of KZF exhibited zero a) b) performed at an initial dye concentration of 30 mg/L, with an adsorbent amount of 0.6g/200mL, at 21 °C and original solution pH of ˜7.4. As seen in Fig. 5, lower removal rates were obtained for both DR-28 and DB-22 in the experi­ments with kaolin compared to those with KZF and KZF­200. c) DR-28 removals at the end of 90 minutes were found to be 82.3% and 81.1% in the experiments with KZF and KZF-200, respectively. The removal rates in the contact time of 90 min were almost the same. However, in the con­tact times of 30 and 60 minutes, KZF provided a higher DR-28 removal than KZF-200. In the experiments, DB-22 Fig. 3. XRD spectra of K, KZF and KZF-200 coercivities and remanences indicating a superparamag­netic behavior of the sample at room temperature.27 3. 2. Effect of Heat Treatment KZF and KZF-200 composites were considered to investigate the effect of the heat treatment on dye removal performance of the composites. The experiments were removal was found to be higher for the composite KZF. In the contact time of 90 min, the removals of DB-22 were found to be 40% and 33.3% in the experiment with KZF and KZF-200, respectively. Therefore, KZF was chosen as the adsorbent, and the effect of the other parameters was examined using KZF. The heat treatment temperature affects the structure of the composite. In one study, Olusegun and Mohallem21 studied adsorption of Congo red using synthesized kaolin­ite supported CoFe2O4 nanoparticles calcined at different temperatures. Adsorption capacity of the composite with­out calcinations was higher than the calcined composite at 700 °C. Calcination temperature up to 700 °C resulted in structural damage to kaolinite. As shown in Fig. 2, KZF and KZF-200 had the same peaks, but the transmittance of KZF was lower than that of KZF-200. The kaolinite had a disordered structure at lower temperatures due to dehydroxylation.28 The removal rate of DR-28 was found to be higher than that of DB-22. DR-28 has two azo bonds, on the other hand, DB-22 has tetra azo bonds. The higher the number of azo bonds, the higher the stability of dye, which makes the dye removal difficult. In addition, the molecular weight of the tetra azo dye DB-22 was higher than that of the diazo dye DR-28. The molecular weights of DB-22 and DR-28 were 1084 g/mol and 696.67 g/mol, respectively. The distribution of dyes in water increases with the increase of the molecular weight of the azo dyes, which causes a decrease in the rate of azo dye degradation.7 3. 3. Effect of Adsorbent Amount In the present study, the effect of adsorbent amount on dye removal was also investigated. Experiments were done with a various amounts of adsorbent at the initial concentration of 30 mg/L, at 21 °C and original pH. Fig. 6 shows the effect of adsorbent amounts on the removal of DR-28 and DB-22. The removal of DR-28 increased with increasing KZF amount from 0.6g/200mL to 0.8g/200mL then remained constant. DR-28 removals at the end of 90 min were found to be 89.7%, 90.2% and 90.1% for the KZF amounts of 0.8, 1, and 1.2g/200mL respectively. At the end of 30 min, the removals of DR-28 were found to be 76.4% and 88.9% in the KZF amounts of 0.8g/200mL and 1g/200mL respectively. Therefore, 1g/200mL of KZF was chosen as the optimum adsorbent amount. The removal of DB-22 increased with the increasing the amount of adsorbent within the range of 0.6g/200mL– 1.2g/200mL. The removals of DB-22 at the end of 90 min, were 40%, 55.3%, 73.2%, and 75.4% in the KZF amounts of 0.6, 0.8, 1, and 1.2g/200mL respectively. There was no sig­nificance difference between the DB-22 removals of 1g/200mL and 1.2g/200mL KZF. DB-22 removal was lower than the removal of DR-28 under the studied conditions. As mentioned in the previ­ous section, removal of tetra azo dyes is more difficult than that of diazo dyes. The removal of DR-28 and DB-22 increased with the increasing adsorbent amount due to the increase in sur­face area and number of active sites. After the optimum adsorbent amount, the effective active surface area de­creased due to the accumulation of adsorbent particles, and as a result, the removal rate remained almost constant. Nicola et al.27 investigated the adsorption of Congo red us­ing magnetic mesoporous silica. The removal of Congo red increased with increasing the adsorbent amount from 0.5 to 1g/L and after 1g/L adsorbent amount removal rate remained nearly constant. Similarly, Boushehrian et al.2, reported that the removal of methylene blue and methyl violet remained almost constant after the adsorbent dos­age of 1.5g/L. Cao et al.29 investigated the adsorption of reactive brilliant red using magnetic Fe3O4/chitosan nano-particles and reported that the optimum adsorbent amount was 0.6 g/L, and after the optimum adsorbent amount, the removal rate was almost the same. In another study, Karthikeyan et al.30 studied the adsorption of phosphate and nitrate ions from water using magnetic kaolin (MK) chitosan beads and reported that, the removal of phos­phate and nitrate increased with the increasing amounts of MK-chitosan beads due to the increase in reactive vacant sites of the adsorbent surface. Moreover, they also report­ed that, there was no significant change in the removal rate at the adsorbent amounts greater than 100 mg. Koohi et al.31 also reported similar results. They found that the re­moval efficiency of Congo red increased with the increas­ing amounts of Fe3O4/NiO due to the increase in the active surface area, and after the adsorbent amount of 15 g/L, no significant change was observed in the removal rate. The decrease in the surface area due to the aggregation of par­ticles causes a decrease in the adsorption capacity. Esvandi et al.3 examined the adsorption of sunset yellow and Nile blue using magnetic nanoparticle, and reported that the removal efficiency increased when the adsorbent amount was increased from 0.4 g/L to 1 g/L (optimum adsorbent amount), and after the adsorbent amount of 1 g/L, the re­moval rate remained constant. In another study, Mahmoo­di et al.32 investigated the adsorption of direct black 22 using polyaminoimide homopolymer. They reported that removal of direct black 22 increases with increasing adsor­bent amount due to increased adsorbent surface and avail­ability of more sites. 3. 4. Effect of Initial pH The pH of solution is an important parameter affect­ing the adsorption of dye onto the adsorbent. DR-28 is a diazo dye which changes colour at low pHs due to proto-nation of its amino groups. The red color of DR-28 chang­es to bluish color below the pH 5.8,21,33 For this reason the effect of pH was not investigated under strong acidic con­ditions. The effect of pH on the removal of DR-28 and DB-22 was studied at the initial pHs of 6.5, ˜7.4, 8.5, and 9. ˜7.4 is the original pH of the dye solution. The results are pre­sented in Fig. 7. The pH of the dye solution was adjusted at the beginning of the experiment and not controlled during the adsorption process. The removal rate of DR-28 increased from 87.5% to 90.5% when the pH of the solution was increased from 6.5 to 7.4. After the original pH, the removal rate decreased with the increase in solution pH, which could be attributed to the de-protonation of the adsorbent surface. Similarly, Koohi et al.31 reported that the removal of Congo red decreased as the pH was increased after the optimum pH. They also reported that, the adsorption capacity gradually decreased, due to the repulsion between the Congo red ions and the adsorbent molecules. Das et al.33 investigated the effect of pH on the decolorization of Congo red and reported that, highly acidic and highly basic conditions were not suitable for the decolor­ization of Congo red solution. They obtained the maximum decolorization of Congo red at pH 7. The removal of DB-22 increased when the pH was increased from 6.5 to 7.4 (original pH). At the pHs of 7.4 and 8.5, there was no significant change in the removal rate. The removal of DB-22 was found to be 80.4% in pH 9. Sun et al.34 reported similar result for the adsorption of anionic dye reactive red 123 and explained the effect of pH on the adsorption by electrostatic interaction between the adsorbent and the dye molecules. Based on these results, it can be asserted that the adsorption mechanism is not de­pendent solely on electrostatic interaction. The molecular structure of dyes may also affect the adsorption process. 3. 5. Effect of Temperature In the study, the effect of temperature on dye remov­al was also investigated to reveal whether the adsorption process is exothermic or endothermic. The experiments were done at different temperatures at the initial concen­tration of 30 mg/L, with the adsorbent amount of 1g/200mL and the contact time of 60 min at original pH. Fig. 8 presents the effect of temperature on the removal of DR-28 and DB-22. The removal of DR-28 decreased with the increasing temperature. This result shows that, the adsorption of DR­28 on KZF was exothermic. As the temperature increases, the tendency of the dye adsorbed on the adsorbent surface to separate from the surface increases.2 According to Es-vandi et al.3, the adsorption efficiency decreases with in­creasing the temperature due to the desorption of dye mol­ecules from the adsorbent surface. Maqbool et al.35 investigated the removal of Congo red using free biomass, Na-Alg/CH and PPY/CH conjugates. It was found that the removal of Congo red decreased with increasing tempera­ture due to the weakened Van der Waals force and the H-bonding between the adsorbent and the sorbate. On the other hand, the removal of DB-22 increased with increasing the temperatures up to 40 °C. The remov­als of DB-22 at the initial concentration of 30 mg/L and the contact time of 60 min were found to be 69.2%, 83.2% and 91.3% at the solution temperatures of 21, 30, and 40 °C, respectively. After 40 °C, the removal rate remained almost constant. So, the adsorption of DB-22 on KZF is an endo­thermic process. Magdy et al.19 reported similar results for the adsorption of Direct Red 23. The mobility of dye mol­ecules changes with temperature. As the temperature of the solution increases, the mobility of the dye molecules increases and as a result, the number of dye molecules in­teracting with the free active sites on the composite surface increases. Based on the experimental results, it can be asserted that the optimum temperatures for the removal of DR-28 and DB-22 were 21 °C and 40 °C, respectively. For this rea­son, the experiments examining the effect of initial dye concentration were carried out at these optimum temper­atures. 3. 6. Effect of Initial Dye Concentration and Contact Time In the study, the effects of initial dye concentration and contact time on dye removal were also investigated. The experiments were done within an initial concentration range of 20–50 mg/L with an adsorbent amount 1g/200mL, at original pH. The experiments examining the effect of contact time and initial dye concentration were carried out at the optimum temperature values specified in the previ­ous section (i.e., 21 °C for DR-28 and 40 °C for DB-22). Fig. 9 presents the effect of initial dye concentration and contact time on the removal of DR-28 and DB-22. The experimental results showed that the removal rates of DR-28 and DB-22 were faster for the first 20 min, and then slowed down. The removal rate was almost con­stant after the contact time of 30 min. At the beginning of the adsorption process, active surface area is high. As the time progresses, empty areas begin to fill and the appropri­ate surface area decreases due to saturation with dye mol­ecules. Accordingly, the rate of the adsorption process de­creases and reaches an equilibrium.2,30 As seen in Fig. 9, the removal of DR-28 and DB-22 decreased with the increasing initial dye concentrations. The removals of DR-28 for the contact time of 120 min were found to be 92.4%, 89%, 86% and 77% at the initial dye concentrations of 20, 30, 40, and 50 mg/L, respectively. Similar result was obtained for DB-22. The removals of DB-22 were found to be 91.7%, 90.2%, 85.5%, and 75.6% at the initial DB-22 concentration of 20, 30, 40, and 50 mg/L respectively. Mass transfer occurs due to the concentration difference between the aqueous phase and the adsorbent surface. Initial concentration of the dye solution is the driving force for mass transfer. The removal rate decreases with the increasing initial dye concentration due to the de­crease in the number of active sites of the adsorbent.16 Magdy et al.19 reported similar results. As the dye concen­tration increases, the adsorbent reaches saturation quickly for a fixed amount of adsorbent, and the active surfaces are covered with dye molecules. According to Sanad et al.36, at high dye concentrations, the unit mass of the adsorbent is exposed to more dye molecules. As a result, the active sites are gradually filled until they reach saturation causing a decrease in removal efficiency. Boushrean et al.2 examined the effect of initial concentration on the removal of meth­ylene blue and methyl violet and reported that the ratio of the active surface area of the adsorbent to the dyes in the solution was high at low dye concentrations, and for this reason, all the dye molecules interacted with the adsor­bent, and thus a higher removal efficiency was achieved. 3. 7. Adsorption Isotherms The adsorption equilibrium data collected at the dye concentrations of 20–50 mg/L were .tted by common iso­therms Langmuir and Freundlich models. The Langmuir isotherm model assumes that the adsorbate is coated in a monolayer on the homogeneous adsorbent surface, and the adsorption takes place only at the active sites on the adsorbent. The Freundlich equation is an empirical equa­tion used to describe heterogeneous systems.29 Langmuir and Freundlich models are given in Eqs. (3) and (4), respectively. 2,22,37 (3) (4) (5) where qmax is the adsorption capacity (mg/g), KL is the ad­sorption energy (L/g), and Kf and n are the Freundlich constants. The value of RL specifies whether the adsorption pro­cess is irreversible (RL=0), desirable (01). The value of n determines whether the adsorption process is linear (n=1), physical (n>1) or chemical (n<1). The adsorption isotherm models were calculated at the initial dye concentrations of 20–50 mg/L. The other factors, such as initial pH, adsorbent amount, and temper­ature were kept constant. For DR-28 and DB-22, the initial pH was original pH, and the amount of the adsorbent KZF was 1g/200mL. While the parameters such as adsorbent amount and pH were the same for both dyes, the tempera­ture was 21 °C for DR-28 and 40 °C for DB-22. The values and constants of the Langmuir and Fre­undlich isotherm models for DR-28 and DB-22 are given in Table 1. The results of the Langmuir adsorption iso­therm models for DR-28 and DB-22 are shown in Fig. 10 and Fig. 11, respectively. The values of R2 for the Langmuir model were found to be 0.9969 and 0.9926 for DR-28 and DB-22, respective­ly. The values of R2 for the Freundlich model were found to be 0.9308 and 0.8381 for DR-28 and DB-22, respectively. DR-28 and DB-22 adsorptions were found to be in a good agreement with the Langmuir isotherm. The calculated RL values of DR-28 and DB-22 were between 0 and 1. Based on these results, it can be asserted that the adsorption pro­cess was desirable. Table 1. Langmuir and Freundlich isotherm parameters for the ad­sorption of DR-28 and DB-22 (pH: original, adsorbent: KZF, adsor­bent amount: 1 g/200mL, temperature: 21 °C for DR-28, 40 °C for DB-22) Isotherm Constants DR-28 DB-22 Langmuir qmax mg/g) 7.640 8.404 KL (L/mg) R2 0.722 0.9969 0.605 0.9926 RL 0.027–0.064 0.032–0.076 Freundlich n 3.59 3.03 Kf (mg/g) 3.71 3.53 R2 0.9308 0.8381 3. 8. Adsorption Thermodynamics Thermodynamic parameters (.G0, .H0, .S0) were calculated using the Eqs. (6), (7), and (8). The values of .H0 and .S0 were calculated from the slope and intercept of a linear plot lnKc versus 1/T.4,37,38 (6) (7) (8) where .G0 is the standard change free Gibbs energy (J/ mol), .H0 is the standard change enthalpy (J/mol), .S0 is the standard change entropy (J/molK), and R is the univer­sal gas constant (8.314 J/molK), Kc is the ratio of the equi­librium concentration of adsorbate (qe) loaded to the equi­librium concentration in solution (Ce). The adsorptions of DR-28 and DB-22 on KZF were thermodynamically studied at an initial concentration of 30 mg/L, with an adsorbent amount of 1g/200mL, and a contact time of 60 min, at the original pH. The results of the thermodynamic study are given in Fig. 12. Table 2 shows the obtained thermodynamic parameters for DR-28 and DB-22. The enthalpy change (.H0) for DR-28 dyes was found to be –24.59 kJ/mol, which shows that, the adsorp­tion of DR-28 using the composite KZF was exothermic. .G0 values were –1.364, –0.653, 0.137 and 0.927 kJ/mol at the adsorption temperatures of 21, 30, 40, and 50 °C, re­spectively. The negative .G0 values at 21 and 30 °C indi­cate that the adsorption process occured spontaneously.29 The positive .G0 values at 40, and 50 °C suggest that the adsorption of DR-28 onto KZF was not a spontaneous pro­ a) b) Fig. 12. The thermodynamics of the adsorption of (a) DR-28 and (b) DB-22 on the adsorbent KZF (pH: original, initial concentra­tion: 30 mg/L, contact time: 60 min, KZF amount: 1 g/mL) cess. The decrease in .G0 value with the decrease in tem­perature indicates that lower temperatures are suitable for the adsorption of DR-28 molecules onto KZF.22 The value of .S0 for DR-28 dye was also found to be negative. Ac­cording to Boushehrian et al.2, the negative .S0 values in­dicate a decrease in the random collision of dye molecules and the adsorbent surface during the adsorption process. Maqbool et al.39 reported similar results for the adsorption of BB-41. The negative value of .S0 reveals that the disor­der at the solid-solution interface is reduced. On the other hand, .H0 value for DB-22 was found to be 61.95 kJ/mol, which shows that the adsorption pro- Table 2. Thermodynamic parameters for the adsorptions of DR-28 and DB-22 onto KZF Dye Temperature .H0 .S0 .G0 (°C) (kJ/mol) (J/molK) (kJ/mol) DR–28 21 30 4050 –24.59 –79 –1.364 –0.653 0.137 0.927 DB–22 21 30 40 61.95 204 1.974 0.138 –1.902 cess was endothermic. .G0 values were 1.974, 0.138, and –1.902 kJ/mol at the adsorption temperatures of 21, 30, and 40 °C, respectively. The positive .G0 value at 21 and 30 °C indicated that the adsorption process was not sponta­neous. The negative .G0 value at 40 °C suggests that the adsorption of DB-22 onto KZF was a spontaneous pro­cess.4 The decrease in .G0 value with the increase in tem­perature shows that higher temperatures are suitable for the adsorption of dye molecules onto adsorbent.22 As mentioned in the section 3.5 there was no increase in the removal rate of dye beyond 40 °C. So, there is no need to work at temperatures higher than 40 °C. .S0 was found to be 204 J/mol. The positive .S0 shows the increase in disor­der and randomness at the composite KZF and DB-22 dye solution interface during the adsorption process.19,40 3. 9. Adsorption Kinetics To understand the dynamics of the adsorption pro­cess of DR-28 and DB-22 onto KZF, the adsorption kinetic experiments were performed at the adsorbent amount 1g/200mL, with the initial dye concentration of 30 mg/L at original pH. Adsorption temperature was 21 °C for DR-28 and 40 °C for DB-22. Common adsorption kinetic models, pseudo .rst order equation, and pseudo second order equation were employed to .t the experimental data. The linear form of the pseudo .rst order kinetic model is presented as Eq. 9: (9) In this relation, qe is the adsorption capacity in the equilibrium state (mg/g), qt is the adsorption capacity at any time (mg/g), and k1 is the rate constant (min–1). k1 can be obtained by drawing the experimental data of ln(qe – qt) versus t.2,37 The pseudo second order kinetic model is given in the Eq. 10: (10) where k2 (g/mg.min) is the rate constant of the pseudo sec­ond order kinetics. Drawing the linear graph of t/qt versus t can provide the pseudo second order kinetic rate param­ eter.2,37,40 Regression coefficient (R2) is important to determine the agreement of calculated qe values with experimental data. A relatively higher R2 value indicates that the model is suitable for the adsorption process. The values and con­stants of the pseudo first order and pseudo second order models for DR-28 and DB-22 are given in Table 3 and Ta­ble 4, respectively. The results of the pseudo second order model obtained for the adsorption of DR-28 and DB-22 are shown in Fig. 13 and Fig. 14, respectively. As can be seen in Table 3 and Table 4, the correlation coef.cient (R2) of the pseudo second order model was Table 3. Pseudo first order and pseudo second order kinetic model constants for DR-28 adsorption using the ad­sorbent KZF Kinetic model Parameters Initial dye concentration of DR-28, mg/L 20 30 40 50 Pseudo First Order R2 0.9531 0.9205 0.9699 0.8018 k1 0.0624 0.0783 0.0727 0.0597 qcal 1.788 3.009 5.385 4.739 qexp 4.166 5.434 6.266 6.633 Pseudo Second order R2 0.9997 0.9980 0.9974 0.9979 k2 0.0755 0.0410 0.0195 0.0177 qcal 4.292 5.692 6.798 7.179 qexp 4.17 5.43 6.270 6.63 Table 4. Pseudo first order and pseudo second order kinetic model constants for DB-22 adsorption using the ad­sorbent KZF Kinetic model Parameters Initial dye concentration of DB-22, mg/L 20 30 40 50 Pseudo First Order R2 0.532 0.8783 0.7863 0.7998 k1 0.0467 0.07 0.0464 0.0338 qcal 0.372 2.749 2.252 2.718 qexp 3.754 5.831 6.898 7.265 Pseudo Second order R2 0.9992 0.9995 0.9993 0.9976 k2 0.145 0.049 0.036 0.021 qcal 3.83 6.05 7.16 7.65 qexp 3.75 5.83 6.90 7.27 higher than that of the pseudo first order. This result shows that the pseudo second order model is the best model for describing the kinetics of KZF toward DR-28 and DB-22. 4. Conclusion In this study, the adsorption of diazo dye Direct Red 28 (DR-28) and tetra azo dye Direct Black 22 (DB-22) us­ing synthesized magnetic kaolin supported zinc ferrite (KZF) were investigated. KZF was prepared by co-precipi­tation method. KZF was heat treated at 200 °C for 3h and coded as KZF-200. KZF provided a higher removal of DR­28 and DB-22 than KZF-200. The characteristics of the KZF and KZF-200 were determined using FTIR, SEM, EDS/Elemental Mapping, XRD, and VSM analyses. The highest removal rates of DR-28 and DB-22 with the com­posite KZF were obtained at the original pH, at the initial dye concentration of 20mg/L, with the adsorbent amount of 1g/200mL, and the contact time of 120 min, and at 21 °C for DR-28 and 40 °C for DB-22. Under these conditions, the removal rates of DR-28 and DB-22 from aqueous solu­tions were found to be 92.4% and 91.7% respectively. The experimental results were better .tted with the Langmuir isotherm model and adsorption process occurred on the homogeneous surfaces. The kinetic behavior of the ad­sorption of DR-28 and DB-22 showed that the pseudo sec-ond-order kinetic model was better .tted with the results. 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DOI:10.1016/j.heliyon.2020.e04975 39. M. Maqbool, H. N. Bhatti, S. Sadaf, M.M. AL-Anazy, M. Iqbal, J Mat. Res. Tech. 2020, 9(6),14729e14741. DOI: 10.1016/j.jmrt.2020.10.017 40. A. Jabeen, H. N. Bhatti, Env. Tech and Inn.2021, 23, 101685 DOI: 10.1016/j.eti.2021.101685 Povzetek Prisotnost molekul barvil v vodah ima škodljiv vpliv na okolje. Zato je pomembno, da jih odstranimo s pomocjo okolju prijaznih materialov. V tej študiji smo preucevali možnost odstranjevanja diazo barvila Direct Red 28 (DR-28) in tetraa­zo barvila Direct Black 22 (DB-22) z adsorpcijo na kompozitni nosilec na osnovi kaolina in cinkovega ferita (KZF). Lastnosti KZF kompozita smo dolocili s pomocjo FTIR, SEM, XRD, VSM in EDS. Adsorpcijo barvil DR-28 in DB-22 na KZF smo preucevali v odvisnosti od kontaktnega casa, zacetne koncentracije barvila, kolicine adsorbenta, tempera­ture, zacetne pH vrednosti raztopine ter toplotne obdelave kompozita. Dosegli smo 92.4 % adsorpcijo DR-28 pri KZF koncentraciji 1g/200 mL, zacetni koncentraciji barvila 20 mg/L, kontaktnem casu 120 min, pri osnovni pH vrednosti in temperaturi 21 °C. Adsorpcija barvila DB-22, pri enakih pogojih le temperaturi 40 °C, pa je bila 91.7 %. Rezultati so pokazali, da je lahko adsorpcijo obeh barvil (DR-28 in DB-22) na KZF opišemo z Langmuirjevo adsorpcijsko izotermo. Adsorpcija barvila DR-28 je eksotermna, medtem ko je adsorpcija barvila DB-22 endotermna. Sprememba entalpije (.H0) pri adsorpciji na KZF je bila 24.59 kJ/mol za DR-28 in 61.95 kJ/mol za DB-22. .S0 adsorpcije je bila pri DR-28 negativna, pri DB-22 pa pozitivna. Hitrost adsorpcije obeh barvil lahko dobro opišemo s kinetiko pseudo-drugega reda. Rezultati kažejo, da lahko pripravljeni KZF kompozit uporabimo za ucinkovito odstranjevanje anionskih barvil. Except when otherwise noted, articles in this journal are published under the terms and conditions of the Creative Commons Attribution 4.0 International License DOI: 10.17344/acsi.2021.7238 Scientific paper Efficient One-Pot Synthesis of 1,4-Dihydropyridines Catalyzed by Magnetic MnFe2O4 Nanoparticles Somayeh Moradi, Mohsen Moradian and Hossein Naeimi* Department of Organic Chemistry, Faculty of Chemistry, University of Kashan, Kashan, Iran * Corresponding author: E-mail: naeimi@kashanu.ac.ir Received: 10-27-2021 Abstract The efficient one-pot synthesis of some 1,4-dihydropyridines is described by a condensation reaction of some aldehyde derivatives, ethyl acetoacetate and ammonium acetate in the presence of superparamagnetic manganese ferrite nano-particles at 80 °C. The advantages of this protocol include selectivity, high purity of the products, excellent yields, short reaction times, ease of processing, and environmentally friendly conditions for the synthesis of 1,4-dihydropyridines. In addition, the catalyst can be recovered and reused in multiple runs without significantly reducing the product yield. Keywords: 1,4-Dihydropyridine; MnFe2O4; Nanoparticles; Catalyst; Superparamagnetism The Hantzsch reaction is one of the most popular MCRs 1.Introduction and generates 1,4-dihydropyridine (1,4-DHP) derivatives. Multicomponent reactions (MCRs) are a very attrac-This reaction, which has received considerable attention in tive approach as well as an efficient and powerful tool for modern synthetic organic chemistry, was first described the synthesis of novel compounds and the discovery of by A. Hantzsch in 1882.7 The Hantzsch reaction is a prac-new drugs.1–6 In MCRs, three or more flexible and simple tical and useful synthetic tool for the preparation of 1,4-di-compounds are reacted to synthesize complex organic hydropyridines by condensation of ethyl acetoacetate, an molecules from commercially available starting materials. aldehyde, and a source of ammonia in the presence of an Figure 1. Some biologically active 1,4-dihydropyridines. alcoholic solvent.8 In modern synthetic organic chemistry, 1,4-dihydropyridines are an important group of drugs9–11 and calcium channel modulators for the treatment of hy-pertension,12 tumors,13,14 HIV,15 cancer,16 diabetes,17 ap­optosis, and18 seizures.19 For example, nifedipine (1), felodipine (2), diludin (3), amlodipine (4), nicardipine (5), nisodipine (6), and nimodipine (7) are synthesized and used worldwide (Figure 1). Recently, methods have been described using differ­ent types of catalysts such as CeCl3.7H2O,20 ionic liq­uids,21,22 TBAB,23 visible light,24,25 chitosan silica sulfate,26 27 molybdic acid-functionalized nano-Fe3O4@TiO2, 28 30 HClO4-SiO2,microwave-assisted,29 PW/SiO2,solar thermal,31 I2,32 sulfonic acid,33 Fe3O4@SiO2@OSO3H,34 heteropolyacids,35 silica gel/NaHSO4,36 organocatalysis,37 AlCl3.6H2O,38 and metal triflates.39 Although many of these methods are effective, the search for a more satisfac­tory catalyst is essential for the preparation of 1,4-DHP. Metal oxide nanocrystals, especially superparamagnetic ones, have recently been used as effective catalysts because of their easy availability and environmentally friendly properties.40 Because of the advantages of magnetic nano-particles, such as low-cost large-scale synthesis, easy sepa­ration and reuse using an external magnet, and applicabil­ity in industrial processes, their use as catalysts in various organic reactions has been developed.41–47 In this research, we describe a useful and simple method for the prepara­tion of 1,4-dihydropyridines from the condensation of ethyl acetoacetate, various aldehydes, and ammonium ac­etate as a source of ammonia using magnetic MnFe2O4 nanoparticles as a catalyst and ethanol as a solvent. 2. Experimental Section 2. 1. Apparatus Chemical reagents and solvents were purchased commercially from Aldrich and Fluka Chemical Compa­nies and used without further purification. A Bruker DRX-400 spectrometer was used to record 1H NMR and 13C NMR spectra in deuterated chloroform solvent. Infra­red spectra (IR) were recorded as KBr pellets using a Nico-let Impact 400 FT-IR spectrophotometer. Elemental ana­lyzes (C, H, and N) were performed using a Perkin Elmer 2400- CHN elemental analyzer. XRD patterns of samples with an X-ray wavelength of 1.54 °A and Cu anode materi­al were recorded using a Philips X’PertPro X-ray diffrac­tometer at a scanning speed of 2°/min over a range of 10° to 80° (2.). The powder morphology of the catalyst was determined using a Hitachi S4160 as a field emission scan­ning electron microscope (FE-SEM). The magnetic prop­erties of the nanoparticles were determined by vibrational magnetometric measurements (VSM, PPMS-9T) at 300 K in Iran (College of Kashan, Iran). The melting points were determined using a Yanagimoto micro-melting point ap­paratus. The course of the reaction was checked by thin layer chromatography (TLC) on silica gel polygram SILG/ UV 254 plates. 2. 2. General Procedure for the Synthesis of MnFe2O4 NPs MnFe2O4 nanoparticles were synthesized by simple coprecipitation of FeCl3/MnCl2 in alkaline NaOH medi­um. First, 100 ml solution of the two salts FeCl3.6H2O and MnCl2.4H2O were prepared such that the molar ratio of Mn2+: Fe3+ molar ratio was 2:1. For this purpose, 0.02 mol of Fe(III) salt and 0.01 mol of Mn(II) salt were dissolved in distilled water and the volume of the solution was made up to 100 mL. Then, the desired solution was added dropwise into a solution of NaOH (100 mL, 3 mol L–1) at 95 °C with constant stirring for 2 h. The solution was then added to the solution of NaOH (100 mL, 3 mol L–1). At the end of the desired time, the solid was collected with an external magnetic field, washed with ethanol (3 × 20 mL) and deionized water (5 × 30 mL), and then dried at 60 °C for 12 h. The solid was then removed from the solution. The Mn­Fe2O4 nanoparticles were characterized by physical and spectroscopic data. 2. 3. A General Method for the Synthesis of 1,4-dihydropyridines A mixture of ethyl acetoacetate (2.4 mmol), selected aldehyde (1 mmol), NH4OAc (1.2 mmol), and MnFe2O4 (5 mol%) in ethanol (5 mL) as solvent was stirred at 80 °C for an appropriate time. The progress of the reaction was fol­lowed by thin layer chromatography (TLC). After comple­tion of the reaction, the mixture was diluted with ethyl acetate and the solid catalyst was separated and collected using a magnetic field. The desired product was extracted with ethyl acetate and water and purified by recrystalliza­tion from ethanol/water (5:1) to afford pure 1,4-dihydro­pyridines. The solid magnetic MnFe2O4 catalyst was care­fully washed with acetone (3 × 10 ml) and distilled water (3 × 10 ml), and dried at room temperature in a desiccator. The structure of the new compounds was identified from the physical and spectroscopic data. 2. 4. Spectral Data for 1,4-dihydropyridine Derivatives Diethyl 2,6-dimethyl-4-(3-nitrophenyl)-1,4-dihydro­pyridine-3,5-dicarboxylate (4a); Yellow solid; m.p. = 162– 167 °C; IR (KBr, cm–1) .: 3344 (NH), 3093 (=CH2), 2987 (-CH, sp3), 1706 (C=O), 1645 (C=C), 1213 (C-O); 1H NMR (400 MHz, CDCl3, ppm) d: 1.23 (t, J=7.2 Hz, 6H, 2CH3), 2.36 (s, 6H, 2CH3), 4.08 (q, 4H, 2CH2), 5.08 (s, 1H), 5.86 (s, 1H, NH), 7.37 (t, J=8 Hz, 1H, ArH), 7.64 (d, J=7.6 Hz, 1H, ArH), 7.99 (d, J=7.2 Hz, 1H, ArH), 8.12 (s, 1H, ArH). Diethyl 2,6-dimethyl-4-phenyl-1,4-dihydropyridine-3,5-dicarboxylate (4b); Yellow solid; m.p. = 150–155 °C; IR (KBr, cm–1) .: 3342 (NH), 3061 (=CH2), 2980 (-CH, sp3), 1690 (C=O), 1651 (C=C), 1211 (C-O); 1H NMR (400 MHz, CDCl3, ppm) d: 1.22 (t, J=7.2 Hz, 6H, 2CH3), 2.33 (s, 6H, 2CH3), 4.10 (q, 4H, 2CH2), 4.98 (s, 1H), 5.66 (s, 1H, NH), 7.10–7.27 (m, 5H, ArH). Diethyl 2,6-dimethyl-4-(4-nitrophenyl)-1,4-dihy­dropyridine-3,5-dicarboxylate (4c); Light yellow solid; m.p. = 125–128 °C; IR (KBr, cm–1) .: 3320 (NH), 3101 (=CH2), 2980 (-CH, sp3), 1701 (C=O), 1646 (C=C), 1214 (C-O); 1H NMR (400 MHz, CDCl3, ppm) d: 1.21 (t, J=7.2 Hz, 6H, 2CH3), 2.36 (s, 6H, 2CH3), 4.06 (q, 4H, 2CH2), 5.09 (s, 1H), 5.74 (s, 1H, NH), 7.44 (d, J=8.8 Hz, 2H, ArH), 8.07 (d, J=8.8 Hz, 2H, ArH). Diethyl 4-(4-chlorophenyl)-2,6-dimethyl-1,4-dihy­dropyridine-3,5-dicarboxylate (4d); Yellow solid; m.p. = 140–145 °C; IR (KBr, cm–1) .: 3356 (NH), 3091 (=CH2), 2988 (-CH, sp3), 1696 (C=O), 1651 (C=C alkene), 1213 (C-O); 1H NMR (400 MHz, CDCl3, ppm) d: 1.22 (t, J=6.8 Hz, 6H, 2CH3), 2.32 (s, 6H, 2CH3), 4.08 (q, 4H, 2CH2), 4.95 (s, 1H), 5.70 (s, 1H, NH), 7.16 (d, J=8.8 Hz, 2H, ArH), 7.21 (d, J=8.4 Hz, 2H, ArH). Diethyl 4-(2-chlorophenyl)-2,6-dimethyl-1,4-dihy­dropyridine-3,5-dicarboxylate (4e); White solid. m.p. = 118–120 °C; IR (KBr, cm–1( .: 3325 (NH), 3060 (=CH2), 2978 (-CH, sp3), 1699 (C=O), 1671 (C=C), 1613, 1491 (C=C, Ar), 1206 (C-O); 1H NMR (CDCl3, 400 MHz, d / ppm): 1.20 (t, J=7.2 Hz,6H, 2CH3), 2.31 (s, 6H, 2CH3), 4.07 (q, 4H, 2CH2), 5.39 (s, 1H), 5.65 (s, 1H, NH), 7.04 (t, J=7.6 Hz,1H, ArH), 7.12 (t, J=7.6 Hz,1H, ArH), 7.22 (d, J=8 Hz, 1H, ArH), 7.37 (d, J=1.6 Hz, 1H, ArH). Diethyl 4-(2,4-dichlorophenyl)-2,6-dimethyl-1,4-di­hydropyridine-3,5-dicarboxylate (4h); Yellow solid; m.p = 140–142 °C; IR (KBr, cm–1( .: 3378 (NH), 3087 (=CH2), 2980 (-CH, sp3), 1699 (C=O), 1679 (C=C), 1617, 1494 (C=C, Ar), 1201 (C-O); 1H NMR (400 MHz, CDCl3, ppm) d: 1.20 (t, J=7.2 Hz, 6H, 2CH3), 2.31 (s, 6H, 2CH3), 4.07 (q, 4H, 2CH2), 5.35 (s, 1H), 5.61 (s, 1H, NH), 7.10 (d, J=7.6 Hz, 1H, ArH), 7.25 (s, 1H, ArH), 7.31 (d, J=8.0 Hz, 1H, ArH). Diethyl 4-(3-methoxyphenyl)-2,6-dimethyl-1,4-di­hydropyridine-3,5-dicarboxylate (4l); Yellow solid; m.p. = 119–123 °C; IR (KBr, cm–1) .: 3342 (NH), 3095 (=CH2), 2983 (-CH, sp3), 1699 (C=O), 1649 (C=C), 1605, 1487 (C=C, Ar), 1215 (C-O); 1H NMR (400 MHz, CDCl3, ppm) d: 1.23 (t, J=7.2 Hz, 6H, 2CH3), 2.32 (s, 6H, 2CH3), 3.76 (s, 3H, CH3), 4.11 (q, 4H, 2CH2), 4.98 (s, 1H), 5.72 (s, 1H, NH), 6.67 (d, J=8.8 Hz, 1H, ArH), 6.84 (s, 1H, ArH), 6.89 (d, J=6.8 Hz, 1H, ArH), 7.13 (t, J=8.0 Hz, 1H, ArH). Diethyl 4-(4-methoxyphenyl)-2,6-dimethyl-1,4-di­hydropyridine-3,5-dicarboxylate (4j); White solid; m.p. = 150–155 °C; IR (KBr, cm–1) .: 3343 (NH), 2983 (-CH, sp3), 1689 (C=O), 1650 (C=C), 1210 (C-O); 1H NMR (400 MHz, CDCl3, ppm) d: 1.23 (t, J=7.2 Hz, 6H, 2CH3), 2.32 (s, 6H, 2CH3), 3.75 (s, 3H, CH3), 4.09 (q, 4H, 2CH2), 4.93 (s, 1H), 5.60 (s, 1H, NH), 6.75 (d, J=8.8 Hz, 2H, ArH), 7.20 (d, J=8.4 Hz, 2H, ArH). Diethyl 4-(4-fluorophenyl)-2,6-dimethyl-1,4-dihy­dropyridine-3,5-dicarboxylate (4i); Yellow solid; m.p. = 145–147 °C; IR (KBr, cm–1) .: 3343 (NH), 3068 (=CH2), 2983(-CH, sp3), 1688 (C=O), 1652 (C=C), 1210 (C-O); 1H NMR (400 MHz, CDCl3, ppm): 1.21 (t, J=7.2 Hz, 6H, 2CH3), 2.33 (s, 6H, 2CH3), 4.04 (q, 4H, 2CH2), 4.95 (s, 1H), 5.67 (s, 1H, NH), 6.88 (t, J=8.4 Hz, 2H, ArH), 7.23 (t, J=5.6 Hz, 2H, ArH). Diethyl 4-(2-bromophenyl)-2,6-dimethyl-1,4-dihy­dropyridine-3,5-dicarboxylate (4f); Yellow solid; m.p. = 128–131 °C; IR (KBr, cm–1) .: 3325 (NH), 3058 (=CH2), 2978 (-CH, sp3), 1698 (C=O), 1673 (C=C), 1613 , 1490 (C=C, Ar),1208 (C-O); 1H NMR (400 MHz, CDCl3, ppm) d: 1.20 (t, J=7.2 Hz, 6H, 2CH3), 2.30 (s, 6H, 2CH3), 4.08 (q, 4H, 2CH2), 5.36 (s, 1H), 5.62 (s, 1H, NH), 6.96 (t, J=8.8 Hz,1H, ArH), 7.17 (t, J=7.2 Hz, 1H, ArH), 7.38 (d, J=1.6 Hz, 1H, ArH), 7.42 (d, J=8.0 Hz,1H, ArH). Diethyl 4-(4-(dimethylamino) phenyl)-2,6-dime-thyl-1,4-dihydropyridine-3,5-dicarboxylate (3k); Yellow solid; m.p. = 195–200 °C; IR (KBr, cm–1) .: 3321 (NH), 3094(=CH2), 2978 (-CH, sp3), 1695 (C=O), 1674 (C=C), 1492, 1613 (C=C, Ar), 1203 (C-O); 1H NMR (400 MHz, CDCl3, ppm) d: 1.24 (t, J=7.2 Hz, 6H, 2CH3), 2.32 (s, 6H, 2CH3), 2.89 (s, 6H, CH3), 4.08 (q, 4H, 2CH2), 4.88 (s, 1H), 5.65 (s, 1H, NH), 6.61 (d, J=8.4 Hz, 2H, ArH), 7.17 (d, J=7.2 Hz, 2H, ArH). Diethyl 2,6-dimethyl-4-(p-tolyl)-1,4-dihydropyri-dine-3,5-dicarboxylate (4o); White solid; m.p. = 139–140 °C; IR (KBr, cm–1) .: 3358 (NH), 2986 (-CH, sp3), 1695 (C=O), 1652 (C=C), 1203 (C-O); 1H NMR (400 MHz, CDCl3, ppm) d: 1.23 (t, J=7.2 Hz, 6H, 2CH3), 2.27 (s, 3H, CH3), 2.32 (s, 6H, 2CH3), 4.08 (q, 4H, 2CH2), 4.95 (s, 1H), 5.62 (s, 1H, NH), 7.01 (d, J=7.6 Hz, 2H, ArH), 7.17 (d, J=8.0 Hz, 2H, ArH). Diethyl 4-(2-fluorophenyl)-2,6-dimethyl-1,4-dihy­dropyridine-3,5-dicarboxylate (4g); Yellow solid; m.p. = 155–157 °C; IR (KBr, cm–1) .: 3332 (NH), 3104 (=CH2), 2982 (-CH, sp3), 1694 (C=O), 1651 (C=C), 1215 (C-O); 1H NMR (400 MHz, CDCl3, ppm) d: 1.19 (t, J=7.2 Hz, 6H, 2CH3), 2.31 (s, 6H, 2CH3), 4.03 (q, 4H, 2CH2), 5.24 (s, 1H), 5.70 (s, 1H, NH), 6.92(t, J=9.6 Hz, 1H, ArH), 6.99 (t, J=7.6 Hz, 1H, ArH), 7.08 (t, J=7.2 Hz, 1H, ArH), 7.30 (t, J=7.2 Hz, 1H, ArH). Diethyl 2’,6’-dimethyl-1’,4’-dihydro-[2,4’-bipyri-dine]-3’,5’-dicarboxylate (4n); Brown solid; m.p. = 188– 191 °C; IR (KBr, cm–1) .: 3172 (NH), 2982 (-CH, sp3), 1695 (C=O), 1670 (C=C), 1478, 1639 (C=C, Ar), 1212 (C-O); 1H NMR (400 MHz, CDCl3, ppm) d: 1.20 (t, J=7.2 Hz, 6H, 2CH3), 2.26 (s, 6H, 2CH3), 4.05 (q, 4H, 2CH2), 5.20 (s, 1H), 7.17 (d, J=5.2 Hz, 1H, ArH), 7.43(d, J=6.8 Hz, 1H, ArH), 7.62(t, J=7.2 Hz, 1H, ArH), 8.51 (d, J=4.4 Hz, 1H, ArH), 8.71(s, 1H, NH). Diethyl 4-(3-hydroxyphenyl)-2,6-dimethyl-1,4-di­hydropyridine-3,5-dicarboxylate (4m); White solid; m.p. = 168–170 °C; IR (KBr, cm–1) .: 3351 (NH), 2978 (-CH, sp3), 1651 (C=O), 1593 (C=C), 1217 (C-O); 1H NMR (400 MHz, CDCl3, ppm) d: 1.23 (t, J=7.2 Hz, 6H, 2CH3), 2.32 (s, 6H, 2CH3), 4.10 (q, 4H, 2CH2), 4.97 (s, 1H), 5.71 (s, 1H, NH), 6.62(d, J=7.2 Hz, 1H, ArH), 6.78(s, 1H, ArH), 6.87 (d, J=7.2 Hz, 1H, ArH), 7.07(t, J=7.6 Hz, 1H, ArH). Diethyl 4-(9,10-dioxo-9,10-dihydroanthra­cen-2-yl)-2,6-dimethyl-1,4-dihydropyridine-3,5-dicarbo­xylate (3p); Brown solid; m.p. = 195–200 °C; IR (KBr, cm– 1) .: 3293 (NH), 2983 (-CH, sp3), 1670 (C=O), 1637 (C=C), 1214 (C-O); 1H NMR (400 MHz, CDCl3, ppm) d: 1.20 (t, J=7.2 Hz, 6H, 2CH3), 2.32 (s, 6H, 2CH3), 4.05 (q, 4H, 2CH2), 5.33 (s, 1H), 6.17 (s, 1H, NH), 7.74(m, 4H, ArH), 8.01(d, J=8.0 Hz, 1H, ArH), 8.21 (m, 2H, ArH); 13C NMR (100 MHz, CDCl3, ppm) d: 15.23, 20.18, 41.58, 62.81, 102.05, 126.94, 130.68, 131.36, 132.62, 133.87, 134.18, 134.94, 135.25, 144.76, 152.63, 167.54, 181.94. Anal. Calcd. for C27H25NO6: C, 70.58; H, 5.48; N, 3.05. Found: C, 70.91; H, 5.51; N, 3.07. 3. Results and Discussion 3. 1. Characterization of MnFe2O4 Nanocrystals. Exploring new efficient strategies for the synthe­sis of organic compounds is a crucial approach for the expansion of science and technology. In this research, a magnetic nanoparticle catalyst was used in the Hantzsch reaction to prepare 1,4-dihydropyridine de­rivatives (DHP) in high yield. Superparamagnetic MnFe2O4 nanocrystals prepared from MnCl2 and Fe­Cl3 via a co-precipitation routine were confirmed and characterized by a vibrating magnetometer (VSM), a scanning electron microscope (SEM), Fourier trans­form infrared spectroscopy (FT-IR), and X-ray dif­fraction (XRD). Figure 2 shows the FT-IR spectra of magnetic MnFe2O4 nanoparticles. The FT-IR spec­trum shows an absorption band at about 550 cm–1 re­lated to the Fe-O stretching vibration. It also shows a band at 1621 cm–1, which is due to the O-H deforma­tion vibration, and a band at 3403 cm–1, which is from the O-H stretching vibration and is related to the sur­face hydroxyl groups and physisorbed water. Figure 2. FT-IR spectra of MnFe2O4 nanoparticles. Intensity and position of all peaks agreed well with the standard MnFe2O4 X-ray diffraction pattern (JCPDS chart No. 73-1964), and the particle size of about 33 nm was estimated from the line broadening at half maximum intensity (FWHM) at 2. = 35.31 using Debye–Scherrer equation; D = 0.9./ß Cos. (Figure 3). Figure 3. The X-ray diffraction patterns of the prepared MnFe2O4 NPs. The micro- and nanoscale size and morphology of MnFe2O4 were studied by scanning electron microscopy (SEM). As Figure 4 shows, MnFe2O4-NP were spherical particles with an average size in the range of 33–35 nm. VSM measures the magnetic properties of MnFe2O4 nanoclusters; hysteresis loops of MnFe2O4 nanoparticles were checked using a vibrating sample magnetometer (VSM). Figure 5 shows that the magnetization loop of MNPs exhibits superparamagnetic property with magnet­ization saturation of 57 emu g–1. The superparamagnetism of MNPs is very advantageous because the particles are magnetized in an existing external magnetic field but show no magnetization in the absence of a magnet. Therefore, they are highly dispersed in the reaction medium and al­low rapid penetration of reactants to the surface of the na­noparticles as an efficient catalyst. Figure 5. Magnetization curves of the prepared MnFe2O4 nanopar­ticles. 3. 2. Optimization and Generalization of Reaction Conditions To investigate the catalytic activity of MnFe2O4 MNP catalysts, their catalytic performance in the reaction of ethyl acetoacetate (1), 3-nitrobenzaldehyde (2), and am­monium acetate (3) was studied (see Scheme 1). Table 1. Optimization of the reaction conditions. a Entry Catalyst Solvent T (°C) Time Yield (mol %) (min) (%)b 1 2 EtOH 78 120 75 2 4 EtOH 78 80 85 3 5 EtOH 78 45 95 4 8 EtOH 78 45 95 5 5 H2O 80 45 80 6 5 THF 60 130 55 7 5 CH3CN 80 150 45 8 5 EtOH 60 80 80 9 5 EtOH 70 60 85 10 5 EtOH 78 45 95 11 5 EtOH 90 50 95 a Reaction conditions: aldehyde (1 mmol), ethyl acetoacetate (2.4 mmol), ammonium acetate (1.2 mmol). b Isolated yields. tained at a reflux temperature of 78 °C (Table 1, entry 10). In this study, the reaction for the synthesis of various 1,4-dihydropyridines using MnFe2O4 nano-particles as a heterogeneous catalyst was carried out to develop the scope and generality of this method con­sidering the optimal reaction conditions. In this step, Scheme 1. Model reaction for the synthesis of diethyl-1,4-dihydro-2,6-dimethyl-4-(3-nitrophenyl)pyridine-3,5-dicarboxylate. To optimize the reaction, the influence of different polar solvents tested for the preparation of 1,4-dihy-dro-2,6-dimethyl-4-(3-nitrophenyl)-pyridine-3,5-dicar­boxylate was first investigated. Among the solvents used, such as acetonitrile (CH3CN), ethanol, THF, and H2O, EtOH is the most suitable for this reaction according to the data in Table 1, entries 5–7, because it is polar and protic (Table 1, entry 3) Different amounts of catalyst (2, 4, 5, and 8 mol%) were used to optimize the amount of catalyst. It was found that 5 mol% catalyst loading gave the high­est yield in the shortest possible time (Table 1, entry 3). Therefore, the optimum catalyst loading was set at 5 mol% MnFe2O4 NPs. To determine the optimum reaction tempera­ture, the sample reaction was carried out at 60 to 90 °C (Table 1, entries 8–11). From the results, it was concluded that the highest yield in the shortest pos­sible time for the synthesis of the product was ob-some aromatic functionalized derivatives of aldehydes with both electron donating and electron withdrawing properties were used for the reaction. The correspond­ing results are summarized in Table 2. It is noteworthy that the benzaldehyde derivatives with electron-with­drawing functional groups, such as 3-nitrobenzalde­hyde and 4-chlorobenzaldehyde, etc., gave excellent yields of the corresponding products (Table 2, entries 1, 3–9), compared with an electron-donating group, which has a lower yield in the reaction (Table 2, en­tries 10–13). The structure of MnFe2O4 is a mixed spinel that has a close packing of face-centered cubic lattices. The octahe­dral (Oh) and tetrahedral (Td) sites are occupied by Mn2+ and Fe3+ cations. The Oh sites are occupied by Mnx2+Fe2–x3+, and the metal ions are coordinated with six oxygen atoms, and the Td sites are occupied by Mn1-x2+Fex3+(0 < x < 1), and the metal ions are coordinat­ed with four oxygen atoms. Since the Td sites have low Table 2. Synthesis of different 1,4-dihydropyridines from several aldehydes. Entry Aldehyde Product Time (min) Yield (%)b Ref. 4a 45 95 [50] 4b 40 90 [51] 4c 45 95 [51] 4d 40 92 [50] 4e 50 89 [50] 4f 40 91 [52] 4g 40 90 [52] 4h 45 92 [53] 4i 40 91 [52] Entry Aldehyde Product Time (min) Yield (%)b Ref. 4j 70 88 [51] 4k 60 85 [50] 4l 70 87 [35] 4m 50 90 [50] 4n 45 90 [54] 4o 80 82 [54] 4p 50 82 – – a Reaction conditions: aldehyde (1 mmol), ethyl acetoacetate (2.4 mmol), ammonium acetate (1.2 mmol), and Mn­Fe2O4 (5 mol%) in ethanol (5 ml) as solvent at 78 °C. b Isolated yield. steric hindrance compared to the Oh sites, the Mn atoms can act as more effective Lewis acid sites. The interaction between the Mn atoms of the nanocatalyst and the active sites of the substrate is more favorable.48,49 In agreement with the reaction mechanism proposed by some research groups,55–57 the role of the nanocatalyst can be explained as follows. The activation of the electro-philic components of the carbonyl groups of aldehyde and acetoacetate adducts by Lewis acid moieties on the mag­netic nanoparticles could be the driving force of the reac­tion. The first step involves the condensation of benzalde­hyde and the enol form of ethyl acetoacetate to form the Knoevenagel adduct. At the same time, another interme­diate is formed by the reaction of ammonia from ammoni­um acetate with the second equivalent of ethyl acetoace­tate. Subsequently, the two intermediates formed in the above steps are reacted with each other, and the subse­quent cyclocondensation and dehydration give the 1,4-di­hydropyridine as the target product. To emphasize the value of this study, the results ob­tained were compared with recently published work in Ta­ble 3. For this comparison, reaction conditions, reaction time and yield were considered in the synthesis of product 4b. It is worth noting that the efficiency of MnFe2O4 NPs as catalyst in this method is higher and easier to handle of the catalyst with a permanent magnet, and reusability of than some of the reported methods (Table 3, entry 11 ver-the catalyst five times without significant deterioration of sus entries 1–10). its activity. Table 3. Comparison of the synthesis of 1,4-DHP (4b) using MnFe2O4 NPs with methods described in the literature. Entry Catalyst (amount) Reaction conditions Time (min) Yield (%) Ref. 1 2 CeCl3·7H2O (10 mol %) [HMIM]BF4 (Excess) H3CCN, r.t., 90 °C 180 10 80 95 [20] [21] 3 [(CH2)4SO3HMIM][HSO4] (25 mol %) C2H5OH, reflux 67 90 [22] 4 MgBr2 (10 mol %) solvent-free, 100 °C 45 82 [23] 5 HClO4–SiO2 (50 mg) solvent-free, 80 °C 25 90 [28] 6 I2 (30 mol %) C2H5OH, r.t., 150 93 [32] 7 SiO2–SO3H (0.2 g) n-Hexane, 60 °C 330 90 [33] 8 H14[NaP5W30O110] (0.01 g) water, reflux 480 76 [35] 9 NaHSO4–SiO2 (5 mol %) H3CCN, r.t., 360 85 [36] 10 AlCl3·6H2O (10 mol %) solvent-free, 60 °C 60 80 [38] 11 MnFe2O4 NPs (5 mol %) C2H5OH, 78 °C 45 95 This work The reusability of the magnetic MnFe2O4 nanocata­lyst, as shown in Figure 6, was investigated using the de­sired reaction under optimized conditions. The catalyst was isolated by an external magnet after each reaction and then carefully washed 3–4 times with ethanol and acetone. The catalyst was dried at a temperature of 60 °C and used five times without significant loss of catalytic activity. The weight percentage recovery of the catalyst after the reac­tion shows a negligible weight loss of about 0.3% and > 99.7% of the catalyst was recovered. Figure 6. Reusability of the MnFe2O4 nanoparticles as catalyst. 4. Conclusion In this protocol, we synthesized superparamagnetic MnFe2O4 nanoparticles and used them as an efficient cat­alyst in the synthesis of 1,4-dihydropyridines by one-pot condensation of aldehydes, ethyl acetoacetate, and ammo­nium acetate. The advantages of this method include high purity, good yield, simple work-up procedures, mild reac­tion conditions, short reaction times, magnetic separation Acknowledgments We thank the Research Council of the University of Kashan for supporting this work through grant No. 159148/71. Declarations: Funding: This research was funded by University of Kashan by grant No. 159148/71. 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DOI:10.1016/S1872-2067(10)60252-5 52. E. Priede, A. Zicmanis. Helv. Chim. Acta 2015, 98, 1095-1103. DOI:10.1002/hlca.201500009 53. Y. Watanabe, K. Shiota, T. Hoshiko, S. Ozaki. Synthesis 1983, 1983, 761-761. DOI:10.1055/s-1983-30505 54. J. Safari, F. Azizi, M. Sadeghi. New J. Chem. 2015, 39, 1905- 1909. DOI:10.1039/C4NJ01730G 55. M. José Climent, A. Corma, S. Iborra. RSC Adv. 2012, 2, 16- 58. DOI:10.1039/C1RA00807B 56. M. Leonardi, M. Villacampa, J. C. Menéndez. Chem. Sci. 2018, 9, 2042-2064. DOI:10.1039/C7SC05370C 57. J.-P. Wan, Y. Liu. RSC Adv. 2012, 2, 9763-9777. DOI:10.1039/c2ra21406g Povzetek V prispevku je opisana ucinkovita sinteza nekaterih 1,4-dihidropiridinov s pomocjo kondenzacijske reakcije aldehidov, etilacetoacetata in amonijevega acetata, v prisotnosti superparamagnetnih nanodelcev manganovega ferita pri 80 °C. Prednosti tega postopka so selektivnost, visoka cistost produktov, odlicni izkoristki, kratki reakcijski casi, enostavna izolacija in okolju prijazni pogoji sinteze. Poleg tega je mogoce katalizator izolirati in veckrat ponovno uporabiti, ne da bi s tem bistveno zmanjšali izkoristek reakcije. Except when otherwise noted, articles in this journal are published under the terms and conditions of the Creative Commons Attribution 4.0 International License DOI: 10.17344/acsi.2021.7245 Scientific paper Opportunities for Learning: Analysis of Czech Lower-Secondary Chemistry Textbook Tasks Karel Vojír1,2 and Martin Rusek1,* 1 Charles University, Faculty of Education, Department of Chemistry and Chemistry Education, Magdalény Rettigové 4, 116 39 Praha 2 Charles University, Faculty of Education, Department of Biology and Environmental Studies, Magdalény Rettigové 4, 116 39 Praha * Corresponding author: E-mail: martin.rusek@pedf.cuni.cz Received: 10-29-2021 Abstract Tasks in Czech lower-secondary chemistry textbooks were analysed to describe their position in textbook chapters, re­quired response type, overall task nature, as well as cognitive requirements. The results showed older textbooks contain task banks at the end of chapters suggest a transmissive teaching paradigm, whereas newer textbooks containing tasks within the chapters. As far as the nature of the tasks is concerned, a strong stereotypical genre was found in the chemistry textbooks. Most of the textbook tasks require open-ended answers and target: factual and conceptual knowledge remem­bering or procedure application. The authors therefore suggest several changes to the tasks, including their position in chapters, cognitive difficulty as well as the required response type in order to meet chemistry education goals. Keywords: tasks; textbook analysis; chemistry education; lower-secondary school; ISCED 2 1. Introduction Changes in society, together with an increasing amount of information, place demands on education’s transformation. The demands for effective participation in society led to key competences for lifelong learning formu­lation.1 As part of the educational process, the emphasis on students was also strengthened. A key element in thinking about education and thus in changing the curriculum is the effort to move from a traditional approach exercising algorithm-based, lower-order cognitive skills to an ap­proach stressing higher-order cognitive skills associated with asking questions, critical or system thinking, decision making and problem solving.2 Changes in access to educa­tion are gradually being incorporated into the curriculum at a national level. A responsible approach to the changes requires an evidence-based approach. Unfortunately, many curriculum reforms take place without adequate re­search evidence or mapping educational reality.3 Although the objectives and education content are set out in state curricular documents, textbooks play an important role in the implementation of teaching. These bring a concrete transformation of educational content and, compared to the higher levels of the curriculum, rep­resent the concretization of the intended curriculum4 into concrete activities. They thus represent not only the selec­tion of particular subject-matter, but also its transforma­tion using specific methods and the form of its mediation. The role of textbooks is twofold: they can be used directly by students as well as by teachers for lesson prepa­ration – which seems to be one of their domineering role.5 Textbooks’ effect is given by their structural components’ utilization. The main structural components in textbooks to mediate the educational content is naturally the text.6 The interpretive text itself is not yet a learning tool, as it does not evoke a specific learning activity. The emphasis on the direction of students’ own learning activities also comes to the fore with regard to the science (chemistry) education objectives, namely science literacy,7 which in­cludes not only the mastery of certain knowledge, but also the development of various science-oriented skills. In or­der for textbooks to perform their function, they cannot be perceived only as educational content intended for sim­ple memorization. Functional textbooks are an aid that helps to condition the diverse learning situations leading to the students’ development in a broad sense. Students’ attention as well as their activity is directed by learning tasks. They represent the students’ opportunity to learn8 and are the key to student activation. Since there is a relationship between questions and answers,9 the form of questions and their context determine students’ activity, i.e. what about and in what way they think. The authentic­ity and context of the tasks also influence the students’ skill in solving problems10 and tasks should be relevant not only to the teaching process but also to address authentic issues11. Students’ activation can be accomplished by using textbook. Xin12 found that task distribution in textbooks affects students’ ability to solve certain types of tasks. Yet, as argued by Yang et al.8, that task representation in text­books does not directly show the textbooks’ quality. Through textbook tasks, students are also presented with the nature of science (NoS), as they are directed to­wards the way of thinking in a given scientific discipline.13 In addition to tools to develop general competences, text­books can also be a means of scientific enculturation. This puts pressure on the form of textbook tasks so that the NoS presented to students is consistent with real science. As pointed out by Wood,14 unlike the classical school concept of natural science teaching, in real chemical (science) problems there is usually more than one correct solution, and in many cases only the most appropriate and achieva­ble solution is sought. In this respect, “school tasks” fre­quently designed to evaluate students deflect from the real tasks as a certain response is needed in order to grade the answer. It is natural to expect these tasks to be placed inside the chapters as they represent the learning content itself, whereas the role of tasks placed at the end is to evaluate the learning progress. Mediating real science practices in school teaching is one of science education’s key challeng­es.15 As shown by Andersson-Bakken et al.13, a number of studies focusing on textbook tasks showed a specific cul­ture is evolving in individual school subjects. Bakken and Andersson-Bakken16 even identified textbook tasks to be a specific genre not only in science textbooks. Textbook tasks are thus influenced by the characteristic standards of their formulation, purpose and educational objectives. The field-specific culture regarding used tasks is further strengthened by textbook use by teachers. A significant proportion of teachers was found to use textbooks for teaching preparation.5, 17 Teachers consider textbooks to be the main source of educational con­tent.18–19 At the same time, they also adopt the teaching concept from textbooks and using textbook models 20, teaching methods and organizational forms suggested in textbooks.21 Since textbooks thus represent curricular ma­terial for many teachers (cf.22) and the conception gives teachers a certain example how to deliver its content.21 Textbooks thus represent a potentially implemented cur­riculum23 with a direct impact on students. Elaborating on textbook components then plays a vital role in under­standing the teaching-learning process.35 The abovementioned trend was observed24 with sig­nificant similarities between the questions used by chem­istry teachers in their lessons and the questions in the text­books they use. Efforts to innovate the curriculum can thus be significantly limited by the lack of textbook inno­vation. Although textbooks follow different curriculum concepts with different emphases and educational objec­tives, this is not always clearly reflected in their content. Even in textbooks following a reformed curriculum de­claring a focus on developing students’ competences, only a limited proportion of tasks targeting higher-order think­ing was found.25 Orientation to tasks, i.e. student activity, remains implicit.26 As seen from the above, by analysing textbook tasks, it is possible to identify both the learning opportunities presented to students and the NoS understanding that textbooks convey. However, these aspects have so far re­ceived rather partial attention in science textbook research internationally.27 2. Research Aim and Research Questions As suggested above, from a certain point of view, task elaboration offers a model of curriculum implementa­tion as seen via the student activity perspective. Textbooks therefore become a tool influencing curriculum concep­tion (when textbooks are being used in education) and tasks in them reflect the potential for active learning. Textbook tasks reflect the potential of students’ ac­tive learning given the textbook is being used. As research shows, textbooks are an important support for teachers in their work, showing them not only what to teach, but also what methods to use,20,22,28 therefore the use of textbook does not mean it is only used in the class with students, but also for teachers’ lesson preparation. Understanding cur­rent tasks is thus an important element for understanding the current state of the concept of teaching. If certain de­flections from an ideal appear, this information is a start­ing point for innovations. For these reasons, the aim of the research was to find out what tasks are contained in lower-secondary school chemistry textbooks and What differences there are between the particular lower-secondary school chemistry textbooks in terms of tasks. These aims were further specified by the following research questions: – In which parts of lower-secondary chemistry text­books are tasks placed? – What type of response is required in the lower-sec­ondary chemistry textbook tasks? – What are the required cognitive processes and the re­quired types of knowledge in the in lower-secondary chemistry textbook tasks? The placement of tasks in textbooks shows their au­thors’ intended conception of chemistry subject-matter presentation – student activation. There are basically two scenarios: Tasks either induce students’ active learning and bring the initial motivation and student activation leading, to their acquisition of new subject-matter, or tasks are de­signed to fixate subject-matter transmitted by a teacher, therefore conclude non-active methods. Different types of response required in assigned tasks correspond with a different type of thinking and de­velopment of problem-solving strategies. While close-end­ed tasks require analysis and differentiation of individual submitted claims, open-ended tasks aim to create an indi­vidual response. Each type of task therefore aims at a dif­ferent science education or literacy goal and are supposed to be balanced. Similarly, the focus on different cognitive processes and types of knowledge shows the emphasis of textbooks on students’ specific thought operations and skills. At the same time, they contribute to the formation of a specific picture of scientific knowledge. 3. Methods To answer the research questions, a quantitative ap­proach based on textbook task closed-coding was chosen. 3. 1. Research Sample The most commonly used chemistry textbooks (text­books used at less than 5% of schools were excluded) for lower-secondary schools in Czechia5 were analysed. With regard to one textbook’s publication of a new edition, this textbook was included in the sample too. It is a direct fol­low-up to the previous series, and it is therefore reasonable to assume that it will continue to be used, replacing the earlier edition. The analysed textbooks are referenced us­ing abbreviations – see Table 1. All the analysed textbooks are intended for the 8th grade. These are textbooks de­signed for teaching in the first year students encounter chemistry in the Czech Republic. By analysing these text­books, the basis of chemistry education is thus evaluated. All analysed textbooks focus on six thematic areas from Framework education programme for basic education (state Table 1: Analysed textbooks curricular document): Observation, experimentation and safety, Mixtures, The molecular composition of substances and chemical elements, Chemical reactions, Inorganic com­pounds and Chemistry and society. In Textbooks FR and nFR attention is also paid to the thematic area Organic compounds. Only the topic of fuel is included in the text­book nFR in addition. The textbook FR includes the the-matical area Organic compounds topics hydrocarbons and hydrocarbon derivates and less attention is paid to the top­ic of chemical reaction. Entire textbooks were analysed to obtain a comprehensive insight covering both the content of individual chapters as well as the complementary tasks. 3. 2. Procedure and Research Tools In the first step, all textbook tasks in terms of the means of inducing, guiding and supporting students’ learning activities29 were identified and quantified. The identified tasks were further evaluated with regard to their position in the chapter/unit, required response type, cog­nitive skills and types of knowledge. The coding was inspired by Gillette and Sanger30 in the categories that corresponded to the component distri­bution in textbooks as identified by the analysis of text­book didactic equipment.31 The following categories were identified: – Inside the chapter – Tasks are integrated between im­age and text components and are thus part of the ex­planatory part of the textbook. – End of page – Tasks are used separately from other text­book components on the page. They are placed in the context of the chapter/unit, but after the exposition part. – Page margins – Tasks are placed visually separately within the chapter/unit among other additional infor­mation. – Tasks banks – Tasks are classified in separate parts of the textbook separately from the other components included in the chapters. Closed-ended tasks and open-ended tasks were identi­fied as the type of response requested. In this sense, tasks were distinguished only according to the form of the re­quested response. The response type was further specified on the basis of task typologies according to Kalhous and Obst32 and Jerábek and Bílek33. The categories were the following: Textbook title Authors Year of publication Publishing house Referenced in the text Základy chemie 1 Beneš, Pumpr, & Banýr 1993 Fortuna ZCH Základy praktické chemie1 Chemie 8 Chemie 8 Chemie 8 (new generation) Beneš, Pumpr, & Banýr Škoda, & Doulík Mach, Plucková, & Šibor Škoda, & Doulík 1999 2006 2016 2018 Fortuna Fraus Nová škola Fraus PCH FR NŠ nFR – Closed-ended tasks – multiple choice – multiple-an­swer, multiple choice – single-answer, dichotomous, coupling/pairing, ordinance, – Open-ended tasks – short answer, gap filling, table fill­ing, long answer. Based on the previous qualitative analysis of text­book tasks,34 tasks with no required verbal response (non-response tasks) were further coded. Typically, these are tasks consisting of an instruction, e.g. observation, but it is not specified that students should express their obser­vations verbally. To assess the tasks’ cognitive and knowledge do­mains, Revised Bloom‘s taxonomy35 was used. Despite its criticism (e.g.36), this taxonomy is used for curricular ob­jectives37–39 as well as textbook task evaluation.40–41 For this reason, it was also used for the purpose of this study. The highest task potential was considered, and the re­quired student activity was decisive for task categorization. For cognitive processes, the categories: remember, under­stand, apply, analyse, evaluate and create were used. In the knowledge domain, factual, conceptual, procedural and metacognitive knowledge were considered.35 3. 3. Data Analysis The data was processed in MS Excel. To verify that the coding was correct, a randomly selected sample of 10% of the tasks was coded by a second researcher and a match was assessed using the Cohen’s kappa coefficient. The kappa values in the individual categories ranging from 0.886 to 1 were found, which can be interpreted as almost perfect or perfect agreement (cf.42) in the 95% confidence interval. With regard to research issues, the Table 2: Number of tasks in lower-secondary chemistry textbooks Textbook Tasks Laboratory Tasks from in activities other fields total (interdisciplinary) ZCH 561 157 4 PCH 320 74 3 FR 651 181 103 NŠ 517 73 16 nFR 695 163 131 data was further quantitatively processed and descrip­tively evaluated. 4. Results 4. 1. Frequency of Tasks in Textbooks In the chemistry textbooks for the 8th grade of low-er-secondary school, relatively high numbers of 695 to 1000 tasks were identified (see Table 2). The different total of tasks in each textbook is mainly related to the different textbook scopes and the topics in­cluded. The highest range for chapter pages in textbooks was found in nFR (120), FR (116) and ZCH (114), which corresponds to the overall higher number of tasks (see Ta­ble 2). In contrast, the lowest number of tasks was found in the PCH textbook, which has the lowest range of chapter pages (62). Although the average subchapter range corre­sponding to one comprehensive teaching unit ranges from 1.6 to 2.8 pages, the average number of tasks included in one subchapter is similar among all analysed textbooks (see Table 3). Differences were identified among the tasks in the individual textbooks. While in most textbooks, about a quarter of the tasks (ZCH and FR – 28%, PCH and nFR – 23 %) fall into the laboratory activities category, i.e. exper­iment or tasks related to the demonstration of substances and their properties, in the NŠ textbook there are only 14%. Significant differences were also identified in the classification of tasks related to educational content of oth­er fields than chemistry. These were found mainly in FR (16%) and nFR textbooks (19%), while other textbooks contain only a few of these tasks (see Table 2). 4. 2. Task Locations Significant differences were identified in the location of the tasks in the textbooks. While earlier published text­books tend to place the tasks at the end of the page (ZCH) or in the task bank (PCH), in the newly published text­books, FR, NŠ and nFR, the tasks being included directly in the chapters prevails. It shows a shift towards greater individual component interconnectedness in the chapters of newer textbooks. In FR and nFR textbooks, page mar­gins are also used as the only ones for assigning additional tasks (see Table 4). Table 3: Subchapter in textbooks and the tasks they contain Textbook Number Number of Mean chapter Mean number of chapters sub-chapters length (number of pages) of tasks in sub-chapters ZCH 9 41 2.8 13.7 PCH 9 40 1.6 8.0 FR 17 55 2.1 11.5 NŠ 6 43 1.9 11.2 nFR 18 55 2.2 12.3 Table 4: Task location in the textbooks Textbook Inside the Page margins End of page Tasks bank chapter (%) (%) (%) (%) ZCH 19 0 69 12 PCH 22 0 0 78 FR 62 36 0 2 NŠ 68 0 14 18 nFR 61 36 0 3 In the tasks located in each section, differences in the tasks’ nature were found in terms of their link with labora­tory activities. The tasks included inside the chapters are practically exclusively related to experimental activities in the ZCH and PCH textbooks (ZCH – 95%, PCH – 100%). Other textbooks include theoretical tasks (tasks related to laboratory activities: FR – 41%, NŠ – 17%, nFR – 33%) as a reaction to more safety restrictions as well as plausible usefulness.43 In all the analysed textbooks, the presence of task banks was found. However, their classification in relation to the content of the chapters varies. While in the ZCH and PCH textbooks, task banks are connected with the chapter (in PCH textbooks, it is the main method of consolidatingthe course), in the FR, NŠ and nFR textbooks, there is also a separate section at the end of the book devoted only to tasks that serve either to repetition of the whole year sub-ject-matter or do not have an direct connection to the sub-ject-matter from the chapters. In the NŠ textbook, 41% of the tasks are placed in the task banks. In the FR and nFR textbooks, all tasks included in task banks are classified in this way and are typically tasks related to laboratory activi­ties (FR – 86%, nFR – 100%). However, compared to other textbooks, the number is low in these categories (cf. Table 4). Tasks linked to laboratory activities are represented in task banks and other textbooks (except for inside the chap­ter placement, this is the second typical classification of lab­oratory activities). 58% of tasks placed in task banks in the ZCH textbook are linked to laboratory activities. However,in the PCH textbook, this is only 1%. In the NŠ textbook, 14% of tasks are associated with laboratory activity in the task bank, which is related to the overall lower proportion of these tasks in this textbook (see Table 2). 4. 3. Requested Response Type In view of the response type required, the analysed textbooks are very similar. In all the textbooks, open-end­ed tasks significantly dominate. They account for 70 to 92% of all assigned tasks (see Table 5) in textbooks. With the exception of FR and nFR, the most frequently required answers are represented only by individual words or phras­es, mainly in the form of free short answers, or by complet­ing a table or filling the gaps. Gap-fill and table-fill is typi­cal, especially for the ZCH textbook (both 5% of tasks). In NS textbooks, 3% of tasks are table-fills, in the case of oth­er textbooks, adding answers appears only for single tasks. In the FR and nFR textbooks, this type of task is non-exist­ent, but open-ended tasks with a long answer dominate, representing more than half of all in the textbooks of the assigned tasks. None of the close-ended task types (multiple-answer, single-answer, dichotomous, coupling/pairing, ordinance) were represented in any textbook in more than 5% of cas­es. The most close-ended tasks were found in the ZCH and PCH textbooks (see Table 5). Both textbooks include tasks of all types. However, coupling/pairing tasks in the ZCH textbook (3%) and multiple choice – single-answer tasks in the PCH textbook (4%) were represented more than once. All types of close-ended tasks were also identified in the NŠ textbook, but only in limited numbers, dichoto­mous tasks (2%) are the most abundantly represented. Di­chotomous tasks are also the most abundant type of close-ended tasks in the FR and nFR textbooks (3%). These contain the lowest variability of close-ended tasks, non-dichotomous tasks did not occur or were represented by only one task. Table 5: Tasks in textbooks according to the type of response requested Textbook Long Short Tables and Closed-ended Non-answer answer (%) answer (%) gaps filling (%) tasks (%) tasks (%) ZCH 31 30 9 9 20 PCH 29 38 2 10 20 FR 52 40 0.5 4 4 NŠ 32 46 5 5 12 nFR 51 42 0.6 3 3 A significant proportion of tasks with no required answer were identified in the ZCH, PCH and NŠ textbooks (see Table 4). In all textbooks, most of these tasks are relat­ed to laboratory activity (ZCH – 87%, PCH – 98%, FR – 61%, NŠ – 87%, nFR – 79%). The form of laboratory activ­ities is thus reflected in the non-answer tasks. While in the ZCH, PCH and NŠ textbooks, most tasks associated with laboratory activities do not require a response (62%, 86% and 71% of tasks related to laboratory activities) and are only instructions, in the FR and nFR textbooks, tasks asso­ciated with laboratory activities are typically open-ended (83 and 82% respectively). Close-ended tasks follow labo­ratory activities only in the FR and nFR textbooks, in which they represent 9% of tasks related to laboratory ac­tivities and are limited in ZCH textbooks (3%). 4. 4. Cognitive and Knowledge Domains in Textbook Tasks The tasks’ cognitive domain analysis also showed significant similarities between textbooks. In all the text­books, a significant prevalence of tasks focused on lower cognitive operations was identified. The tasks focused on analyse, evaluate and create were not identified in more than 2% of the tasks (see Figure 1), i.e. textbooks practical­ly do not aim at these operations. Tasks targeting understanding account for the high­est share in all textbooks (50–63%). The lowest proportion of these tasks was identified in NŠ textbooks, thanks to a higher representation of tasks aimed at remembering (31%). About a third of the tasks focused on remember were also found in FR and nFR textbooks (37 and 32% respec­tively). These textbooks however contained the highest variety of tasks compared to the other textbooks. A higher proportion of remember-oriented tasks is influenced by a significantly higher proportion of tasks focused on other educational areas (see Table 2), most of which (79 and 61% of these tasks) aim at remember. The remaining tasks in other educational areas in these textbooks are almost ex­clusively aimed at understand. The low representation of application-oriented tasks is also specific to FR and nFR textbooks. Although in other textbooks these tasks repre­sent 1826%, in FR and nFR textbooks it is only 5 and 4% respectively. This difference partially reflects the different classification of the topic of chemical calculations, which in the case of the FR and nFR textbook series are included in the textbook for the 9th grade which was not analysed. While in the ZCH and PCH textbooks the numerical tasks represent 8% and in the NŠ textbook they represent 7%, in the FR and nFR textbooks they constitute less than 1% of the tasks. However, the main difference is due to a different Figure 1 Proportion of tasks in textbooks according to required cognitive operations Figure 2 Proportion of textbook tasks according to required knowledge types approach in the chapters focused on selected substances. Most application tasks are focused on chemical nomencla­ture and balancing chemical equations. These skills are significantly less stressed in FR and nFR textbooks, and on the contrary, the connection in the wider context and with everyday life is emphasized significantly more. The abovementioned differences of FR and nFR text­books are also shown in the tasks according to the required types of knowledge. A lower proportion of chemical no­menclature and equations balancing tasks, respectively calculations, is reflected in a lower proportion of tasks re­quiring procedural knowledge. These represent only 3 and 4% of the tasks in FR and nFR textbooks, compared to 16–22% in other textbooks (see Figure 2). As far as the knowledge types are concerned, signif­icant similarities were identified between the textbooks’ knowledge. Factual knowledge prevails in all textbooks (47–70% of tasks), and the focus on conceptual knowl­edge was found in about a third of the tasks. No metacog­nitive knowledge tasks were identified in any of the text­books. The higher proportions of tasks requiring factual knowledge in FR and nFR textbooks partly reflect the in­clusion of tasks focused on other educational areas / school subjects, which target factual knowledge (94% and 90% of these tasks, respectively). Even if these tasks were removed from the sample, the proportion of factual knowledge tasks is still the highest of all analysed textbooks (65 and 60%, respectively). Table 6. Most frequent tasks in textbooks 5. Discussion The analysed lower-secondary chemistry textbooks contain a relatively high number of tasks, which is also typical for textbooks in other countries.16 On average, there are 8 to 14 tasks per subchapter/unit. Due to the usu­al number of two teaching lessons per week,44 the subchap­ters basically represent the content of one lesson. As the teaching content does not consist only of tasks, the text­books offer (1) more tasks for teachers to choose from and/or (2) tasks of a nature that allow for short, quick an­swers. Based on Bakken’s45 findings, it is reasonable to ex­pect teachers to have the tendency to cover all textbook content. Previous research5 showed teachers’ use questions and tasks from textbooks often. Therefore, the impact of the tasks is immense. As long as the tasks are an integral part of the textbook (mainly the case of the newer text­books), those which fit this approach need to be short. This corresponds to the results of the qualitative analysis34 – the tasks in Czech chemistry textbooks typically consist only of a question possibly supplemented by a short accompa­nying text (maximum a few short sentences). Due to this concept, chemistry textbooks lack tasks focused on work­ing with content representations and a more complex con­text. The results of this study fully agree with Bakken and Andersson-Bakken.16 From various angles, textbook tasks are a very specific genre whose concept, however, is worth reconsidering. Although most of the assigned tasks have Textbook Remember factual Understand factual Understand conceptual Apply procedural knowledge (%) knowledge (%) knowledge (%) knowledge (%) ZCH 15 31 26 21 PCH 12 31 32 19 FR 35 35 22 3 NŠ 27 29 21 15 nFR 31 34 26 3 A more detailed analysis taking into account both cognitive and knowledge domains identified only four types of tasks that occur in at least 5% of textbook tasks (see Table 6). In the ZCH and PCH textbooks, the most commonly represented task types are the ones on under­stand factual knowledge and understand conceptual knowledge. The FR, NŠ and nFR textbooks also include tasks focused on remember factual knowledge more abun­dantly. Tasks from the other fields included mainly in FR and nFR (see Table 2) textbooks are reflected in this cat­egory of tasks. When analysing only tasks focused on chemical content, the tasks targeting factual knowledge remembering in these textbooks represent 27 and 24% re­spectively. an open form of response which seems to require a more complex student answer, if all of them were used, there would not be enough time in class for thorough thought or answer. The culture of using learning tasks is therefore very probably not exploiting the potential of open-ended tasks fully: teachers transform the questions into the for­mat which fits the time constraints46 or leave students a very short time to think about the answer after asking a question.47 Either way of using the tasks does not leave enough space for the students’ learning activity itself and does not provide learning opportunities for all students, thus not fully exploiting the tasks’ potential. Although the analysed textbooks are similar as far as the structural components are concerned,31 connecting these components within the chapters differs significantly. Task analysis then offers better understanding to the text­book authors’ conception of chemistry teaching reflected in their textbook. In this study, this was revealed in the textbooks’ focus on students’ activity, accent on science lit­eracy and, a domain dominated by science education, ex­perimental activities. The positioning of tasks suggests the required course of the learning activity. In the formerly published ZCH and PCH textbooks (first published before the contempo­rary curriculum was released), tasks are placed at the end of the chapter. This in itself is an indicator of a proposed learning conception which builds upon an initial trans­missive transfer of knowledge to end with questions and task designed to evaluate students’ knowledge, i.e. the shape of textbooks from the 90’s.48 In newer textbooks, tasks are typically placed inside chapters which suggest their authors’ attempt to activate students earlier, during new knowledge presentation. The concept of scientific literacy spread mostly thanks to the PISA project. Naturally, textbooks published before 2000 did not have it incorporated in them. Never­theless, as curriculum analysis showed, despite not explic­itly being named, the content of scientific literacy is re­flected in the national curriculum for chemistry.49 The declaration that all the chemistry textbooks with a valid approval clause,5 are in accordance with the curriculum, is not fully valid. It is the interconnectedness of topics and especially their links to other fields of human knowledge which is (or is not) reflected in textbook tasks. Only the textbooks by one publishing house (FR) contain such links. Other textbooks seem to concentrate on chemistry only, containing interdisciplinary relations only in individ­ual cases. Another aspect of scientific literacy specific to sci­ence disciplines is the accent of experimental activities. Notwithstanding the safety issues, especially in chemistry experiments, see e.g.50, educational experiments are cor­nerstones of science teaching. In this respect, chemistry textbooks with the exception of NŠ textbook maintain the status quo, containing about 25% of the tasks aimed at lab­oratory activities. However, laboratory activities in Czech chemistry textbooks are typically only demonstrations performed by the teacher and not experiments in the true sense of the word, i.e. laboratory activities performed by students.34 This explains Rusek et al.’s findings43 about demonstrations predominating all chemistry experimen­tal activities in chemistry lessons in Czechia. Moreover, in this particular respect, there seems to be a contradiction between textbook content and teachers’ actions. Textbook use reported worldwide shows several aspects teachers from different countries share. They were reported to fol­low textbooks for the content, i.e. subject-matter (e.g.18,19), used analogies,20 questions24 and also methods21 – some­times even feeling under pressure, considering them com­pulsory.51 Yet, as far as laboratory activities are concerned, despite textbooks being found to contain a high share of experimental tasks, such activities are only seldom.43 Due ZCH, PCH and NŠ’s focus on identical topics, the lower proportion (although the absolute number is still high) of the laboratory tasks in the textbook NŠ seems to reflect a school practice more. The fact this textbook is most pre­ferred by majority of Czech teachers5 indicates a shift in thinking about didactic transformation in chemistry edu­cation. There is also a thought deviation from school ex­periments visible, which, after all, corresponds to the over­all trend of chemistry teaching.44 This is, naturally a serious finding as it may lead to students’ underdevelopment in one of crucial parts of chemistry/scientific literacy (cf.52– 53). As shown in the requested task answer analysis as well as the targeted cognitive and knowledge domain, the genre is not represented only by the tasks’ structure. This inertia seems to block any distinct change. A similar phe­nomenon was also captured by Abd-El-Khalick et al.54 in American science textbooks. The impact of former curric­ulum is therefore being strengthened not only by teachers’ relationships to the familiar and well-tried, but also by even new textbooks taking over former structures, includ­ing teaching conceptions (see55), including tasks. Although the intended curriculum declares a shift in teaching con­ception, the school reality, i.e. the implemented or experi­enced curriculum56 reflects this shift only in a limited way. The required task response type analysis showed a strong task culture. All the analysed textbooks significant­ly predominate open response type tasks, which represent 70–96% of all tasks. In all analysed textbooks, both tasks with a required short and long answer are abundantly rep­resented. In contrast, close-ended tasks are represented only from 3–10%. Although the use of open-ended tasks can be viewed positively due to the students’ need to for­mulate their own response, thus also develop students’ communication competence (see e.g.57) this significant imbalance in all textbooks points to the one-sidedness of assignment wording. Textbooks thus do not provide stu­dents with tools to adopt strategies for comparing, evaluat­ing and selecting the options offered. In contrast, the cur­rently close-ended tasks are widely used for both admission procedures and national and international surveys aimed at students’ scientific literacy.7,58 Differences between textbooks in the area of re­quired response type were shown, in particular for tasks related to laboratory activity. While in the ZCH, PCH and NŠ textbooks, tasks associated with laboratory activities are typically formulated without specific verbal response requirements, FR and nFR textbooks typically use open-ended tasks. Students’ attention in these textbooks is more clearly directed in the case of these tasks, so it is pos­sible to guide them.29 The high homogeneity of the tasks was also identi­fied in their cognitive demands. All analysed textbooks are dominated by tasks aimed at understanding, followed by tasks focused on remembering and application. On the contrary, tasks targeting higher-order cognitive operations are almost neglected in textbooks (only 1–3% of tasks were identified). This fact, however, is in contrast with the re­quired students’ problem solving skills,59 as these students’ skills remain underdeveloped,60 probably partly because of the lack of emphasis on problem solving in the textbooks. This is another aspect of textbooks only partial align­ment with the national curriculum. Compared to the cur­ricular objectives’ cognitive demands (see38), it was ex­pected that most tasks would target understanding followed by remembering and application. Nevertheless, the share of objectives on evaluation is also significant. Al­though outcomes in the state curriculum target mostly concepts (53%), followed by facts (28%), procedures (21%), but also metacognitive knowledge (20% cit.38). The textbook task analysis, however, revealed that factual knowledge prevails (47–70% of tasks), conceptual knowl­edge is covered by about one third of the tasks. With the exception of the FR and nFR textbooks, procedural knowl­edge is represented in about 20% and no metacognitive knowledge tasks were identified in any of the textbooks. Given the fact the procedural knowledge was repre­sented by considerably one-sided tasks (training nomen­clature, balancing formulas and performing calculations), even this category which, judging by the numbers, fits the curriculum needs to be considered only partially fitting. When combined, most objectives – translated in tasks – are supposed to target understanding concepts, re­membering facts, applying procedure and evaluating con­cepts. The oldest textbooks meet the curriculum in their focus on understand conceptual knowledge. The FR, NŠ and nFR textbooks include tasks focused on remember factual knowledge, especially when only the chemistry-ori­ented tasks in FR and nFR are considered. None of the textbooks addressed concept evaluation. This phenomenon, however, is not caused by the na­ture of the field requiring only a certain type of questions or tasks. Kácovský et al.’s38 analysis showed Polish or Slove­nian curriculum contains a wider range of objectives than the Czech chemistry curriculum. In cases when even cur­riculum objectives do not contain larger objective variabil­ity than the students’ cognitive activities’, it is not reasona­ble to expect them in textbooks. This suggests a certain chemistry education culture in Czechia. To confirm, anal­ysis of Polish or Slovenian textbooks tasks needs to be per­formed. Also, in their analysis of Turkish chemistry text­books, Zorluoglu, et al.41 identified not only tasks targeting students’ remembering , understanding and application as expected. A significant proportion of tasks targeting eval­uation were identified. This is especially surprising in con­nection to the Turkish curricular objectives’ analysis37 which showed the intended chemistry curriculum not to significantly target this cognitive domain. This example then shows a textbooks’ positive role in developing the state curriculum in the required manner. Rethinking edu­cational content and its didactical transformation with re­gard to active learning and the objectives of science educa­tion is thus shown to be crucial in the creation of new textbooks. The fact that the Czech chemistry textbooks which claim their alignment to the curriculum do not even con­tain the range from the curriculum, is alarming. Consider­ing the textbooks’ effect and the number of tasks, more elaboration, not less, was expected from the textbooks. This finding then stresses an important issue in chemistry (science) education. Despite various efforts to change sci­ence education at the level of the intended curriculum, the continued emphasis on knowledge transfer and thus the low proportion of tasks accentuating higher-order cogni­tive operations is also shown in other countries (cf.26). However, as shown by Gillette and Sanger,30 many studies found that the ability to solve tasks requiring simple knowledge equipment, or their direct application, does not translate into the ability to solve tasks requiring higher cognitive operations. Understanding the concepts of edu­cational content does not in itself lead to the development of skills to analyse, evaluate or create. Surely, many stu­dents who underwent such an education (with such text­books) developed their cognitive skills, though the effect or even contribution of school is disputable. Also, these students’ skills could have been developed to a greater ex­tent if addressed at school. As proved by Robinson,61 learning to solve tasks al­gorithmically often leads to mere manipulation with vari­ables according to the learned procedure. Simple task rep­etition requiring simple application of a known procedure does not lead to understanding scientific concepts the tasks’ solution is based on, and learning becomes just a mechanical imitation. In view of the effective learning and deep development of field knowledge and skills, the full absence of metacognitive knowledge tasks identified in all the analysed textbooks also seems problematic. Students are supposed to be encouraged to learn how to learn through textbook tasks, estimate their performance and eventually adapt their learning approach. As shown by Pappa and Tsaparlis,62 this aspect is also a challenge for curriculum in Greece. Systematic work with tasks in curricular materials is also proving desirable from the NoS point of view. The analysis performed in this study identified only four dom­inant types of tasks in which there is only limited scope for students’ autonomy in their solution. Therefore, the nature of chemistry (NoS) emphasizes existing knowledge as fixed, while lacking the procedural aspect of the NoS and room for new (out-of-the-box thinking). Simon and Bud­ke63 in their textbook research reached similar conclu­sions. They identified stress on goals associated with con­tent as opposed to tasks related to methodological aspects and competence development. As pointed out by Os-borne,15 freely paraphrasing scientific literacy definition, natural science knowledge does not only consist of factual knowledge, but also highlights the way science works, how individual phenomena are related to other aspects, and how knowledge is acquired. These aspects of the NoS in relation to the analyzed textbooks are all the more impor­tant as chemical education begins in the 8th grade in the Czech Republic. Since the abovementioned aspects of nat­ural sciences cannot be developed solely through lower cognitive operations, there is a risk of consolidating the image of chemistry as mere isolated, mostly symbolic,59 facts students are supposed to acquire (cf.64). This tradi­tion needs to be revisited and incorporated into pre-ser­vice teacher training as a part of the teachers’ professional vision (see65) since it is this exact domain in which the content meets the pedagogy.66 There are some limitations to the approach present­ed in this study. First, only the intended curriculum was analysed. In connection with international research, the results provide a vivid picture about the teaching practice, however, without analysing the implemented curriculum, i.e. the textbook tasks in action, conclusions need to be formulated carefully. For this reason, students’ activity, performance and results are the next logical step in this area of research. Second, this study contains results of 8th grade text­books tasks analysis, despite chemistry is mostly being taught in both 8th and 9th grade (the last two years of com­pulsory, lower-secondary school in Czechia). Neverthe­less, the textbooks are published by the same teams of au­thors. As a pilot sample of tasks from 9th grade textbooks showed no differences in the tasks’ conception, the second books were not submitted to a thorough analysis. Third, with respect to the analysis’ focus, other tasks’ qualities should such as their relation to the key compe­tences as the major curricular goal, their accent on science literacy or the Nature of Science be investigated. These are topics which would inform all actors in the field of chem­istry education about necessary steps in order textbooks – as the most influential medium in contemporary school – to aim at contemporary (not former) goals of chemistry/ science education. 6. Conclusions This study is directed at chemistry lower-secondary textbook tasks’ analysis. It showed several trends which confirm other findings worldwide as well as several specif­ics which revealed trends in Czech chemistry education conception. Particular chemistry textbooks differ in task place­ment. Whereas formerly published textbooks contain tasks at the end, newer textbooks contain them within the chapters. These show authors’ effort to connect the tasks with other structural textbook elements and the field’s content. The textbooks contain a relatively large number of tasks offering a vast database to be used in education. The analysis of tasks from Czech chemistry textbooks con­firmed there is a certain task genre from a required answer type to a required performance. Textbooks were found to follow the curriculum as far as the subject-matter is con­cerned, however they vary from the curriculum as far as the required cognitive processes are concerned. The textbooks’ integral culture consists in the tasks’ nature: open-ended tasks predominate, followed by tasks of laboratory nature without required answers. Higher task variability is required. With respect to students’ more complex development, richer response types as well as more varying information explanation are suggested. A greater variability is also suggested from the cognitive op­erations’ point of view. Tasks on remembering or under­standing facts and conceptual knowledge predominate in the textbooks. Three out of four most often used chemistry textbooks in Czechia also contain procedure application. Tasks on analysis, evaluation or creation, or tasks operat­ing with metacognitive knowledge are lacking. With re­spect to scientific literacy development, it is important to focus on these domains too. So far, Czech chemistry textbooks were found to be a suitable educational tool for students to master factual and conceptual knowledge. 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[Selective survey on students’ results at the level of 5th and 9th lower-secondary school grade in the school year 2016/2017 – Final report]; Ceská školní inspekce: Praha, 2017; 59. N. Graulich, Chem. Educ. Res. Pract. 2015, 16, 9–21. DOI:10.1039/C4RP00165F 60. M. Tóthová, M. Rusek, Acta Chim. Slov. 2021, 68, 1016–1026. ttps://doi.org/10.17344/acsi.2021.7082 61. W. R. Robinson, J. Chem. Educ. 2003, 80, 978–982. DOI:10.1021/ed080p978 62. E. T. Pappa, G. Tsaparlis, Evaluation of questions in general chemistry textbooks according to the form of the questions and the Question-Answer Relationship (QAR): the case of intra- and intermolecular chemical bonding 2011, 12, 262–270. DOI:10.1039/C1RP90031E 63. M. Simon, A. Budke, Sustainability – Basel 2020, 12, 1–19. DOI:10.3390/su12208344 64. J. K. Gilbert, A. M. Bulte, A. Pilot, Int. J. Sci. Educ. 2011, 33, 817–837. DOI:10.1080/09500693.2010.493185 65. L. Honskusová, K. Vojír, M. Rusek, J. Balt. Sci. Educ. 2022, 21, in press. DOI: 10.33225/jbse/22.21.00 66. L. S. Shulman, Educ. Researcher 1986, 15, 4–14. DOI:10.3102/0013189X015002004 Povzetek Analizirali smo naloge v ceških ucbenikih kemije za nižje srednje šole, da bi opisali njihovo umestitev v poglavjih ucbeni­ka, vrsto zahtevanega odgovora, splošno naravo naloge in kognitivne zahteve. Rezultati so pokazali, da starejši ucbeniki vsebujejo banke nalog na koncu poglavij, kar kaže na transmisivno paradigmo poucevanja, medtem ko novejši ucbeniki vsebujejo naloge znotraj poglavij. Kar zadeva naravo nalog, je bila v ucbenikih za kemijo ugotovljena stereotipna zvrst. Vecina ucbeniških nalog zahteva odprte odgovore in je usmerjena v pomnjenje dejanskega in konceptualnega znanja ali uporabo postopkov. Avtorji zato predlagajo vec sprememb nalog, vkljucno z njihovim položajem v poglavjih, kognitivno zahtevnostjo ter zahtevano vrsto odgovora, da bi izpolnili cilje kemijskega izobraževanja. Except when otherwise noted, articles in this journal are published under the terms and conditions of the Creative Commons Attribution 4.0 International License DOI: 10.17344/acsi.2021.7250 Scientific paper To What Extent do Freshmen University Chemistry Students Master Chemistry Calculations? Martin Rusek,1,* Karel Vojír,1,2 Iveta Bártová,3 Marta Klecková,3 Vladimír Sirotek4 and Jitka Štrofová4 1 Department of Chemistry and Chemistry Education, Faculty of Education, Charles University, M. Rettigové 4, 116 39 Praha 1, Czech Republic 2 Department of Biology and Environmental Studies, Faculty of Education, Charles University, M. Rettigové 4, 116 39 Praha 1, Czech Republic 3 Department of Inorganic Chemistry, Faculty of Science, Palacký University Olomouc, 17. listopadu 1192/12, 779 00 Olomouc, Czech Republic 4 Department of Chemistry, Faculty of Education, University of West Bohemia, Veslavínova 42, 306 14 Plzen 3, Czech Republic * Corresponding author: E-mail: martin.rusek@pedf.cuni.cz Received: 11-04-2021 Abstract The research mapped chemistry-oriented university freshman students’ ability to solve chemical calculations. Their suc­cess was monitored based on several factors such as their faculty, field of studies, the type of calculation and the assign­ment type (word problem vs. formula). The results indicate a significant need to change the approach to teaching chemi­cal calculations - the students were rather unsuccessful in the tasks. The obstacles they face include the ability to identify a problem, understand the concepts of calculation and appropriately adjust the correct use of the mathematical apparatus. These findings represent an impulse for teaching in upper-secondary school as well as to introductory university courses. Keywords: Chemistry calculations; students’ success; teaching chemistry; freshman students 1.Introduction Chemistry calculations are a widely discussed area of chemistry in many countries.1–4 On the one hand, they represent a very substantial base for chemistry students, which they apply practically throughout their entire uni­versity studies, mainly in the laboratory.5 On the other hand, research6–7 has shown the topic is considered critical in schools for several reasons: it is a link between mathe­matical skills and chemical content, pupils consider it dif­ficult and unimportant.7 In professional discourse, ques­tions arise as to whether this topic is appropriate in the chemistry curriculum at primary school and in the general educational fields of secondary schools.8 As research shows various parallels worldwide, this research conducted in Czechia has the potential to inform other researchers as well as teachers internationally. 2. Theoretical Background 2. 1. Research on Chemistry Calculations Chemistry calculation tasks represent a special form of tasks. They build on field concepts, however, these are not the only variable. Mathematical skills, reading skills as well as general problem-solving skills are necessary for successful solutions. The already mentioned difficulty in students ability to do chemistry calculations has been reported in several studies.2–4,9,10 Firstly, some identified the aforementioned mathematical skills to be the cause of pupils or students’ failure in chemistry calculations. Leopold and Edgar 10 ar­gued that students’ unsatisfactory mathematical skills (mathematics fluency) could be the reason for the limited skills necessary to further understanding of chemical con­cept development. In connection with this, another study9 found unsatisfactory skills among students entering che­mistry-oriented university studies. In reaction to this phe­nomenon, some authors suggested new algorithmic approaches which would make the topic more accessible to students.11 In other studies,3,4 however, students’ mathematical skills were not found to be such a strong factor, although they appear to play a role. A possible explanation for stu­dents’ lower ability to solve chemistry calculations may also be caused by the lack of the students’ ability to operate with concepts as well as their ability to identify the pro­blem in the given role. Research showed that pupils have significant gaps in this area.12 With the obstacle constitu­ted by the difficulty of chemistry concepts and the need to select only the most important,13 supportive formulas to work with the commonly used rules were also suggested.14 Secondly, students’ results can be affected by the tasks’ context which is carried by text. Added context on one hand brings relevance,15 but on the other hand it in­creases the task’s difficulty, as students need a certain level of reading literacy.16,17 In contrast to rigid tasks containing only values and variables together with a problem to solve, tasks containing context are word problems requiring text processing and understanding and realization of the pro­blem. Only then can particular relationships be under­stood and a correct mathematical model applied in order to solve the problem.18,19 Thirdly, calculation tasks represent a special form of problems, therefore require problem-solving skills. A sig­nificant body of research stresses problem-solving skills as a vital agent in chemistry education.20–22 However, several studies suggest these skills are underdeveloped in many students. As far as particular chemistry calculations topics are concerned, there was only a limited amount of informati­on on the topics’ difficulty. Childs & Sheehan found the most difficult topic for upper-secondary and university students were volumetric analysis calculations, redox reac­tions and concentration of solutions.1 Rusek et al. identi­fied pH calculations, calculations with the equation of sta­te, calculations from chemical equations to be the most difficult.3 There was an intersection with the aforementio­ned research in the topic of redox reactions, dilution solu­tions calculations and calculations of molar concentration. 2. 2. Chemistry Calculations in the Czech Curriculum According to the currently valid chemistry curricu­lum, chemical calculations are already encountered by lower-secondary school students and later by the overwhel­ming majority of upper-secondary school students. The current version of the lower-secondary chemistry curricu­lum23 contains the expected outcomes as follows: „calcula­tes the composition of solutions, practically prepares a solu­tion for a given composition“. The grammar school curriculum24 mentions „a student performs chemical cal­culations and applies them in solving practical problems“. This general outcome is complemented by, in secondary schools, binding subject-matter: quantities and calculati­ons in chemistry. Therefore, there is no concretization, so the scope of teaching is left to individual schools or tea­chers. In the Framework Educational Programmes for Se­condary Vocational Education (FEP SOV) with a non-aca­demic focus,25,26 the topic of chemical calculations is represented by two expected outcomes: „expresses the composition of solutions in different ways, prepares a solu­tion of the desired composition“ and „performs simple chemical calculations in solving practical chemical pro­blems“ and further specifies the subject-matter „calculati­ons in chemistry“. However, the importance of the turno­ver of ‚simple calculations‘ is further unspecified. As can be seen above, although the topic of chemical calculations is mandatory in teaching, the content is not further speci­fied and is thus influenced mainly by tradition or with re­gard to the use of numerous published textbooks27 based on former curriculum (cf. 28). 3. Aims and Methodology 3. 1. Aims As mentioned above, the extent of chemistry calcula­tion subject-matter may vary from school to school. It is therefore very difficult to map the entire student populati­on’s chemical calculation solving skills. However, this skill plays a crucial role for students who have chosen chemis­try as their field of study, especially in the early stages of their studies. These students are, on one hand, expected to have mastered the topic of calculations at a higher than basic level, thanks to their study ambitions and interest in the field. At the same time, it is these students who, in case of chemistry calculation solving skills deficits, can be sig­nificantly limited in their university studies. With respect to understanding the results of secon­dary education in this area as well as mapping the baseline to which higher education courses must respond, the aim of this study was to find out what chemistry calculation knowledge and skills freshman university students focused on chemistry have at the beginning of their studies. The general expectation was that the students are able to solve the chemistry calculation tasks at the chosen level. The study’s aim was specified by the following hy­potheses: 0H1: There is no statistically significant difference in chemistry calculation test results between students in study programmes focused on chemistry and on chemis­try education. With regard to the double task type (word problem and symbols), the second hypothesis was tested: 0H2: There is no statistically significant difference be­tween the students’ results in word problems and tasks as­signed using symbols. This hypothesis was supported by Tóthová and Rusek.12 3. 2. The Research Sample The research sample consisted of 220 students in the first year of bachelor‘s degrees. 42 % were students of che­mistry-focused fields (environmental chemistry, bioche­mistry, restoration, etc.), 58 % of the sample were students of chemistry teaching-focused fields (combined with bio­logy, mathematics, health education, etc.). In the academic year 2020/2021, these students studied at four different universities in Czechia. 3. 3. The Research Tool A test consisting of five pairs of tasks was used in the study. The tasks focused on: mass fraction, mixing solu­tions, molar concentration of solutions, calculations from chemical equations and pH calculations. Each pair of tasks was represented by a word problem and an example speci­fied in symbolic notation using formulas and variables. The aim was to include only basic calculations with few partial steps,19 which would then not allow us to map the causes of possible solver failures. For illustration, two types of example are shown: The word problem example Saline used for medical purposes is a solution of so­dium chloride in water in 0.15 mol/dm3 concentration. Count how much sodium chloride is needed to prepare 30 litres of saline. Molar mass of NaCl is 58.5 g/mol. The symbol type example Count m(CuCl2) in a solution when you know that: V(solution) = 24 dm3, c(CuCl2) = 0.1 mol/dm3, M(CuCl2) = 134.45 g/mol. The test underwent a multi-cycle validation process in an expert panel consisting of six university teachers fo­cused on chemistry calculation teaching and/or on chem­istry education. The resulting test was piloted on a conven­ient sample of bachelor students. After slight changes in formulations, the final version of the test arose. It was giv­en to freshman university students at the beginning of their studies (late September – mid October) in order not to affect the results of ongoing courses. 3. 4. Used Methods Statistical analysis was performed in IBM SPSS Sta­tistics 26. Based on the normality test, which did not ena­ble rejection of the hypothesis about normal distribution of the data (the Saphiro-Wilk test results p > 0.05), non-parametric tests were used. To examine differences among students from different universities/faculties, Kruskal-Wallis’ test designed for K independent samples’ comparison was used. To evaluate both students’ results according to their study field as well as the results divided according to the task type, Wilcoxon’s single-rank test de­signed for two dependent samples comparison was used. To calculate the effect-size, r was used as an alternative to the parametric Cohen’s d test. 4. Results and Discussion 4. 1. Overall Results The results of the statistical analysis among the stu­dents from different universities/faculties (p > 0.05) sug­gested there are no statistically significant difference – the students from faculties of science did not reach better re­sults than students from the faculties of education. Like­wise, the differences between the students focused on chemistry and chemistry education (p > 0.05) did not sug­gest any statistically significant difference. The original ex­pectation about chemistry students outperforming chem­istry education students was not proven. The sample was then considered homogenous. The students’ success in solving individual calculati­on types is shown in Figure 1. The students achieved the highest success rate in the mass fraction calculations, with some distance from solutions’ concentration and composi­tion. However, even these results do not correspond to ex­pectations for students who have chosen chemistry as their field of study. Although the composition of solutions calculation is already an obligatory output in lower-secon­dary education,23 a fourth of the students failed to solve the task about the components of the mixture’s mass pro­portions. The results for pH calculations, solution mixing and chemical equations, which were solved by only a third of students, are all the more dramatic. These results com­plete the previously identified problematic calculation ty­pes.1,3 4. 2. Differences Between Students’ Results by Assignment Type As proven by Drummond and Selvaratnam,9 stu­dents often struggle to process mathematical information given in word problems and convert them into appropriate relationships and formulas. This involves both the use of the corresponding mathematical relationships expressed in the word input and the conversion of the verbally described quantities into the marks used in the formulas. Therefore, a higher success rate was assumed when using chemical formulas and variable symbols to specify exam­ples, due to a clearer definition of the example type leading directly to the placement of specific symbols in known de­finition relationships. Based on previous research, reading demands were considered a hurdle too.6,17 The Kruskal-Wallis test value (p < 0.05) enabled us to reject the zero hypothesis of comparable student outcomes according to the assignment type. Specifically, there were differences in calculations focused on the mass fraction (p = 0.022; r = 0.01), molar concentration and composition of solutions (p = 0.03, r = 0.143), and calculations from che­mical equations (p < 0.001, r = 0.337). While for examples of calculating the mass fraction and chemical equation cal­culations, students were statistically significantly more su­ccessful in the tasks assigned in formulas and variables, but in the solution concentration calculations, this was the opposite. However, the effect-size value in cases of mass fraction and concentration calculations indicates a low effect-size. For calculations from chemical equations, the effect-size was medium. For this reason, the initial as­sumption was only partially confirmed, and further rese­arch is needed on the effect of longer text on student resul­ts. Especially in calculations from chemical equations, the result could also be influenced by students’ reading litera­cy. The task focused on this type of calculation contained the longest text describing the reaction, its course and amount of individual reactants due to the information needed to describe the situation. In contrast, in the assignment using chemical equations and variable sym­bols, it was not necessary to analyze the input to under­stand the context and identify the variables, and the infor­mation was already ready to apply the algorithmic calculation. A sufficient level of reading literacy appears to be a prerequisite to develop scientific literacy29 and in con­sequence also for problem solving, including chemical cal­culations. To initiate any change in this area, it seems desi­rable to shift the choice of teaching from tasks focusing on isolated field knowledge30 towards the use of tasks inclu­ding analyzing and choosing appropriate solutions. Since chemistry textbooks do not seem to offer such opportuni­ty,31 before the new chemistry textbook paradigm takes over, it is important to develop other supporting materials. Another explanation could be in the context of the word problems. Students are known to be more active in task solving when they relate to the context.32 The word problem in which the students reached better results – molar concentration – built on a medical context. This could be closer to the students than the chemistry context of the other two types of calculations. 4. 3. Causes of Students’ Failure In the tasks aimed at calculating the mass fraction, the most common cause of students’ failure was the non­-mention of the relevant mathematical relationship for the weight of the individual components and the whole sys­tem. The students who failed usually did not include the dissolved substance’s mass to the solvent’s mass. Also, so­me of them failed to provide a mathematical formula or other proportional calculation. Furthermore, numerical errors or unit mis-indications appeared, indicating a fairly common misunderstanding of the concept of mass fracti­on as a relative quantity. Similarly, for the second type of expressing the solution’s composition included in the test (examples of molar concentration calculation), typically unsuccessful students did not even state the basic relation­ship (formula) required for the calculation. 77 % of the students reported a general formula or otherwise expre­ssed mathematical relationship to calculate molar concen­tration, and most of them were also able to use it. A similar proportion of students (78 %) introduced a relationship to calculate the weight of a substance from a substance amount. Lower shares of successful solutions were mainly due to the addition of specific values according to the assignment or numerical errors. While in simple calculations focused on solution composition the success rate was relatively higher, in the examples aimed at mixing solutions, only 34 % of students reported a relevant mathematical relationship (mixing equation or other calculation involving weight-to-mass fraction relationships in mass fraction) and only 30 % of respondents used it in their calculation. Many students were able to express the composition of solutions only in simple examples based on the application of a direct algo­rithmic solution. Nevertheless, their conceptual under­standing of individual quantities and their relationships may not be sufficient to use them in examples requiring more comprehensive judgment. This problem seems to ha­ve a wider validity in relation to chemical problem solving (cf.21,33). At the same time, the problems identified in int­roducing adequate relationships for calculations further confirm that the low success rate in solving chemical cal­culations cannot be attributed only to students’ insufficient mathematical skills3–4 and there is a need to focus more on the conceptual understanding of calculated quantities. Students’ difficulties were also identified in the field of using the relationships of quantities students encounter already in the early years of science education. Only 57 % of students provided the relationship for calculating wei­ght when volume and density is given in these examples. This in itself does not represent any significant problem, nevertheless only 50 % used the calculation correctly in their test, which was represented as their understanding of this relationship. In the examples aimed at calculating pH, ignorance of basic relationships was significant. Only 49 % of stu­dents reported the correct relationship to calculate pH and 27 % of pH–pOH relationship which was later mirrored in their test results. However, even if the right relationship was stated, there was a lack of conceptual understanding. Only 20 % of students took into account that sulfuric acid is dibasic. The rest of these only mechanically added a con­centration value to the pH calculation formula. The result suggests that these students do not understand the princi­ple of calculation and do not distinguish between the con­centration of hydronium and the concentration of acid. As far as the calculations from chemical equations are concerned, many students failed at the very beginning, i.e. balancing the formula. The chemical formula in the word-problem task was correctly solved by 50 % students, 64 % wrote reactants and products without balancing the formula. The symbolic-assignment task’s formula was cor­rectly balanced by 77 % students. Since in both cases the equations contained only two reactants and one product, which can be considered one of the easiest versions, the result is surprising. Both the no­menclature of inorganic compounds and the balancing of chemical equations are given extensive attention in chemi­stry teaching,7 these topics also take up a significant num­ber of textbook pages even for lower-secondary schools, although at the state curriculum level the emphasis on this topic is being gradually limited.28 The findings show that, although it is a traditional chemistry subject-matter, tea­ching does not seem to lead to the desired educational out­comes, whereby the series of problematic topics is exten­ded.34 The seriousness of the findings is enhanced by the fact that the respondents were students interested in the field of chemistry. It therefore seems desirable to revise ge­neral education leading either to a large-scale innovation in teaching of this topic or to some form of its replacement with other key topics (big ideas), leaving the focus of no­menclature and chemical equation balancing for special seminars. Although 48 % of respondents correctly established the relationship to calculate the amount of substance in the context of calculations from chemical equations, only 12 % of respondents correctly used it. The most common pro­blems were the faulty determination of the reactant in sur­plus, and by far errors in the stoichiometry. These pro­blems suggest only a superficial understanding of the chemical equations’ meaning. The simple algorithmic application of relevant quantities’ relationships does not subsequently lead to the correct result. A similar problem was found by Mensah and Morabe 35. For the relevance of learning numerical algorithms, it is necessary to closely link the tasks with the described situation’s conceptual understanding. The teaching process based on the ex­pectation that students will understand the concept of the reaction’s course and its outcome based on learning the algorithmic numerical procedures was proven ineffecti­ve.35 Due to the fact the student sample was convenient, their relatively high number still did not allow the genera­lization of results to the entire population of students in the fields of chemistry or chemistry teaching. However, due to the variability of the sample (students came from a high number of secondary schools), the results show po­ssible shortcomings in their preparation and significant limitations that need to be addressed at the beginning of chemistry’s higher education. 5. Implications The results showed critical problems in students’ abi­lity to solve chemistry calculations consists in their lack of conceptual understanding to the included variables (cf.13). This finding seems to be a product of the contemporary conception of chemistry (calculations’) education. Its im­provement, however, heavily depends on students’ actual potential as too abstract concepts cannot be processed by students not even at lower-secondary,36 but also in upper secondary school.37 With respect to the results, implicati­ons for lower-secondary school chemistry calculations’ education will be omitted with just a brief remark that on­ly mass-fraction calculations and basic calculations of the amount of substance seem to be sufficient. More concrete implications in the light of this rese­arch’s results need to be divided in two parts respecting the actions: 1. secondary school curriculum and 2. reaction from universities. In order to promote interest in the study of natural sciences as well as the use of skills acquired in general ed­ucation in an extracurricular environment, rethinking the concept of a curriculum for chemical education needs to be considered. Since there are only sporadic situations when people need to solve a specific chemistry calculation task (in many cases mere rule of three suffices), the topic can hardly be considered important for the field’s inner structure. Therefore, it can be removed from the core sub-ject-matter topics placed among broadening topics taught in selective seminars for students with further chemistry studies ambitions. This act is also in accordance with other research which identified this topic is seen by students as too difficult, useless and with too much attention given to it.7 Considering its negative effect on students’ attitudes and the fact that despite the perceived high number of les­sons devoted to the topic, students mostly fail the tasks, its removal from core-curriculum (at the current extent) should be considered. The finding that students’ results coming into higher education from secondary schools are insufficient in the field of chemical calculations should be reflected in the rel­evant courses at higher education institutions. This condi­tion represents a potential risk to their successful study. The courses simply cannot start as an extension of up-per-secondary subject matter but need to consider starting from a considerably lower level. Also, the students’ prob­lems should be taken into account and tackled in the uni­versity courses to prevent the students from unnecessary struggle at the very beginning of their university studies. The key aspects are: understanding the key concepts, un­derstanding the role of the formulas and relations in par­ticular calculation types, followed by mastery of these pro­cedures’ application first on known, and later on unknown problems.38 Also, with the less-problematic topics identi­fied in this study, such introductory courses could save time not focusing on these topics and directly address the bigger, conceptual, problem before indulging in calcula­tions as such. 6. Conclusion Although the research sample does not fully allow generalization of the findings, parallels with previous rese­arch suggest that students‘ results in chemical calculations have significant reserves. This is an impulse for a wide range of areas and actors in education. The result is a me­ssage towards the content as well as concept of teaching this topic in upper-secondary, and even lower-secondary schools. According to these partial findings, teaching chemi­stry at secondary school does not lead to the students mas­tering basic calculations. The study’s results suggest that the output of secondary education is to adopt some algo­rithmic procedures consisting of placement in remembe­red definition relationships, but with conceptual under­standing of calculated quantities and their relationships at a very low level. The findings are even more dramatic when the research sample is considered. Low conceptual under­standing was found in individuals interested in chemistry. It is therefore reasonable to assume that the results would be even more unsatisfactory for secondary school students with interests in a different field than chemistry. For this reason, suggestions to remove this topic from the chemis­try curriculum for everyone and its placement among a broadening topic for students with interest in chemistry was made. However, even for the group of students with chemistry aspirations, the identified problems need to be taken into account during this topic’s instruction. This na­turally applies also for university courses. Being better pre­pared for these students’ chemistry calculation problems could significantly improve their dropout rate and make their freshman semesters, but also following years, more pleasant and efficient. 7. References 1. P. E. Childs, M. Sheehan, What’s difficult about chemistry? An Irish perspective 2009, 10, 204–218. DOI:10.1039/b914499b 2. F. Ö. 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Vojír, M. Rusek, J. Balt. Sci. Educ. 2021, 20, 316–331. DOI:10.33225/jbse/21.20.316 28. K. Vojír, M. Rusek, Chem. Listy 2020, 114, 366–369. http:// ww.chemicke-listy.cz/ojs3/index.php/chemicke-listy/article/ view/3606/3552 29. OECD, PISA 2018 Assessment and Analytical Framework. OECD Publishing: Paris, 2019; https://www.oecd-ilibrary. org/content/publication/b25efab8-en 30. K. Vojír In: Rusek, M.; Tóthová, M.; Vojír, K. (Eds.) What tasks are included in chemistry textbooks for lower-secondary schools: A qualitative view: Project-based Education and oth­er activating Strategies in Science Education XVIII., 2021; pp 247–256. 31. K. Vojír, M. Rusek, Opportunities for learning: Analysis of Czech lower-secondary chemistry textbook tasks 2022, 69, in press. 32. R. Havlícková, Sci in Educ. 2020, 11, 2–21. DOI:10.14712/18047106.1715 33. M.-H. Chiu, Algorithmic problem-solving and conceptual un­derstanding of chemistry by students at a local high school in Taiwan 2001, 11, 20–38. 34. M. Rusek, M. Tóthová In: Rusek, M.; Tóthová, M.; Vojír, K. (Eds.) Did students reach the periodic table related curricular objectives after leaving from lower-secondary education?: Pro-ject-Based Education and Other Activating Strategies in Sci­ence Education XVIII., Prague, CZECH REPUBLIC, 2021; pp 178–185. 35. A. Mensah, O. N. Morabe, African J. Res. Mat. Sci. Tech. Educ. 2018, 22, 174–185. DOI:10.1080/18117295.2018.1475908 36. J. Piaget, The Construction of reality in the child. Routledge & Kegan Paul: London, 1954; DOI:10.1037/11168–000 37. J. D. Herron, Piaget for chemists – explaining what good stu­dents cannot understand 1975, 52, 146–150. :// WOS:A1975W028900005 DOI:10.1021/ed052p146 38. A. H. Johnstone, Uni. Chem. Educ. 2001, 5, 12–18. DOI:10.7748/mhp.4.5.18.s16 Povzetek Raziskava je prikazala zmožnost reševanja nalog s podrocja kemijskega racunstva pri študentih prvega letnika s kemij­sko usmeritvijo. Uspešnost reševanja nalog smo spremljali glede na vec dejavnikov, kot so fakulteta, smer študija, vrsta racunske naloge in vrsta naloge (besedilna naloga ali formula). Rezultati kažejo na precejšnjo potrebo po spremembi pristopa k poucevanju kemijskega racunanja, saj so bili študenti pri nalogah dokaj neuspešni. Ovire vkljucujejo sposob­nost prepoznavanja problema, razumevanja konceptov kemijskega racunanja in nenazadnje ustrezna uporaba matema­ticnega znanja. Ta opažanja predstavljajo vzpodbudo za poucevanje kemije na srednjih šolah in uvodnih univerzitetnih predmetih. Except when otherwise noted, articles in this journal are published under the terms and conditions of the Creative Commons Attribution 4.0 International License DOI: 10.17344/acsi.2021.7301 Scientific paper The Use of Yeast Saccharomyces Cerevisiaeas a Biorecognition element in the Development of a Model Impedimetric Biosensor for Caffeine Detection Zala Štukovnik,1 Regina Fuchs Godec1 and Urban Bren1,2,* 1 University in Maribor, Faculty for Chemistry and Chemical Engineering, Smetanova 17, 2000 Maribor, Slovenia 2 University of Primorska, Faculty of Mathematics, Natural sciences and Information Technologies, Glagoljaška 8, 6000 Koper, Slovenia * Corresponding author: E-mail: urban.bren@um.si Received: 11-26-2021 Abstract In the present study, an electrochemical-impedimetric biosensor using Saccharomyces cerevisiae as an effective biorecog­nition element was designed to detect caffeine. The presented biosensor consists of a previously developed stainless steel electrochemical cell constructed as a three-electrode system in the RCW side-by-side configuration. The electrochemical stability of the sensing electrode was evaluated by measuring the open circuit potential (OCP), and electrochemical im­pedance spectroscopy (EIS) was applied to determine the impedimetric response of the biosensor with Saccharomyces cerevisiae cells attached to the working electrode (WE) in the absence (0.9% NaCl) and presence (10 mg/mL in 0.9% NaCl) of caffeine. Moreover, the limit of detection (LOD) was determined. In this way, a new approach in biosensor de­velopment has been established, which involves assembling a low-cost and disposable electrochemical system to detect alkaloids such as caffeine. The developed biosensor represents a good candidate for detecting caffeine in beverages, foods, and drugs with the merits of time-saving, robustness, low cost, and low detection limit. Keywords: Impedimetric biosensor, Saccharomyces cerevisiae, electrochemical impedance spectroscopy, caffeine 1. Introduction The demand for biosensors has increased significant­ly in the recent years due to the need for specific sensors that can provide fast and reliable measurements in various research areas. The development of biosensors is of interest for different applications ranging from biochemical profil­ing of normal and pathologic cells, over clinical diagnos­tics and drug discovery to more straightforward analyses such as fermentation, process monitoring, environmental testing, and food and beverages quality control.1–3 Detec­tion of alkaloids such as caffeine has attracted abundant attention due to their extensive occurrence in beverages and drugs.4 However, conventional detection methods for caffeine (high-performance liquid chromatography-mass spectrometry (HPLC-MS), thin-layer chromatography (TLC), and immunoassay) have several drawbacks, in­cluding expensive equipment as well as complex and labo­rious sample preparation.5 Biosensors, as analytical devices, convert a biological response into an electrical signal and provide us with the information on the concentration of the target analyte.6,7 Biosensors may present the best candidates for detecting caffeine with the merits of high sensitivity and specifici­ty, convenience, time-saving, low cost, and low detection limit.8 Biosensors can be based on animal tissues, bacteria, or eukaryotic microorganisms such as yeasts.9 Although yeasts are highly resistant to adverse environmental con­ditions, they can sense and respond to a variety of stimuli and, unlike several alternative biological components, do not require sophisticated sterile techniques or complex media.10,11 Yeast Saccharomyces cerevisiae represents a sin-gle-cell eukaryotic organism used primarily in the food in­dustry to produce bakery products and alcoholic beverag­ es.9,12 It is chemoorganotrophic and anaerobic organism classified in the kingdom of Fungi, phylum Ascomycota, class Saccharomycetes, order Saccharomycetales, and family Saccharomycetaceae.13 Saccharomyces cerevisiae can exist in two different forms, the haploid or the diploid form.13,14 A yeast cell possesses the typical characteristics of a eu­karyotic cell and characteristic organelles such as vacuoles and lipid droplets.12 It is usually spherical to slightly spher­ical and occasionally ellipsoidal to cylindrical.15 Caffeine (1,3,7-trimethylxanthine), with the chemi­cal molecular formula of C8H10N4O2, has been used for thousands of years and represents one of the most widely consumed food ingredient throughout the world.1,16 It is found in common beverages such as coffee, tea, and soft drinks, as well as in products containing cocoa or choc­olate, and in a variety of medications and dietary supple­ments.17,18 Due to the high consumption of caffeinated foods, beverages, and medicines worldwide, caffeine is also considered to be the most representative pharmaceu­tically active pollutant with regard to its abundance in the environment.19 Based on the data reviewed, it is concluded that in the healthy adult population, daily caffeine intake at a dose exceeding 400 mg is associated with adverse effects such as general toxicity, cardiovascular effects, effects on bone status and calcium balance, changes in adult behav­ior, increased cancer incidence and effects on male fertil­ity.20 The caffeine content in coffee products ranges from 0.27 to 1.85 mg/mL, in tea from 0.11 to 0.23 mg/mL, in energy drinks from 0.30 to 0.37 mg/mL, and in soft drinks such as regular cola from 0.10 to 0.13 mg/mL.21–23 Given these values, the biosensor may be sufficiently sensitive and robust enough to cover the range of caffeine concen­trations present in beverages. Figure 1: Chemical structure of the caffeine molecule For the detection of different caffeine concentra­tions, the electrochemical impedance spectroscopy (EIS) method was applied. EIS is widely used in the production and optimization of biosensors as this method allows for the characterization of the biological component attached to the sensor and of the analyte present in the sample.24,25 Because biosensors produce a rapid response, they can be applied to monitor molecular events in real-time.26 The EIS method was used to measure the frequency response of the electric current, which provides data on the adhe­sion layer of Saccharomyces cerevisiae on the electrode surfaces. The aim of this study was to develop a model elec­trochemical impedimetric biosensor for the detection of caffeine using Saccharomyces cerevisiae as an effective bi-orecognition element with many advantageous properties such as cell robustness, ease of maintenance, and cell pro­duction rate. 2. Experimental The developed biosensor consists of a stainless steel electrochemical cell (Figure 2) constructed as a three-elec­trode system in the RCW-side by side configuration, in­cluding the working electrode (WE) with yeast cells on the surface, the reference electrode (RE), and the counter electrode (CE). Such electrochemical cell was previously developed and tested.10 In assembling the electrochemical cell, stainless steel type SS316 (manufacturer TBJ Indus­tries, Germany) was used. The electrodes were manufac­tured with a dimension of electrode 20 mm × 5 mm, where the active component was applied to the 5 mm × 5 mm. The electrodes were insulated on the fixation side, and the system was sealed with glass. Saccharomyces cerevisiae was applied to the working electrode using a technique involving a mold made with a 3D printer, which ensured that the layer thickness (0.10 mm) was similar for all measurements. 3.8 g Saccharomy­ces cerevisiae was mixed with 1 mL 0.9% NaCl to ensure that the mixture was viscous. The mixture was applied on the stainless-steel electrode inserted in the mold. The pro­cess of coating the working electrode (WE) was taken at 25 °C and took approximately 30 seconds. CAS: 58-08-2, M: 194.19 g/mol) in 0.9% NaCl solution. The electrochemical cell with Saccharomyces cerevisiae on the working electrode was connected to the Multi Palms­ens4 potentiostat. Initially, 1 mL of the 0.9% NaCl solution was injected into the system, and the open circuit potential (OCP) and EIS measurements were performed. Then, the excess saline (0.9% NaCl) was drained. Afterward, 1 mL of the 10 mg/mL caffeine solution was injected into the system, and OCP and EIS measurements were repeated. The electrochemical characterization of the working electrode was evaluated by measuring the OCP to assess the stability of the electrode. The duration of the measure­ment was 600 seconds since living yeast cells were utilized. The EIS method was applied to evaluate the biosen­sor with yeast on the stainless-steel surface. Impedance spectra were obtained at a steady open circuit potential (OCP) in the frequency range from 100 kHz to 10 mHz with 10 points per decade and a 20 mV amplitude of the excitation signal. The potential amplitude was chosen at 20 mV since the lower amplitudes have not provided the stable signal due to the yeast layer on the stainless-steel electrode The EIS measurement‘s expected duration was 2 minutes and 15 seconds, although this time was often ex­tended up to 3 minutes. EIS was used to obtain data on the processes on the surface of the electrode and the applied layers, and the Bode and Nyquist plots were interpreted as the results. Moreover, the limit of detection (LOD) was deter­mined based on the impedance drop with the increasing concentration of caffeine in 0.9% NaCl. For the measure­ment, eight different caffeine concentrations in saline (0.0 mg/mL, 0.05 mg/mL, 0.01 mg/mL, 0.1 mg/mL, 0.25 mg/ mL, 0.5 mg/mL, 1.0 mg/mL, and 5.0 mg/mL) were pre­pared and 1mL of each sample was injected into the sys­tem separately. The blank solution consisted of 0,9% NaCl solution (saline). Three measurements were taken for each concentration, where the mean value (MV), standard de­viation (SD), precision, and accuracy of the measurements were calculated. The measurements were taken using iden­tical parameters as in the measurements mentioned before, and the data were obtained at the frequency 125 mHz. 3. Results and Discussion 3. 1. Open Circuit Potential (OCP) Measurements The electrochemical characterization of the sensing electrode was evaluated by measuring the OCP to assess the stability of the electrode. The OCP provides valuable insight into the thermodynamic stability of the electrode material involved in the electrochemical response.27 The results are represented in Figure 1S in the Supple­mentary information. The data provided from the meas­urements indicate that the system was thermodynamically stable. When measuring with Saccharomyces cerevisiae on the stainless steel surface of the working electrode (WE) with the addition of 0.9% NaCl and the addition of 10 mg/ ml caffeine in 0.9% NaCl, a slight difference is observed at OCP in each measurement. The slight change in OCP is due to the living cells on the surface, which react to envi­ronmental conditions. 3. 2. Electrochemical Impedance Spectroscopy (EIS) EIS represents a non-destructive method that can be used to quantify specific parameters and simultaneously monitor multiple electrochemical processes.28 The meas­urements are explained with the real (electrical resistance) and imaginary (capacitance) components of the imped­ance response of an electrochemical system.10 In the Nyquist diagram (Figure 3), the decrease in the resistance (real component, x-axis) and the decrease in the capacitance (imaginary component, y-axis) when 10 mg/mL caffeine in 0.9% NaCl was added to the system compared to the blank solution (0.9% NaCl) is seen. The results indicate that the electrode surface was released due to the detachment of yeast cells from the electrode surface. Figure 3: Nyquist diagram of the EIS measurement of the biosensor containing Saccharomyces cerevisiae on the WE, with the addition of 0,9% NaCl (blue) and 10 mg Caffeine in 1 mL 0.9% NaCl (red). Bode plots consist of two spectra simultaneously, the impedance spectrum and the phase spectrum, in which the dependence of impedance (Z) and the dependence of phase angle on the frequency is shown. In the impedance spectrum, the activity at the working electrode is deter­mined from the slopes of the line, and in the phase spec­trum, the activity is determined from a phase angle. Based on the impedance spectrum of the Bode dia­gram (Figure 4), the solution resistance (Rs) was determined with a slope of approximately 0 (high-frequency range), the capacitance of the electrical double layer (Cdl) was deter­mined with a slope of approximately –0.8, which occurs at the phase boundary between the electrode and the electro­lyte (middle-frequency range), the charge transfer resistance was determined with a slope of approximately 0 (Rct) which occurs due to the electrochemical reaction or due to the charge transfer between the electrolyte and the metal (mid­dle- frequency range), as well as the diffusion with a slope approximately –0.5 (low-frequency range) was determined. In the phase spectrum of the Bode diagram (Figure 5), the resistance is described as the negative phase at ap­proximately 0°, the non-ideal capacitance with the nega­tive phase at approximately 55°, the diffusion at the nega­tive phase at approximately 45°. Figure 4: Bode diagram of the EIS measurement of the biosensor containing Saccharomyces cerevisiae on the WE, with the addition of 0,9% NaCl (blue) and 10 mg Caffeine in 1 mL 0.9% NaCl (red). The Bode diagram includes an impedance diagram described with squares and a phase diagram described with triangles, where I Z I represents an impedance and -Phase presents a negative phase shift. The equivalent electrical circuits (EEC) of the stain­less steel electrochemical cell without and with the yeast on the working electrode (WE) are shown in Figure 5. The EEC of the electrochemical cell without yeast cells attached to the stainless steel electrode is depicted in Figure 5A. The equivalent circuit consists of the solution resistance (Rs), the capacitance of the electrical double lay­er (Cdl), the charge transfer resistance (Rct), and the War-burg impedance (Wo). The EEC of the electrochemical cell with the yeast cells attached to the working electrode is shown in the Figure 5B and consists of the solution resistance (Rs), the yeast layer capacitance (Cy), the yeast layer resistance (Ry), the capaci­tance of the electrical double layer (Cdl), the charge transfer resistance (Rct), and the Warburg open diffusion (Wo). The comparison of the values of the parameters where the 0.9% NaCl and the 10 mg/mL caffeine in 0.9% NaCl were separately added to the system is provided in Table 1. When caffeine was added to the system, the Sac-charomyces cerevisiae cells detached from the stainless steel surface, and consequently, the electrode surface was released. Consequently, the resistance of the system dropped, and the capacitance and the impedance of dif­fusion increased. The .2 of the measurement where 0.9% NaCl was added to the system was 2.94 × 10–3, and for the measurement where the 10 mg/mL caffeine in 0.9% NaCl was added 1.48 × 10–3. Table 1: Comparison of the EEC parameters when the 0.9% NaCl and the 10 mg/mL caffeine in 0.9% NaCl were added to the system. Parameters 0.9% NaCl 10 mg/mL Caffeine Rs (kOhm/cm2) 3.07 0.69 Ry (kOhm/cm2) 55.65 1.08 Rct (kOhm/cm2) 103.35 78.97 Cy (µF/cm2) 21.17 2237.20 Cdl (µF/cm2) 77.26 208.72 Wor (kOhm/cm2) 8.42 12.68 Woc (Ohm/cm2) 0.09 1.21 Figure 5: The equivalent electrical circuit (EEC) of the electrochemical cell without the yeast attached to the working electrode (Figure 5A) and with the electrochemical cell with yeast attached to the working electrode (Figure 5B). Figure 5A consists of the solution resistance (Rs), the capacitance of the electrical double layer (Cdl), the charge transfer resistance (Rct), and the Warburg impedance (Wo). Figure 5B consists of the solution resistance (Rs), the yeast layer capacitance (Cy), the yeast layer resistance (Ry), the capacitance of the electrical double layer (Cdl), the charge transfer resistance (Rct), and the Warburg open diffusion (Wo). 3. 3. Limit of Detection (LOD) and Limit of Quantification (LOQ) In Table 2, the decrease in impedance (|Z|) with the increasing concentration of caffeine in saline (c) is re­ported. Compared to the blank solution, the decrease in impedance is observed with the addition of 0.1 mg/mL of caffeine in 0.9% NaCl. The data were obtained at a fre­quency of 125 mHz. Three measurements were taken for each concentration, where the mean value (MV), standard deviation (SD), precision, and accuracy of the measure­ments were calculated. Table 2: The decrease in impedance with increasing concentration of caffeine in 0.9% NaCl C logC MV log|Z| SD Precision Accuracy (mg/mL) (mg/mL) (kOhm (kOhm (%) (%) /cm2) /cm2) 0.00 / 237.472 0.539 99.503 99.681 0.01 –2.00 231.935 2.895 97.051 98.318 0.05 –1.30 221.839 2.998 96.827 98.166 0.10 –1.00 106.922 1.929 96.161 97.515 0.25 –0.61 98.402 0.555 98.627 99.310 0.50 –0.30 94.551 0.272 99.312 99.616 1.00 0.00 88.514 0.583 97.949 98.859 5.00 0.70 75.801 0.332 98.175 99.010 The impedance decrease with increasing concen­trations is also represented as a box plot (Figure 6). The data were obtained at a frequency of 125 mHz. The box plot shows the mean values as a dot, the upper whiskers represent the maximum, the lower whiskers represent the minimum, and the box represents the interquartile range. The distinct decrease in the impedance is observed at 0.1 mg/mL concentration. The system‘s linearity was obtained in the concentra­tion range from 0.1 mg/mL to 5 mg/mL with R2 of 0.997 (Figure 7). The concentrations were calculated to logarith­mic values (Table 2) to obtain linear regression since the impedance values read from the Bode plot were logarith­mic. Based on the 3-Sigma criteria, the LOD was deter­mined at 0.728 mg/mL, and based on the 10-sigma criteria, the LOQ was determined at a concentration of 0.382 mg/ mL. It was observed that the impedance decreased with the increasing concentration of the caffeine in the solution. Thus, it can be concluded that the biosensor can sense the presence of caffeine in the solution. Figure 7: Calibration curve between the impedance (log|Z|) and the caffeine concentrations (logc) In conclusion, the Nyquist plot and the Bode plot of the investigated biosensor show that adding 10 mg/mL of caffeine in saline decreased the resistance and increased the capacitance, indicating that the electrode surface is released. Due to the Saccharomyces cerevisiae detachment from the stainless steel surface, the parameters of the EEC changed: the system‘s resistance decreased, and the capaci­tance increased. Consequently, it can be indeed concluded that caffeine can cause the desorption and death of Saccha­romyces cerevisiae cells. Some other research was done in the field of electro­chemical biosensors and caffeine detection. An ampero-metric biosensor for the determination of caffeine in solu­tions was developed, where whole cells of Pseudomonas alcaligenes were utilized. The biosensor system was able to detect caffeine in solution over a concentration range of 0.1 to 1 mg/mL.1 In comparison, the linearity range with our biosensor was in the range of 0.1 to 5 mg/mL. A biosensor based on the inhibition of alkaline phosphatase (ALP) en­zyme was developed for caffeine determination, where caf­feine concentration can be determined accurately between 0.1 and 10 µM and the LOD of the biosensor was 0.08 µM. This biosensor, compared to ours, had a lower LOD and linearity range as the enzymes were used as a biorecogni­tion element. Also, an electrochemical impedance aptasen­sor based on a porous organic framework supported silver nanoparticles for ultrasensitively detecting theophylline, with the LOD of 0.191 pg/mL (1.06 pmol/L) in a wide con­centration range of 5.0 × 10–4 to 5.0 ng/mL (2.78 × 10–3 to 27.8 nmol/L) was developed.29 4. Conclusions A new approach in biosensor development has been established, which involves assembling a low-cost and disposable electrochemical system for the detection of alkaloids such as caffeine. The caffeine detection with the presented method avoids an excessive use of solvents, requires only a small amount of analyte, and does not re­quire lengthy preparation. It was observed that the impedance decreased with the increasing concentration of the caffeine in the solution. It can be concluded that the developed biosensor is ro­bust enough to detect the various caffeine concentrations. Based on the linear calibration curve of the impedance de­crease with the increasing caffeine concentration, the LOD was determined at 0.728 mg/mL, and the LOQ was deter­mined at 0.382 mg/mL. Therefore, it can be concluded that yeasts, although very resistant to adverse environmental conditions, can sense and respond to caffeine as stimuli. Biosensors have the potential to represent the best candidate for caffeine detection with the merits of time-saving, robustness, low cost, and low detection limit. In the future, an upgraded impedimetric biosensor with the biorecognition element Saccharomyces cerevisiae could be used to detect the caffeine content in various beverages, foods, and medicines. Furthermore, impedimetric biosen­sors based on the described approach and using a simpli­fied potentiostat/galvanostat, which is transferable and compared to other conventional methods cost less, could also be applied on a large scale for food monitoring, envi­ronmental monitoring, or even medical diagnostics. Acknowledgments Financial support from the Slovenian Research Agency through project (J1-2471) and programme (P2­0046) grants is gratefully acknowledged. 5. References 1. V. R. S. Babu, S. Patra, N. G. Karanth, M. A. Kumar and M. S. Thakur, Analytica Chimica Acta 2007, 582, 329-334. DOI:10.1016/j.aca.2006.09.017 2. H. Kaur, A. Bhosale and S. Shrivastav, Int J Health Sci Res 2018, 8, 315–333. 3. P. Mehrotra, J Oral Biol Craniofac Res 2016, 6, 153–159. DOI:10.1016/j.jobcr.2015.12.002 4. J. dePaula and A. Farah, Beverages 2019, 5, 37. DOI:10.3390/beverages5020037 5. W. Ye, T. Liu, W. Zhang, M. Zhu, Z. Liu, Y. Kong and S. Liu, Toxins (Basel) 2019, 12, 1. 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DOI:10.1016/B978-0-12-384730-0.00292-5 13. S.-O. Suh, M. Blackwell, C. P. Kurtzman and M.-A. Lachance, Mycologia 2006, 98, 1006–1017. DOI:10.1080/15572536.2006.11832629 14. B. C. Viljoen and G. M. Heard, in: R. K. Robinson (Ed.): En­cyclopedia of Food Microbiology, Elsevier, Oxford, 1999, pp. 1918–1925. DOI:10.1006/rwfm.1999.1355 15. J. I. Pitt and A. D. Hocking: Fungi and Food Spoilage, Spring­er US, Boston, MA, 1997, pp. 439–468. DOI:10.1007/978-1-4615-6391-4_10 16. S. Li, J. Wen, B. He, J. Wang, X. Hu and J. Liu, Environmental Pollution 2020, 263, 114371. DOI:10.1016/j.envpol.2020.114371 17. M. A. Heckman, J. Weil and E. G. De Mejia, Journal of Food Science 2010, 75, R77-R87. DOI:10.1111/j.1750-3841.2010.01561.x 18. J. L. Temple, C. Bernard, S. E. Lipshultz, J. D. Czachor, J. A. Westphal and M. A. Mestre, Front Psychiatry 2017, 8, 80–80. DOI:10.3389/fpsyt.2017.00080 19. C. V. T. Rigueto, M. T. Nazari, C. F. De Souza, J. S. Cadore, V. B. Brião and J. S. Piccin, Journal of Water Process Engineering 2020, 35, 101231. DOI:10.1016/j.jwpe.2020.101231 20. P. Nawrot, S. Jordan, J. Eastwood, J. Rotstein, A. Hugenholtz and M. Feeley, Food Additives & Contaminants 2003, 20, 1–30. DOI:10.1080/0265203021000007840 21. J. C. Verster, C. Aufricht and C. Alford, Int J Gen Med 2012, 5, 187–198. DOI:10.1080/10408390701522445 22. L. Bagwath Persad, Frontiers in Neuroscience 2011, 5. DOI:10.3389/fnins.2011.00116 23. S. E. George, K. Ramalakshmi and L. J. Mohan Rao, Critical Reviews in Food Science and Nutrition 2008, 48, 464–486. DOI:10.1080/10408390701522445 28. N. Meddings, M. Heinrich, F. Overney, J.-S. Lee, V. Ruiz, E. 24. X. Fan, Z. Li, S. Wang, L. Liu, P. Liu, F. Chen and X. Zheng, Napolitano, S. Seitz, G. Hinds, R. Raccichini, M. Gaberšcek Journal of the Brazilian Chemical Society 2019, 30, 1762–1768. and J. Park, Journal of Power Sources 2020, 480, 228742. 25. E. Randviir and C. Banks, Analytical methods 2013, 5, 1098– DOI:10.1016/j.jpowsour.2020.228742 1115. DOI:10.1039/c3ay26476a 29. Z.-Y. Han, H.-K. Li, Q.-Q. Zhu, R. Yuan and H. He, Chinese 26. K. Kivirand, M. Min and T. Rinken: Environmental Biosen-Chemical Letters 2021, 32, 2865–2868. sors, IntechOpen, 2019. DOI:10.1016/j.cclet.2021.02.013 27. A. S. Tanak, B. Jagannath, Y. Tamrakar, S. Muthukumar and S. Prasad, Analytica Chimica Acta: X 2019, 3, 100029. DOI:10.1016/j.acax.2019.100029 Povzetek Za detekcijo kofeina smo razvili elektrokemijski-impedancni biosenzor, ki kot biološko komponento uporablja kvasovke vrste Saccharomyces cerevisiae. Predstavljen biosenzor je sestavljen iz predhodno razvite elektrokemijske celice, narejene iz nerjavnega jekla v RCW konfiguraciji. Elektrokemijska stabilnost delovne elektrode je bila ocenjena s potencialom odprtega tokokroga (OCP). Elektrokemijska impedancna spektroskopija (EIS) je bila uporabljena za spremljanje imped­imetricnega odziva biosenzorja s celicami Saccharomyces cerevisiae na površini delovne elektrode (WE) pri odsotnosti (0.9% NaCl) in prisotnosti (10 mg/mL v 0.9% NaCl) kofeina. Dolocena je bila tudi meja zaznavnosti (LOD). Razvit je bil nov pristop v razvoju biosenzorjev, ki vkljucuje sestavo ekonomicno dostopnega biosenzorja, namenjenega enkratni up-orabi za detekcijo alkaloidov kot je kofein. Razvit biosenzor je dober kandidat za detekcijo kofeina v pijaci, hrani ter zdra­vilih, saj omogoca hitro detekcijo, z nizko mejo zaznavnosti ter z nizko mejo dolocljivosti, hkrati pa je tudi ekonomicen. Except when otherwise noted, articles in this journal are published under the terms and conditions of the Creative Commons Attribution 4.0 International License DOI: 10.17344/acsi.2021.7252 Scientific paper Synthesis, Biological Evaluation, and Molecular Docking Studies of Hydrazones as Novel Xanthine Oxidase Inhibitors Ling-Wei Xue,1,* Shi-Tong Li,2 Yong-Jun Han1 and Xiao-Qiang Luo1 1 School of Chemical and Environmental Engineering, Pingdingshan University, Pingdingshan Henan 467000, P. R. China 2 Department of Materials, School of Natural Sciences, The University of Manchester, Manchester M13 9PL, United Kingdom * Corresponding author: E-mail: pdsuchemistry@163.com Received: 11-02-2021 Abstract A series of hydrazones, 2-cyano-N’-(4-diethylamino-2-hydroxybenzylidene)acetohydrazide (1), N’-(5-bromo-2-hy­droxy-3-methoxybenzylidene)-3-chlorobenzohydrazide monohydrate (2·H2O), N’-(2-hydroxy-3-methylbenzylidene) -4-nitrobenzohydrazide (3), and N’-(2-hydroxy-3-trifluoromethoxybenzylidene)-4-nitrobenzohydrazide (4), were pre­pared and structurally characterized by elemental analysis, IR and 1H NMR spectra, and single crystal X-ray determina­tion. Xanthine oxidase inhibitory activities of the compounds were studied. Among the compounds, 2-cyano-N’-(4-di­ethylamino-2-hydroxybenzylidene)acetohydrazide shows the most effective activity. Docking simulation was performed to insert the compounds into the crystal structure of xanthine oxidase at the active site to investigate the probable binding modes. Keywords: Hydrazone; xanthine oxidase; inhibition; crystal structure; molecular docking study. 1.Introduction Enzyme inhibitors can interact with enzymes and block their activity towards natural substrates. The impor­tance of enzyme inhibitors as drugs is enormous since these molecules have been used for treating a number of pathophysiological conditions.1 Xanthine oxidase (XO; EC 1.17.3.2), a molybdenum hydroxylase, catalyses the hy­droxylation of hypoxanthine and xanthine to yield uric acid and superoxide anions. These superoxide anions have been linked to post ischaemic tissue injury and edema as well as to vascular permeability.2 XO can oxidize synthetic purine drugs, such as antileukaemic 6-mercaptopurine, with the loss of their pharmacological properties. XO has also been linked to conditions such as hepatic and kidney damage, atherosclerosis, chronic heart failure, hyperten­sion and sickle-cell disease due to the production of reac­tive oxygen species (ROS) alongside uric acid.3 Then, con­trol of the action of XO may help the therapy of some diseases. Nowadays, the treatment of gout makes use of allopurinol, a potent inhibitor of XO known for a long time.4 The mode of action of allopurinol involves the di­rect coordination of its active metabolite, oxypurinol (al-loxanthine), to the molybdenum centre in the active site of the enzyme.5 However, given its side effects, toxicity, and its inability to prevent the formation of free radicals by the enzyme,6 the research on new XO inhibitors is needed. A number of compounds with various types like carboxylic acids and pyrimidines,7 pyrimidinones and 3-cyano in-doles,8 amides,9 pyrazoles,10 thiobarbiturates,11 hydrozin-gerones,12 have been reported with XO inhibitory activi­ties. Schiff bases have been of great interest in biological chemistry for a long time.13 Leigh and co-workers have reported some Schiff bases as novel XO inhibitors.14 How­ever, the study on hydrazones is limited, and no rational structure-activity relationships have been achieved so far. As an extension of the work on the exploration of effective XO inhibitors related to Schiff bases, in this paper, a series of hydrazone type Schiff bases, 2-cyano-N’-(4-diethylami­no-2-hydroxybenzylidene)acetohydrazide (1), N’-(5-bro­mo-2-hydroxy-3-methoxybenzylidene)-3-chlorobenzo­hydrazide monohydrate (2·H2O), N’-(2-hydroxy -3-methylbenzylidene)-4-nitrobenzohydrazide (3), and N’-(2-hydroxy-3-trifluoromethoxybenzylidene)-4-ni­trobenzohydrazide (4), were synthesized and structurally characterized. The XO inhibitory activities of the com­pounds were investigated from both experimental and molecular docking study. 2. Experimental 2. 1. Materials and Methods Starting materials, reagents and solvents with AR grade were purchased from commercial suppliers and used without further purification. Elemental analyses were performed on a Perkin-Elmer 240C elemental analyzer. IR spectra were recorded on a Jasco FT/IR-4000 spectrometer as KBr pellets in the 4000–400 cm–1 region. 1H NMR data were recorded on a Bruker 300 MHz instrument. X-ray diffraction was carried out on a Bruker SMART 1000 CCD area diffractometer. 2. 2. General Method for the Synthesis of the Compounds The compounds were prepared according to the literature method.15 Equimolar quantities (1.0 mmol each) of hydrazide and aldehyde were dissolved in methanol (30 mL) and stirred at room temperature for 30 min to give clear solution. X-ray quality single crystals were formed by slow evaporation of the solution in air for a few days. 2-Cyano-N’-(4-diethylamino-2-hydroxybenzylidene) acetohydrazide (1) Yield: 0.22 g (82%). Mp 133–135 °C, IR (KBr, cm–1) . 3195, 3143, 2066, 1649, 1466, 1395, 1323, 1264, 1127, 1074, 963, 859, 820, 748, 520. 1H NMR (300 MHz, DMSO-d6) d 10.97 (s, 1H, OH), 9.70 (s, 1H, NH), 9.62 (s, 1H, CH=N), 7.41 (d, 1H, J = 9.0 Hz, ArH), 6.25 (d, 1H, J = 9.0 Hz, ArH), 6.24 (s, 1H, ArH), 3.42 (m, 4H, CH2), 3.33 (s, 2H, CH2), 1.15 (t, 6H, J = 6.0 Hz, CH3). Anal. Calcd for C14H18N4O2: C, 61.3; H, 6.6; N, 20.4; Found: C, 61.1; H, 6.7; N, 20.5%. N’-(5-Bromo-2-hydroxy-3-methoxybenzylidene)-3­chlorobenzohydrazide monohydrate (2·H2O) Yield: 0.37 g (93%). Mp 221–223 °C, IR (KBr, cm–1) . 3430, 3195, 1668, 1629, 1518, 1401, 1342, 1244, 1127, 1081, 781, 709, 520. 1H NMR (300 MHz, DMSO-d6) d 12.20 (s, 1H, OH), 11.72 (s, 1H, NH), 8.64 (s, 1H, CH=N), 8.02 (s, 1H, ArH), 7.87 (d, 1H, J = 9.0 Hz, ArH), 7.68 (d, 1H, J = 9.0 Hz, ArH), 7.56 (t, 1H, J = 9.0 Hz, ArH), 7.41 (s, 1H, ArH), 7.19 (s, 1H, ArH), 3.83 (s, 3H, CH3). Anal. Calcd for C15H14BrClN2O4: C, 44.9; H, 3.5; N, 7.0; Found: C, 44.7; H, 3.6; N, 6.8%. N’-(2-Hydroxy-3-methylbenzylidene)-4-nitrobenzohy drazide (3) Yield: 0.26 g (86%). Mp 252–253 °C, IR (KBr, cm–1) . 3435, 3228, 1651, 1605, 1554, 1519, 1341, 1287, 1075, 853, 713, 627. 1H NMR (300 MHz, DMSO-d6) d 12.67 (s, 1H, OH), 11.83 (s, 1H, NH), 8.60 (s, 1H, CH=N), 8.40 (d, 2H, J = 8.6 Hz, ArH), 8.19 (d, 2H, J = 8.6 Hz, ArH), 7.32 (d, 1H, J = 7.6 Hz, ArH), 7.25 (d, 1H, J = 7.2 Hz, ArH), 6.88 (t, 1H, J = 7.5 Hz, ArH), 2.23 (s, 3H, CH3). Anal. Calcd for C15H13N3O4: C, 60.2; H, 4.4; N, 14.0. Found: C, 60.4; H, 4.3; N, 14.0%. N’-(2-Hydroxy-3-trifluoromethoxybenzylidene)-4­nitrobenzohydrazide (4) Yield: 0.33 g (89%). Mp 245–247 °C, IR (KBr, cm–1) . 3434, 3112, 1648, 1536, 1401, 1255, 1160, 1115, 1040, 862, 785, 710, 670. 1H NMR (300 MHz, DMSO-d6) d 12.15 (s, 1H, OH), 12.01 (s, 1H, NH), 8.71 (s, 1H, CH=N), 8.39 (d, 2H, J = 8.6 Hz, ArH), 8.18 (d, 2H, J = 8.5 Hz, ArH), 7.67 (s, 1H, ArH), 7.33 (d, 1H, J = 9.0 Hz, ArH), 7.03 (d, 1H, J = 8.9 Hz, ArH). Anal. Calcd for C15H10F3N3O5: C, 48.8; H, 2.7; N, 11.4. Found: C, 48.7; H, 2.9; N, 11.5%. 2. 3. Measurement of the XO Inhibitory Activity The XO activities with xanthine as the substrate were measured spectrophotometrically, based on the procedure reported by L. D. Kong et al., with modification.16 The ac­tivity of xanthine oxidase is measured by uric acid forma­tion monitored at 295 nm. The assay was performed in a final volume of 1 mL 50 mM K2HPO4 at pH 7.8 in a quartz cuvette. The reaction mixture contains 200 mL of 84.8 mg/ mL xanthine in 50 mM K2HPO4, 50 mL of the various concentrations tested compounds. The reaction is started by addition of 66 mL 37.7 mU/mL xanthine oxidase. The reaction is monitored for 6 min at 295 nm and the product is expressed as mmol uric acid per minute. The reactions kinetic were linear during these 6 min of monitoring. 2. 4. Docking Simulations Molecular docking study of the compounds into the 3D X-ray structure of XO (entry 1FIQ in the Protein Data Bank) was carried out by using the AutoDock version 4.2. First, AutoGrid component of the program precalculates a 3D grid of interaction energies based on the macromolecular target using the AMBER force field. The cubic grid box of 60 × 70 × 60 Å3 points in x, y, and z direction with a spacing of 0.375 Å and grid maps were created representing the catalytic active target site region where the native ligand was embedded. Then automated docking studies were carried out to evaluate the biding free energy of the inhibitor within the macromolecules. The GALS search algorithm (genetic algorithm with local search) was chosen to search for the best conformers. The parameters were set using the software ADT (AutoDockTools package, version 1.5.4) on PC which is associated with AutoDock 4.2. Default settings were used with an initial population of 100 randomly placed individuals, a maximum number of 2.5 × 106 energy evaluations, and a maximum number of 2.7 × 104 generations. A mutation rate of 0.02 and a crossover rate of 0.8 were chosen. Given the overall consideration of the most favorable free energy of biding and the majority cluster, the results were selected as the most probable complex structures. 2. 5. Data Collection, Structural Determination and Refinement Diffraction intensities for the compounds were col­lected at 298(2) K using a Bruker D8 VENTURE PHO­TON diffractometer with Mo Ka radiation (. = 0.71073 Å). The collected data were reduced using the SAINT pro­gram,17 and multi-scan absorption corrections were per­formed using the SADABS program.18 The structures were solved by direct methods and refined against F2 by full-ma­trix least-squares methods using the SHELXTL.19 All of the non-hydrogen atoms were refined anisotropically. The amino and water H atoms were located in difference Fou­rier maps and refined isotropically, with N–H, O–H and H···H distances restrained to 0.90(1), 0.85(1) and 1.37(2) Å, respectively. All other H atoms were placed in idealized positions and constrained to ride on their parent atoms. The crystallographic data for the compounds are summa­rized in Table 1. Hydrogen bonding information is given in Table 2. Table 1. Crystallographic and experimental data for compounds 1–4. 3. Results and Discussion 3.1. Chemistry The compounds were readily synthesized by reaction of 1:1 molar ratio of aldehydes with primary amines in methanol at room temperature (Scheme 1), with high yields (over 90%) and purity. Single crystals suitable for X-ray diffraction were obtained by slow evaporation of the solutions containing the compounds in air. The com­pounds have been characterized by elemental analyses and IR spectra. Structures of the compounds were further con­firmed by single crystal X-ray crystallography. Compound 1 2·H2O 3 4 Formula C14H18N4O2 C15H14BrClN2O4 C15H13N3O4 C15H10F3N3O5 Mr 274.3 401.6 299.3 369.3 T (K) 298(2) 298(2) 298(2) 298(2) Crystal system Monoclinic Monoclinic Monoclinic Monoclinic Space group a (Å) b (Å) c (Å) P21/c 12.582(3) 14.562(3) 8.389(2) P21/n 5.999(2) 14.323(2) 19.372(2) P21/c 11.158(1) 13.448(1) 9.278(1) P21/c 11.672(2) 15.011(3) 8.785(2) a (°) b (°) 107.116(2) 96.624(2) 91.366(2) 94.744(3) g (°) V (Å3) 1469.0(6) 1653.3(5) 1391.8(2) 1533.9(5) Z 4 4 4 4 Dc (g cm–3) m (Mo-Ka) (mm–1) 1.240 0.086 1.614 2.668 1.428 0.106 1.599 0.145 F(000) 584 808 624 752 Reflections collected 11691 8041 13528 14416 Unique reflections 3213 3590 2582 2697 Observed reflections (I = 2s(I)) 1345 1939 2146 1471 Parameters 187 219 204 241 Restraints 1 4 1 2 Goodness-of-fit on F2 0.999 0.999 1.055 1.028 R1, wR2 [I = 2s(I)]a 0.0548, 0.1269 0.0412, 0.0801 0.0424, 0.1142 0.0568, 0.1124 R1, wR2 (all data)a Large diff. peak and hole (eÅ–3) 0.1442, 0.1709 0.202, –0.139 0.1059, 0.1006 0.315, –0.362 0.0515, 0.1216 0.186 and –0.146 0.1358, 0.1427 0.331 and –0.284 2)2]1/2 aR1= Fo – Fc/Fo, wR2 = [åw(Fo 2 – Fc2)/åw(Fo Table 2. Hydrogen bond distances (Å) and bond angles (°) for the compounds. D–H···A d(D–H) d(H···A) d(D···A) Angle (D–H···A) 1 N3–H3···O2#1 0.91(1) 1.98(2) 2.889(3) 172(2) O1–H1···N2 0.82 1.90 2.631(3) 147(2) 2·H2O O4–H4B···O3#2 O4–H4A···O2#3 O4–H4A···O1#3 N2–H2···O4#4 0.84(1) 0.84(1) 0.84(1) 0.90(1) 1.96(2) 2.44(3) 2.28(2) 1.96(2) 2.754(3) 3.070(4) 3.042(4) 2.830(3) 158(4) 133(3) 150(4) 162(4) O1–H1···N1 0.82 1.86 2.580(3) 145(3) 3 N2–H2···O2#5 0.90(1) 2.08(1) 2.920(2) 159(2) O1–H1···N1 0.82 1.94 2.654(2) 146 4 O1–H1···N1 N2–H2···O2#6 0.85(1) 0.90(1) 1.91(2) 2.02(1) 2.661(4) 2.900(3) 150(4) 166(4) Symmetry codes: #1) 2 – x, – y, 3 – z; #2) 1 + x, –1 + y, z; #3) x, –1 + y, z; #4) 1/2 – x, 1/2 + y, 1/2 – z; #5) x, 1/2 – y, –1/2 + z; #6) x, 3/2 – y, –1/2 + z. nohydrate,20 and similar to N’-[(E)-5-bromo-2-hydroxy­ 3. 2. Structure Description of the Compounds 3-methoxybenzylidene]benzohydrazide monohydrate.21 Figure 1 gives perspective view of compounds 1–4 All the benzohydrazone molecules of the compounds with atomic labeling systems. X-ray crystallography adopt E configuration with respect to the methylidene reveals that the compounds are similar benzohydrazone units. The distances of the methylidene bonds, rangingderivatives. The asymmetric unit of compound 2·H2O from 1.26 to 1.29 Å, confirm them as typical double bonds. contains a benzohydrazone molecule and a water molecule The shorter distances of the C–N bonds and the longer of crystallization, which is isostructural with the bromo-distances of the C=O bonds for the –C(O)–NH– units containing hydrazone compound 3-bromo-N’-(5-bromo-than usual, suggest the presence of conjugation effects 2-hydroxy-3-methoxybenzylidene)benzohydrazide mo-in the molecules. The remaining bond lengths in the Figure 1. A perspective view of the molecular structures of the compounds 1–4 with the atom labeling scheme. Thermal ellipsoids are drawn at the 30% probability level. Hydrogen bonds are shown as dashed lines. Figure 2. Molecular packing diagrams of the compounds 1–4. Hydrogen bonds are shown as dashed lines. compounds are comparable to each other, and are within 12.01–9.70 ppm and imine CH proton in the region d normal values.22 The dihedral angles between the two 9.62–8.60 ppm confirmed the synthesis of the hydrazones. aromatic rings are 9.8(3)° for 2·H2O, 12.9(5)° for 3, and The aromatic proton signals were found in their respective 14.9(3)° for 4. The crystal structures of the compounds regions with different multiplicities, confirming their are stabilized by intermolecular hydrogen bonds (Table relevant substitution pattern. 2, Figure 2). As expected, the crystal packing structure of compound 2·H2O is also very similar to the similar 3.3. Pharmacology compounds mentioned above.13,14 In 1H NMR, the absence of NH2 signals and the The measurement of XO inhibitory activity was car-appearance of a peak for NH proton in the region d ried out for three parallel times. The percents of inhibition at the concentration of 100 µM and IC50 values for the compounds against XO are summarized in Table 3. Table 3. Inhibition of XO by the tested materials. Tested materials Percent of inhibitionb IC50 (µM) 1 74.0 ± 3.3 15.8 ± 1.5 2 44.5 ± 2.8 — 3 25.5 ± 2.0 — 4 31.2 ± 1.7 — Allopurinol 80.7 ± 4.3 8.7 ± 2.3 b The concentration of the tested material is 100 µM. Allopurinol was used as a reference with the percent of inhibition of 80.7 ± 4.3 and with IC50 value of 8.7 ± 2.3 µM. Compound 1 shows the most effective activity with the percent of inhibition of 74.0 ± 3.3 and with IC50 value of 15.8 ± 1.5 µM. Although the number of tested compounds is limited, some structural features, important to the xanthine oxidase inhibitory effect, can be inferred. The merely difference of compounds 3 and 4 is the substituent groups of the benzene rings, viz. CH3 for 3 and OCF3 for 4. As a result, the XO inhibitory activity of 3 is less than 4. Detailed investigation of the structure-activity relationship reveals that the presence of Cl and Br substituent groups may contribute to the inhibition, which is revealed by compound 2. These findings are coherent with the results reported in the literature that the presence of electron-withdrawing groups in the benzene rings can enhance the activities,23 and also comparable to that compounds with the presence of bulky ethyl group have stronger activity than those bearing methyl group.24 As a comparison, compound 1 has stronger activity than the Schiff base copper complexes,14b and the OH and Br substituted hydrazone compounds,26 but lower activity than N-(3-cyano-1H-indol-5/6-yl)-6-oxo-1,6-dihydropyrimidine-4-carboxamides and 5-(6-oxo­1,6-dihydropyrimidin-2-yl)-1H-indole-3-carbonitriles,8 N-(3-cyano-1-cyclopentyl-1H-indol-5-yl)-1H-benzo[d] imidazole-5-carboxamide,9 and the Cl and CN substituted hydrazone compounds.14b 3. 4. Molecular Docking Study In order to give an explanation and understanding of potent inhibitory activity observed from the experiment, molecular docking study was performed to investigate the binding effects between the compound 1 and the active sites of XO (entry 1FIQ in the Protein Data Bank). Allopurinol was used to verify the model of docking, and gave satisfactory results. Figure 3 is the binding model for the compound 1 in the enzyme active site of XO. The docking score is –7.12. As a comparison, the docking score for Allopurinol is –6.27. From the docking results, it can be seen that the molecule of compound 1 is well filled in the active pocket of XO. The molecule of 1 binds with the enzyme through four hydrogen bonds with Ser876, Ala1011 and Thr1010. In addition, there exist hydrophobic interactions among the compounds with the active sites of the enzyme. The results of the molecular docking study could explain the effective inhibitory activity of compound 1 on XO. 4. Conclusion The present study reports the synthesis, crystal structures and XO inhibitory activities of a series of hydrazones. The compounds were characterized by elemental analysis, IR and 1H NMR spectra, as well as single crystal X-ray diffraction. Among the compounds, 2-cyano-N’-(4-diethylamino-2-hydroxybenzylidene) acetohydrazide has effective XO inhibition with IC50 value of 15.8 ± 1.5 µM, which may be used as a potential XO inhibitor, and deserves further study. The molecule of the compound can be well filled and combined with hydrogen bonds in the active pocket of XO. Figure 3. 3D (left) and 2D (right) binding mode of 1 with the active site of XO. Hydrogen bonds are shown as dashed lines. Supplementary Material CCDC – 892363 for 1, 892364 for 2·H2O, 1041580 for 3, and 1041581 for 4 contain the supplementary crystallographic data for this article. These data can be obtained free of charge at http://www.ccdc.cam. ac.uk/const/retrieving.html or from the Cambridge Crystallographic Data Centre (CCDC), 12 Union Road, Cambridge CB2 1EZ, UK; fax: +44(0)1223-336033 or e-mail: deposit@ccdc.cam.ac.uk. 5. References 1. (a) R. Roncato, J. Angelini, A. Pani, E. Cecchin, A. Sartore-Bi­anchi, S. Siena, E. de Mattia, F. Scaglione, G. Toffoli, Inter. J. Mol. Sci. 2020, 21, 6350; DOI:10.3390/ijms21176350 (b) S. Kaur, Y. Bansal, R. Kumar, G. Bansal, Bioorg. Med. Chem. 2020, 28, 115327; DOI:10.1016/j.bmc.2020.115327 (c) D. W. Banner, P. Hadvary, J. Biol. Chem. 1991, 266, 20085– 20093; DOI:10.1016/S0021-9258(18)54894-8 (d) E. Sandstrom, Drugs 1989, 38, 417–450. DOI:10.2165/00003495-198938030-00005 2. (a) T. Mori, H. Muramatsu, T. Matsui, A. McKee, T. Asano, Neuropath. Appl. Neurobiol. 2000, 26, 31–40; DOI:10.1046/j.1365-2990.2000.00215.x (b) A. Pinto, M. Kornfeld, H. Aubin, P. Akhyari, A. Lichten-berg, Free Rad. Biol. 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DOI:10.1007/s11243-009-9309-6 Povzetek Sintetizirali smo serijo hidrazonov: 2-ciano-N’-(4-dietilamino-2-hidroksibenziliden)acetohidrazid (1), N’-(5-bro-mo-2-hidroksi-3-metoksibenziliden)-3-klorobenzohidrazid monohidrat (2·H2O), N’-(2-hidroksi-3-metilbenzi­liden)-4-nitrobenzohidrazid (3) in N’-(2-hidroksi-3-trifluorometoksibenziliden)-4-nitrobenzohidrazid (4). Pripravljene spojine smo strukturno karakterizirali z elementno analizo, IR in 1H NMR spektroskopijo ter rentgensko difrakcijo na monokristalu. Preucili smo tudi inhibitorno aktivnost proti ksantin oksidazi. Izmed vseh spojin se je 2-ciano-N’-(4-di­etilamino-2-hidroksibenziliden)acetohidrazid izkazal kot najbolj ucinkovit. Da bi ugotovili verjetne nacine vezave preucevanih hidrazonov v aktivno mesto ksantin oksidaze, smo izvedli tudi molekulsko sidranje spojin v kristalno struk­turo tega encima. Except when otherwise noted, articles in this journal are published under the terms and conditions of the Creative Commons Attribution 4.0 International License DOI: 10.17344/acsi.2021.7267 Scientific paper Computational Molecular Modeling Studies of Some Mycobacterium Tuberculosis Alanine Racemase Inhibitors Unni Jayaram* and Mohammed Afzal Azam Department of Pharmaceutical Chemistry, JSS College of Pharmacy, Udhagamandalam - 643001, Tamil Nadu, India (A Constituent College of JSS Academy of Higher Education and Research, Mysuru). * Corresponding author: E-mail: jayaramkvt@gmail.com Received: 11-10-2021 Running title: Mycobacterium tuberculosis alanine racemase. Abstract Alanine racemase is a pyridoxal-5´-phosphate dependent bacterial enzyme that provides the essential peptidoglycan precursor D-alanine, utilized for cell wall synthesis. This enzyme is ubiquitous throughout bacteria, including Mycobac­terium tuberculosis, making it an attractive target for antibacterial drug discovery. We investigated the binding mode of twenty five reported Mycobacterium tuberculosis alanine racemase inhibitors. The results obtained from molecular dock­ing studies emphasized the importance of inhibitor interaction with Lys42, Tyr46, Arg140, His172 and Tyr175 residues at the catalytic binding pocket of alanine racemase enzyme. The predicted binding free energies showed that van der Waals and nonpolar solvation interactions are the driving force for binding of inhibitors. Molecular dynamics simulation studies of four such inhibitor-alanine racemase systems were further explored to study the inhibition mechanism. The quantum chemical parameters calculated at the B3LYP/6-31G**++ level of theory indicated that the inhibitors must have low values of the lowest unoccupied molecular orbital energy and high values of electrostatic potential for stronger interactions. We expect that this study can provide significant theoretical guidance for design of potent Mycobacterium tuberculosis alanine racemase inhibitors in future. Keywords: Molecular docking; binding free energy; alanine racemase; molecular dynamics simulations. 1.Introduction One-third of world’s population is infected with My-cobacterium tuberculosis (M. tuberculosis) and remains a serious global health concern. Hence, discovery and de­velopment of effective chemotherapeutic agents against M. tuberculosis is of top health priority.1 Alanine race-mase (AlaR) is a pyridoxal-5´-phosphate (PLP) depend­ent enzyme required for bacterial cell wall synthesis. This enzyme catalyses the conversion of L-alanine to D-ala­nine which is required for the synthesis of the peptidogly-can in both Gram-positive and Gram-negative bacteria.2 Generally, AlaR is absent in higher eukaryotes but is ubiq­uitous throughout bacteria. Its inhibition is lethal to prokaryotes, hence making it an attractive target for the antibacterial drug discovery.3 This enzyme exists as a ho-modimer in which two monomers interact in a head-to-tail fashion, making two active sites, where PLP and ala­nine bind. AlaR from numerous bacteria has been structurally characterized including M. tuberculosis ala­nine racemase (MtAlaR).4 The substrate entryway and ac­tive site of enzymes are shown to be highly conserved.5,6 The 3D crystal structure of MtAlaR monomer consists of 384 residues and two different domains.4 The N-terminal domain is made up of an eight-stranded a/ß-barrel from residues 1–246 and the C-terminal domain contains pre­dominantly ß-strands formed by residues 247–384. The approximate 130° angle between the N- and C- terminal domains is unique for MtAlaR. The substrate binding cav­ity having cofactor PLP390 forms an internal aldimine linkage with the highly conserved catalytic Lys42 residue which is surrounded by the side chains of Tyr175, Tyr46 and Tyr364. The binding cavity of this enzyme is compact (5.5 Å × 5.0 Å × 2.5 Å), making it difficult for the larger molecules to reach the active site. The putative substrate binding site of MtAlaR exhibits structural similarity with AlaR binding sites of Pseudomonas aeruginosa,4 Geobacil­lus stearothermophilus7 and Staphylococcus aureus.8,9 MtAlaR uses Lys42 and Tyr271 for the racemization reac­tion.10–12 Site-directed mutagenesis and kinetic studies revealed that Tyr271 serves as the base acting on the L-enantiomer13 and Lys42 on the antipode. It also showed that mutation of Arg219 to a glutamate residue reduced the pH independent Kcat value by more than 3 orders of magnitude11 which is supported qualitatively by the quan­tum chemical calculations.12 Several reports described the role of key binding residues Lys42, Tyr46, His172, Arg228, Tyr271 and Met319 in the enzyme catalytic steps.5,7,13,14 The amino acid residues outside the binding site but near to the entryway of the active site are highly conserved4 and can be exploited for the design of larger and poten­tially more specific inhibitors. There are several well-known inhibitors of AlaR. Among them D-cycloserine and O-carbamyl-D-serine are two natural compounds known to inhibit AlaR.15,16 But only D-cycloserine is approved commercially for the treatment of tuberculosis. However, its clinical use is lim­ited due to severe toxic effects arising due to the lack of target specificity.17 D-cycloserine inhibits alanine race-mase by interacting with the enzyme-bound co-factor PLP. There are several PLP dependent enzymes in nature, hence D-cycloserine is not target-specific. Several struc­tural modifications of D,L-cycloserine have been made to improve the activity and target specificity but not to date resulted in development of AlaR specific inhibitors18a,b with high potency. Several other scaffolds have also been investigated as AlaR inhibitors. These include ß,ß,ß-trif­luoroalanine, alanine phosphonate,19 1-amino-cyclopro-pane phosphonate,20 ß-chloro- and ß-fluoroalanine,21 phosphonopeptides,22,23 and thiadiazolidinones.24 Most of the substrate analogs, like D-cycloserine engage co-fac­tor PLP. The interest in identifying small molecule inhibitors of AlaR to overcome the liabilities of the existing com­pounds has intensified the structure-based drug design approach.25,26 In addition, there is a considerable research effort to discover AlaR inhibitors that are not substrate an­alogs and that act through different mechanisms of en­zyme inhibition.24,27 In this study, we present the results of molecular docking, binding free-energy calculation, mo­lecular dynamics (MD) simulation and quantum chemical calculation on literature reported MtAlaR inhibitors to get further insight into the binding mechanism of these inhib­itors. The role of hydrogen bonding with key residues of MtAlaR catalytic pocket with these inhibitors is examined in detail. Binding free energies of protein with inhibitors were calculated by molecular mechanics-generalized born/surface area (MM-GBSA) and analysed. MD simula­tions were performed to investigate the stability and dy­namical changes of predicted binding conformations. Us­ing B3LYP/6-31G**++ level of theory, quantum chemical parameters were calculated to understand the mechanism of interaction between inhibitors and biological system. The information from this study provides further insight into the key structural features required to design or opti­mize alanine racemase inhibitors. 2. Materials and Methods Computational Methods 2. 1. Molecular Docking Twenty-five MtAlaR inhibitors (1-25) with wide range biological activity and structural diversity (Supple­mentary Table S1) were collected from literatures24,27 (Ta­ble 1) and used in the present study. The 3D structures of ligands were generated using the builder panel in Maestro 10.5 and subsequently optimized with the LigPrep module (Schrödinger 2017-2, LLC, New York, NY). Partial atomic charges were ascribed and possible ionization states were generated at a pH of 7.0. The energy minimization for each ligand was performed using OPLS3 force field.28 The structure of MtAlaR (PDB-ID: 1XFC, 1.9 Å resolution) was retrieved from the protein data bank and prepared us­ing protein preparation wizard29 (Epik v4.0, Schrödinger 2017-2). Crystallographic water molecules (less than 3 hy­drogen bonds) were deleted and hydrogen bonds (corre­sponding to pH 7.0) were added. Missing side chain atoms were added and breaks present in the structure were built using Prime (v4.8, Schrödinger 2017-2).30 Then the energy of protein was minimized under OPLS3 force field28 with the convergence of heavy atoms to a RMSD of 0.3 Å. The Ramachandran plot31 (Supplementary figure S1) showed 97.90% of the residues in the most favoured regions and none of the non-glycine residues in disallowed regions. The active site was defined with a 10 Å radius around the Lys42 residue present in the crystal structure and a grid box was generated at the centroid of this active site for docking. All compounds were docked into the catalytic pocket of prepared protein using Glide (v7.5, Schrödinger 2017-2)32 in extra precision (XP) mode without applying any constraints (Figure 1a-d and Supplementary figure S2). Best docked structures were selected based on Glide score function, Glide energy and Glide model energy (Supplementary Table S2). The best pose of 10, 19, 21 and 25/1XFC complexes were selected to run the molecular dynamics. Further, electrostatic potential surfaces for 21 and 25 (Supplementary figure S3a and S3b) and hydro­phobic and hydrophilic map for 19, 21 and 25 (Supple­mentary figure S3c) were generated in the binding pocket of 1XFC enzyme. 2. 2. Binding Free Energy Calculation Molecular mechanics-generalized born surface area (MM-GBSA) method is used to calculate the binding free energies of macromolecules, and serve as a powerful tool for the design of inhibitors.33 The XP-molecular docking receptor-ligand complex structures ranking were mini­mized with Prime (v4.8, Schrödinger 2017-2).30 Binding free energies of complexes were computed using MM-GB­SA continuum solvent model (Table 1) with OPLS3 force field28 and VSGB 2.0 solvation model.34 2. 3. Quantum Chemical Parameters The molecular electrostatic potential (MEP)35 calcu­lations provide detailed stereoelectronic information and are useful to study the biological activity of a compound.36 We calculated mean MEP of best binding conformations of inhibitors 1-25 by Jaguar (v9.1, Schrödinger 2017-2) us­ing basis set 6-31G**++ and hybrid density functional the­ory with Hamiltonian-Nonrelativistic correlation func­tional (B3LYP) in gas phase. ESP maps of compounds 19, 21 and 25, which is an estimate of overall molecular size and location of regions of negative and positive electro­static potential are shown in supplementary figure 4a-c. The deepest red color represents the most electronegative potential, whereas deepest blue indicates the most positive potential site in inhibitors.37 A semi-empirical Neglect of Diatomic Differential Overlap (NDDO) module with RM138 method and RHF wave function (Schrödinger 2017-2) was used to calculate highest occupied molecular orbital (HOMO), lowest unoccupied molecular orbital (LUMO), electrophilic, nucleophilic and radical super delocalizibility energies (Supplementary Table S3). The frontier molecule orbital density distributions of HOMO and LUMO for compounds 19, 21 and 25 are shown in supplementary figure S5. 2. 4. Molecular Dynamics Simulations In order to investigate the stability of the XP-docked inhibitor 10, 19, 21 and 25/1XFC complexes, 10 ns molec­ular dynamics simulations39 were performed using the Desmond (v5.0) computational package. The systems were explicitly solvated in orthorhombic boxes (volume 10/1XFC = 460130 Å3; volume 19/1XFC = 1016870 Å3; volume 21/1XFC = 459351 Å3; 25/1XFC = 985370 Å3) with a shell of TIP4P40a,b water molecules within Desmond molecular dynamics system and allowing for a 10 Å buffer region between protein atoms and box sides. Overlapping water molecules were deleted and the systems were neu­tralized by counter ions. The total numbers of atoms in solvated protein structures of 10, 19, 21 and 25/1XFC complexes for the MD simulations are 43992, 44104, 44348 and 42692, respectively. Each system was placed at a dis­tance of 10 Å from the edge of the box and LBFGS minimi­zation was performed with 3 vectors and minimum 10 steepest descent steps until a gradient threshold of 25 kcal/mol/Å was reached. Smooth particle mesh Ewald41 meth­od was used for long range electrostatic interactions at a tolerance of 1e-09 and a cut-off radius of 9 Å was selected for short range electrostatic interactions. After minimiza­tion, systems were gradually heated in the NPT ensemble to 300° K with a time step of 2 fs. For bonded, near non-bonded, and far nonbonded interactions, a multiple time step RESPA integration algorithm was used throughout the dynamics with time steps of 2, 2 and 6 fs, respectively. Systems were then subjected to 10 ns MD simulations in the NPT ensemble (T = 300° K, thermostat relaxation time = 200 ps; P = 1 atm; barostat relaxation time = 200 ps) us­ing a Nose-Hoover thermostat42 and Martyna-Tobi-as-Klein barostat.43 For each system, trajectories and 3D structures were visually analysed using the Maestro graph­ical interface. 3. Results and Discussion 3. 1. Molecular Docking We compared different XP-docked poses (Figure 1 and also Supplementary figure S2) for the better under­standing of interactions between MtAlaR and its inhibitors 1-25. It is evident that most of these inhibitors adopt similar hydrogen bond pattern at the ligand-receptor interface. Ear­lier studies have indicated that the difference in activities of different AlaR inhibitors are primarily due to their binding orientations and spatial arrangement towards Lys42, Tyr46, Arg140, His172, Tyr175 and Tyr364 core residues and for­mation of hydrogen bonding network and hydrophobic in­teraction4,6,7,9 within the catalytic pocket. In the present in­vestigation, inhibitors binding to the MtAlaR were observed to be driven by the p-p stacking, p-cation and hydrogen bonding interactions with the conserved residues near to the PLP390. It is important to note that none of the selected inhibitors showed any interaction with the co-factor PLP390. To explain the binding mode, compounds 10, 19, 21 and 25 were selected for more detailed analysis. Figure 1d shows the docked pose of the most active compound 25 (IC50 0.03 µM) within the active site of 1XFC protein. Pre­cisely, fifth position >C=O of thiadiazolidine-3,5-dione ring accepted a hydrogen bond from the backbone OH of Tyr175(OH···O=C<, 2.1 Å). Another hydrogen bonding interac­tion was observed between third position >C=O of thiadi­azolidine-3,5-dione ring and the side chain NH of Met173(NH···O=C<, 2.0 Å). These hydrogen bonds are important for positioning and orienting the thiadiazolidine-3,5-dione ring of this inhibitor closer for p-p interactions with imida­zole ring of His172 and phenyl ring of Try175. In another high active compound 19 (Figure 1b, IC50 0.05 µM), a total of three hydrogen bonds were observed with 1XFC. The ni­tro oxygen atom of 2-chlorophenyl-4-nitrophenyl moiety accepted hydrogen bond from the side chain NH of Asn141 (NH···–O(NO), 1.8 Å). Carbonyl oxygen atoms of thiadiazo­lidine-3,5-dione ring exhibited hydrogen bonding interac­tions one each with the side chain imidazole ring NH ofHis172 (rNH···O=C<, 2.1 Å) and the side chain OH ofTyr46 (OH···O=C<, 2.1 Å). Our Glide XP-docking also ex­posed two p-p stacking interactions of this inhibitor. Phenyl ring of 2-chlorophenyl-4-nitrophenyl moiety and phenyl ring of 2,3-dihydro-1H-indene moiety exhibited p-p inter­actions, respectively with imidazole ring of His172 and phe­nyl ring of Tyr175. These attractive and non-covalent inter­actions are responsible for further stabilization of inhibitor 19 at the active site. In the lowest active inhibitor 21 (Figure 1c, IC50 28.76 µM), only one p-cation interaction was ob­ b) a) c) served between thiadiazolidine-3,5-dione ring and nitrogen atom of Lys42 side chain NH2. For this inhibitor, hydrogen bonding and p-p stacking interactions were not observed and probably this may be the reason for the lower activity of this compound. In case of another less active compound 10 (Figure 1a, IC50 13.1 µM) a single hydrogen bond was ob­served. Precisely, (carbamothioylsulfanyl)acetic acid moiety NH formed a hydrogen bond with the side chain imidazolering nitrogen of His172 (r =N···NH-C(=S)S-) (2.0 Å). This compound also showed a p-p stacking interaction of its pyridine ring with indole phenyl ring of Trp88. However, like high active inhibitors 19 and 25, no p-p stacking inter­actions with Tyr46, His172 and Tyr175 residues were ob­served for this inhibitor. It is evident from the docking re­sults that apart from the hydrogen bonding network, p-p stacking interactions with Tyr46, His172 and Tyr175 resi­dues are important for the stabilization within the catalytic pocket and inhibitory activity of MtAlaR inhibitors. As shown in supplementary figure S3b, 25 fully oc­cupied the binding pocket and oriented in a way to block the approach of substrate near to the Lys 42 and co-factor PLP390. Moreover, the positively charged (blue colour) Lys42 residue is in the region of negative (red colour) sur­face electrostatic potential of 25, which favour the stabili­zation of this inhibitor within the catalytic pocket. While the inhibitor 21, did not occupy the whole binding pocket (Supplementary figure S3a) and oriented in a way that in-dene ring is exposed to the solvent pocket, disfavouring the binding of this inhibitor. Further, it can be seen from the supplementary figure S3c that inhibitors 19 and 25 phenyl rings are in close contact with the hydrophobic pocket, while the thiadiazolidine-3,5-dione rings are bur­ied in the hydrophilic pocket. In case of inhibitor 21, part of the 2,3-dihydro-1H-indene ring is buried in hydrophilic pocket while, the ethyl carboxylate group is located within the hydrophobic pocket (Supplementary figure S3c), disfa­vouring the binding. 3. 2. Binding Free Energy Calculation To get a quantitative comparison of the binding strengths between diverse structure inhibitors 1-25 and 1XFC, the binding free energies (.Gbinds) were computed by using the MM-GBSA approach (Li et al., 2011). In the present study a moderate correlation (R2 = 0.573) was ob­served between the computed .Gbinds and IC50 values of selected inhibitors 1-25. It is evident from the energy com­ponents of the calculated binding free energies (Table 1) Table 1: Contribution to the free energy of binding (mm–gbsa) for ligands 1–25 with 1XFC (kcal/mol). Compd. IC50 (µM) a.GCoul b.GCov c.GH–bond d.GvdW e.GLipo f.GSolv g.Gbind No. 1. 5.2 –33.713 2.605 0.317 –23.690 –21.036 13.815 –60.087 2. 8.2 –29.817 –1.214 –0.428 –21.324 –22.045 28.677 –45.4426 3. 1.0 –43.348 –1.849 –5.124 –31.954 –21.937 39.614 –53.3249 4. 5.7 –48.829 –2.445 –0.657 –10.810 –14.137 29.585 –48.7813 5. 0.12 –14.652 8.125 –0.322 –27.265 –29.148 17.119 –45.1766 6. 4.48 –25.131 15.666 –1.376 –27.085 –34.652 23.502 –47.7372 7. 3.3 –3.618 –1.077 0.797 –24.274 –25.189 12.226 –41.4227 8. 6.5 21.218 –21.045 0.975 –26.127 –31.154 1.225 –56.0666 9. 9.0 77.783 –1.501 1.083 –31.825 –26.723 –23.612 –32.1513 10. 13.1 –9.601 6.504 –1.333 –20.895 –9.6544 19.690 –21.5392 11. 0.82 –9.454 –3.512 0.927 –25.370 –38.742 8.915 –61.989 12. 0.29 –54.104 24.440 –2.991 –25.605 –33.075 22.886 –57.7316 13. 2.6 –13.699 5.534 –1.351 –10.300 –18.288 –1.185 –41.3644 14. 8.2 –8.832 6.480 –1.556 –32.968 –32.636 24.397 –51.4349 15. 1.46 –40.730 6.015 –2.836 –26.248 –35.996 39.831 –59.7989 16. 0.13 3.913 –1.156 0.893 –27.383 –28.121 13.004 –42.6172 17. 4.9 20.307 1.304 –0.315 –17.582 –22.819 –30.085 –50.5578 18. 6.8 24.923 6.666 –2.189 –27.740 –18.081 –8.493 –29.8858 19. 0.05 –18.082 –4.375 –0.779 –22.054 –33.262 16.623 –63.3841 20. 6.0 8.003 –9.162 0.950 –20.613 –27.058 –1.843 –48.2484 21. 28.76 –8.562 –6.816 –1.094 –17.350 –26.901 15.372 –33.1315 22. 2.8 –1.611 –7.988 –3.683 –16.635 –10.579 6.723 –36.685 23. 0.17 0.258 –3.931 –0.221 –19.133 –26.945 –4.626 –57.9106 24. 1.6 6.098 5.407 –0.682 –29.088 –30.205 –26.703 –58.8693 25. 0.03 –23.355 3.334 –0.215 –24.938 –29.498 25.507 –63.1989 a Coulomb energy; bcovalent energy (internal energy); chydrogen bonding; dvan der Waals energy; ehydrophobic energy (nonpolar contribution estimated by solvent accessible surface area); ffree energy of binding; gelectrostatic solvation energy. that the major favourable contributors to ligand binding are van der Waals (.GvdW) and nonpolar solvation (.Glipo) terms. Whereas in all active inhibitors covalent and elec­trostatic solvation (.Gsolv) energy terms strongly disfa­vour the inhibitor binding except inhibitors 9, 17, 18 and 24 which showed moderate (.Gsolv) energy terms. The highly active inhibitors 19 and 25 (IC50 0.05 and 0.03 µM respectively) showed a slightly higher van der Waals (–22.05 and –24.938 kcal/mol, respectively) and nonpolar solvation (–33.26 and –29.49 kcal/mol, respectively) con­tributions. Also, both inhibitors exhibited higher favoura­ble Coulomb energy terms (–18.082 and –23.355 kcal/mol, respectively) compared to the other less active inhibitors 10 and 21 (–9.601 and –8.562 kcal/mol, respectively). It is clearly evident from result that .GvdW and .Glipo terms are the driving force for ligand binding and this is in well agreement with our XP-Glide docking result (Supplemen­tary Table S2). 3. 3. Quantum Chemical Parameters The mean electrostatic potential (ESP) of potent in­hibitors 19 and 25 are +3.55 and +4.45 kcal/mol, respec­tively, clearly indicating that positive electrostatic potential of molecules will favour the binding of inhibitors into the binding pocket of MtAlaR. The negative electrostatic po­tential of inhibitor 8, 10 and 21 (–0.03, –71.79 and –0.12 kcal/mol, respectively) may be one of the reason for low inhibitory activity (IC50 8.2, 13.1 and 28.76 µM, respec­tively) against 1XFC. In addition, the similarity in electro­static potential profile of compound 19 and 25 was akin to the similarity in the inhibitory activity (IC50 0.05 and 0.03 µM, respectively) against MtAlaR enzyme. The quantum chemical calculation results show that the optimizations of ESP may lead to the potent MtAlaR inhibitor. It is evident from supplementary Table S3 that in high active com­pounds 19 and 25, the high negative ELUMO (–1.555 and –1.630 eV, respectively) and positive nucleophilic super-delocalizibility (703.455 and 178.047, respectively) favours the inhibitors binding. Whereas in the less active com­pounds 10 and 21 positive ELUMO (1.810 and 0.681 eV, re­spectively) and negative nucleophilic superdelocalizibility (–8.571 and –79.09, respectively) disfavour inhibitor bind­ing into the catalytic pocket of 1XFC. It appears from the LUMO shape that the molecular orbital involved in bonding with the catalytic pocket resi­dues is located on the 2-(2-chloro-4-nitrophenyl)-1,2,4-thi­adiazolidine-3,5-dione ring of compounds 19 and 25, but it is spread over the 3-aminopyridine ring in case of com­pound 10. The relative smaller size of LUMO lobes in com­pound 10 explains the low inhibitory activity of this com­pound. A plot of the LUMO vs. HOMO energies (Supplementary figure S6) showed statistically significant correlation coefficient of R2 = 0.71 with a slope and inter­cept of 0.67 and 5.14 eV, respectively. The slope showed consistent increase in the excitation binding energy with increasing electron affinity. These data indicates that the potent molecules should have a higher negative ELUMO, positive nucleophilic superdelocalizibility and positive ESP values for the inhibitory activity. This is in well agree­ment with the MM-GBSA result where Coulomb energy term favours binding of compounds 19 and 25 in the cata­lytic pocket of 1XFC. 3. 4. Molecular Dynamics Studies A 10 ns molecular dynamics (MD) was performed to explore the atomic details of molecular interactions. Root a) b) mean square deviations (RMSD) from the starting struc­tures were analysed to explore the dynamic stability of sys­tems. The convergence of RMSD values (Figure 2a-d) at approximately 2 ns of the simulation time indicates that the systems were well equilibrated and have attained sta­bility. The time-dependent average RMSDs of 10, 19, 21 and 25/1XFC complexes backbone (0.37, 0.26, 0.74, 0.605 Å) and C-a (0.38, 0.345, 0.72, and 0.61 Å) atoms indicated less conformational changes in protein during MD simula­tion. It is also evident from the RMSD plots (Figure 2a-d) that the tendency of catalytic pocket residues RMSD copes well with the ligand movement. During MD simulations ligands 10, 19, 21 and 25 showed conformational varia­tions up to 2 ns and then were stable for rest of the simula­tion period. The protein backbone, C-a and heavy atoms of li­gand binding residues for complexes 10, 19, 21 and 25/1XFC showed root-mean-square fluctuations (RMSF) values in the range 0.62–2.87, 0.5–2.10 and 0.48–3.13 Å, respectively (Figure 3a-d) which is indicative of the mod­erate fluctuations in these residues. The high fluctuations in RMSFs of 10, 19, 21 and 25/1XFC complexes backbone (2.55–2.91, 2.87–2.96, 2.98–3.32 and 1.76–1.86 Å, respec­tively) and C-a (2.51–2.89, 2.61–2.88, 2.86–3.11 and 1.72– 1.83 Å, respectively) atoms were observed inflexible loop between amino acid stretch Ser270 to Thr274, connecting two short ß-sheets (amino acid stretches Trp275-Ile276 and Val269-Gly268). These residues are away from the cat­alytic pocket. During MD simulation the low mean fluctuations of radius of gyration (rGyr) in the backbone (blue circle, 0.677 and 0.30 Å, respectively) and Ca (red thin diamond, 0.35 and 0.31 Å, respectively) atoms for 19 and 25/1XFC complexes (Supplementary figure S7b and S7d) further in­dicated low degree of flexibilities in protein complexes. However, in 10 and 21/1XFC complexes, a little higher c) d) mean fluctuations of rGyr in the backbone (0.677 and 1.0 Å, respectively) and Ca atoms (1.37 and 0.81 Å, respec­tively) (Supplementary figure S7a and S7c) showed higher degree of flexibilities in protein complexes. These results indicate that the whole 19 and 25/1XFC systems were in more relaxed conformations compared to the 10 and 21/1XFC complexes. In comparison to the protein crystal structure average B-factor (28 Å2), a little higher average B-factor of 30.26 Å2 was observed for the catalytic pocket residues during MD simulations of four complexes. Dur­ing MD simulations of all four complexes, five residues Gly137, Leu138, Asn139, Arg140 and Asn141 located on the flexible loop between amino acids stretch Asp135­Gln147 showed higher B-factor in the range 48.40–74.30 Å2 but lower RMSF values for the protein backbone and Ca atoms (0.68–1.46 Å and 0.67–1.31 Å, respectively). MD simulations of 10, 19, 21 and 25/1XFC com­plexes showed similar binding interactions as predicted in the extra-precision molecular docking. During MD simu­lation of 19/1XFC complex, all three hydrogen bonds (Asn141 NH···-O(NO), His172 rNH···O=C< and Tyr46 OH···O=C<) predicted in the XP molecular docking was preserved, respectively in 7, 34 and 62% of MD trajectory (Figure 4b also Supplementary figure S8b and S9b). For this inhibitor a strong hydrogen bond interaction was ob­served between fifth position >C=O of thiadiazoli­dine-3,5-dione ring and the backbone OH of Tyr46 (>C=O···HO) (~62% of the trajectory) in a bidentate man­ner. Another moderate frequency hydrogen bonding in­teraction was observed between third position >C=O of thiadiazolidine-3,5-dione ring and the side chain NH of His172 ring (>C=O···HNr) (~34% of the trajectory). Con­formational flexibility of His172 side chain (. = 110°, Sup­plementary figure S10b) was observed to be less during first 8.5 ns of simulation and it is considerably less for this inhibitor compared to conformational flexibility of His172 observed for the lowest active inhibitor 10. These hydro­gen bonding networks stabilized the positions of thiadi­azolidine-3,5-dione and 4-nitro-2-chlorophenyl rings for p-p stackings respectively, with Tyr364 phenyl (~21% of the trajectory) and His172 imidazole (~26% of the trajec­tory) rings. Additional stabilization was achieved by the low frequency p-p stacking of 2,3-dihydro-1H-indene phenyl ring with Tyr175 (~11% of the trajectory) and hy­drogen bonding network between the backbone of Tyr175 and third position >C=O of thiadiazolidine-3,5-dione ring (~13% of the trajectory). These low frequency interactions are due to the conformational flexibility of Tyr175 back­bone brought about by the flexible body movement of the Ser171-Asp181 stretch in loop. Our MD simulation result also exposed salt bridge interaction of nitro oxygen atom with Arg140 (~21% of the trajectory). Another salt bridge interaction was observed between oxygen atom of fifth po­sition >C=O of thiadiazolidine-3,5-dione ring and Lys42 (~13% of the trajectory). These interactions are in good agreement with the calculated .Gbinds value of this inhibi­tor (Table 1). These hydrogen bonds, hydrophobic and salt bridge interactions are responsible for the stabilization of this inhibitor within the catalytic pocket. During MD simulation of the highest active inhibi­tor 25/1XFC complex, among the two hydrogen bonds (Tyr175 OH···O=C<, Met173 NH···O=C<) predicted in XP-docking, only one (Met173 NH···O=C<) was preserved in 37% of MD trajectory (Figure 4d also Supplementary figure S8d and S9d). A moderate frequency hydrogen bond was observed between the second position >C=O of thiadiazolidine-3,5-dione ring and backbone NH (r >C=O···HN) of Met173 (~37% of the trajectory). Precisely, fifth position >C=O of thiadiazolidine-3,5-dione ring formed low frequency hydrogen bond networks with the side chain of Asn212 (>C=O···HN(H)- and >C=O···H-O­H···HN(H)) in a bidentate manner (~18% of the trajecto­ry). Imidazole ring of His172 residue exhibited weak p-p stacking interactions, one each with thiadiazolidine-3,5-di-one and phenyl rings of inhibitor 25. This weak interaction is due to the higher conformational flexibility of His172 side chain (. = 340°, Supplementary figure S10d during first 5 ns of MD simulation and then (. = 80°) during fur­ther last 5 ns. These hydrophobic interactions further sta­bilized the inhibitor 25 in the catalytic pocket of 1XFC and positioned the inhibitor near to the Lys42 and PLP390. Additionally, nitro group of 2-chloro-4-nitrophenyl moie­ty showed two moderate frequency water mediated hydro­gen bonding networks with the side chain of Lys133 (~21% of the trajectory), back bone of Arg140 (~29% of the tra­jectory) and a low frequency water mediated hydrogen bonding interaction with the backbone of Leu138 (~18% of the trajectory) during MD simulation. These hydrogen bonding networks probably forms the basis for the high activity of this inhibitor compared to the low active com­pounds 10 and 21. For the low active 10/1XFC complex, one hydrogen bond (His172r =N···NH-C(=S)S-), predicted in glide XP-docking simulation was preserved in 25% of MD sim­ulation trajectory. This inhibitor exhibited a total of six moderate to low frequency hydrogen bonds (Figure 4a, also Supplementary figure S8a and S9a). Precisely, nitro­gen of pyridine ring accepted a moderate frequency hy­drogen bond from the side chain of Arg228 (~45% of the trajectory) and also exhibited salt bridge interaction (~39% of the trajectory) with the same residue. These interactions stabilize the position of the pyridine ring with regard to the Trp88 phenyl and His172 imidazole rings to form low frequency p-p stacking interactions. His172 imidazole ring nitrogen atom also accepted a low frequency hydro­gen bond (~25% of the trajectory) from the NH of (carba­mothioylsulfanyl)acetic acid moiety (r =N···HN) of this compound. This low frequency hydrogen bond is due to the higher conformational flexibility of His172 side chain (. = 350°, Supplementary figure S10a) during last 4 ns of simulation. This conformational flexibility is brought about by the flexible body movement of the Ser171-Asp181 stretch in loop. Another moderate frequency hydrogen bond was observed between >C=O of carboxylate and the side chain OH (>C=O···HO) of Tyr46 (~31% of the trajec­tory). In addition, compound 10 showed four more low frequency hydrogen bond networks with Arg140, Ser213 and Arg228 residues. During MD simulation of 21/1XFC complex, the p-cation interaction between thiadiazolidine-3,5-dione ring and the side chain NH2 of Lys42 predicted in the XP molecular docking was preserved during MD simulation (~11% of MD trajectory) (Figure 4c, also Supplementary figure S8c and S9c). An additional p-cation interaction c) d) (~11% of MD trajectory) was observed between Lys42 and the phenyl ring of ethyl benzoate moiety. The side chain NH2 of this residue also exhibited a weak hydrogen bond­ing interaction (~9% of MD trajectory) with position five >C=O of thiadiazolidine-3,5-dione ring. This carbonyl ox­ygen atom showed another weak hydrogen bonding inter­action (~16% of MD trajectory) with the side chain imida­zole ring NH of His172. This low frequency hydrogen bonding interaction is due to the higher conformational flexibility of His172 side chain (. = 325°, Supplementary figure S10c), observed during entire 10 ns MD simulation. This conformational flexibility of His172 is more com­pared to the conformational flexibility of His172 observed for inhibitors 19 and 25. In addition, three weak p-p stack­ing interactions (~5–17% of MD trajectory) were also ob­served for this inhibitor with Tyr46, Trp88 and Tyr175 residues. Probably these weak hydrophobic and hydrogen bonding interactions are responsible for the low activity of this compound. The gyration radius (rGyr) measures the extended­ness of a ligand. It was found to be stable after 2 ns of MD simulations of 21 and 25/1XFC complexes and the average values were 0.215, 0.24, 0.67 and 0.11 Å, respectively (Sup­plementary figure S12a-d). The average RMSD of these inhibitors were 0.48, 0.465, 0.67 and 0.475 Å, respectively. As evident by the lower RMSD values, inhibitors 19 and 25 showed less conformational changes and more stability during simulation. Inhibitors 19 and 25 also exhibited lower changes in the polar surface area (PSA) (157.75– 169.16 and 156.71–139.4 Å2, respectively) compared to the less active compounds 10 and 21 (MolSA: 203.5–210 and 344.6–355.58 Å2, respectively; PSA: 121.20–141.44 and 86.72–108.73 Å2, respectively) during MD simulation, fur­ther indicating less conformational flexibility of inhibitors 19 and 25 before stabilization within the catalytic pocket. Further, similar orientation was observed between the superposition of the conformations of 10 after MD simulation and best XP-docking pose (RMSD: 1.23 Å) (Supplementary figure S11a); conformations of 19 after MD simulation and best XP-docking pose (RMSD: 1.53 Å) (Supplementary figure S11b); conformations of 21 after MD simulation and best XP-docking pose (RMSD: 1.35 Å) (Supplementary figure S11c) and conformations of 25 af­ter MD simulation and best dock pose (RMSD: 0.77 Å) (Supplementary figure S11d). Conformational changes of 2-chloro-4-nitrophenyl moiety arising due to the rotation about PhC-N< thiadiazolidine-3,5-dione ring bond prob­ably resulted in slightly higher RMSDs after superposition of the conformations of 19 best XP-docking pose with the pose of MD simulation. The designing of novel MtAlaR inhibitors targeting the blockade of bacterial AlaR enzyme is considered to be a futuristic approach as reflected by the availability of lim­ited number of known inhibitors. The only marketed drug D-cycloserine however, faces the issues associated with the lack of target specificity and selectivity. The partial ago-nism offered at the glycine site of NMDA receptor and non-selective inhibition towards other PLP dependent en­zymes are proven causes for the concerned drawbacks. For further evaluation, the results of the current study were compared with the AlaR inhibitory activity of the litera­ture molecules as reported by Anthony K.G. et al. 24,27 The compound 19 (IC50 = 0.05 µM) and 25 (IC50 = 0.03 µM) showed potent activity against the MtAlaR. The com­pounds (19; IC50 = 0.36 µM) and 25; (IC50 = 0.49 µM) were also able to exhibit prominent inhibitory activity against Figure 5: Structural comparison of designed AlaR inhibitors (A10, A17, A26 and A27) with that of reported AlaR inhibitors (19 and 25). Staphylococcus aureus AlaR. When the structural aspects of the inhibitors 19 and 25 were compared, it was found that, three ring system scaffold, aryl ring with electron withdrawing groups like -Cl and -NO2, and thiazoli-din-2,4-dione/thiadiazolidine-3,5-dione ring scaffold are essential requirements for imparting the activity (Figure 5). The reported molecule although showed high inhibito­ry action towards MtAlaR, but their further development as drug candidate still needs to be investigated in terms of target specified action and non-interaction with other PLP dependent enzymes. Based on the above observation, some novel AlaR inhibitors (A10, A17, A26 and A27) were designed which may be considered as promising lead mol­ecules for AlaR inhibitory action. These designed mole­cules may assure target selectivity and enzyme inhibitory action which has to be evaluated by computational proto­cols and in vitro enzymatic assays. 4. Conclusion To date, most known inhibitors of alanine racemase bind solely to the substrate binding region proximal to the PLP. In the present study, a combined computational ap­proach was applied to gain insight into the structural basis for MtAlaR inhibitors. We obtained several possible binding poses for structurally diverse set of 25 MtAlaR inhibitors us­ing Glide XP-docking. Docking interaction profile showed that residues Lys42, Tyr46, Arg140, His172 and Tyr175 are mainly responsible for the stabilization of inhibitors within the catalytic pocket. MM-GBSA rescoring result revealed that .GvdW and .Glipo terms are the driving force for ligand binding. Further, the negative values of van der Waals (.G­vdW –22.05 and –24.93 kcal/mol, respectively) and non-polar solvation (.GLipo–33.26 and –29.49 kcal/mol, respectively) binding energy terms for the high active inhibitors 19 and 25, also indicated that hydrophobic interactions are primari­ly responsible for the stable complex formation. A computa­tional study on inhibitors 1-25 with the B3LYP/6-31G**++ quantum chemical method was performed in an attempt to correlate stereoelectronic features with AlaR inhibitory ac­tivity. The molecular electrostatic potential of the inhibitors in specific regions of the molecules appears to play a pivotal role in the activity. Further, results showed that positive elec­trostatic potential, high negative ELUMO and positive nu-cleophilic superdelocalizibility favours inhibitors binding in the catalytic pocket. 10 ns MD simulations used here re­vealed the importance of hydrogen bonding and hydropho­bic interactions of compounds with M. tuberculosis AlaR. Moreover, similar orientations were observed between the superposition of the conformations of 21 and 25 after MD simulation and respective best XP-docking poses. Based on the above finding we hereby propose the chemical structures of 4 lead molecules which may exhibit potent MtAlaR action and can be considered as a promising scaffold for further drug design and synthesis. Acknowledgements We would like to thank University Grants Commis­sion (UGC), Government of India, for the financial support (No. F1-17.1/2014-15/RGNF-2014-15-SC-KER-61880). 5. References 1. D. T. Hoagland, J. Liu, R. B. Lee, R. E. Lee, Adv Drug Del Rev 2016, 102, 55–72. DOI:10.1016/j.addr.2016.04.026 2. N. V. Grishin, M. A. Phillips, E. J. Goldsmith, Protein Sci 1995, 4, 1291. DOI:10.1002/pro.5560040705 3. A. Watanabe, T. Yoshimura, B. Mikami, H. Hayashi, H. Kag­amiyama, N. Esaki, J Biol Chem 2002, 277, 19166–19172. DOI:10.1074/jbc.M201615200 4. P. LeMagueres, H. Im, J. Ebalunode, U. Strych, M. J. Benedik, J. M. 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DOI:10.1016/S0009-2614(03)00526-8 41. U. Essmann, L. Perera, M. L. Berkowit, T. Darden, H. Lee, L. G. Pedersen, J. Chem. Phys. 1995, 103, 8577–8593. DOI:10.1063/1.470117 42. G. J. Martyna, M. L. Klein, M. Tuckerman, J. Chem. Phys. 1992, 97, 2635–2643. DOI:10.1063/1.463940 43. G. J. Martyna, D. J. Tobias, M. L. Klein, J. Chem. Phys. 1994, 101, 4177–4189. DOI:10.1063/1.467468 Povzetek Alanin racemaza je piridoksal-5´-fosfat odvisen bakterijski encim, ki zagotavlja esencialni peptidoglikanski prekurzor D-alanin, ki ga bakterija uporablja za sintezo celicne stene. Ta encim je prisoten v vseh bakterijah, vkljucno z Mycobac­terium tuberculosis, zaradi cesar je pomembna tarca za odkrivanje novih antibakterijskih zdravil. Raziskali smo nacin vezave petindvajsetih znanih zaviralcev alanin racemaze bakterije Mycobacterium tuberculosis. Rezultati pridobljeni s študijami molekulskega prileganja so pokazali interakcije zaviralcev z aminokislinskimi ostanki Lys42, Tyr46, Arg140, His172 in Tyr175 na katalitskem veznem mestu encima alanin racemaze. Izracunane proste vezavne energije so pokazale, da predstavljajo van der Waalsove in nepolarne solvatacijske interakcije gonilno sila vezave inhibitorjev. Inhibicijski me-hanizem štirih preucevanih sistemov inhibitor-alanin racemaza smo nadalje preucili s pomocjo simulacije molekulske dinamike. Kvantno kemijski parametri izracunani z B3LYP/6-31G**++ pristopom so pokazali, da morajo imeti zaviralci nizke energijske vrednosti najnižje nezasedene molekulske orbitale in visoke vrednosti elektrostaticnega potenciala za mocnejše interakcije. Rezultati te teoreticne študije lahko služijo kot smernica pri bodocem nacrtovanju mocnih zaviral­cev alanin racemaze Mycobacterium tuberculosis. Except when otherwise noted, articles in this journal are published under the terms and conditions of the Creative Commons Attribution 4.0 International License DOI: 10.17344/acsi.2021.7305 Scientific paper Synthesis, Characterization, Biological Activities and Ab-initio Study of Transition Metal Complexes of [Methyl 2-((4-chlorophenyl)(hydroxy)methyle) Acrylate] Shazia Ishfaq,1 Shazia Nisar,1,* Saqib Ali,2 Sadaf Iqbal,1 Saima Imad,3 Samina Iqbal,3 Saeeda Bano,3 Syeda Kanwal Zahid,1 Nasreen Fatima1 and Muhammad Shahzaman4 1 Department of Chemistry, Faculty of Sciences, University of Karachi, 75270, Pakistan 2 Department of Chemistry, Faculty of Sciences, University of Kotli, 11100, Pakistan 3 PCSIR Laboratories Complexes, Karachi, 75270, Pakistan 4 School of Atmospheric Sciences, Nanjing University of Information Science and Technology, Nanjing 210044, China * Corresponding author: E-mail: shazian@uok.edu.pk Phone No.00923343519338 Received: 11-29-2021 Abstract Taking cognizance of the medicinal significance and diverse functions of synthetic Morita-Baylis-Hillman adducts (MB­HA), the title ligand was synthesized and purified through column chromatography. Cr+3, Mn+2, Co+3, Ni+2, Cu+2 com­plexes of the ligand were synthesized under basic conditions, subjected to characterization through spectral analyses and verified with the IR spectrum that was generated computationally by the DFT B3LYP method, with 6-311++ G (d,p) basis set and Hartree Fock (HF) B3LYP method in conjunction with 3-21G(d,p) basis set. Powder XRD helped to testify crystals of the complexes. Moreover, the antibacterial, and antioxidant characteristics of MBHA and its complexes were also established. All of them were found to be active antioxidants. The antibacterial activities, examined against S. aureus, E. coli, B. pumilis and S. typhi have revealed that its Cobalt complex has an excellent potential to act against all of them. Hence, these compounds maybe having potentialities for the discovery of new, cheaper and efficient drugs against various infectious diseases. The study also uncovers the first example of utilization of MBHA towards metal complex formation. Keywords: MBH Adduct; transition Metal Complexes; XRD; antibacterial Activities; antioxidant Activities 1.Introduction The significance of reactions that proceed through Carbon-Carbon bond forming, which in turn involves the coupling of the a-position of activated alkenes with carbon electrophiles under the influence of a catalyst, cannot be overlooked in atom economy.1,2 The reaction ends up in the formation of Michael type dimers. There are many oth­er synthetic routes by which effectual and perfect C-C bond emerging reactions take place.3 Further to that me­dicinally relevant pharmaceutical compounds and natural products are very useful multifunctional building blocks for C-C bond formation which is vital for the synthetic organic chemistry.4,5 Besides this, Morita-Baylis-Hillman (MBH) compounds, especially the ones which are carbon­ate, aldehyde, ester, or acetate-based derivatives act as an important intermediate in the synthesis of different struc­tures. These compounds have become important MBH originators and represent a valuable class of substrates for synthetic purposes. These derivatives are widely employed as intermediates in the preparation of significant classes of compounds, as building blocks for natural products. Moreover, Michael/aldol cyclic reactions, that involve multiple C–C bond formations, also lead to important car­bon frames that can be employed in drug synthesis.6 The MBH reaction is mainly a three-component reaction be­tween the a-position of activated alkenes e.g ethyl or methyl acrylate, acrylonitrile and methyl vinyl ketones and electrophilic sp2 carbon of aldehyde or imines in the presence of an appropriate catalyst, usually a tertiary amine or phosphine.5 The resulting Morita-Baylis-Hill­man adducts (MBHA) have diverse functionalities and versatile reactivity. Consequently, they have been widely used as powerful precursors for the building of various carbocyclic, heterocyclic and biologically important com­pounds.7 These compounds that are economical and bio­logical friendly, perform a number of functions and most importantly can be synthesized following green- one-step synthetic protocols. The anti-fungal, antimalarial, anti-tu­bercular activities of some MBHAs have been reported.8 The antileishmanial/antibacterial activities of some MBHA indicate that they can prove to be a novel and promising class of anti-parasitic compounds.9,10 In this scenario a discovery that paved the way for some newer treatments has been that of an uncharted world of metal-based chemotherapeutic agents which have dissimilar kinetic and mechanisms of action from those of traditional organic medications. Curative potenti­alities of metal complexes in tumor/cancer therapy as well as other biological functions develop certain intricacies because central metals exhibit some special features. These metal-based complexes have variable coordination modes, that generate curing drug activity and provide excellency in terms of reactivity in the realm of the organic sub­strate.11,12 Through metal complexation of ligands, com­prising of oxygen, nitrogen, hydroxyl, or ester functional groups, with metals like copper, zinc, cobalt, and iron, nu­merous finely-tuned therapeutic activities such as antihy­pertensive, antimalarial, and antimicrobial get enhanced. In addition, transition metal ion complexes are known to have a key electron transfer role in many biological pro­cesses.13 Metals have long been used for medicinal purpos­es in a more or less empirical fashion, in a wide variety of chelating modes, geometrical forms, and redox states. Presently identified thermodynamic and kinetic proper­ties of the metal cation and ligand itself, however, provide the medicinal chemist a wide spectrum of reactive sites that can be highly mobilized.14 Thus, the biological diver­sity of the transition metal complexes along with their pharmacological specificity is to be established. Promising physiological responses would be drugs need to be demon­strated by in vitro study with targeted biomolecules and tissues as well as in-vivo. Therefore, studies pertaining to ligands with multiple, medicinally dynamic binding sites may serve as models for ensuring biochemical process­es.11,15,16 In this context, the wide variety of potent biotic activities of MBHA encouraged us to synthesize new series of products belonging to this class of compounds, possess­ing different functionalities and exhibiting potential anti­oxidant activity. Keeping in view the significance of re­ported research on synthetic MBH adducts as medicinally important candidates and having the diversity of func­tions, we conferred them as ligands for the novel drug syn­thesis composed of their transition metal complexes. The present study is directed towards synthesis, characteriza­tion, and computational studies of halo/chloro-benzalde­hyde based MBH adducts and some of their novel metal complexes. 2. Materials and Methods 2. 1. General In the present study various solvents such as n-hex­ane, methanol and ethyl acetate of synthetic grade were used. In addition to that chemicals 4-Chlorobenzaldehyde (Sigma Aldrich), DABCO (Sigma Aldrich), Methyl Acry­late (Dae-Jung Kosdaq Reagents and Chemicals), NaCl (Sigma Aldrich), MgSO4 anhydrous (Sigma Aldrich), NaOMe (Dae-Jung Kosdaq Reagents and Chemicals), CuCl2 · 2H2O (Sigma Aldrich), MnCl2 · 2H2O (Sigma Al­drich), NiCl2 · 6H2O (Sigma Aldrich), CoCl3 · 6H2O (Sig­ma Aldrich) and CrCl3.6H2O (Sigma Aldrich) were used. Further to that equipments like UV lamp (monitoring TLC card), Digital hot plate with constant temperature and stirring system, Gallen Kamp melting point instru­ment, LAMBDA 1050+ UV/Vis/NIR spectrophotometer, FTIR Spectrophotometer of Shimadzu Company pres­tige-21. The 1H NMR spectrum of the ligand was recorded on AVANCE NEO NMR spectrometer (500 MHz for 1H) in CDCl3 solvent. Chemical shifts are reported as d ppm against TMS as internal reference and coupling constants (J) are reported in Hz units. Mass spectrophotometer in­strument of JEOL 600H1, for transition metal complexes used Electrospray ionization mass spectrometry (ESI MS) at QTrap hybrid triple-quadrupole (QTrap 2000, Applied Biosystems/MDS Sciex) and XRD Pananalytical company X-pro serial No. DYH313 for powder diffraction were the major instruments used for data analysis. 2. 2. Synthesis of MBHA (Methyl 2-((4-chlorophenyl) (hydroxy)methyle) acrylate) L1 MBHA was prepared by addition of 5 equivalents of methyl acrylate to 4-chloro benzaldehyde (1 equivalent). The reaction mixture was further treated with 0.65 equiva­lents of Lewis base DABCO, using 5% THF solvent in round bottom flask with constant stirring at room temperature. The progress of the reaction was monitored at regular inter­vals of one hour through TLC under UV lamp and the com­pletion time was recorded as 24 hours. The excess of methyl acrylate was evaporated which furnished gummy crude ma­terial upon complete evaporation. This was diluted further with 15 mL each of ethyl acetate and brine solutions. The organic layer developed in this mixture was separated through separating funnel, collected and this process was repeated thrice. Furthermore, the organic phase was con­centrated by removing EtOAc through evaporation and concentrated crude was subjected to column chromato­graphic purification. Mobile phase system of ethyl acetate and n-hexane in ratio of 5:95 to 35:65 (v/v) was used for this Scheme 1. Morita-Baylise Hillman reaction with e.g. aldehydes by using tertiary amine (DABCO) as catalyst at room temperature and other stand­ ard reaction conditions purpose. The main spot was collected as product in pure form and it’s purity was confirmed through spectral analy-sis.7,9,17 The experimental 1H NMR and mass spectrum is in good agreement with previous studies18,19 and hence proves the purity of the final MBHA synthesized using this proce­dure. Preparation of the MBHA is shown below in scheme 1. A pearl white color gummy mass was found in the form of pure product of C11H11ClO3 (compound 3) yield 80% (226.66 g/mol). B.P: 125.3–125.5 °C. IR data (KBr: .max, cm–1): 3446, 2953, 2897, 1714, 1631, 1487, 1440, 1278, 1151, 1045,827 and 540. 1H NMR (500 MHz, CDCl3): d 3.29 (s, 1H, OH), 3.71 (s, 3H, OCH3), 5.55 (m, 2H, CH2), 5.83 (s, 1H, CH), 6.35 (s, 1H, H-1), 7.49 (d, J2,3 =8 Hz, H-2, H-3, Ar), 7.60 (d, J5,6 =6.5 Hz, J5,1 = 1.5 Hz, H-4, H-5, Ar), ppm. UV-visible data in DMSO [., nm (e, M–1 cm–1)]: 267(124.23). [(m/z) M+, Cal./Obs. (%)] for C11H11ClO3: 227 (56) M+, 207 (25), 191 (100), 160 (90), 132 (66), 104 (70), 77 (64). 2. 3. Synthesis of Metal Complexes with Ligand MBHA (L1) About 20 mL of 0.02M each metal salt solutions in distilled water and 0.04M solution of L1 in hot ethanol were prepared separately (1:2). The metal salt solution was added with continued stirring to adduct solution drop wise, few drops of NaOH were added to maintain the pH at 11. The reaction mixture was refluxed at 60–70 °C with constant stirring for 3–5 hours until solid mass formed in the mixture.15,20 Only one spot appeared for the desired molecule on TLC strip suggesting its purity. After comple­tion of the reaction (complete solid mass formation), the reaction mixture flask was cooled slowly at room tempera­ture and precipitates were collected using vacuum filtra­tion. The precipitates were washed thrice with ethanol and distilled water. Then the obtained product was dried in a desiccator. The deduced structural formula for each of the transition metal complex is shown in scheme 2. 2. 4. Analytical Data of Transition Metal Complexes CrL1 complex (compound 4) 577.37(g/mol), Light blue: m.p: >300 °C. IR data (KBr: .max, cm–1): 3500–3380, 2915, 2980, 1708, 1625, 1550, 1438, 1382, 1105, 835, 603 and 520. UV-visible data in DMSO [., nm (e, M–1 cm–1)]: 262(145.9). [(m/z) M+, Cal./Obs.(%)] for C22H30Cl2O10 Cr3+: 192.02(100)/192.69(65.1)M+. CoL1 complex (compound 5) 584.31(g/mol), Dark brown: m.p: >300 °C. IR data (KBr: .max, cm–1): 3500– 3400, 2935, 1651, 1620, 1483, 1386, 1109, 837, 653, 607 and 518. UV-visible data in DMSO [., nm (e, M–1 cm–1)]: 260(136.58). [(m/z) M+, Cal./Obs.(%)] for C22H30Cl2O10 Co3+:194.35(100)/195.02(63.9) M+. NiL1 complex (compound 6) 548.04 (g/mol), Parrot green: m.p: >300 °C. IR data (KBr: .max, cm–1): 3450– 3350, 3005, 2850, 1640–1566, 1643, 1566, 1463, 1388, 1109, 609 and 455. UV-visible data in DMSO [., nm (e, M–1 cm–1)]: 255(139.54). [(m/z) M+, Cal./Obs.(%)] for C22H26Cl2O8Ni2+: 274.01(100)/273.02(97.6)M+. MnL1 complex (compound 7) 544.28(g/mol), Black­ish brown: m.p: >300 °C. IR data (KBr: .max, cm–1): 3505– 3310, 3005, 2980, 1629, 1500, 1566, 1386, 1111, 748, 603 and 523. UV-visible data in DMSO [., nm (e, M–1 cm–1)]: 253(128.10). [(m/z) M+, Cal./Obs.(%)] for C22H26Cl2O8 Mn2+: 271.52(100)/272.52(65.6)M+. CuL1 complex (compound 8) 552.89 (g/mol), Paris green: m.p: >300 °C. IR data (KBr: .max, cm–1): 3480–3350, 3115, 2980, 1673, 1604, 1521, 1388, 1112, 746, 605 and 486. UV-visible data in DMSO [., nm (e, M–1 cm–1)]: 250(161.10). [(m/z) M+, Cal./Obs.(%)] for C22H26Cl2O8Cu2+: 276.51(100) /275.51 (89.5)M+. 2. 5. Computational Studies All the calculations were performed with the Gauss­ian 16W 21 program package on a ACPI x64 Core i7 laptop with 3.0 GHz processor and 16 GB Ram. The molecular structure of ligand in the ground state is optimized by us­ing DFT B3LYP method and 6-311++ G (d,p) basis set.23,24 The molecular structures of remaining metal complexes are optimized by UFF molecular mechanics. The vibra­tional frequencies of ligand and nickel metal complex were also calculated along with optimization. The assignment of the calculated wavenumbers is aided by the animation op­tion of Gauss View 6.0 graphical interface for Gaussian program, which gives a visual presentation of the shape of the vibrational modes. Scheme 2. Molecular modifications by reacting the MBHA as Ligand with transition metal salts to synthesize corresponding metal complexes 2. 6. X-Ray Diffraction The XRD study (powder pattern) of the complexes were made with the help of X-ray diffractometer (Goni­ometer Radius mm = 240.00 and Dist. Focus-Diverg. Slit mm = 91.00) with Cu- anode material, Ka [A°] = 1.54060 and the generator settings 30 mA, 40 kV. 2. 7. Antibacterial Assay The antibacterial activity of L1 and its complexes was evaluated by agar diffusion method,26,27 against two Gram-negative bacteria (Staphylococcus aureus, Escherich­ia coli) and two Gram- positive bacteria (Bacillus pumilis, Salmonella typhi). All extracts were sterilized by sterile membrane syringe filter (pore size 0.45µm, Millipore). For assay, 1mL of culture suspension of each strain (25% trans­mittance, 530 nm) was added in 100 mL antibiotic agar No.11 (45 °C) and mixed well. 25mL of inoculated agar was poured in each petri dish (20 × 100 mm) and kept for solidification. After solidification 4 holes were made using sterile borer of 8mm diameter with 6mm internal diame­ter. Holes were marked and each extract (100 µL) was poured in the respective well, and incubated for 24 hours at 37 °C. The experiment was performed in triplicate un­der strict aseptic conditions. After incubation, zone of in­hibition (mm) produced by each extract was measured and antibacterial activity expressed in terms of percent in­hibition. Gentamycin (0.3%) was used as a standard anti­biotic in comparison to L1 and complexes. 2. 8. DPPH Radical Scavenging Activity The free radical scavenging action was determined by using the DPPH (2,2-diphenyl-1-picrylhydrazyl) radi­cal scavenging method with some modification.28,29 The 0.3 mM solution of DPPH was prepared in methanol. 10 µL of each sample of different concentration (25 µg–100 µg) was mixed with 1.0 mL of DPPH solution. The reac­tion mixture was incubated at 37 °C for about 30 min. The absorbance at 515 nm was measured by spectrophotome­ter and percent radical scavenging activity was determined in comparison with the methanol treated control. Ascorbic acid was used as standard for further samples analysis.30 By using this standard the antioxidant activity of the MBHA (L1) and its metal complexes were determined and recorded in the Table 4. 3. Results and Discussions The present work was initiated with (i) the synthesis of ligand MBHA (C11H11ClO3) through MBH reaction scheme where the choice of catalyst in appropriate amount and sol­vent often have a significant effect on the efficiency, rate and stability of the reactions.31,32 Using DABCO as catalyst and THF as solvent, the MBH adducts form exclusively or as major products in moderate to good yields in stable form upon continues stirring.33,34 The stability of the adduct was further confirmed via spectral analysis, which demonstrat­ed no dimerization (Scheme 1), its transition metal com­plexes with five biologically significant transition metal ions Fig. 1. Theoretically optimized geometry of the (a) MBHA (L1), and its corresponding complexes, (b) Cr+3L1, (c) Co+3L1, (d) Ni+2L1, (e) Mn+2L1 and (f) Cu+2L1. The results have confirmed that each metal is coordinated by two ligand molecules, Cr+3, Co+3 are octahedral and Ni+2, Mn+2, Cu+2 are tetrahedral as per optimized geometrical structures. (Scheme 2), (ii) characterization of these compounds and finally (iii) their subsequent biological and antioxidant in­vestigations. The UV-Visible spectroscopic results have re­vealed the formation of metal complexes, which was ob­served between 250 nm–265 nm of wavelength, in contrast to MBHA (L1) that shows a .max = 267 nm. Additionally, the m/z ratio and relative abundance (%) of MBHA have also been observed using Electrospray ionization-mass spec­trometry (ESI-MS). 35While, the 1H NMR of MBHA was recorded at 500MHz using CDCl3 as solvent. MBHA (com­pound 3) showed four signals at d 3.29, 3.71, 5.83 and 6.34 ppm as singlet corresponding to proton of –OH and three proton of –OCH3, one proton of –CH and one –CH aro­matic proton, respectively, in 1H NMR spectrum. Further­more, one signal of multiplet was appeared at d 5.55 because of two protons of –CH2 and neighboring proton effect. The other aromatic protons have been shown in the region d 7.47–7.49 and 7.60–7.62 as doublet ppm in consensus with the structure.36–38 In Mass spectrum,8 a prominent peak of 227 m/z at 56.5% noted for M +, while 207 m/z (25%), 191 m/z (100%), 160 m/z (90%), 132 m/z (66%), 104 m/z (70%) and 77 m/z (64%) for fragmentation peaks pertaining to synthesized MBHA were recorded. The 1H NMR and ESI­MS spectral data are consistent with the assigned structure (compound 3). Additionally, the results of computational and spectral studies confirm that each transition metal (Cr+3, Co+3, Ni+2, Mn+2, Cu+2) complex with MBHA (L1) has 1:2 stoichiometric ratios (Fig. 1). 3. 1. Theoretical and Experimental FTIR Analysis of Ligand (MBHA)/Compound 3 The FTIR spectra of MBHA (L1) and their complex­es [M(L1)2(H2O)4]+3, [M(L1)2(H2O)2]+2 were recorded between 4000–500 cm-1 using KBr disk and the data is presented in Table-2. The FTIR data corresponding to compound 3, has shown the characteristics -OH broad peak in 3446 cm–1, prominent stretch of =C-H at 2953 cm–1 and Sp3 C-H stretch at 2897cm–1. The band observed at 1714 cm–1 is due to ester carbonyl vibration. Further­ – more, the frequency (.) of C=C aromatic, -CH3, C-O and C-Cl were designated as 1631 cm–1, 1440 cm–1, 1278cm–1 and 540 cm–1 respectively (Fig. 2a).39 Moreover, in compu­tational IR analysis, it was observed that OH peak is some­what different from the observed spectrum. This fact is because of the breadth of OH band appeared by the water vapors. It might be obtained due to greater number of OH ions appeared in the same intensity.40 The peak intensities Fig. 2a. Experimental FTIR spectrum of Methyl 2-((4-chlorophenyl)(hydroxy)methyle) acrylate [MBHA (L1)] of left aforementioned fragments (Fig. 2b) in calculated spectrum more about the similar values. The slight change may be observed because of the theoretical results present­ed to isolated functional groups in gaseous state, while the experimental calculations belong to the molecules in solid state.41 3. 2. Characterization of MBHA Based Metal Complexes The transition metals (Cr+3, Co+3, Ni+2, Mn+2, Cu+2) complexes of MBHA (L1) were synthesized. Initially, Cr+3 based complex 4 of ligand was prepared by employing a reported method.42 The existence of a single complex spe­cie in the reaction mixture and hence, the purity of the complex was confirmed through TLC, until a single spot was achieved on TLC plate (expected product 4) (Scheme 2). In addition to this, the major spot upon purification was subjected to UV- visible analysis, which presented consid­erable difference from MBHA band (expected product 4). By repeating the procedure with slight modification such as increasing the pH, precipitates were observed in 2.5 hours. Furthermore, the TLC analysis indicated 20–30 per­cent conversion with less spots and change in color. Based on current observation (TLC in 1.5h), any increase in the concentration of base up to 4M, maximum conversion of the reactants into product was observed (Scheme 2). All the transition metal complexes (4-8) were sub­jected to characterization through their physical character­istics. Their molecular and structural formulae were estab­lished with the help of m/z ratio obtained through powder XRD and FTIR analyses. The data presented for m/z ratio of the ligand and compound 4-8 shows a good agreement between the calculated and observed values and thus sup­port the suggested structures of the complexes. 3. 3. Theoretical and Experimental FTIR Analysis of the Compounds 4-8 Table 1 provides the details of the experimental and computed IR vibrational frequencies of MBHA (com­pound 3) and metal complexes (compounds 4-8). Con­sidering FTIR spectrum of compound 4, a broad band at 3500–3380 cm-1 (–OH) can be observed, which might be either due to moisture or coordinated water in the com­plex. Moreover, we observed peak shifts toward lower fre­quency value for C=O group from a sharp peak of ligand at 1725 cm–1 to a small peak at 1625 cm–1. It suggests the coordination of carbonyl group through oxygen to the Cr+3 metal which ultimately reduces strength of the car­bonyl group in complex. These results are also supported by literature.14 Similarly, the peaks of ligand and its C-O (of ester) appeared 1278 cm–1 which showed a shift from 1105 cm–1 (Table 1). Which may change because of envi­ronmental effect regarding C=O coordination towards the metal ion.43,44 Some additional peaks have also been observed to appear at 603 and 520 cm–1. These peaks were assigned to Cr-O and C-Cl, respectively.45 These findings have confirmed the participation of carbonylic functional group of the ligand in complex formation. No significant change in the IR frequency of C=C double bond was seen upon complex formation and hence, it is inferred that this group doesn’t participate in complexation (Fig. 1b). Based on these observations, the molecular and structural for-mulae of the compound 4 were deduced as [Cr(L1)2 (H2O)4]+3. In FTIR spectral results for the compound 5 we ob­served that –OH group has shown a broad signal ranging from 3400–3300 cm–1. This signal was relatively at similar position as in the ligand. While comparing the C=C fre­quencies of both the ligand and complex 5, the peak was found to be slightly shifted from 1631 cm–1 to 1620 cm–1 which suggests that this functional group was not involved in coordination. The aromatic ring has shown frequencies (1604 and 1521cm–1) almost at similar position as that of the ligand aromatic pi bonds which also suggests non co­ordination behavior of the aromatic ring in respective complex formation. A clearer signal appeared at 1651 cm–1 was probably due to the carbonyl group and has signifi­cantly shifted from 1714 cm–1 to 1651 cm–1 upon complex-ation. This kind of shift has also been reported in litera-ture,14 thus confirming the coordination of the ligand’s carbonyl site to the Co+3 metal. Another significant varia­tion in the IR spectrum of complex was a slight shift to the higher frequencies in IR signal of C-O (of ester) i.e. ligand C-O frequency shifted from 1278 to 1386 (C-O of the complex) (Table 1). Additionally, a unique signal attribut­able to Co-O appeared at 607 cm–1 and another noticeable Fig. 3a. Observed FT-IR spectrum of Nickel Transition Metal with MBHA (L1) Fig. 3b. Theoretical FT-IR spectrum of Nickel Transition Metal with MBHA (L1) variation in C-Cl frequency from 540 cm–1 to 518 cm–1 was also observed. Based on the overall information dis­cussed above, the molecular formula [Co(L1)2(H2O)4]+3 and structural formula (Fig. 1c) was established for the compound 5. FTIR analysis for the Ni+2 complex (compound 6) showed peaks for all the prominent functional groups along with some informative shifts which revealed the complex formation. Such as, an extremely weak band, al­most negligible at 1640 cm–1 correspond to carbonyl group which was comparatively at lower frequency than its precursor (Ligand) carbonyl absorption (1714 cm–1). It suggests carbonyl coordination upon complex forma­tion. Moreover, the peaks at 609 cm–1 and 455 cm–1 were assigned to Ni-O and C-Cl groups, respectively. Addi­tionally the –OH and =CH groups exhibited peaks at 3450–3350 cm–1 (broad) and 3005 cm–1, respectively. Furthermore, we have also observed peaks for C=C bond at 1643 cm–1 with no significant shift and simultaneously demonstrating that C=C do not participate in complex-ation (Fig. 1d). In spite of that we have also observed sharp peak instead at 3880–3886 cm–1 of same (-OH) functional group. The difference is observed due to the presence of moisture in the compound, which has pro­vided interference during recording of spectrum.41 Like­wise, the remaining functional groups show a little change in the calculated values as is evident from their sharp pointed peaks at exact frequencies. In the observed spectrum these small peaks overlap with each other to give loops instead of sharp pointed peaks obtained in the theoretical spectrum (Fig. 3a & Fig. 3b).40 Nevertheless, we have obtained good agreement in the observed and calculated values of frequencies and deduced the formula 46 [Ni(L1)2(H2O)2]+2. FTIR spectral results for the compound 7 (Fig. 1e) reveal that –OH group gives a broad band ranging from 3505–3310 cm–1 with relatively no change in its position as compared to the ligand. Similarly, the C=C .– (1631 cm–1) remains about the same upon complex formation i.e. at 1629 cm–1 suggesting that this functional group was not involved in coordination. A clear disappearance of signal of the carbonyl group and its appearance at an entirely new position (Table 1) was probably due to its coordination with the metal ion. This kind of shift has also been report­ed in the literature,14 hence confirms the coordination of the ligand’s carbonyl site to the Mn+2 metal ion. Moreover, the appearance of new peak at 603 cm–1was attributed to Mn-O while C-Cl 540 cm–1 shifted towards 523 cm–1. The – Table 1. Characteristic FTIR bands of the MBHA (L1) and its metal complexes (cm-1) . Functional (L1) Calc. CrL1 CoL1 NiL1 Calc. MnL1 CuL1 groups (L1) NiL1 – (-OH) 3446 3808 3500–3380 3300–3400 3350–3450 3880–3886 3310–3505 3350–3480 – . (=C-H) 2953 3150 2915 2950 3005 3194 3005 3115 – . (Sp3 C-H stretch), 2897 3020 2810–2980 2860–2935 2850 3244 2980 2980 – . (ester C=O), 1714 1760 1625 1651 1640 1669 – – – . C=C (aromatic), 1631 1637 1550 1620 1643 1627 1629 1673 – . C=C – 1446 1438 1483 1566 1576 1500 1604 – . (-CH3) 1440 1482 – – 1463 1485 1566 1521 – . (C-O) 1278 1299 1105 1386 1388 1358 1386 1388 – . (C-OCH3) – 1212 1382 1109 1109 1128 1111 1112 – . (M-O) – – 603 607 609 606 603 605 – . (C-Cl) 540 459 520 518 455 485 523 486 Only the observed changes in specified functional groups are represented molecular [Mn(L1)2(H2O)2]+2 and structural formula were established for the compound 7. FTIR results for the compound 8 (Fig. 1f) have re­vealed the presence of –OH functional group by a peak almost at similar position (3480–3350 cm–1) as that of the ligand precursor. Almost similar results were obtained for the presence of sp2 and sp3 C-H bonds of the complex i.e. their peaks were observed at 3115, 2980 cm–1 respectively. The ester carbonyl absorption (1705 cm–1) has completely disappeared as compared to that of the ligand carbonyl (1714 cm–1) which suggests its participation in complex formation. No marked difference was observed in the fre­quency due to the C=C double bond (1673 cm–1) and ar­omatic C=C bond (1604 cm–1) suggesting no contribu­tion in complex formation. These assumptions were further supported by appearance of IR peaks at 605 cm–1, and 486 cm–1 due to presence of Cu-O and C-Cl bond. Based on all these physical, analytical and spectral results the prepared complex was assigned the structure present­ed above in scheme 2 as compound 8 formulated as [Cu(L1)2(H2O)2]+2. 3. 4. X-Ray Diffraction The structure resolved from powder XRD analysis is a successful process that leads to evaluation of an exact crystal structure depending on the cautious handling of each step. Microcrystalline materials mainly organic based metal complexes can easily be resolved by XRD powder diffraction.13,28 The accuracy of a material whose structure is to be determined depends on the quality of the powder diffraction data. Besides, nature of the asym­metric unit such as the number, type of atoms, atomic arrangement and molecular geometry involves to prove the quality of the complex. However, for successful struc­ture determination, monophasic powder sample of good crystallinity should be available for the material of inter­est.47 The recorded result was summarized in Table 2. & Table 3. X-ray diffraction was performed for further confir­mation of the structure of transition metal complexes. The patterns of diffractograms obtained for complexes are giv­en in Fig. 4. Each metal complex pattern has one sharp peak of metal oxide (Fig. 4). This clearly gives an idea of the coordination of one oxygen atom from the ligand to the metal ions. Hence, the idea of coordination of ligand with the metal ions through the same site has been confirmed. Moreover, these patterns indicate crystalline nature of all the complexes. It can be observed that the rest of the pat­tern of metal complexes is almost dissimilar from each oth­er owing to the formation of a distinct distorted crystalline structure. Most probably, this difference is due to the coor­dination of water molecules into the coordination sphere.15,16 Besides, they have different unit cell number and radius intensity ratio, because of this they belong to non-identical but unique crystal class. The resultant XRD output such as density, volume, radius intensity ratio, lat­tice parameters (a, b, c, a, ß, .) and probable crystal class have been given in the Table 2. The chromium complex (compound 4) was isolated, and crystallized in the orthor­hombic system (space group Pmmn (59) with Z = 2). In the cobalt complex, the molecule contains one Cr+3 atom, two Table 2. Crystallographic data and observed refinement parameters for complexes 4-8 Sample S. Group Volume Density Unit Cell Crystal No. of unit Reference Intensity S. No. complexes (G. No.) (106pm3) (g/cm) Dimensions (Å) Class cells (Z) Ratio (RIR) 1 CrL1 Pmmn 94.58 3.63 a.b.c Orthorhombic 2 4.60 (59) a = 3.8630 b = 3.1820 c = 7.6940 2 CoL1 Fd-3m 93.82 6.09 a=b=c Cubic 2 6.39 (223) a = 8.0840 b = 8.0840 c = 8.0840 3 NiL1 R-3m 99.78 4.53 a=b.c Hexagonal 3 6.38 (166) a = 2.8353 b = 2.8353 c = 14.3319 4 MnL1 141/amd 518.94 ……. a=b=c Cubic 4 4.21 (141) a = 8.0360 b = 8.0360 c = 8.0360 5 CuL1 Cc 81.12 6.51 a.b.c Monoclinic 4 3.72 (9) a = .6890 b = 3.4200 c = 5.1300 Table 3. Crystal lattice Data and Summary of Data Collection and Refinement for complexes 4-8 S.NO. Complex Compound Miller Indices d [Å] 2. Intensity (%) hk l 0 1 [Cr(L1)2(H2O)4]+3 1 1 1 0 0 1 1 1101 7.6940 3.4523 2.4519 2.3397 11.492 25.78536.62038.443 100.0 48.3 37.2 14.1 2 2 [Co(L1)2(H2O)4]+3 3 5 4 2 1 1 4 0110 2.8600 2.4380 1.5559 11.4293 31.24936.837 59.35065.222 40.0 100.0 35.0 45.0 1 3 [Ni(L1)2(H2O)2]+2 1 1 1 1 0 0 0 0147 4.7773 2.4201 2.0254 1.5724 18.558 37.11944.70658.663 100.0 26.9 36.9 9.4 2 4 [Mn(L1)2(H2O)2]+2 1 2 2 1 0 2 2 1304 3.4037 2.9359 2.8850 1.8266 26.160 30.42130.97249.883 100.0 20.8 22.1 30.9 0 5 [Cu(L1)2(H2O)2]+2 2 0 2 2 0 0 2 2240 2.5300 2.3600 2.3350 2.0241 35.452 38.10038.52544.735 100.0 79.7 36.2 42.2 Each set of indices was chosen on the basis of highest intensity (%) of sharp peak coordinated MBHA (L1) molecules at the two sites in a amd (141), whereas they have 2 and 4, Z values, respective-mononuclear complex. ly. The indexing related to their structure elucidation as It is noteworthy that the complexes cobalt and man-well as peak intensities and diffraction angle (2.) is given ganese (compound 5 and 7) were found to have cubic crys-in the Table 3. It is illustrated that the prepared complex of talline nature, having space group Fd-3m (223) and 141/ nickel (compound 6) has the space group R-3m (166), Z = Fig. 4. X-ray diffraction patterns of complexes [M(L1)2(H2O)4]+3 with M=Cr+3, Co+3and [M(L1)2(H2O)2]+2 with M= Ni2+, Mn+2 and Cu2+ 3 and belongs to hexagonal crystal class. While, copper metal (compound 8) coordinated with MBHA has mono­clinic crystalline nature. It has the space group Cc (9) and Z = 4. Based on aforementioned analysis and to provide more strength in verdicts (energy balance and mass bal­ance) the computational modeling was further performed. 3. 5. Antioxidant Activities of MBHA and Metal Complexes These compounds (3-8) were tested to determine their efficiency for scavenging the DPPH radicals using ascorbic acid as standard, which has a known free radical scavenging activity. The potential of MBHA and metal complexes as antioxidants has been plotted in (Fig. 5 and Fig. 6). The alcoholic MBHA and ester containing com­pounds are highly active against antioxidant exhibi­tion.28,48 The antioxidant potential of the MBHA and its transition metal complexes along with the standard (ascor­bic acid) is documented in Table 4. The results have indicated that ascorbic acid has ex­hibited the highest antioxidant activity at high concentra­tion. When the highest antioxidant exhibition percent among synthesized metal moieties was compared with Table 4. Antioxidant activity of MBHA (compound 3) and its formed metal complexes (compound 4-8) S. No. Concentration Ascorbic Acid Ligand/Complexes Antioxidant Activity (mM) (%) (%) 1 1.00 20 L1 56.7 2 2.00 39.4 CrL1 9.7 3 3.00 58.1 CoL1 17.1 4 4.00 76 NiL1 10.8 5 5.00 92.7 MnL1 19.7 6 6.00 96.8 CuL1 18.7 DPPH scavenging effect (%) = Ac – As/Ac × 10, Ac = Absorbance of Control MBHA, it was observed that MnL1 (compound 7) has the highest (19.7%) antioxidant activity. Additionally, the cop­per complex with MBHA has shown almost the same inhi­bition. However, L1 (MBHA) has comparatively maxi­mum antioxidant activity. It is interesting to note that all these novel compounds (MBHA, [M(L1)2(H2O)4]+3 and [M(L1)2(H2O)2]+2) possess positive inhibition (Fig. 6). 3. 6. Antibacterial Assay The antibacterial activity of these compounds was screened by using agar disc diffusion method against four Table 5. Antibacterial activities of the MBHA and the complexes (compound 4-8) S. Ligand/ S.aureus E.coli B.pumilis S.typhi No Complexes (%) (%) (%) (%) 1 L1 71.4 62.5 80.3 45.8 2 CrL1 A A 40.7 A 3 CoL1 95.21 99.1 81.4 50.0 4 NiL1 44.6 45.8 40 50.0 5 MnL1 47.6 A 46.2 A 6 CuL1 52.3 A 44.4 48.0 Antimicrobial activity, expressed as inhibition zone diameter in millimeters (mm), of synthesized compounds against pathological strains based on agar well diffusion assays at 1 mg mL–1. The exper­iment was carried out in triplicate and the average zone of inhibi­tion was calculated. A = Non active inhibition zone bacterial strains (Staphylococcus aureus, Escherichia coli, Bacillus pumilis, Salmonella typhi) while Gentamycin is used as standard (Table 5). The efficiency of compounds was categorized into three classes such as sensitive, transitional and resistant. The reported analysis is consistent with our findings that if a compound is found to be sensitive, it could be a valuable medication against bacterial disease. Though, non-active compounds are found to be as least resistant.26,49,50 In con­trast to the ligand an enhanced antibacterial potential was observed in metal complexes. Consequently, opening new ways in the fight against antibiotic resistance. This is be­cause of coordination (ligand to metal) which could be best explained on the basis of chelation theory.16,51 The comparison of antibacterial activity, shown in Fig. 7 con­firms that metal complex of cobalt (compound 5) was highly active than its corresponding MBHA ligand against all bacterial strains. It is evident from the results that all metal complexes and MBHA are significantly active against B.pumilis, however, chromium (compound 4) complex is completely resistive against the remaining bac­terial strains (S.aureus, E.coli, S.typhi). It is interesting to note that compound 7 (manganese complex) is found non-reactive against two bacterial strains (E.coli and S. ty-phi), while compound 8 (copper complex) is resistant against E.coli only. Thus, all remaining complexes indicate extremely positive activity against the bacterial strains. 4. Conclusions An aromatic MBHA Methyl 2-((4-chlorophenyl) (hydroxy)methyle) acrylate (compound 3) was prepared successfully by a well-known MBH reaction scheme and used further in synthesis of metal complexes by transition metal salts (Cr+3, Co+3, Ni+2, Mn+2,Cu+2). Through modi­fied aforementioned scheme synthesis of five novel com­pounds is being reported (4-8). The ligand MBHA (L1) and its corresponding complexes were characterized by carrying out their physical, analytical, spectral and com­putational analysis. The IR spectrum of the synthesized ligand is optimized by using DFT B3LYP method and 6-311++ G (d,p) basis set. Whereas, five metal complexes were generated computationally by the Hartree Fock (HF) B3LYP method in conjunction with the 3-21G(d,p) basis set and we found good agreement with their observed FT­IR spectrum. These investigations have collectively sug­gested the molecular and structural formula. It has been found that MBHA (L1) coordinated with all the central metal ion in stoichiometric of 2:1 ligand to metal ratio. The well-defined crystalline homogeneous nature of all metal complexes was observed and confirmed through XRD powder diffraction analysis. Moreover, different crystal structures were found for each metal complex. Such as, compound 4 is orthorhombic, compound 6 is hexagonal, and compound 8 is monoclinic while com­pounds 5 and 7 both lie in cubic crystal class. For antibac­terial assay, four pathogenic strains (S. aureus, E. coli, B. pumilis, P. mirabilis, and S.typhi) were examined and compared with standard antibiotic, Gentamycin. It was inferred that Cobalt complexes displayed uppermost anti­bacterial potential compared to MBHA (L1). Whereas, Nickle based complex showed good inhibition against all four bacterial strains. Chromium based complexes were found active against B.pumilis only. Both Manganese and Copper based complexes were seen inactive against E. coli. Manganese complex inhibition potential against S.ty-phi indicated non-active, conversely, again showed good inhibition zone against the remaining bacterial strains. Furthermore, the DPPH radical scavenging assay (antiox­idant) of compounds 3-8 demonstrated the positive inhi­bition with context to the standard (ascorbic acid). This study can be expanded further by synthesizing some oth­er novel MBHA ligands and their metal complexes, in or­der to identify the most suitable medication against infec­tious diseases. Acknoledgement The authors are thankful to University of Karachi for providing such an appreciative infrastructure and envi­ronment. 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Harmon, J. R. Clugston, K. Dec, B. Hainline, S. Herring, F. Hossen, M. F. Haque, C. M. Zakaria, Asian J. Appl. Chem. S. F. Kane, A. P. Kontos, J. J. Leddy, M. McCrea, S. K. Poddar, Res. 2019, Vol. 3 (3), pp. 1–15. et al., Br. J. Sports Med. 2019, Vol. 53 (4), pp. 213–225. DOI:10.9734/ajacr/2019/v3i330093 DOI:10.1136/bjsports-2018-100338 47. A. Jacques, T. Auvray, R. Bevernaegie, F. Loiseau, M. Cibian, 51. M. I. Khan, A. Khan, I. Hussain, M. A. Khan, S. Gul, M. Iqbal, G. S. Hanan, A. Kirsch-De Mesmaeker, B. Elias, Inorganica Inayat-Ur-Rahman, F. Khuda, Inorg. Chem. Commun. 2013, Chim. Acta. 2018, Vol. 471 (Iii), pp. 8–16. Vol. 35 (June), pp. 104–109. DOI:10.1016/j.ica.2017.10.018 DOI:10.1016/j.inoche.2013.06.014 Povzetek Naslovni ligand smo sintetizirali in ocistili s kolonsko kromatografijo, pri cemer smo upoštevali pomen in razlicna po­drocja uporabe sinteticnih Morita-Baylis-Hillmanovih aduktov (MBHA). Pod bazicnimi pogoji smo pripravili Cr+3, Mn+2, Co+3, Ni+2 in Cu+2 komplekse s sintetiziranim ligandom, ga karakterizirali s spektroskopskimi metodami in pre­verili IR spekter z izracunanim z DFT B3LYP metodo, z 6-311++ G (d,p) naborom osnov in Hartree Fock (HF) B3LYP metodo z 3-21G(d,p) naborom osnov. Dobljene kristale smo dodatno karakterizirali z rentgensko praškovno difrakcijo. Preucevali smo tudi antibakterijske in antioksidativne lastnosti MBHA in njegovih kompleksov. Vse navedene spojine so aktivni antioksidanti. Antibakterijske študije, opravljene na S. aureus, E. coli, B. pumilis in S. typhi so pokazale odlicno delovanje kobaltovega kompleksa. Tovrstne spojine imajo potencial za razvoj novih, cenejših in ucinkovitih zdravil za bakterijska obolenja. Raziskava predstavlja prvi primer uporabe MBHA za tvorbo kovinskih kompleksov. Except when otherwise noted, articles in this journal are published under the terms and conditions of the Creative Commons Attribution 4.0 International License DOI: 10.17344/acsi.2021.7314 Scientific paper Novel Benzimidazole-Based Compounds as Antimicrobials: Synthesis, Molecular Docking, Molecular Dynamics and in silico ADME Profile Studies Elif Yesilçayir,1 Ismail Çelik,2 Hasan Tahsin Sen,1,3 Suna Sibel Gürpinar,4 Müjde Eryilmaz,4 Gülgün Ayhan-Kilcigil1* 1 Ankara University, Faculty of Pharmacy, Department of Pharmaceutical Chemistry, Ankara, Turkey 2 Erciyes University, Faculty of Pharmacy, Department of Pharmaceutical Chemistry, Kayseri, Turkey 3 Lokman Hekim University, Faculty of Pharmacy, Department of Pharmaceutical Chemistry, Ankara, Turkey 4 Ankara University, Faculty of Pharmacy, Department of Pharmaceutical Microbiology, Ankara, Turkey * Corresponding author: E-mail: kilcigil@pharmacy.ankara.edu.tr Received: 12-03-2021 Abstract Some novel benzimidazole derivatives were synthesized and their antimicrobial activities were evaluated. Compounds 3a and 3b exhibited excellent antibacterial activity with MIC values <4 µg/mL against Staphylococcus aureus ATCC 29213 (MSSA) and Staphylococcus aureus ATCC 43300 (MRSA). Molecular docking analyzes of compounds with MIC values of 16 µg/mL and below against gram-positive bacteria and fungi were performed using FabH (ß-ketoacyl-acyl carrier protein synthase III) as bacterial protein and CYP51 (sterol 14a-demethylase) as the fungal target protein. According to the mo­lecular docking analysis, it was calculated that sufficient protein-ligand interaction energy was liberated between the com­pounds 2f, 3a, 3b, 3e and 3h and the antibacterial target protein FabH and strong interactions were formed between 2f and 3h and the antifungal target protein. According to RMSD, RMSF and MMPBSA measurements obtained from molecular dynamics, it is understood that compounds 3a and 3b maintain protein-ligand stability in silico physiological conditions. Keywords: Benzimidazole, antimicrobial activity, molecular docking, molecular dynamics 1.Introduction Microbes are disease agents that cause death. Today, the transmission of diseases to large masses has become an increasing threat to human health. Antibiotic resistance remains at dangerously high levels around the world. This situation leads to new resistance mechanisms and spreads the resistance globally, making it difficult to treat infec­tious diseases. Furthermore, antimicrobial resistance is recognized globally as one of the greatest health threats; thus, the discovery of alternative antibacterial agents to address antimicrobial resistance is a priority target. Effec­tive treatment of infections and complete elimination of antimicrobial resistance can be achieved with the use of new antimicrobial compounds. It is well known that benzimidazoles have antibacteri-al,1 antimicrobial,2–5 and antifungal6 activities. Furthermore, several benzimidazoles show promising pharmacological activities such as antioxidant,7–9 anticancer,10 anti-inflam­matory,11 antiprotozoal,12 antiviral,13 antidiabetic,14 antihy-pertensive,15 antimycobacterial,16 and antithrombin,17 as well as tubulin18 and dipeptidyl peptidase III19 inhibitors. In view of extending our previous studies on the syn­thesis and bioactivity of benzimidazole derivatives,20–21 we synthesized a series of 4-(1H-benzimidazol-2-yl)-6-arylpy-rimidin-2-amines. Moreover, we also evaluated their anti­ bacterial and antifungal activities and carried out molecu­ lar docking and molecular dynamics simulation studies. 2. Experimental 2. 1. Chemistry All reagents and solvents were used as purchased, without further purification. The reactions were moni­tored by thin-layer chromatography (TLC) analysis using silica gel plates (Kieselgel 60F254, E. Merck). Column chromatography was performed on Silica Gel 60 M (0.040–0.063 mm, E. Merck). Melting points were deter­mined on a Büchi B540 capillary melting point apparatus and are uncorrected. The 1H and 13C NMR spectra were recorded on a Varian 400 MHz and Bruker 500 MHz FT spectrometer in DMSO-d6, shift values are given in parts per million relative to tetramethylsilane as internal refer­ence and coupling constants (J) are reported in Hertz. Mass spectra were taken on a Waters Micromass ZQ con­nected with Waters Alliance HPLC, using ESI+ method, with the C-18 column. Elemental analyses were performed by Leco CHNS-932 analyzer. 2. 1. 1. Synthesis of 2-(a-Hydroxyethyl)benzimidazole o-Phenylenediamine (0. 025 mol) and lactic acid (3.2 mL) were refluxed for 3 h. The reaction mixture was cooled and made alkaline with 10% aq. NaOH. The crude product obtained was dissolved in boiling water and decolorized with activated charcoal. The mixture was filtered and washed with cold water.22–24 2. 1. 2. Synthesis of 2-Acetylbenzimidazole To the solution of K2Cr2O7 (0.15 mol) in H2SO4 (25%, 10 mL) was added dropwise a solution of 2-(a-hy­droxyethyl)benzimidazole (0.01 mol) in 5% H2SO4 (5 mL) while stirring at room temperature over a period of 20 min. The reaction mixture was stirred at room tempera­ture for 2 h. The reaction mixture was neutralized with aqueous NH3 solution (1:1) and the precipitated solid was filtered, washed with water, dried and recrystallized from ethyl acetate.23,24 2. 1. 3. Synthesis of 1-(1H-Benzimidazol-2-yl)-3­aryl-prop-2-en-1-ones 2a–h 2-Acetylbenzimidazole (0.01 mol) and aromatic al­dehydes (0.01 mol) were mixed with ethanol (20 mL) and added 60% aq. KOH (5 mL) at 0 °C and the mixture were stirred at room temperature for 4 h. After completion of the reaction (controlling TLC, chloroform:hexane 1/3), the reaction mixture was poured into ice-cold water and neutralized with dilute HCl solution. The solid formed was filtered, washed, dried and recrystallized from ethanol.23,24 (E)-1-(1H-Benzimidazol-2-yl)-3-(3-bromo-4-fluoro­phenyl)prop-2-en-1-one (2a) Yield 73%; mp 214 °C. 1H NMR (400 MHz, DM­SO-d6) d 7.3–7.5 (m, 3H, Ar-H), 7.56 (d, 1H, Jo = 8 Hz, Ar-H), 7.83–7.95 (m, 3H, Ar-H and CH=CH), 8.09 (d, 1H, Jtrans = 16 Hz, CH=CH), 8.25–8.26 (m, 1H, Ar-H), 13.51 (s, 1H, NH); 13C NMR (100 MHz, DMSO-d6) d 108.9 (d, J = 21.4 Hz), 112.94, 117.4 (d, J = 22.9 Hz), 121.7, 112.7, 123.2, 125.8, 130.3 (d, J = 8.45 Hz), 132.8 (d, J = 3.11 Hz), 134.0, 134.8, 141.6, 143.0, 148.8, 159 (d, J = 250.05 Hz), 180.8; MS (ESI+) m/z 345.40 (M+H), 347.39 (M+H+2). Anal. Calcd for C10H10BrFN2O: C, 55.68; H, 2.92; N, 8.12. Found: C, 55.83; H, 3.19; N, 7.88. (E)-1-(1H-Benzimidazol-2-yl)-3-(naphthalen-2-yl) prop-2-en-1-one (2b) Yield 34%; mp 225 °C. 1H NMR (500 MHz, DM­SO-d6) d 7.35–7.37 (m, 1H, Ar-H), 7.41–7.44 (m, 1H, Ar-H), 7.90 (d, 1H, Jo = 8.15 Hz, Ar-H), 7.98–8.06 (m, 4H, Ar-H), 8.15 (d, 1H, Jtrans = 16 Hz, CH=CH), 8.26 (d, 1H, Jtrans = 16 Hz, CH=CH), 8.43 (s, 1H, Ar-H), 13.52 (s, 1H, NH); 13C NMR (125 MHz, DMSO-d6) d 113.39, 121.63, 122.32, 123.71, 124.76, 126.28, 127.39, 128.23, 129.24, 129.28, 131.42, 132.39, 133.45, 134.62, 135.29, 143.54, 144.80, 181.39; MS (ESI+) m/z 299.60 (M+H). Anal. Calcd for C20H14N2O: C, 80.52; H, 4.73; N, 9.39. Found: C, 80.23; H, 4.41; N, 9.80. (E)-1-(1H-Benzimidazol-2-yl)-3-(naphthalen-1-yl)prop-2-en-1-one (2c) Yield 44%; mp 216 °C. 1H NMR (500 MHz, DM­SO-d6) d 7.35–7.41 (m, 2H, Ar-H), 7.35–7.43 (m, 2H, Ar-H), 7.61–7.70 (m, 4H, Ar-H), 7.90 (d, 1H, J = 7.6 Hz, Ar-H), 8.04 (d, 1H, J = 8.30 Hz, Ar-H), 8.10 (d, 1H, J = 8.10 Hz, Ar-H), 8.18–8.23 (2H, Ar-H and CH=CH), 8.36 (d, 1H, J = 8.45 Hz, Ar-H), 8.82 (d, 1H, Jtrans = 15.85 Hz, CH=CH), 13.57 (brs, 1H, NH); 13C NMR (125 MHz, DM­SO-d6) d 113.41, 121.68, 123.50, 123.70, 124.57, 126.28, 126.35, 126.93, 127.91, 129.36, 131.51, 131.70, 131.83, 133.91, 135.31, 140.85, 143.57, 149.44, 181.29; MS (ESI+) m/z 299.55 (M+H). Anal. Calcd for C20H14N2O: C, 80.52; H, 4.73; N, 9.39. Found: C, 80.33; H, 4.92; N, 9.15. (E)-1-(1H-Benzimidazol-2-yl)-3-(4-bromophenyl)prop-2-en-1-one (2d) Yield 80%; mp 229 °C. 1H NMR (400 MHz, DM­SO-d6) d 4.82 and 5.46 (td, 1H, J = 7.6 Hz, Ar-H), 7.21– 7.47 (m, 5H, Ar-H), 7.67–7.85 (m, 3H, Ar-H), 7.94 and 8.14 (d, 1H, Jtrans = 16 Hz, CH=CH), 13.18 and 13.53 (s, 1H, NH); 13C NMR (100 MHz, DMSO-d6) d 119.81, 122.27, 124.52, 129.86, 130.84, 130.86, 132.13, 133.57, 138.21, 142.87, 147.76, 148.90, 180.88, 192.09; MS (ESI+) m/z 327.51 (M+H), 329.49 (M+H+2). Anal. Calcd for C16H11BrN2O: C, 58.74; H, 3.39; N, 8.56. Found: C, 59.07; H, 3.72; N, 8.24. (E)-1-(1H-Benzimidazol-2-yl)-3-phenylprop-2-en-1­one (2e) Yield 71%; mp 224 °C (lit.23 162–164 °C). 1H NMR (400 MHz, DMSO-d6) d 7.36–7.38 (m, 2H, Ar-H), 7.47– 7.49 (m, 3H, Ar-H), 7.70–7.89 (m, 4H, Ar-H), 7.98 (d, 1H, Jtrans = 16 Hz, CH=CH), 8.13 (d, 1H, Jtrans = 16 Hz, CH=CH), 13.51 (brs, 1H, NH); 13C NMR (100 MHz, DM­SO-d6) d 121.53, 128.94, 129.14, 131.11, 134.30, 144.25, 148.99, 180.96; MS (ESI+) m/z 249.47 (M+H). Anal. Calcd for C16H12N2O: C, 77.40; H, 4.87; N, 11.28. Found: C, 77.65; H, 5.21; N, 10.91. (E)-1-(1H-Benzimidazol-2-yl)-3-(2-fluorophenyl)prop-2-en-1-one (2f) Yield 13%; mp 212 °C. 1H NMR (400 MHz, DM­SO-d6) d 7.29–7.38 (m, 4H, Ar-H), 7.52–7.55 (m, 2H, Ar-H), 7.86 (d, 1H, J = 6.8 Hz, Ar-H), 7.97–8.01 (m, 2H, Ar-H and CH=CH), 8.18 (d, 1H, Jtrans = 16.4 Hz, CH=CH), 13.52 (brs, 1H, NH); 13C NMR (100 MHz, DMSO-d6) d 113.36, 116.75 (d, J = 21.3 Hz), 121.70, 123.47 (d, J = 11.5 Hz), 123.71, 124.49 (d, J = 6.1 Hz), 125.73, 126.36, 130.66, 133.52 (d, J = 8.4 Hz), 135.29, 136.60, 143.49, 149.23, 160.66 (d, J = 250.7 Hz), 181.33 (d, J = 3.8Hz); MS (ESI+) m/z 267.49 (M+H). Anal. Calcd for C16H11FN2O: C, 72.17; H, 4.16; N, 10.52. Found: C, 71.79; H, 4.49; N, 10.86. (E)-1-(1H-Benzimidazol-2-yl)-3-[4-(benzyloxy)phenyl] prop-2-en-1-one (2g) Yield 45%; mp 240 °C. 1H NMR (400 MHz, DM­SO-d6) d 5.2 (s, 2H, CH2), 7.13 (d, 2H, J = 8.8 Hz, Ar-H), 7.34–7.49 (m, 7H, Ar-H), 7.6 (brs, 1H), 7.84–8.03 (m, 5H, Ar-H and CH=CH); 13C NMR (100 MHz, DMSO-d6) d 69.88, 115.73, 115.92, 119.54, 127.59, 128.24, 128.32, 128.51, 128.94, 128.95, 131.42, 132.34, 137.07, 144.69, 149.62, 161.32, 181.27; MS (ESI+) m/z 355.54 (M+H). Anal. Calcd for C23H18N2O2: C, 77.95; H, 5.12; N, 7.90. Found: C, 77.52; H, 4.79; N, 8.28. (E)-1-(1H-Benzimidazol-2-yl)-3-(thiophen-2-yl)prop-2-en-1-one (2h) Yield 67%; mp 224 °C. 1H NMR (400 MHz, DM­SO-d6) d 7.22–7.24 (m,1H, Ar-H), 7.31–7.42 (m, 2H, Ar-H), 7.73 (d, 1H, J = 3.6 Hz, Ar-H), 7.80 (d, 1H, Jtrans = 15.6 Hz, CH=CH), 7.85–7.88 (m, 2H, Ar-H), 8.15 (d, 1H, Jtrans = 16 Hz, CH=CH); 13C NMR (100 MHz, DMSO-d6) d 180.35, 148.94, 143.51, 139.69, 137.01, 135.26, 134.27, 131.17, 129.09, 126.23, 123.68, 121.62, 119.86, 113.31; MS (ESI+) m/z 255.30 (M+H). Anal. Calcd for C14H10N2OS: C, 66.12; H, 3.96; N, 11.02; S, 12.61. Found: C, 65.83; H, 3.50; N, 11.45; S, 12.97. 2. 1. 4. Synthesis of 4-(1H-Benzimidazol-2-yl)-6­arylpyrimidin-2-amines 3a–h 0.81 mmol arylidene benzimidazole 2a–h was added at 0 °C to the mixture of 1.08 mmol (103.4 mg) guanidine hydrochloride and 2.16 mmol (51.94 mg) sodium hydride in 2.7 mL DMF, stirred for 1 h at room temperature and for another 3 h at 100 °C. The reaction mixture was poured onto the crushed ice and pH adjusted to 7 with dilute HCl. The precipitate was filtered and purified by column chro­matography using chloroform/methanol, 10/0.5 as the elu­ent.25 4-(1H-Benzimidazol-2-yl)-6-(3-bromo-4-fluorophenyl) pyrimidin-2-amine (3a) Yield 26%; mp 130 °C. 1H NMR (500 MHz, DM­SO-d6) d 6.91 (brs, 2H, NH2), 7.24–7.32 (m, 2H, Ar-H), 7.55 (td, 1H, J = 8.65 Hz, 8.60 Hz, Ar-H), 7.61 (d, 1H, Jo = 7.85 Hz, Ar-H), 7.75 (d, 1H, Jo = 7.9 Hz, Ar-H), 7.98 (s, 1H, pyrimidine H-5), 8.25–8.28 (m, 1H, Ar-H), 8.54 (dd, 1H, Jo = 6.8 Hz, Jm = 2.6 Hz, Ar-H), 13.04 (s, 1H, NH); 13C NMR (125 MHz, DMSO-d6) d 102.93, 109.10–109.27 (d, J = 21.26 Hz), 112.96, 117.56–117.73 (d, J = 22.46 Hz), 120.03, 122.77–124.29 (d, J = 190.65 Hz), 128.92–128.98 (d, J = 7.96 Hz), 132.39, 135.24–135.31–135.34 (2×d, J = 9.34 Hz, 3.44 Hz), 144.13, 149.85, 158.00, 158.25, 161.24, 163.00, 164.20; MS (ESI+) m/z 384.48 (M+H), 386.50 (M+H+2). Anal. Calcd for C17H11BrFN5: C, 43.90; H, 2.60; N, 15.06. Found: C, 44.39; H, 2.98; N, 14.85. 4-(1H-Benzimidazol-2-yl)-6-(naphthalen-2-yl)pyrimi­din-2-amine (3b) Yiel 29%; mp 231 °C. 1H NMR (500 MHz, DM­SO-d6) d 6.87 (brs, 2H, NH2), 7.25–7.33 (m, 2H, Ar-H), 7.60–7.64 (m, 3H, Ar-H), 7.77 (d, 1H, Jo = 7.9 Hz, Ar-H), 8.00–8.02 (m, H, Ar-H), 8.09 (d, 1H, Jo = 8.75 Hz, Ar-H), 8.15–8.17 (m, 2H, Ar-H), 8.34 (dd, Jo = 8.65 Hz, Jm = 1.75 Hz, Ar-H), 8.83 (s, 1H, pyrimidine H-5), 13.05 (s, 1H, NH); 13C NMR (125 MHz, DMSO-d6) d 103.28, 112.97, 120.01, 122.73, 124.20, 124.41, 127.15, 127.45, 127.91, 128.06, 128.81, 129.47, 133.33, 134.59, 134.64, 135.28, 144.18, 150.08, 157.68, 164.35, 165.41; MS (ESI+) m/z 338.58 (M+H). Anal. Calcd for C21H15N5: C, 74.76; H, 4.48; N, 20.76. Found: C, 74.29; H, 4.75; N, 20.38. 4-(1H-Benzimidazol-2-yl)-6-(naphthalen-1-yl)pyrimi­din-2-amine (3c) Yield 26%; mp 152 °C. 1H NMR (500 MHz, DM­SO-d6) d 6.91 (brs, 2H, NH2), 7.24 (td, 1H, J = 8.15 Hz, 1.15 Hz, Ar-H), 7.30 (td, 1H, J = 8.15 Hz, 1.15 Hz, Ar-H), 7.57–7.67 (m, 5H, Ar-H), 7.71 (d, 1H, J = 8.10 Hz, Ar-H), 7.78 (dd, 1H, J = 7.05 Hz, 1.15 Hz, Ar-H), 8.04–8.09 (m, 2H, Ar-H), 8.29–8.31 (m, 1H, Ar-H); 13C NMR (125 MHz, DMSO-d6) d 107.82, 112.95, 120.07, 122.73, 124.23, 125.74, 125.90, 126.64, 127.24, 127.68, 128.92, 130.19, 130.47, 133.89, 135.27, 136.80, 144.15, 149.91, 157.11, 164.14, 168.33; MS (ESI+) m/z 338.56. Anal. Calcd for C21H15N5: C, 74.76; H, 4.48; N, 20.76. Found: C, 75.11; H, 4.82; N, 20.99. 4-(1H-Benzimidazol-2-yl)-6-(4-bromophenyl)pyrimi­din-2-amine (3d) Yield 24%; mp 102 °C. 1H NMR (500 MHz, DM­SO-d6) d 6.87 (brs, 2H, NH2), 7.26–7.30 (m, 2H, Ar-H), 7.60–7.61 (m, 2H, Ar-H), 7.75 (d, 2H, Jo = 8.5 Hz, Ar-H), 7.97 (s, 1H, pyrimidine H-5), 8.15 (d, 2H, Jo = 8.5 Hz, Ar-H), 13.3 (s, 1H, NH); 13C NMR (125 MHz, DMSO-d6) d 102.88, 112.97, 120.05, 122.78, 124.25, 124.91, 129.37, 131.56, 132.29, 135.25, 136.45, 144.17, 149.93, 157.87, 164.29, 164.39, 172.49; MS (ESI+) m/z 366.51 (M+H), 368.51 (M+H+2). Anal. Calcd for C17H12BrN5: C, 55.75; H, 3.30; N, 19.12. Found: C, 56.18; H, 3.74; N, 19.50. 4-(1H-Benzimidazol-2-yl)-6-phenylpyrimidin-2-amine (3e) Yield 17%; mp 172 °C (lit.24 192–194 °C). 1H NMR (500 MHz, DMSO-d6) d 6.95 (brs, 2H, NH2), 7.20 (brs, 1H), 7.35–7.36 (m, 2H, Ar-H), 7.56–7.59 (m, 2H, Ar-H), 7.72–7.74 (m, 3H, Ar-H), 8.16 (s, 1H, Ar-H), 8.21–8.23 (m, 2H, Ar-H); 13C NMR (100 MHz, DMSO-d6) d 104.47, 116.18, 124.32, 127.08, 127.37, 129.34, 129.51, 131.50, 136.93, 149.23, 152.20, 156.00, 158.76, 164.12, 165.85; MS (ESI+) m/z 288.5 (M+H). Anal. Calcd for C17H13N5: C, 71.06; H, 4.56; N, 24.37. Found: C, 69.59; H, 4.82; N, 24.80. 4-(1H-Benzimidazol-2-yl)-6-(2-fluorophenyl)pyrimi­din-2-amine (3f) Yield 15%; mp 121 °C. 1H NMR (500 MHz, DM­SO-d6) d 6.88 (brs, 2H, NH2), 7.23–7.31 (m, 2H, Ar-H), 7.38–7.42 (m, 2H, Ar-H), 7.57–7.76 (m, 2H, Ar-H), 7.75 (d, 1H, Jo = 7.95 Hz, Ar-H), 7.87 (d, 1H, J = 2.45 Hz, Ar-H), 8.09 (tdd, 1H, J = 7.9 Hz, 7.8 Hz, 1.8 Hz, 1.75 Hz, Ar-H), 13.04 (s, 1H, NH); 13C NMR (125 MHz, DMSO-d6) d 106.85–106.93 (d, J = 10.27 Hz), 112.95, 116.93–117.12 (d, J = 22.58 Hz), 120.12, 122.74, 124.26, 125.29–125.31 (d, J = 2.98 Hz), 125.51–125.60 (d, J = 10.66 Hz), 130.87, 132.85– 132.92 (d, J = 8.77 Hz), 135.27, 144.14, 149.80, 157.48, 160.09, 161.95–162.08 (d, J = 16 Hz), 164.24; MS (ESI+) m/z 306.5 (M+H). Anal. Calcd for C17H12FN5: C, 66.88; H, 3.96; N, 22.94. Found: C, 66.39; H, 3.74; N, 23.26. 4-(1H-Benzimidazol-2-yl)-6-[4-(benzyloxy)phenyl]py­rimidin-2-amine (3g) Yield 10%; mp 240 °C. 1H NMR (500 MHz, DM­SO-d6) d 5.24 (2, 2H, CH2), 6.72 (brs, 2H, NH2), 7.15–7.50 (m, 9H, Ar-H), 7.60 (d, 1H, J = 7.80 Hz, Ar-H), 7.75 (d, 1H, J = 7.85 Hz, Ar-H), 7.91 (s, 1H, pyrimidine H-5), 8.17 (d, 2H, J = 8.80 Hz, Ar-H); 13C NMR (125 MHz, DM­SO-d6) d 69.87, 102.26, 112.90, 114.77, 115.47, 119.98, 122.67, 124.11, 128.26, 128.42, 128.96, 129.76, 131.37, 135.21, 137.24, 144.14, 150.17, 157.33, 161.09, 164.20, 165.04; MS (ESI+) m/z 394.7 (M+H). Anal. Calcd for C24H19N5O: C, 73.27; H, 4.87; N, 17.80. Found: C, 73.61; H, 4.45; N, 18.09. 4-(1H-Benzimidazol-2-yl)-6-(thiophen-2-yl)pyrimi­din-2-amine (3h) Yield 13%; mp 118 °C. 1H NMR (400 MHz, DM­SO-d6) d 6.77 (brs, 2H, NH2), 7.21–7.26 (m, 3H, Ar-H), 7.60 (brs, 1H, Ar-H), 7.71 (brs, 1H, Ar-H), 7.78 (dd, 1H, J = 4 Hz, 1.2 Hz, thiophene-H), 7.87 (s, 1H, pyrimidine H-5), 8.05 (dd, 1H, J = 4 Hz, 1.2 Hz, thiophen-H), 13.00 (brs, 1H, NH); 13C NMR (100 MHz, DMSO-d6) d 101.45, 112.94, 120.02, 122.74, 124.23, 128.69, 129.18, 130.86, 135.19, 143.01, 144.10, 149.89, 157.29, 160.67, 163.93; MS (ESI+) m/z 294.47 (M+H), 296.27 (M+H+2). Anal. Calcd for C15H11N5S: C, 61.42; H, 3.78; N, 23.87; S, 10.93. Found: C, 61.20; H, 3.39; N, 24.15; S, 10.58. 2. 2. Antimicrobial Activity Tests In the antibacterial activity tests, Staphylococcus au­reus ATCC 29213 (methicillin-susceptible, MSSA), Staph­ylococcus aureus ATCC 43300 (methicillin-resistant, MRSA), Escherichia coli ATCC 25922, and Pseudomonas aeruginosa ATCC 27853 were used as test bacteria. For the determination of minimum inhibitory concentration (MIC) values, the broth microdilution method was used.26 Serial two-fold dilutions ranging from 512 µg/mL to 4 µg/ mL were prepared in Mueller-Hinton Broth (Difco, Difco Laboratories, Detroit, MI, USA). The inoculums were pre­pared from subcultures for 24 h. The final test concentra­tion of the bacteria was adjusted to 5 × 105 cfu/mL. The microplates were incubated at 35 °C for 18–24 h. The last well that completely inhibited visual microbial growth was noted as the MIC value (µg/mL). The antifungal activity of the compounds was also evaluated by the determination of the MIC values (µg/ mL). Candida albicans ATCC 10231 was used as the test organism. Serial two-fold dilutions ranging from 512 µg/ mL to 4 µg/mL were prepared in RPMI 1640 broth (ICN-Flow, Aurora, OH, USA, with glutamine, without bicar­bonate, and with pH indicator). The final test concentra­tion of the fungus was 0.5 to 2.5 × 103 cfu/mL. The microplates were incubated at 35 °C for 48 h. The last well that completely inhibited visual microbial growth was not­ed as the MIC value (µg/mL). 27 Test compounds were dissolved in dimethyl sulfox­ide (DMSO; Sigma, USA) and 10% DMSO was used as the negative control. Ciprofloxacin (Sigma, USA) and genta­micin (Sigma, USA), fluconazole (Sigma, USA) were used as reference drugs.28 Each experiment was performed in triplicate. 2. 3. Molecular Docking Molecular docking studies were performed using the Maestro module of Schrödinger software 2021.2 version. Protein preparation was done with the ‘Protein Prepara­tion Wizard’ module. FabH and CYP51 target proteins were prepared to add H atoms, creating disulfide bonds and removing waters and other heteroatoms. H bonds as­signment for protein optimization according to sample water orientations with PROPKA pH:7.0 was performed. The protein minimization stage was performed with con­verging heavy atoms to RMSD:0.3 Å and OPLS428 force field. Ligand 3D minimized structures were prepared us­ing OPLS4 force field in pH 7±2 with the ‘LigPrep’ module. The active site was determined according to the native li­gands of target proteins, and the 20·20·20 Å3 area was cre­ated by the ‘Receptor Grid Generation’ module. Molecular docking was carried out using the ‘Glide XP’29 module, and Molecular Mechanics Generalized Born Surface Area (MM-GBSA) dG bind (binding free energy, kcal/mol) was measured using Prime module of Schrödinger software. 2D protein-ligand interactions and 3D binding mode analysis were performed with Chimera v.1.15, and Discov­ery Studio Visualizer v2021. 2. 4. Molecular Dynamics Molecular dynamics simulation was performed with Gromacs 2019.2 version (GROningen MAchine for Chem­ical Simulations) to investigate the FabH·3a and the FabH·3b complex’s protein-ligand stability. The 3a and 3b compounds structure’s topology was created by the Glyco-BioChem PRODRG2 server, the topology file of the FabH enzyme was created with the GROMOS 43a1 force field31,32 and SCP water model. The energy of the formed pro­tein, ligand, ion, and solvent system was minimized in 5000 steps with the steepest descent integrator algorithm. The system was balanced with 0.3 ns NVT and 0.3 ns NPT stages at 1 atm pressure and 300 K temperature according to the V-rescale32 thermostat and Parrinello-Rahman34 barostat. The 100 ns molecular dynamics simulation was performed with leap-frog MD integrator. Trajectory anal­ysis was performed with gmx scripts, the root mean square deviation (RMSD) and the root mean square fluctuation (RMSF) measurements were performed. MD trajectory analysis results were monitored with VMD-Visual Molec­ular Dynamics v.1.9.3, BIOVIA Discovery Studio Visualiz­er v.2021, and graphs were generated with GraphPad Prism v.8.0.1. The binding free energy calculation by mo­lecular mechanics Poisson–Boltzmann surface area (MM-PBSA) was performed between 80 and 100 ns using RashmiKumari’s g_mmpbsa package.35–37 The average binding free energy was calculated by using the ‘MmPbSa-Stat’ Python script provided in g_mmpbsa. 2. 5. ADME Predictions The theoretical ADME parameters of the selected compounds were calculated with the Schrödinger software ‘QikProp’ module. Molecular weight, QPlogPo/w, QPlogHERG, QPPCaco, QPlogBB, QPPMDCK, percent­age human oral absorption, rule of five, and rule of three were calculated. 3. Results 3. 1. Chemistry Novel 1-(1H-benzimidazol-2-yl)-3-aryl-prop-2-en­1-ones and 4-(1H-benzimidazol-2-yl)-6-arylpyrimi­din-2-amine derivatives were synthesized as described in Scheme 1. 2-Acetylbenzimidazole (1) was prepared by condensation of o-phenylenediamine and lactic acid22–24 and followed by oxidation with potassium dichromate in the presence of sulfuric acid.23–24 The arylidene derivatives 2a–h were synthesized via Claisen–Schmidt condensation of 2-acetyl benzimidazole (1) with aromatic aldehydes in ethanol at room tempera­ture, the reaction was catalyzed by potassium hydroxide solution.25 Although two types of geometric isomers could Scheme 1. Synthesis of compounds 2 and 3 be expected for compounds 2a–h, only (E) isomers were obtained. This is demonstrated by the 1H NMR spectra, supported by the appearance of characteristic trans-cou­pling constants belonging to the aryldene protons in the range of 15.85–16.4 Hz. The reaction of compounds 2a–h with guanidine hy­drochloride in the presence of NaH25 was conducted to give the respective 4-(1H-benzimidazol-2-yl)-6-(aryl)py-rimidin-2-amines 3a–h. 3. 2. Antimicrobial Activity All the synthesized compounds 2 and 3 were evalu­ated for their antimicrobial activities in vitro against Staph­ylococcus aureus (ATCC 29213-methicillin-susceptible, MSSA, and ATCC 43300-methicillin-resistant, MRSA) as gram-positive, two gram-negative (Escherichia coli ATCC 25922, and Pseudomonas aeruginosa ATCC 27853) bacte­ria, and Candida albicans ATCC 10231 as fungus using the standard two-folds serial dilution method in 96-well mi-cro-test plates recommended by the National Committee for Clinical and Laboratory Standards Institute.26,27 Mini­mal inhibitory concentration (MIC, µg/mL) was defined as the lowest concentration of new compounds that com­pletely inhibited the growth of bacteria and fungus. Cipro-floxacin, gentamicin, and fluconazole were used as the ref­erence drugs.28 The antimicrobial results in vitro (Table 1) revealed that most of the prepared compounds could effectively in­hibit the growth of some tested strains and that gram-pos­itive bacteria are more sensitive to the tested compounds Table 1. MIC values (µg/mL) of the synthesized compounds than the gram-negative bacteria and fungus. Moreover, in most of the compounds, amino pyrimidines were ob­served to be more active than the arylidene counterparts. Regarding the activity of individual compounds, it is noteworthy that bearing 4-fluoro-3-bromophenyl (3a) and 2-naphthyl (3b) as aryl group at the position 4 of pyrimi­dine ring are the most active analogs; they exhibited <4 µg/ mL MIC values against both S. aureus. 2f (Ar = 2-fluoro-phenyl), 3e (Ar = phenyl), and 3h (Ar = thienyl) also dis­played moderate to good activities against the gram-posi­tive bacterial strains. In addition, compounds 2f and 3h showed moderate antifungal efficacy toward C. albicans with 16 µg/mL MIC values. The rest of the investigating benzimidazoles exerted either weaker activity or were to­tally inactive toward the tested microbial strains. 3. 3. Computational Studies 3. 3. 1. Molecular Docking Analysis Molecular docking studies are computational meth­ods frequently used in drug design to predict how small molecule compounds interact with target macromolecules at the atomic level.38–40 In this study, molecular docking analyses of compounds with MIC values of 16 µg/mL and below against gram-positive bacteria and fungi were per­formed. FabH (ß-ketoacyl-acyl carrier protein synthase III) was preferred as the bacterial target protein and CYP51 (sterol 14a-demethylase) was preferred as the fungal target protein. To validate the molecular docking process, the natural ligand re-docking process in the crystal structures of the target enzymes was performed. Ligand and protein Gram-positive bacteria Gram-negative bacteria Fungus Compound S. aureus S. aureus E. coli P. aeruginosa C. albicans ATCC 29213 (MSSA) ATCC 43300 (MRSA) ATCC 25922 ATCC 27853 ATCC 10231 2a 64 64 256 128 – 2b 128 128 – – 128 2c 64 64 – 128 128 2d 64 64 – – – 2e 128 128 256 128 64 2f 16 16 256 256 16 2g 64 64 – – – 2h 128 128 256 256 128 3a <4 <4 256 256 – 3b <4 <4 256 256 64 3c 32 64 256 256 128 3d 64 128 – 128 128 3e 16 16 256 128 64 3f 32 64 256 256 64 3g 64 128 – – 128 3h 16 16 256 256 16 Ciprofloxacin <0.25 0.5 <0.25 <0.25 NT Gentamicin 0.5 <0.25 0.5 <0.25 NT Fluconazole NT NT NT NT 1.56 NT: Not tested “–“: represents no activity Table 2. Glide XP molecular docking, prime binding free energy and protein-ligand interactions results performed against bacterial (FabH) and fungal (CYP51) target enzymes Target enzymes Comp. XP GScore MMGBSA dG Bind Protein-ligand interactions FabH 2f –8.75 –60.11 Asn253 (2.50 Å), Asn280 (2.80 Å, 1.96 Å), Asn280, Phe312, (PDB ID: 3IL5) 3a –8.50 –65.52 Ile223, Ala227, Ala252, Ile223, Ala252 Ala252 (2.06 Å), Gly313, Phe312, Cys117, Leu194, His250, Arg221, Ala252, Ile223, Ala227, Ala252 3b –7.90 –60.64 Cys117, His250, Phe224, Arg221, Ile223, Ala252, Ile256, Leu194, 3e –8.33 –56.69 Ile223, Ala252 Gly220 (2.51 Å), Ala252 (1.97 Å), Met218, Phe312, Ala252, 3h –7.51 –61.75 Ile223, Ala227, Ala252 Gly220 (2.42 Å), Ala252 (2.24 Å), Ala252, Met218, Phe312, Ile223, Ile223, Ala227, Ala252 CYP51 2f –7.97 –47.97 Phe380, Phe233, Leu376, Leu376, Ile131, (PDB ID: 5TZ1) 3h –7.07 –47.94 Hem601 Met508 (3.69 Å), Tyr118, Tyr118, His377, Pro230, Pro230, Met508, Leu121, Leu376, Met508, Leu376, Met508 FabH: ß-ketoacyl-acyl carrier protein synthase III (PDB ID: 3IL5), CYP51: sterol 14a-demethylase, XP Gscore (kcal/mol): Extra Precision Glide Score, MMGBSA dG Bind (kcal/mol): Molecular Mechanics Generalized Born Surface Area total binding energy. structures were minimized using the OPLS4 force field. As the FabH active site are shown in Figure 1. Compound 3a given in Table 2, Glide XP binding energies were below –7 showed H bond between –NH group of 2-aminopyrimi­kcal/mol and MMGBSA binding free energy values were dine structure and Ala252, hydrophobic interactions with below –47 kcal/mol. Again, in Table 2, the protein–ligand Gly313, Phe312, Cys117, Leu194, His250, Arg221, Ala252, interaction details of the selected compounds are ex-Ile223, and Ala227. Compound 3b, on the other hand, plained. formed hydrophobic interactions with Cys117, His250, The binding poses and schematic protein-ligand in-Phe224, Arg221, Ile223, Ala252, Ile256, Leu194, and teractions of the two most active compounds, 3a and 3b, at Ile223, although there was no H bond formation. Figure 1. Visualization of the results from the Glide XP molecular docking study performed against bacterial target enzyme FabH. (a) Binding pose of compound 3a and (b) compound 3b, and 2D schematic protein-ligand interactions of (c) compound 3a and (d) compound 3b 3. 3. 2. Molecular Dynamics Simulations Molecular dynamics simulations are widely used to study the stability of protein-ligand complexes obtained from molecular docking.37,40,41 By modeling the pro-tein-ligand complex in silico physiological conditions, the variation and stability of the protein-ligand interaction can be predicted. Accordingly, the protein-ligand interac­tion of FabH·3a and FabH·3b complexes obtained from the Glide XP molecular docking study was investigated for the two most active compounds. 100 ns molecular dynam­ics simulation was performed, RMSD and RMSF trajecto­ry analyzes were performed. RMSD is one of the most ba­sic parameters used to analyze aberrations in protein structure. As seen in Figure 2, after the first 20 ns, just above 0.4 nm, the deviations continue to be stable at a mi­nor level after the system stabilizes. The mean RMSD value of the FabH·3a and FabH·3b complex was measured as 0.44 nm and 0.41, respectively. RMSF is another analysis parameter that provides information on protein fluctua­tions and conformational changes. As seen in Figure 2, some different fluctuations occurred with the binding of 3a and 3b with FabH. The lower RMSF value than 3b was measured around the Ser41 residues, where 3a gave H-bond interaction. This H bond reduced protein mobili­ty and made it more stable. 3b, on the other hand, formed strong hydrophobic interactions with Phe312, significantly reducing FabH mobility. To examine the protein-ligand interaction and bind­ing pose during the molecular dynamics simulation of compound 3a, its changes in the middle and at the end of the 100 ns simulation were analyzed. As shown in Figure 3, compound 3a remained stable at the active site. The H bond and basic hydrophobic interactions between –NH of the benzimidazole core and Ser41 were preserved. Measuring the binding free energy between protein and ligand in molecular dynamics simulations is another important approach. MMPBSA is obtained by summing the averages of Van der Waals, electrostatic, polar solva­tion, and solvent accessible surface area (SASA) energies. In this study, the average binding free energy of 3a and 3b compounds with FabH was calculated between 80 ns and Table 3. MM-PBSA binding free energies of FabH with compounds 3a and 3b between 80 ns and 100 ns. Enzyme-ligand complexes Parameters FabH · 3a FabH · 3b (Energy) (kJ/mol) (kJ/mol) Van der Waals –207.305 ± 12.271 –272.200 ± 10.109 Electrostatic 3.695 ± 3.356 1.563 ± 2.200 Polar solvation 41.219 ± 13.297 45.524 ± 6.006 SASA –18.603 ± 10.999 –20.690 ± 0.835 Binding free –180.993 ± 15.364 –245.803 ± 10.893 Figure 2. Molecular dynamics simulation trajectory analysis of 3a and 3b with FabH enzyme throughout 100 ns (a) RMSD of ligand-bound FabH·3a (magenta) and FabH·3b (green), (b) RMS fluctuation values during the period of simulation Figure 3. Binding pose and protein-ligand schematic interactions of compound 3a in the FabH active site (a, a’) in the middle (50 ns), (b, b’) and at the end (100 ns) of the molecular dynamics simulations. 100 ns. The average of –180.993 kJ/mol and –245.883 kJ/ mol binding free energy was found between 3a and 3b compounds and FabH, respectively. Interactions energy details are given in Table 3. 3. 3. 3. ADME Estimations Drug discovery is a long, expensive and risky process that includes drug candidate identification, candidate val­idation, pharmacokinetics, and preclinical toxicity assess­ment studies. In silico, drug discovery technology plays an important role in the pharmaceutical industry. One of these technologies is in silico ADME prediction. ADME parameters, together with the drug discovery process, con­tribute to the selection of the therapeutic dose and identi­fication of molecules with the optimal safety profile. Early prediction of ADME parameters has been shown to sig­nificantly reduce the pharmacokinetics failure rate at clin­ical stages during the discovery phase and avoid wasting time and resources in the discovery of drug molecules.43 ROF value Lipinski’s rule of five, also known as Pfizer’s five rules or only the five rules (ROF), is a rule of thumb for assessing drug similarity or determining whether a chem­ical compound with a particular pharmacological or bio­logical activity has favorable chemical and physical prop­erties. According to this rule, the ligand molecule should have no more than 5 hydrogen bond donors, no more than 500 molecular weight, no more than 5 log P, and no more than 10 N and O atoms.44 ROT value should be greater than the estimated aqueous solubility (logS) -5.7, predict­ed apparent Caco-2 cell permeability (PCaco) greater than 22 nm/s, and primary metabolites (PM) less than 7 ac­cording to Jorgensen’s rule of three. The QPlogPo/w value is the estimated octanol/water coefficient and should be in the range of –2.0 to 6.5. QPlogHERG is the estimated IC50 for blocking HERG K+ channels below –5 is of concern. QPPCaco Estimated apparent Caco-2 cell permeability in nm/second Caco-2 cells are a model for the intestinal blood barrier. QikProp estimates are for inactive trans­port. Values below 25 are weak, above 500 are great. QPlogBB is the estimated brain/blood partition coefficient and should has a value between –3.0 and 1.2. QPPMDCK Estimated apparent MDCK cell permeability in nm/s. MDCK cells are considered a good mimic of the blood-brain barrier. QikProp estimates are for inactive transport. Percent Human Oral Absorption (PHOA) is estimated hu­man oral absorption on a scale of 0% to 100%. Values above 80% are great, below 20% are weak. For this pur­ Table 4. ADME parameters data of selected compounds Compounds MW QPlogPo/w QPlogHERG QPPCaco QPlogBB QPPMDCK PHOA ROF ROT 2f 266.2 3.287 –6.0 1483.2 –0.411 1226.717 100.0 0 0 3a 384.2 3.507 –6.2 652.5 –0.531 1330.029 100.0 0 0 3b 337.3 3.677 –7.0 639.4 –0.845 305.129 100.0 0 0 3e 287.3 2.752 –6.4 643.1 –0.780 307.012 93.3 0 0 3h 293.3 2.672 –6.0 637.6 –0.599 529.965 92.7 0 0 pose, some important physicochemical properties, and descriptors of 2f, 3a, 3b, 3e, and 3h were calculated theo­retically using Schrödinger Maestro’s QikProp module and are presented in Table 4. According to Lipinski’s five rules and Jorgensen’s three rules in these calculations, drug can­didates should not have more than one violation in their ADME profile. All compounds in the table appear to com­ply with these rules. These results increase the possibility that the com­pounds are potential drug molecules, given the promising antimicrobial activity results. 4. Conclusion Results from molecular docking and molecular dy­namics simulation studies show that active compounds 3a and 3b form strong interactions at the FabH active site. According to the molecular docking analysis, it was calcu­lated that sufficient protein-ligand interaction energy was formed between the compounds 2f, 3a, 3b, 3e, and 3h and the antibacterial target protein FabH, and strong interac­tions were formed between the compounds 2f and 3h and the antifungal target protein. It is understood that com­pounds 3a and 3b with MIC values below 4 µg/mL main­tain protein-ligand stability in silico physiological condi­tions, according to RMSD, RMSF, and MMPBSA measurements obtained from molecular dynamics. Conflicts of Interests The authors declare that there are no conflicts of in­terests. Acknowledgement The authors thank to Ankara University-Scientific Research Unit supplying the Schrödinger software pro­gram purchased under grant project number BAP­21B0237004. All molecular dynamics simulations reported were performed utilizing TÜBITAK (The Scientifc and Techno­logical Research Council of Turkey), ULAKBIM (The Turkish Academic Network and Information Center), and the High Performance and Grid Computing Center (TRU­BA resources). 5. References 1. R. Srivastava, S. K. Gupta, F. Naaz, A. Singh, V. K. Singh, R. Verma, N. Singh, R. K. Singh, Comput. Biol. Chem. 2018, 76, 1–16. DOI:10.1016/j.compbiolchem.2018.05.021 2. M. Abdel-Motaal, K. Almohawes, M. A. 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Divers. 2021, 1–14 DOI:10.1007/s11030-021-10215-5 43. L. L. Ferreira, A. D. Andricopulo, Drug Discov. Today 2019, 24(5), 1157–1165. DOI:10.1016/j.drudis.2019.03.015 44. I. Sahin, M. Çesme, F. B. Özgeris, F. Güngör, F. Tümer, J. Mol. Struct. 2022, 1247, 131344. DOI:10.1016/j.molstruc.2021.131344 Sintetizirali smo serijo novih derivatov benzimidazola ter dolocili njihove antimikrobne aktivnosti. Spojini 3a in 3b sta proti Staphylococcus aureus ATCC 29213 (MSSA) in Staphylococcus aureus ATCC 43300 (MRSA) pokazali odlicno anti-bakterijsko aktivnost z MIC vrednostjo <4 µg/mL. Molekulsko sidranje spojin, ki so se proti gram-pozitivnim bakterijam in glivam izkazale z MIC vrednostmi 16 µg/mL in manj, smo izvedli z uporabo bakterijskega proteina FabH (ß-ketoac­il-acil protein sintaza III) oz. CYP51 (sterol 14a-demetilaza), ki je protein glive. Glede na rezultate molekulskega sidran­ja, smo ugotovili, da se pri interakciji proteina z ligandom sprosti dovolj energije v primerih, ko spojine 2f, 3a, 3b, 3e in 3h interagirajo z antibakterijskim tarcnim proteinom FabH; mocne so tudi interakcije v primeru, ko spojini 2f in 3h int-eragirata s tarcnim proteinom v primeru glive. Skladno z RMSD, RMSF in MMPBSA rezultati, dobljenimi z molekulsko dinamiko, izgleda, da spojini 3a and 3b ohranjata stabilno interakcijo proteina in liganda pod in silico fiziološkimi pogoji. Except when otherwise noted, articles in this journal are published under the terms and conditions of the Creative Commons Attribution 4.0 International License DOI: 10.17344/acsi.2021.7332 Scientific paper Effects of Extraction Period on Bioactive Compounds Extracted from Olea Europaea (var. Domat) Leaves by Ultrasound-Assisted Extraction Dua Danjolli-Hashani1,2,* and Sebnem Selen-Isbilir2 1 Heimerer College, Health Sciences of Diagnostic Profile: Laboratory Technician, 10000, Pristina, Kosovo 2 Trakya University, Faculty of Science, Department of Chemistry, 22030, Edirne, Turkey * Corresponding author: E-mail: dua_danjolli@hotmail.com Phone: +383 49 850662 Received: 12-22-2021 Abstract In this study, polyphenols from olive leaves (Domat var.) were compared by shaking water bath and ultrasound-assisted extraction to compare the polyphenol contents and antioxidant activity in olive leaves. The effects of the extraction time on the antioxidant activity of olive leaves will be analyzed depending not only on the extraction method but also on the extraction time, due to the extraction yield, antioxidant activity, as well as the type of polyphenols recovered. Objective: To obtain high antioxidant results and to determine the phenolic compounds contained in olive leaf (Domat var.) by LC-MS/MS after ultrasonic water bath and shaking water bath extraction comparison in a shorter time instead of 2 h. Conclusions: The phenolic compounds contained in olive leaf by ultrasound-assisted extraction were higher than water bath extraction. We said that there is no significant difference between the extraction time and TPC and TFC. There was also no relationship between extraction time and DPPH and ABTS EC50 values (p < 0.05), which means that 15 min of sonication can be performed instead of 120 min. Keywords: Ultrasound-assisted extraction; Domat olive tree leaves; antioxidant activity; phenolic compounds; LC-MS/MS 1.Introduction Antioxidants are endogenous or exogenous sub­stances that protect metabolism against the harmful effects of free radicals. They are also used in the food industry, cosmetics, and pharmaceutical industries to prevent lipid peroxidation. In these sectors, it is preferred to use natural antioxidants found in herbal sources instead of the syn­thetic antioxidants. The antioxidant properties of plants are due to their contents phytocomponents, especially polyphenolic compounds, carotenoids, and ascorbic acid. Polyphenolic compounds including mainly phenolic ac­ids, flavanones, flavones, anthocyanins, isoflavones, tan­nins which are the secondary metabolites of plants. It is known that olive tree (Olea europaea L.) leaf, which is rich in phenolic compounds,1–3 has antioxidant properties4 as well as antimicrobial,5–6 antihypertensive7–8 and cholester­ol-lowering9 properties. The positive effects and antioxi­dant properties of olive tree leaf on health are related to its phenolic components such as oleuropein, verbascoside, rutin, hydroxytyrosol, luteolin-7-glucoside, and tyrosol. Besides there are studies on the pharmacological effects of oleuropein, which is the major phenolic component of ol­ive tree leaf.10–11 Olive tree is an economically important tree, espe­cially in Mediterranean countries. Although the minor part of leaves is traditionally drunk as tea, the others are by-product of table olive and olive oil industries, or natural waste as fallen leaf. Therefore, the phenolic-rich extract could be obtained from olive leaves, and this extract can be evaluated in food additive as antioxidant, functional food production or drug candidate search studies because of their rich bioactive components and inexpensiveness. There is an increasing effort of some industries to obtain bioactive compounds from natural products through ex­traction and purification for food additive manufactur­ing.12–14 The first and most important step in obtaining the phytocomponents of natural products such as plant leaves is an efficient extraction process. Especially oleuropein is extracted from olive leaves, fruits, and olive seeds.15 To ex­traction of phenolic compounds from plants the organic solvents are used most and, the extraction procedures are divided into two groups as traditional and modern meth­ods. Traditional methods such as maceration, percolation, infusion and soxhlet extraction are inexpensive, but have very solvent-used, longer extraction time. Recently, mod­ern techniques such as supercritical fluid extraction, mi-crowave-assisted extraction (MAE), ultrasound-assisted extraction (UAE) are replacing these traditional meth­ods.16–17 These alternative techniques are green extraction processes, and both reduce the use of solvents and speed up the extraction procedure. Polyphenolic compounds are secondary metabolites of plants with various biological effects18. Due to the vari­ety and structural differences of phenolic compounds, it is difficult to develop an optimum extraction procedure. In recent years, techniques such as UAE, MAE, supercritical and accelerated extraction have been used to obtain bio­logically active extracts from plants19–20. These techniques have specific benefits such as environmental friendliness, cost, and time savings. In addition, the fact that some of them offer high temperature or high-pressure operation which significantly shortens the extraction time.21–22 In recent years, studies on ultrasonic applications have been increasing due to its use in food processing and preserva­tion.23 The efficiency of ultrasound-assisted extraction is that it allows the intracellular material to pass into the sol­vent by disrupting the plant cell wall. Various parameters such as extraction method, time, temperature, solvent type and solvent-sample ratio affect the yield of the bioactive compounds obtained. Since olive tree leaf is known to be effective as antioxidants,4 it was used as a model in this study. In the study, the olive tree (Olea europaea L.) leaves belong to Domat variety were ex­tracted both by shaking in a water bath, which is a tradi­tional extraction method, and ultrasound-assisted ex­traction method at the same conditions as a modern extraction method. Domat variety orginates from Akhisar district of Manisa province (Turkey) and, has large fruits. It is the best green olive variety grown in Turkey. Firstly the two extraction methods were compared in terms of an­tioxidant activity and polyphenolic compounds content. In the other part of study the UAE conditions were con­stant, namely, extraction solvent, temperature and sol-id-solvent ratio, and the ultrasound treatment was per­formed in intervals 15 min, 30 min, 60 min and 120 min. Although there are few studies about the UAE of polyphe­nols from other varieties of olive leaves (Serrana variety from Spain and Tavsan Yuregi variety from Turkey),24–25 this is the first report on time effect in UAE for antioxidant compound recovery from Domat variety of olive leaves. 2. Experimental 2. 1. Materials All the reagents and solvents used in the experiments were of analytical grade. Olive tree leaf which is Domat variety (2019 November) was obtained from Olive Re­search Institute in Izmir-Turkey. Olive tree leaves were dried at room conditions in airy environment without di­rect sunlight and then ground in a Waring blender. 2. 2. Extraction Procedure In the extraction with shaking water bath (WBE), samples are shaken with a 75% (v/v) ethanol solvent at 30 °C and 125 rpm with a sample:solvent ratio of 1:10 (w/v) during 2 h. Ultrasound- assisted extraction (UAE) was carried out in an ultrasonic bath (Daihan WiseClean, Ko­rea, 230 V, 296 Watt and 50 Hz) at 30 °C for 2 h with 75% (v/v) ethanol solvent at a sample:solvent ratio of 1:10 (w/v). In the second part of the study, samples were sonicated for 15, 30, 60 and 120 min under the same conditions. After the extraction procedures, the samples were filtered through filter paper and their solvents were evaporated at 35 °C in the evaporator. The obtained crude extracts were used in all experiments. 2. 3. Determination of Total Phenolic Content The determination of total phenolic content was per­formed according to the method of Singleton and Rossi 26 using Folin-Ciocalteau reagent (FCR). Gallic acid solu­tions in the concentration range of 50–500 µg / mL were used as the standard phenolic substance. The total pheno­lic content of the samples was calculated using the equa­tion (y = 0.0011x – 0.0372, R2 = 0.996) obtained from the concentration-absorbance graph. 2. 4. Determination of Total Flavonoid Content It was determined according to Zhishen, Mengcheng and Jianming.27 Rutin solution was used as standard flavo­noid in the concentration range of 25–200 µg / mL. Total flavonoid contents of the samples were calculated from the equation of the standard graph (y = 0.0012 + 0.0206, R2 = 0.9996). 2. 5. DPPH Radical Scavenging Assay The free radical scavenging capacity of the samples was determined using the DPPH radical scavenging method. According to the method of Blois,28 0.1 mM DPPH solution was added to the sample or standard solutions (BHA, butylatedhydroxy anisole) which were prepared at different concentrations (100–1000 µg / mL). The samples were kept in the dark for 30 min at room conditions, and absorbances at 517 nm were measured. The control was prepared using ethanol or water instead of the sample. Free radical scavenging efficiencies of the standards and samples were calculated as % inhibition using Equation 2.1. Acontrol is the absorbance of the con­trol tube and Asample is the absorbance of the sample or standard compound. % I = [ (AControl – A Sample) / AControl] × 100 (1) 2. 6. ABTS Cationic Radical Scavenging Assay In the ABTS radical scavenging method, ABTS solu­tion and sodium persulphate solution were mixed at a ra­tio of 1: 0.5 and kept for 16 h and then ABTS radical (ABTS• +) was formed. The absorbance of the prepared solution was used after diluting with ethanol to give an ab-sorbance of 0.70 at 734 nm. The samples (100–1000 µg/ mL) to which ABTS radical solution was added were kept in the dark for 30 min and their absorbance were mea­sured at 734 nm.29 Radical scavenging capacities were cal­culated using the formula in Equation 2.1. Free radical scavenging results of olive leaf extracts were given as EC50 value. EC50 (effective concentration) value is defined as the amount of extract required to re­move half of the radical in the environment and is calcu­lated by plotting inhibition (%) against extract concentra­tions graph. The EC50 value is the most used parameter to evaluate antioxidant activity. 2. 7. Identification of Phenolic Content with LC-MS/MS LC-MS/MS analysis was used for identification of phenolic compounds quantitatively. Phenolic content of olive leaf extracts was compared to standard thirty-three organic compounds. The chromatographic separation was performed on a C8 (150 mm × 3 mm, 3.5 µm) reversed phase analytical column (Agilent Zorbax SB-C8). The mo­bile phase A consisted of 5 mM ammonium acetate and ultrapure water. The mobile phase B consisted of 5 mM acetonitrile: methanol (1:1, v/v) and 0.1 % acetic acid. The injection volume of sample was 5 µL. It was runned at 0.7 mL/min flow. For the mass spectrometry analysis, it was carried out using the Agilent Technologies 6460 Triple Quad LC/MS system. The ionizations were detected by ESI. The other parameters were ion spray (IS) voltage 3500 V; 10 L/min for nebulizing gas flow; nitrogen as nebulizer gas and source temperature 375 °C. The multiple reaction monitoring (MRM) mode was used to quantify the ana­lyzes. 2. 8. Statistical Analysis In the experiments, each sample was analyzes two times in parallel, and each experiment was carried out in dublicate (n=2). Data were expressed as means ± standard deviation. Pearson correlation test was used to determine the correlation between the antioxidant properties and ex­traction time. The value of p < 0.05 was considered to be statistically significant. The statistical analysis was done using SPSS 21 programme. 3. Results and Discussion Due to the biological activities of polyphenolic com­pounds, they are extract from plant materials using vari­ous extraction methods. The ultrasound-assisted ex­traction is also used as a cheap and simple extraction technique that provides high extraction yield and quality, especially in the extraction of phenolic compounds from various natural and waste herbal sources.30–31 The ex­traction yield, total phenolic and flavonoid contents of ex­tracts obtained from ultrasound-assisted and shaking wa­ter bath extraction techniques are presented in Table 1. The extraction yields (gram extract/100 g dried leaves) were 18.7% for WBE and 16.5% for UAE. Although UAE has a slightly lower extraction yield than WBE, total phe­nolic amount of UAE was twice that of WBE (Table 1). The extract obtained by the ultrasound-assisted extraction showed higher antioxidant activity due to its high phenolic and flavonoid contents. In addition, the low EC50 value indicates high antioxidant capacity. The EC50 values of ex­tracts obtained from UAE are lower than these of other extraction method in both of free radical scavenging as­says (Table 1). According to Table 1, the difference was found between the two methods in the free radical activi­ties, the phenolic and flavonoid contents of the extracts. Phenolic contents of olive leaf extract were com­pared to standard thirty-three organic compounds and thirteen of them were identified in both extracts at quanti­fiable levels. The flavonol quercetin, isorhamnetin, luteo­lin, five phenolic acids (chlorogenic, protocatechuic, 2,5-dihydroxybenzoic, caffeic and gallic acid) and as well as glycosides oleuropein, verbascoside and rutin were de­termined higher amount at olive leaf extract obtained by UAE (Table 2) and (Figure 1). As seen in Table 1, in the determination of total phenolic and flavonoid contents by spectrophotometric method, the contents of ultrasonic ex­tract were already determined higher than the water bath Table 1. Effect of UAE and WBE methods on antioxidant properties of olive leaf extract. Extraction Extraction Extract TPC TFC DPPH EC50 ABTS assay Conditions Method yield (%) (GAE µg/mg) (Rutin µg/mg) assay mg/mL EC50 mg/mL 75% EtOH, UAE 16.5 111.9 ± 0.018 534.44 ± 0.045 138.1 245.9 2h, 30 °C WBE 18.7 56.9 ± 0.0013 528.11 ± 0.072 284.1 650.0 Table 2. Content of individual phenolics of olive leaf extracts by LC­MS/MS Phenolic compound UAE (ppb) WBE (ppb) Oleuropein 34090.33 30715.84 Chlorogenic Acid 2621.13 2395.64 Protocatechuic Acid 1088.58 710.85 Verbascoside 515.37 178.54 Rutin 417.76 325.78 Quercetin 224.19 20.80 Isorhamnetin 197.97 39.02 2,5-dihyrdoxybenzoic acid 121.96 88.76 Luteolin 111.37 76.36 Caffeic acid 88.35 76.12 Gallic acid 20.88 5.58 Salicyclic acid 9.95 15.94 Apigenin 6.52 10.47 extract. This indicates that the high antioxidant capacity in the olive leaf extract obtained UAE is due to its high phe­nolic composition in comparison with WBE. Researchers who interested in natural products have been studying on extraction process to compare the effi­ciency of the UAE method with others by using various plant sources. Similar to our study, it was observed that the extracts obtained from ultrasonic application were more efficient in other studies performed to compare traditional and ultrasound-assisted extraction techniques for the ex­traction of bioactive components. It has been reported that UAE technique from the leaves of lemon scented tea tree (Leptospermum petersonii) is more efficient in terms of to­tal phenolics and antioxidant capacity compared to tradi­tional shaking water bath extraction.17 Ali and Kumar (2015) used two extraction methods (UAE and Soxhlet extraction) for the extraction of bioactive compound from the pomegranate peel. They reported that UAE extract showed higher antioxidant activity and had a higher amount of chlorogenic acid.30 In another study, Bauhinia purpurea leaf was extracted by Soxhlet, ultrasonication and maceration extraction methods, and it was reported that UAE was better method for the extraction of antioxi­dant and antibacterial substances compared to others.32 Similarly, Wang et al. (2011) compared ultrasound ex­traction, high-pressure liquid extraction, and Soxhlet ex­traction for antioxidants from C. sulcate fruit and peel and suggested that UAE is the ideal method for the extraction of antioxidants.31 In our study, similarly other studies in the literature, the extract from UAE showed better results compared to its extract by obtained WBE for Domat variety of olive leaf. Since extraction conditions have an effect on the extraction of phenolic and biocomponents, it was aimed to investi­gate the effect of extraction duration on extraction yield by keeping other conditions same in the continuation of the study. If the extraction time is shortened without changing antioxidant capacity of the extract significantly, time and energy will be saved that is important for manufacturing cost industrially. Therefore, in the present study, olive leaf Table 3. Effect of ultrasonication period on antioxidant properties of olive leaf extract Extract Yield TPC TFC EC50 mg/mL (%) (µg GAE/mg) (µg Rutin/mg) DPPH assay ABTS assay UAE-15 14.2a 99.7 ± 0.12a 421.3 ± 0.10a 130.3a 411.3a UAE-30 15.4a 97.5 ± 0.03a 410.1 ± 0.04a 137.6a 376.9a UAE-60 19.1a 102.7 ± 0.04a 418.7 ± 0.02a 127.6a 409.2a UAE-120 19.4a 104.5 ± 0.018a 530.2 ± 0.045a 122.8a 406.7a SD = standard deviation (n=2) Different letters of upper index within the column indicate significant differences at p<0.05 level. Table 4. Linear correlations between the analyzed parameters DPPH assay ABTS assay Time TPC (GAE) TFC (Rutin) (EC50 value) (EC50 value) Time 1 0.865 0.911 –0.789 –0.904 0.135 0.089 0.211 0.096 TPC (GAE) 1 0.763 –0.972* 0.334 0.237 0.028 TFC (Rutin) 1 –0.777 –0.982* 0.223 0.018 DPPH assay (EC50 value) 1 0.656 0.344 ABTS assay (EC50 value) 1 *Correlation is significant at the 0.05 level (2-tailed). was extracted by ultrasound-assisted extraction at a differ­ent interval of time (15 min, 30 min, 60 min, 120 min) at 30 °C using ethanol (75%) as the solvent. According to re­sults (Table 3), it was seen that the maximum extraction yield was obtained by 120 min of sonication in comparison with 15 min, 30 min and 60 min intervals of sonication. The sonication interval was increased the extraction yield (%), however, there are not significantly differences be­tween the sonication time and both total phenolic and fla­vonoid contents (p < 0.05). According to Table 4, it can be inferred that ex­traction time values are not significantly correlated with TPC, TFC, DPPH, ABTS, but DPPH values are significant­ly correlated with TPC, and ABTS values are significantly correlated with TFC. We said that there is no significant difference between the extraction time and TPC and TFC which responsible antioxidant activity. There was also no relationship between extraction time and DPPH and ABTS EC50 values (p < 0.05), which means that 15 min of sonica­tion can be done instead of 120 min. In recent studies, the extraction time has been reported at different timings. The most efficient extraction time of the total polyphenol con­tained in soybeans extract with solid-liquid extraction has been reported as 120 min.33 It has been shown that the pep­per pulp extract obtained by ultrasonic bath and shaking water bath methods was obtained with the 20 min ex­traction process in terms of the highest total phenolic sub­stance and antioxidant activity which is similar with our results.34 Altemimi et al. was reported the optimum ex­traction process time of spinach leaves with ultrasound-as­sisted extraction method was reported as 30 min.35 In other studies, about comparing microwave-assist­ed extraction and ultrasound-assisted extraction methods, the extraction times for optimum antioxidant activity were reported as 30.5 for Prunella vulgaris L.extract.36 Similarly, the extraction time was reported as 5–30 min by keeping temperature and power constant as the optimum ex­traction conditions of Nettle and Chokeberries extracts by comparing microwave-assisted extraction and ultra-sound-assisted extraction methods.37–38 According to the reports of other researchers, Wang et al. also indicated that the ultrasound-assisted extraction method is a convenient and economical method for the extraction of total pheno­lic compounds for Inula helenium sample.39 The olive tree leave is a source of valuable active compounds such as oleuropein, verbascoside and rutin, which has benefit in health promoting potencial, and also have an importance in agricultural as industrial waste. Therefore, the extraction process for biophenols obtained from olive leaf is important and the researchers focused on optimum extraction conditions. Nowadays, ultrasound-as­sisted extraction methods have been studied mostly be­cause of its advantages. There are few studies about olive leaf extraction by ultrasound-assisted extraction method related to Serrana variety from Spain25 and Tavsan Yuregi variety from Tur­key.24 In this study, we studied another variety of olive leaf which is Domat variety from Manisa/Turkey. Ahmad-Qa­sem et. al. has addressed the ultrasound-assisted extraction of Serrana var. olive leaf bioactive compounds and evaluat­ed the influence of some process parameters such as the electric amplitude, the emitter surface and temperature. Also, they reported that olive leaf extracts were similar content of bioactive compounds, such as oleuropein, ver­bascoside and luteolin-7-O-glucoside in comparing the conventional technique with ultrasound-assisted ex­traction method. In their results, the extraction time of conventional technique reduced from 24 h reduced to 15 min with ultrasound-assisted extraction method.25 In an­other study by Sahin and Samli for ultrasound-assisted extraction of olive leaves (Tavsan Yuregi variety) with re­sponse surface methodology, were reported the optimum extraction conditions such as 0.5 g sample to 10 mL sol­vent ratio, 50% ethanol and 60 min of extraction time24. As stated above, different extraction process times have been reported so far regarding to antioxidant activity, total phe­nolic and flavonoid contents of different plants, so the re­sults of our study may suggest the best extraction time for olive leaf (Domat var.) as 15 min with ultrasound-assisted extraction. Comparing to the conventional extraction pro­cess, ultrasound-assisted extraction can use for reducing the long extraction time, and to decrease the risk of bio-compound degradation. 4. Conclusions This study aimed to analyse whether high antioxi­dant results of olive leaf extracts in a shorter time instead of two hours by ultrasound-assisted extraction, and to de­termine the phenolic compounds contained in olive leaf (Domat var.) by LC-MS/MS. So, choosing the right ex­traction method is important for obtaining extracts with various pharmacological activities expected from plants and also it is important to minimize or avoid degradation of biocompounds and to select the best working condi­tions. For the extraction of antioxidant compounds from Domat variety olive leaves, the UAE technique gave better results than the extraction with shaking water bath tech­nique. When the effect of time on ultrasound-assisted ex­traction is examined, it can be said that the extraction time of 15 minutes instead of 2 hours is sufficient, according to the results obtained from the antioxidant activity trials of the extracts and their extract yields. The results show that the UAE method provides time and energy saving, and can be used in biocomponent recovery, which is faster and more effective than traditional methods. This technique can also find application in the development of industrial extraction processes. In order to contribute to the indus­trial field, extraction-time optimization or other extraction parameters can be also investigated by using different herbal materials with biological activity. Acknowledgements The authors wish to thank TUTAGEM for the phe­nolic profile analysis by LC-MS/MS, and Olive Research Institute-Izmir for providing the olive leaves sample. This work was supported financially by the Scientific Research Fund of Trakya University, Project number TUBAP-2019/159. 5. References 1. S. De Marino, C. Festa, F. Zollo, A. Nini, L. Antenucci, G. Raimo, M. Iorizzi, Anticancer Agents Med. Chem. 2014, 14, 1376–1385. DOI:10.2174/1871520614666140804153936 2. A. Guinda, J. M. Castellano, J. M. Santos-Lozano, T. Delga­do-Hervás, P. Gutiérrez-Adánez, M. Rada, Food Sci. Tech-nol. 2015, 64, 431–438. DOI:10.1016/j.lwt.2015.05.001 3. S. Lockyer, P. Yaqoob, J. Spencer, I. Rowland, Nutrition Ag­ing 2012, 1, 125–140. DOI:10.3233/NUA-2012-0011 4. H. H. Orak, S.S. Isbilir, H. Yagar, Food Sci. Biotechnol. 2012, 21, 1065–1074. DOI:10.1007/s10068-012-0138-6 5. S. Himour, A. Yahia, H. Belattar, Eur. Sci. J. 2017, 13, 1857– 7881. DOI:10.19044/esj.2017.v13n6p342 6. M. Bayram, S. Topuz, C. Kaya, Turk. J. Agric. - Food Sci. Tech-nol. 2020, 8, 337–347. DOI:10.24925/turjaf.v8i2.337-347.3039 7. K. Mmopele, K, S. Combrinck, J. Hamman, C. Willers, W. Chen, A. 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DOI:10.1155/2013/157527 Povzetek V tej raziskavi smo primerjali ucinkotiost ekstrakcije polifenolov iz oljcnih listov (Domat) s pomocjo stresane vodne kopeli in ultrazvoka ter rezultate primerjali z vsebnostjo polifenolov in antioksidativno aktivnostjo oljcnih listov. Ucink­ovitost ekstrakcije na antioksidativno aktivnost oljcnih listov smo preverjali preko izkoristka ekstrakcije, antioksidativne aktivnosti in vrste pridobljenih polifenolov, pri cemer smo poleg same metode spreminjali tudi trajanje ekstrakcije. Cilj: doseci visoke antioksidativne vrednosti in dolociti vrste fenolnih spojin v oljcnih listih (Domat) z LC-MS/MS pri uporabi ekstrakcije z ultrazvokom ali stresano vodno kopelijo v prej kot 2 urah Zakljucki: Ugotovili smo, da z ekstrakcijo z ultrazvokom dosežemo višjo koncentracijo fenolnih komponent kot pri ekstrakciji s stresano vodno kopeljo. Obenem pa smo ugotovili, da pri tem tipu ekstrakcije cas ekstrakcije ne vpliva na celotno vsebnost fenolov in flavonoidov, kot tudi ne na antioksidativne vrednosti dolocene z DPPH in ABTS EC 50 (p < 0.05), kar pomeni, zadostuje že 15 minut namesto 120 min ultrazvocne ekstrakcije. Except when otherwise noted, articles in this journal are published under the terms and conditions of the Creative Commons Attribution 4.0 International License DOI: 10.17344/acsi.2022.7333 Scientific paper Design of “Turn-Off ” Fluorescent Nanoprobe for Highly Sensitive Detection of Uric Acid using Green Synthesized Nitrogen-Doped Graphene Quantum Dots Sopan Nangare,1 Shweta Baviskar,2 Ashwini Patil3 and Pravin Patil2,* 1 Department of Pharmaceutics, H. R. Patel Institute of Pharmaceutical Education and Research, Shirpur-425405, Dist: Dhule, Maharashtra state, INDIA-425405 2 Department of Pharmaceutical Chemistry, H. R. Patel Institute of Pharmaceutical Education and Research, Shirpur-425405, Dist: Dhule, Maharashtra state, INDIA-425405 3 Department of Microbiology, R. C. Patel Arts, Science, and Commerce College, Shirpur, Dist: Dhule, Maharashtra state, INDIA-425405 * Corresponding author: E-mail: rxpatilpravin@yahoo.co.in Received: 02-24-2022 Abstract Green synthesized graphene quantum dots (GQD) have been doped with nitrogen in an attempt to boost their opti­cal characteristics and application sectors. In the present investigation, the blue luminescent nitrogen-doped GQDs (N-GQDs) were synthesized by single-step hydrothermal synthesis using tamarind shell powder as a precursor. The particle size and zeta potential of N-GQDs were found to be 11.40 nm and be –35.53 mV, respectively. A quantum yield as high as 23.78 % was accomplished at an excitation wavelength of 330 nm at neutral pH. It gets quenched sensitively in the existence of uric acid (UA) combining static quenching, electron transfer, and an inner filter effect mechanism. A linear range was obtained for UA from 10 µM to 100 µM, with a limit of detection (LOD) of 401.72 ± 0.04 pM. Addition­ally, the N-GQDs were selective toward UA in presence of metal ions and biomolecules that indicated its impending use to monitor UA in clinical samples. In conclusion, this work demonstrates that the N-GQDs as a sensing probe for UA recognition with notable advantages including socioeconomic, simple, and less time-consuming methods as compared to other methods. In the future, it can be potentially explored as a biosensor for UA detection in clinical samples. Keywords: Graphene Quantum Dots; N-GQDs; Uric acid; Biosensor; Tamarind Shell Powder 1.Introduction Principally, UA (2.6,8-trihydroxypurine) is the pri­mary product of purine synthesis.1 As per literature, in the general population, UA is referred to between 0.13 mM to 0.46 mM and 2.49 mM to 4.46 mM in serum and urine, respectively.2 As we know, the abnormal levels of such me­tabolites in body fluids can cause several diseases.3 Plenti­ful literature revealed that the increased UA levels in body samples are indicative of hypertension, gout, cardiovas­cular disease, kidney disease, high cholesterol, and many more.4 In comparison, low concentrations of UA are also connected with multiple sclerosis and oxidative stress.5,6 In diagnosis and healthcare, it is crucial to quantify me­tabolites in blood or other biological samples. Therefore, a rapid, responsive, precise, and cheap method of assess­ment must be developed to track such metabolites in body fluids including serum and urine.5 Literature survey reported that electrochemical sensing,7 a colorimetric method,8 a chromatograph­ic method,9 etc. are currently engaged detection tech­niques for UA in different body fluid samples. However, some in-conveniences such as complicated synthesis or challenging extraction, advanced equipment, expensive and tedious limiting their practical uses, are present in these approaches.5 There are no exceptions for benefit of fluorescence. It is highly sensitive, and it shows a fast reaction, and operative simplicity in contrast to the oth­er existing approaches.5, 10 From the last decades, ample reports have been reported for the detection of UA us­ing fluorescent nanomaterials. Ding et al., designed the graphene oxide-based carbon quantum dots (CQDs) as an electrochemical sensor for detection UA.11 In another study, CQDs- hybrid composites-based electrochemical sensor has been constructed by Abbas and colleagues for recognition of UA.12 Currently, application of GQDs as a sensing material is gaining much consideration from re­search fraternities. Thanks to its tunable photolumines­cence properties GQDs have created substantial interest as a potential new class of carbon-based, luminescent na­nomaterials.10 The growing advancement in fluorescent sensors and bioimaging has been growing for the GQD because of their high bio-compatibility and photostabili­ty.13,14 Applications of GQDs in UA sensing a redox mod­ulated fluorescent strategy for a finding of UA and glucose using fluorescent GQDs based probe has been reported.5 Kunpatee and co-authors reported the detection of UA using GQDs mediated ionic liquid modified screen-print­ed carbon electrode that offers the 0.03 µM of lowest de­tection limit.15 However, the QY of GQDs mentioned is far lower than traditional quantum dots (QDs), which restricts QD’s wide-ranging use for different purposes. Hence, the improvement of QYs has become one of the obstacles to the use of GQDs. Several scholars have suc­cessfully shown that GQDs can be reliably transformed into an amino-functionalized GQD or p-conjugated sys­tem employing doping of heteroatoms.14 As per litera­ture, doping is an efficient method to alter the electronic density of bulk semiconductor materials. Besides, it helps to tune the optical and electrical properties. Recently, ni­trogen doping in graphene structure enabled the in-plane substitution of nitrogen atoms to graphene as an n-type semiconductor.16 Therefore, in this work we developed the doped GQDs as fluorescent nanomaterials. Nitrogen doping is a feasible approach for altering the electrical, chemical, and structural functions of GQDs to boost their efficiency in sensing applications.17 Li and co-authors found, owing to the intense electron-with­drawing outcome of the doped ‘N’ atom, that the as-pre­pared N-GQDs have special optoelectronic properties.18 Besides, a potential mechanism for tuning GQDs fluores­cent properties via the charge transfer effect of the func­tional group was suggested by the Chattopadhyay group.19 Due to the high QY and superb bleaching resistance of fluorescent materials, many research groups have been used N-GQDs as fluorescent sensors for sensing biological small macromolecules, organic small molecules, and met­al or non-metal ions.20 It is intended, by adjusting the sur­face characteristics and the associated fluorescent proper­ty, the design of realistic sensors for the assessment of UA in the urine can be promising. In the meantime, N-GQDs normally have greater amounts of QY than GQDs and are good for the fluorescence study in spiked real-time body fluid (urine). A preference of methods has been developed for the N-GQDs preparation including hydrothermal method,21 direct electrolysis,22 pyrolysis,23 etc. using dif­ferent chemicals as a precursor. Additionally, owing to the costly facilities, complex and time-consuming processes, the aforementioned protocols are unconvincing. In this study, we illustrate a single step, relatively in­expensive and environmentally safe in preparing N-doped GQDs through hydrothermal treatment of Tamarind seed as a green source. We present a new kind of fluorescent sensor for UA detection based on the fluorescence Turn On-Off strategy. The synthesized N-GQDs show a high quantity of QY and emit light blue luminescence. Inter­estingly, owing to the stronger electrostatic interaction and electron transfer between blue luminescent N-GQDs and UA, the photoluminescence intensity of the N-GQDs could be quenched efficiently (Scheme 1). Finally, based Scheme 1: One spot synthesis of N-GQDs using tamarind shell powder and sensing of UA on a finding of the proposed N-GQDs fluorescent sensor, it could be an exceptional alternative as high sensitivity and selectivity for UA recognition. 2. Materials and Methods 2. 1. Materials Uric acid (2, 6, 8-trihydroxypurine, =99%) and urea (=99%) were purchased from Sigma-Aldrich. The sodium hydroxide, sodium chloride, calcium chloride, glycine, glucose, sucrose, lysine, alanine, quinine sulfate were col­lected from Lobachemie, Chemicals. Pvt. Ltd. Mumbai, India. All chemicals plus reagents utilized for synthesis and characterization of N-GQDs were of analytical grade (AR) and applied as received without purification. Dou­ble distilled water (DDW) was prepared in the laborato­ry. Tamarind seed was collected from the local market of Shirpur, Maharashtra (India). 2. 2. Methods 2. 2. 1. Processing of Tamarind Shell In the beginning, 0.5 kg of tamarind shell (Tama-rindus indica) waste was collected from the local market, Shirpur city (Dhule, Maharashtra). Further, the tamarind shell waste was dried using a laboratory hot air oven. The temperature was programmed at 60 °C and then de­creased to 40 °C at the rate of –0.14 °C/minutes for 12 h. After drying, the dried shells were checked for stuck dirty materials on the shells. Finally, shells were crushed into tiny pieces via a mortar pestle. Further, the obtained powder was subjected to grinding to obtain fine tamarind shell powder. 2. 2. 2. Synthesis of N-GQDs The N-GQDs synthesis from Tamarind shell powder and urea was achieved through the one-pot hydrothermal method. Briefly, 500 mg of fine tamarind shell powder was dispersed in 20 mL of double-distilled water (DDW) followed by 20 minutes of sonication. After that, 100 mg urea was added to the above dispersion. Finally, the pre­pared dispersion was transferred into a Teflon-lined auto­clave in a stainless-steel hydrothermal reactor in a hot air oven at 200 °C for 8 h. After the end of the hydrothermal process, the resultant solution was kept to bath sonica­tion for 20 minutes at 25 °C, which gives uniform disper­sion of N-GQDs. For purification purposes, the obtained N-GQDs were shifted to the dialysis (for 24 h) using a di­alysis bag (12,000 kDa, 0.22 µm), which help to remove the insoluble carbon materials. Afterward, the final dialyzed solution was transferred to centrifugation (Refrigerated Centrifuge, Elteck Overseas Pvt. India) at 9000 rpm for 20 minutes (4 °C to 10 °C) to remove the impurities. 2. 2. 3. Freeze-Drying of N-GQDs In this study, liquid N-GQDs solution was processed for lyophilization by using a laboratory freeze dryer (Free­zone12, Labconco, MO, USA). Briefly, the N-GQDs solu­tion was pre-freeze at –30 °C for 36 h at the laboratory deep freezer (Southern Scientific Lab Instrument, India). After that, the primary drying of N-GQDs was performed at –53 °C and 0.016 mBar for 24 h. Afterward, the second­ary drying of N-GQDs was performed at 10 °C for 8 h and was followed by drying at 25 °C for 4 h with a steady in­crease in temperature at 1 °C/min. Finally, the temperature of the cold trap was maintained at –53 °C until the end of the drying process. 2. 2. 4. Characterizations of N-GQDs The synthesized N-GQDs were characterized by the UV-visible spectra was recorded between 800 to 400 nm on UV–vis spectrophotometer (UV 1800 Shimadzu, Japan) using a quartz cuvette. FT-IR spectra of N-GQD were re­corded using an FT-IR spectrophotometer (Agilent CARY 630 FT-IR) at 4 cm-1 resolution in the absorption area of 4000 to 1000 cm-1. Energy Dispersive X-ray Spectroscopy (EDAX) was used to provide qualitative and quantitative surface analysis. The particle size of synthesized N-GQDs was measured via Nanoplus 3 Particulate System (Mi-cromeritics, USA). The green synthesized N-GQDs shape, size, and crystalline nature were obtained using HR-TEM (HR-TEM, Jeol/JEM 2100) with a LaB6 light source at 200 kV. The fluorescence study of N-GQDs was performed us­ing a UV chamber (details) at different wavelengths such as 254 nm and 365 nm along with visible light. 2. 2. 5. pH Influence Study The pH of the N-GQDs solution actively affects the fluorescence properties of the N-GQDs since the pH af­fects the surface stability and molecularity of the surface functional groups. The prepared N-GQDs were subject­ed to different pH ranges from 1 to 12 to study the pH dependant changes in the fluorescence intensity.24 Here­in, different predefined pH such as 1, 3, 7, 9, and 12 of N-GQDs solution was adjusted using 1.5 M solution of sodium hydroxide and 0.5 M hydrochloric acid at room temperature. Then, the pH value of the N-GQDs solution was recorded using a digital pH meter, and solutions were kept aside for 60 min. After that, the fluorescence intensi­ty was recorded at excitation 330 nm and corresponding changes in the intensities were recorded. 2. 2. 6. Quantum Yield (QY) In this study, the ‘QY’ of the N-GQDs was calculated by using a comparative method. Quinine sulfate was dis­solved in 0.1 M sulphuric acid (fst = 54%) as a standard for QY determination. Then, the dilutions were prepared using double distilled water (DDW). The absorbance was measured at 350 nm via a UV-Vis spectrophotometer and optical density values were kept below 0.1 to reduce the re-absorption effect. Fluorescence emission spectra of qui­nine sulfate, as well as N-GQDs, were recorded at 350 nm excitation and a graph was plotted for integrated fluores­cence intensity against absorbance. The ‘QY’ of N-GQDs was calculated using the following equation, wherein, ‘f’ is representing the QY. The subscripts ‘x’ and ‘st’ represent test (N-GQDs) and standard (quinine sulfate) respectively. Grad is the slope of fluorescence intensity vs. absorbance plot. The ‘.’ is the refractive index of the sol­vent. 2. 2. 7. Uric Acid-Sensing Study Detection of UA was carried out by using a spectro­fluorometer (JASCO FP8200 spectrofluorometer) at neu­tral pH. Initially, 2 mL of synthesized N-GQDs solution were taken in a quartz cuvette and excited at 330 nm with a slit width of 5 nm for both excitations as well as emission wavelength at a scanning range of 335 nm to 520 nm at room temperature. To study the sensitivity of N-GQDs for UA detection, different concentrations of UA from 10 µM to 1000 µM were incorporated into an aqueous solution of N-GQDs (2 mL) and uniformly shaken followed by 5 min­utes of incubation time at controlled room temperature (25 °C). Afterward, fluorescence intensities were measured in triplicate for more accuracy. Notably, N-GQDs probe fluorescence intensity and the concentration of UA were found to have a linear function as the calibration curve. Different analytical characteristics including the limit of detection and the limit of quantification were calculated using slope and standard deviation. Moreover, the preci­sion of the proposed sensing method was measured as in-terday and intraday variations. 2. 2. 8. Interference Study The selectivity of N-GQDs (2 mL) was analyzed against a range of metal ions and biomolecules. For the interference study, an aliquot of the stock solutions of non-target samples namely, Na+, K+, Ca+, Ag+, bovine serum albumin, lysine, glycine, alanine, glucose, sucrose, lactose, ascorbic acid, etc. were prepared in DDW (pH 7). A concentration of 100 µM was chosen for all the metal ions and biomolecules for interference study. Briefly, the 1 mL of N-GQDs solution was mixed with 500 µL of these interfering substance solutions, separately. After 10 min­utes, the fluorescence intensity of all samples was recorded at 330 nm (excitation wavelength) with 5.0 nm silt width for both excitation and emission wavelength. In addition, the mixture of all metal ions and biomolecules with UA (sample A) was investigated to confirm the interference of foreign substances in the UA sample. The repeatability of N-GQDs was measured by the proposed method using 30 µM of UA samples (n = 6). 3. Results and Discussion 3. 1. Characterization of N-GQDs In the present study, the applicability of N-GQDs as a fluorescent nanomaterial belonging to the ‘C’ family was explored for the recognition of UA in aqueous media. Briefly, a one-step, facile and eco-friendly method was pre­ferred to synthesize N-GQDs using tamarind shell powder in a Teflon-lined hydrothermal stainless-steel autoclave reactor. Accordingly, the tamarind shell was proved to be an efficient and reliable waste material for synthesiz­ing of GQDs while urea successfully donated nitrogen for the doping of nitrogen on the GQD structure. Inside the reactor, under high pressure and temperature conditions, tamarind shell powder forms the primary graphitic frame, and the nitrogen atoms were doped onto the graphitic structure. The synthesis procedure was optimized by al­tering the precursor’s concentration as well as by chang­ing the reaction time. The various batches were thus syn­thesized. As a result, it displayed momentous variation in the fluorescence properties. From these results; the final optimized parameters were identified for the synthesis of N-GQDs. Briefly, the fluorescence spectra were recorded at a fixed and varying wavelength to measure the excita-tion-dependent behavior. The synthesized N-GQDs were obtained in liquid form and then subjected to the different wavelengths in a UV- cabinet for fluorescence study. The solution of synthesized N-GQDs was looking pale yellow (or brownish) in visible light (Figure 1A). When a solu­tion was transferred to the UV light excitation wavelength 254 nm, green fluorescence was observed (Figure 1B). In the case of UV light excitation wavelength 365 nm, the bright blue fluorescence of N-GQDs was obtained.25 The varied colors observed in N-GQDs fluorescence were most possibly caused by solvent attachment/ different emissive traps of ‘N’ on green synthesized GQDs surface.26 The ox-ygen-rich group provides the blue shift in photolumines­cence emission from N-GQDs. Moreover, ‘N’ contains a strong electron affinity that offers the photoluminescence blue shift.18 For further investigation of optical properties of green synthesized GQDs, the UV-vis absorption spectra and fluorescence spectra were recorded. Figure 1D depict­ed the typical absorption peak at 229 nm that confirmed establishment of the graphitic structure of N-GQDs. It shows the absorption peak of N-GQDs in water at 200 nm to 800 nm against DDW (as a blank). The UV visible spec­tra of synthesized N-GQDs showed a maximum absorp­tion peak at ~235 nm due to the p-p* transition of C=C. The shoulder peak at ~344 nm is due to n-p* transitions of Figure 1: A) N-GQDs in Visible Light, (B) N-GQDs excited in UV light wavelength 254 nm, (C) N-GQDs excited in UV light wave­length 365 nm, (D) UV-Visible spectra of N-GQDs C=O.27 Herein, these two absorption peaks confirmed that there is a certain absorption peak in N-GQDs. In conclu­sion, it confirmed the green synthesis of N-GQDs using the hydrothermal method.20, 25 FT-IR was used to characterize the presence of ma­jor functional groups in synthesized N-GQDs (Figure 2A). The FT-IR spectrum of N-GQDs showed the absorption peak of the stretching vibration of the –OH group at 3412.0 cm–1 whereas the strong absorption peak 3230 cm–1 can vibrate of amine (NH2) bond. The overlapping of –OH and Figure 2: (A) FT-IR Spectra of N-GQDs, (B) EDAX spectra of N-GQDs NH2 was obtained due to the aqueous form of N-GQDs. Moreover, the peak at 1408.0 cm–1 can be attributed to the bimodal bending vibration of –NH. The C-N stretching vibration was obtained at 1276.92 cm–1. Accordingly, it confirms the chemical interaction between the ‘N’ source and carboxyl. Moreover, it assures the existence of N on green synthesized GQDs surface.25 The vibration peaks at 1658.84 cm–1, 1120.68 cm–1, and 1026.16 cm–1 can be as­cribed to C=O stretching, C–O stretching, and C–O bend­ing, respectively. In conclusion, the different functional Figure 3: Particle size analysis (A) and zeta potential (B) of green synthesized N-GQDs groups including –OH, –NH, and –COOH on N-GQDs surface confirmed the hydrophilicity and stability of the N-GQDs in an aqueous system.23 The elemental analysis of synthesized N-GQDs was conducted for authentication of the occurrence of C, O, and N (Figure 2B) elements. In brief, it provides the de­tails of wt.% of these mentioned elements in synthesized N-GQDs from green precursors. Herein, the presence of C, O, and N was found to be 41.09 wt%, 49.00 wt%, and 9.91 wt% respectively. Therefore, the existence of these elements demonstrates the successful formation of the N-GQDs.28 Moreover, the confirmation of the amide group in N-GQDs can form the hydrogen bonds between water molecules and N-GQDs, which helps to improve the water solubility of prepared N-GQDs.20 It is worthy to mention that particle size is the foremost critical factor since particle size is a potential technique for confirming the transformation of a micro-sized structure to a na-no-sized structure. In this study, the particle size of green synthesized N-GQDs was found to be 12.6 ± 11.1 nm that confirming the synthesis of nanosized N-GQDs (Figure 3A). The polydispersity index (PDI) was found to be 0.35 ± 0.09. It assured that the prepared N-GQDs exhibited uni­form distribution in media. The zeta potential of N-GQDs was checked to confirm the surface charge as well as stabil­ity. The zeta potential of N-GQDs was found to be –35.53 mV (Figure 3B), which assures that the prepared N-GQDs exhibited good stability. The morphological features of purified N-GQDs were characterized using HR-TEM. As depicted in Figure 3A, the GQDs are small, rounded, and uniformly dispersed with an average particle of size 7 nm. Moreover, the typical lattice spacing was found to be 0.23 nm demonstrating a graphitic structure of green synthesized N-GQDs. Figure 3B showed the selected area diffraction (SAED) pattern of N-GQDs that assured the crystalline nature of N-GQDs. In conclusion, it confirmed the successful synthesis of na­nosized N-GQDs.20 3. 2. pH-Dependent Fluorescence Study of N-GQDs Nitrogen doping is a feasible approach for altering the electrical, chemical, and structural functions of GQDs to boost their efficiency in sensing applications. The syn­thesized N-GQDs solution was excited at different excita­tion wavelengths from 300 to 400 nm length, with an in­crement of 10 nm, slit width is 5 nm and scanning range is 315–525 nm at 25 °C (Figure 5A). The N-GQDs showed the emission at 400 nm when excited from 300 nm to 330 nm and the emission peak was decreased and shifted to­wards the longer wavelength when excited from 340 nm to 400 nm. The strongest emission peak was obtained at 400 nm when excited at 330 nm. In this study, the emis­sion spectrum of the N-GQDs was showed excitation-de­pendent PL behavior. Literature survey reported that the shift in emission peak positions with different excitation wavelengths is depending on several key factors such as quantum confinement effect, size effect, elemental compo­sition, edge states, surface functional groups, conjugated p-domains, and defects in the carbon framework.10 The effect of pH on synthesized N-GQDs was also studied by fluorescence spectroscopy. The fluorescence intensity of N-GQDs at different pH was shown in Figure 5B. The syn- Figure 4: (A) HR-TEM and (B) SAED images of N-GQDs thesized N-GQDs were dispersed in a solution of varying pH mainly 1, 3, 7, 9, and 12. The N-GQDs fluorescence intensity was gradually increased with increment in pH of N-GQDs. As a result, the synthesized N-GQDs show the highest fluorescence intensity at neutral pH. On contrary, the fluorescence intensity of N-GQDs was decreased at pH 9 and 12.29 Therefore, neutral pH was selected for further sensing study of UA. Importantly, the protonation state of N-GQDs could influence fluorescence intensity. In brief, owing to protonation or deprotonation of N-GQDs con­taining amide and carboxylic functional groups, it gives a carbene-like triplet state that causes changes in photolu­minescence.30–32 The change in pH from 1 to 7 resulted in a high degree of deprotonation of N-GQDs. Subsequently, it generates the deprotonated oxygen atoms and nitrogen atoms on the N-GQDs surface that facilitated the altera­tion in electron distribution. Hence, the present N-GQDs mediated sensor shows a high fluorescence intensity at the physiological range.32 At neutral pH, the Quantum yield (QY) of synthesized N-GQDs was found to be 23.78%. Figure 4B concludes that the neutral pH (7) has the high­est fluorescence intensity as compared to acidic pH (1, 3) and basic pH (9, 12). 3. 3. Detection of UA using N-GQDs The fluorescence quenching of N-GQDs was studied by adding different concentrations of UA at 25 °C (Figure 6A). The fluorescence of N-GQDs was observed in the UV cabinet shown in Figure 6B (i and ii). In this study, the flu­orescence of N-GQDs (i) gets quenched after UA addition (ii). To study the sensitivity of N-GQDs for UA detection, different concentrations of UA were added to the aqueous solution of N-GQDs followed by measurement of fluores­cence intensities. When UA with concentrations ranging from 10 µM to 1000 µM was added to the as-prepared N-GQDs, the fluorescence intensity of N-GQDs was grad- Figure 5: (A) Fluorescence spectra of N-GQDs at different excitation wavelength pH study; (B) Fluorescence intensity of N-GQDs at dif­ferent pH ually decreased at 245 nm. It presents the relationship of the fluorescent quenching value .F = F0 – F*100 (F0 and F are the fluorescence intensities of N-GQDs at 400 nm in the absence and presence of UA, respectively) in varying concentrations of UA (Figure 6C and D). As a result, the .F has a good linear relationship with the concentration of UA in the range of 10 µM to 50 µM and 60 µM to 100 µM wherein the linear regression equation was found to be y = 0.337x + 2.614 [R2 = 0.989, CI: (0.202, 0.472)] and y = 0.300x + 8.947 [R 2 = 0.981, CI: (0.165, 0.435)], respec­tively at 95% of confidence level. It shows both static and dynamic fluorescence quenching in presence of UA33. The lowest detection limit of 401.72 ± 0.04 pM (n = 3) was ob­tained based on a 3.3 ./Slope (.: Standard deviation). The comparison of different sensors for the detection of UA is depicted in Table 1. The limit of quantification was found to be 1.217 ± 12 nM, which was calculated based on 10* ./Slope. The precision of the sensor was measured based on intraday (n = 6) and interday (n = 6) analysis of UA concentration. The percent relative standard deviation (% RSD) was found to be 0.901% and 2.547% for intraday and interday, respectively assuring the high precision for sens­ing of UA. Importantly, at neutral pH, the fluorescence of N-GQDs was gets quenched sensitively in the existence of UA. It may be because of different possible mechanisms such as static quenching, electron transfer, and an inner filter effect mechanism. Notably, the N-GQDs were exhib­ited as hydrophilic which may be due to the continuation of surface nitrogen, oxygen functionality. This hydrophilic surface can facilitate the better accessibility of interest an-alytes in the aqueous media to the ‘C’ surface. Particularly, it may because of high surface coverage, and more efficient adsorption ability towards interest analyte. This mecha­nism can alter the surface ion interaction at the edge or on the basal plane of N-GQDs by the modification of surface charge properties. Besides, the occurrence of oxygen func­tionalities in N-GQDs offers more interaction among the analyte and carbon surface of N-GQDs.16 Literature survey divulged that nitrogen doping on nanomaterials surface could change the electrical conductivity and alter the band-gap. Additionally, it can produce electronic defects that can increase the charge transfer ability of nanomaterials. Ac­cordingly, it gives good sensitivity and stability.34 Herein, doping of nitrogen into GQDs shows high electronegativity and it can transfer lone pair of an electron to electron-de­ficient molecules.35 In the present investigation, N-GQDs can transfer two electrons to UA which shows the quench­ing of fluorescence of N-GQDs.36 Overall, it is notable that the doping of nitrogen not only enhanced the QY but also improves the chelation ability like oxygen functional groups with the presence of a target marker such as UA that offers the effectual quenching of fluorescence.14 Taken as a whole, modification of chemical and electronic properties by ni­trogen doping into GQDs shows the boosted fluorescence presentation for UA.33 Herein, more research is necessary to comprehend the quenching mechanics of N-GQDs. For further study, optimizing the doping of nitrogen in the structure of GQDs is essential in N-GQDs-based fluores­cent nanosensors for monitoring an analyte. 3. 4. Interference Study To estimate the selectivity of the proposed method for the determination of UA, the effects of some common metal ions and biomolecules such as NaCl, KCl, CaCl2, AgNO3, lysine, glycine, alanine, glucose, sucrose, and lactose were investigated in triplicates, individually (n = 3). Figure 7 shows the selectivity of N-GQDs towards dif­ferent interfering substances in presence of the different interfering substances. Herein, common ions and bio­molecules had little effect (or slight quenching effect) on the fluorescence of N-GQDs, which was considered to be tolerable. It may be due to available active sites in metal ions and biomolecules that get co-interacted with func­tionality present on the surface of N-GQDs. Importantly, slight N-GQDs fluorescence quenching effect of different interfering substances was observed that assured the low affinity of N-GQDs towards the selected metal ions and biomolecules.44 In addition, the fluorescent color alters of N-GQDs upon adding the common ions and biomolecules can be examined by the naked eye below the UV lamp. Moreover, a mixture of numerous metal ions and biomol­ecules (sample A) had no obvious interference in the de- Figure 6: (A) Fluorescence spectra of N-GQDs with different concentrations of UA; (B) Fluorescence of N-GQDs before (i) and after addition of UA (ii); Fluorescence quenching linearity curve [.F vs UA concentration ranging from 10 µM to 50 µM (C) and (D) 60 µM to 100 µM, n = 3]. Table 1: Summary of different types of sensors for detection of UA Sr. No. Composite Sensor type Linearity range Limit of detection Ref. 37 1. Citrate-capped -PtNPs Colorimetric 0 mM to 8 mM 4.2 µM 38 2. Poly(dipicolinic acid)/SiO2@Fe3O4 Electrochemical 1.2 µM to 1.8 µM 0.4 µM 39 3. CNCo/GCE Electrochemical 2 µM to110 µM 0.83 µM 40 4. MoS2 Electrochemical 10 µM to 400 µM 1.169 µM 5. ZnNi@f-MWCNT Electrochemical 0.2 mM to 1.1 mM 0.51 µM 41 42 6. r-GO/AuNPs Electrochemical 10 to 500 µmol dm–3 3.6 µM 43 7. Cu2+@MIL-91(Al:Eu) Optical 10 µM to 1200 µM 1.6 µM 8. N-GQDs Optical 10 µM to 100 µM 401.72 ± 0.04 pM Present work tection of UA. These results demonstrate that the method possesses a good selectivity for the determination of UA. Overall, N-GQDs could be a promising biosensing stage for UA detection in complex samples. The repeatability of the proposed N-GQDs based fluorescent sensor was per­formed (n = 6) for detection of UA. As a result, it provides the %RSD of 0.802 that confirmed the repeatability of a proposed sensor for the detection of UA. Table 3 shows the summary of analytical characteristics of the N-GQDs based fluorescent biosensor for the determination of UA. 4. Conclusion In conclusion, a novel, extremely sensitive, and ef­ficient label-free fluorescence sensing substrate has been developed using N-GQDs. Compared to methods report­ed in previous studies for N-GQDs synthesis, we have re­ported a single step, facile, and green synthesis method, which is more economical and less time-consuming. As a result, the prepared N-GQDs were demonstrated excel- Table 3: Analytical characteristics of the N-GQDs based fluorescent biosensor for determination of UA. Analytical parameters Uric acid 10 µM to 50 µM y = 0.337x + 2.614 [R2 = 0.989, CI: (0.202, 0.472)] 60 µM to 100 µM y = 0.300x + 8.947 [R2 = 0.981, CI: (0.165, 0.435)] Limit of detection (n = 3) 401.72 ± 0.041 pM Limit of quantification (n = 3) 1.217 ± 12 nM Precision (% RSD) Intraday analysis (n = 6) 0.901 % Interday analysis (n = 6) 2.547 % Repeatability ( RSD, n = 6) 0.802 % lent optical properties and graphene structure. The as-syn­thesized N-GQDs emitted strong blue color fluorescence and showed good stability at neutral pH. Afterward, the prepared N-GQDs were utilized for fluorescence detection Figure 7: Fluorescence intensity (n = 3) in presence of different ions and biomolecules of UA. Owing to synergistic static quenching, electron transfer, and inner filter effect mechanisms, the fluorescent N-GQDs nanoprobe exhibited high sensitive detection of UA upto 401.72 ± 0.04 pM. Under the optimum condi­tion, it shows a wide linear response and high selectivity in presence of numerous interfering agents. For further development, there is a major need to control on doping of nitrogen in the structure of GQDs. In a nutshell, N-GQDs can be synthesized from different kinds of waste materials, which are existed in nature. In the upcoming days, the pro­posed N-GQDs based fluorescent nanoprobe can be used for the determination of UA and other biomarkers in hu­man plasma and urine samples. 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Qi, and X. Cui, J Electroanal Chem 2017, 797, 113–120. DOI:10.1016/j.jelechem.2017.05.031 Povzetek Zelene sintetizirane grafenske kvantne pike (GQD) smo dopirali z dušikom, da bi povecali njihove opticne znacilnosti in sektorje uporabe. V tej preiskavi so bili modri luminiscentni GQD, dopirani z dušikom (N-GQD), sintetizirani z enost­openjsko hidrotermalno sintezo z uporabo prahu lupine tamarinda kot prekurzorja. Ugotovljeno je bilo, da sta velikost delcev in zeta potencial N-GQD 11,40 nm in –35,53 mV. Kvantni izkoristek do 23,78 % je bil dosežen pri valovni dolžini vzbujanja 330 nm pri nevtralnem pH. Fluorescenca se obcutljivo zniža v prisotnosti secne kisline (UA) na podlagi treh mehanizmov: staticno dušenje fluorescence, prenos elektronov in mehanizem ucinka notranjega filtra. Za UA smo dobili linearno obmocje od 10 µM do 100 µM z mejo detekcije (LOD) 401,72 ± 0,04 pM. Poleg tega so bili N-GQD selektivni proti UA v prisotnosti kovinskih ionov in biomolekul, kar kaže na njihovo potencialno uporabo za spremljanje UA v klinicnih vzorcih. Skratka, to delo dokazuje, da imajo N-GQD kot zaznavna sonda za prepoznavanje UA dolocene pred­nosti pred ostalimi metodami, kot je enostavnost, hitrost analize, pa tudi z družbenoekonomskih aspektov. V prihodnos-ti je mogoce razcito metodologijo potencialno uporabiti za odkrivanje UA v klinicnih vzorcih. Except when otherwise noted, articles in this journal are published under the terms and conditions of the Creative Commons Attribution 4.0 International License DOI: 10.17344/acsi.2021.7336 Scientific paper Optimisation of Amphiphilic-Polymer Coatings for Improved Chemical Stability of NaYF4-based Upconverting Nanoparticles Tina Cernic,1,2 Monika Koren,3,4 Boris Majaron,3,4 Maja Ponikvar-Svet5 and Darja Lisjak1,* 1 Jožef Stefan Institute, Department for Materials Synthesis, Ljubljana, Slovenia 2 Jožef Stefan International Postgraduate School, Ljubljana, Slovenia 3 Jožef Stefan Institute, Department of Complex Matter, Ljubljana, Slovenia 4 Faculty of Physics and Mathematics, University of Ljubljana, Slovenia 5 Jožef Stefan Institute, Department of Inorganic Chemistry and Technology, Ljubljana, Slovenia * Corresponding author: E-mail: darja.lisjak@ijs.si +386 1 4773879 Received: 12-20-2021 Abstract NaYF4 nanoparticles codoped with Yb3+ and Tm3+ exhibit upconversion fluorescence in near-infrared and visible spec­tral range. Consequently, such upconverting nanoparticles (UCNPs) can be used as contrast agents in medical diagnos­tics and bioassays. However, they are not chemically stable in aqueous dispersions, especially in phosphate solutions. Protective amphiphilic-polymer coatings based on poly(maleic anhydride-alt-octadec-1-ene) (PMAO) and bis(hexam­ethylene)triamine (BHMT) were optimised to improve the chemical stability of UCNPs under simulated physiological conditions. Morphologies of the bare and coated UCNPs was inspected with transmission electron microscopy. All samples showed intense UC fluorescence at ~800 nm, typical for Tm3+. The colloidal stability of aqueous dispersions of bare and coated UCNPs was assessed by dynamic light scattering and measurements of zeta potential. The dissolution of UCNP in phosphate-buffered saline at 37 °C, was assessed potentiometrically by measuring the concentration of the dissolved fluoride. Protection against the dissolution of UCNPs was achieved by PMAO and PMAO crosslinked with BHMT. Keywords: Nanoparticles; upconversion; dissolution; chemical stability; coatings ological tissues compared to visible or ultraviolet light. To 1.Introduction achieve a high quantum yield of UC fluorescence, fluoride Upconverting nanoparticles (UCNPs) show great crystalline matrices are preferred to incorporate lantha-potential for various applications in biomedicine as multi-nide dopants. One of such matrices is ß-NaYF4 with hex-modal contrast agents in bioimaging and bioanalysis, nan-agonal structure, where Na+ and Y3+ occupy two different othermometry, biosensorics and nanotheranostics.1–4 sites among the close-packed F- ions.6 These applications are based on the UC fluorescence of The first condition for practical application of any visible or near-infrared (NIR) light. The sensitising Yb3+ nanoparticles is their chemical and colloidal stability, ions are excited with NIR light around 980 nm, and the which strongly depends on their surface chemistry and absorbed energy is transferred to the activator Er3+, Yb3+ surrounding medium. Nanoparticles are prone to fast sur-or Ho3+ ions emitting light with lower wavelengths.5 The face reactions, dissolution and severe agglomeration due NIR emission of Tm3+ around 808 nm is of particular in-to their large surface area. Specifically, UCNPs intended terest for biomedicine, due to its deeper penetration in bi-for biomedicine must be stable in aqueous media, includ­ing physiological buffers. The colloidal stability of nanopar­ticles is achieved by optimising their surface chemistry to induce: (i) electrostatic repulsion by tuning the surface charge or/and (ii) steric repulsion with large molecules (e.g., polymers). The chemical and colloidal stability of nanoparticles becomes more challenging with the increas­ing complexity of the aqueous media. The primary origin of the colloidal destabilisation of nanoparticles in physio­logical buffers is high ionic strength that decreases the re­pulsive electrostatic energy between the nanoparticles, which is otherwise sufficiently high in pure water. Addi-tionaly, the colloidal destabilisation of nanoparticles in complex aqueous media can also be induced by the ex­change of the stabilising surface ligands with species hav­ing a higher affinity towards the surface metal ions but lower surface charge. Similarly, the dissolution of nanopar­ticles can be promoted when the exchanging species bond more strongly with the surface ions than the latter are bonded with the crystalline core. For example, the fluo­ride-based UCNPs dissolve in phosphate solutions (e.g., in phosphate-buffered saline; PBS) due to strong interaction between the surface rare-earth ions and dissolved phos­phate ions, thus inducing the coprecipitation of highly sta­ble rare-earth phosphates.7 Any dissolution of fluo­ride-based UCNPs in vivo can cause various adverse effects4,8–11 which questions their suitability for applica­tions in biomedicine. The protection of fluoride-based UCNPs against the dissolution and interaction with sol­utes from the surrounding media is necessary also to en­sure stable optical properties. It was shown that even a minor dissolution of the NaYF4-UCNPs deteriorates their UC fluorescence properties,12 –15 limiting their general ap­plicability. Possible protective coatings against the dissolution of the NaYF4-based UCNPs in aqueous media were stud­ied in the last few years, starting from silica and phospho­nates to more complex polymer coatings.15–21 Most of these coatings decelerated the dissolution of the UCNPs in water or PBS at room temperature. However, only thick microporous silica coating and amphiphilic poly(maleic anhydride-alt-octadec-1-ene) crosslinked with bis(hexam­ethylene)triamine (PMAO-BHMT) coating significantly suppressed the dissolution of NaYF4-UCNPs in PBS at body temperature. Under the same conditions, bare UCNPs almost completely disintegrated.16,20 Such a high protective efficiency of the thick silica coatings was at­tributed to the mechanical barrier for the diffusion of aqueous molecules and solute ions to/from the UCNPs surface. Alternatively, the protection of UCNPs by the PMAO-BHMT was attributed to the coating stability, achieved by crosslinking PMAO with BHMT at the UCNPs surfaces. This study focuses on amphiphilic coatings since they have been recognised as a suitable strategy towards biocom­patible inorganic NPs.22 We hypothesise that a hydrophobic surface layer from an amphiphilic polymer can form an ef­fective diffusion barrier and suppresses the dissolution of UCNPs. We coated Yb3+,Tm3+-codoped ß-NaYF4 with dif­ferent surface fractions of amphiphilic PMAO or PMAO additionally crosslinked with BHMT (PMAO-BHMT, Scheme 1). We compared the colloidal and chemical stabil­ity of differently coated UCNPs in aqueous media. Scheme 1. Chemical formulae of poly(maleic anhydride-alt-octadec-1-ene) (PMAO) and bis(hexamethylene)triamine (BHMT) with a schematic presentation of the PMAO (top) and PMAO-BHMT (bottom) coating on an upconverting nanoparticle (UCNP). The inner layer of the coating (yellow) is hydrophobic, while the outer layer is hydrophilic. 2. Experimental 2. 1. Chemicals Unless otherwise specified, chemicals were used as received. Deionised water was used in all experiments. Ammonium fluoride (98.0%), yttrium(III) chloride hy­drate (99.99%), ytterbium(III) chloride hydrate (99.9%), thulium(III) chloride hydrate (99.9%), 1-octadecene (tech. 90%) and oleic acid (tech. 90%) were purchased from Alfa Aesar. The concentration of rare earths in chlorides were determined with an inductively-coupled optical emission spectrometer (ICP-OES, Agilent 720). Poly(maleic anhy­dride-alt-1-octadecene) (PMAO, Mm 30000–50000), bis(hexamethylene)triamine (BHMT, high purity), sodi­um hydroxide (=98%) and chloroform (=99.8%) were pur­chased from Sigma Aldrich. Acetone (=99.8%) and abso­lute ethanol (=99.9%) were purchased from Carlo Erba Reagents, while methanol (100%) was purchased from VWR Chemicals. Phosphate-buffered saline (PBS, 10x) was purchased from Alfa Aesar and diluted 10-times be­fore usage to mimic blood’s pH and ionic strength. 2. 2. Materials ß-NaYF4 upconverting nanoparticles codoped with Yb3+ and Tm3+ (named shortly as UCNPs in the subse­quent text) with nominal composition NaY0.78Yb0.20Tm0.02F4 were synthesised with a high-temperature coprecipitation, similarly as reported previously.23,24 In short, lanthanide Table 1. Selected samples used in specific comparative studies chlorides (2 mmol) in the stoichiometric ratio were mixed with 12 ml of oleic acid and 30 ml of 1-octadecene in a 100 ml three-neck flask and heated to 156 °C for 30 min. After that, the solution was cooled down to 70 °C, and 10 ml methanol solution of NH4F (8 mmol) and NaOH (5 mmol) was slowly added. The mixture was stirred for 40 min at 50 °C until methanol evaporated. After that, the solution was heated to 300 °C under an argon atmosphere for 1.5 h. When the reaction solution cooled down to room tempera­ture, acetone was added to sediment the UCNPs. UCNPs were washed several times with ethanol and deionised wa­ter, centrifuged (3016 rcf for 5 min), and finally dispersed in chloroform. Three UCNPs batches were distinguished by the average particle diameter (Table 1 and Figure S1 in Supporting Information). The UCNPs were coated with poly(maleic anhy­dride-alt-octadec-1-ene) (PMAO) and subsequently cross-linked with bis(hexamethylene)triamine (BHMT) by opti­mising the procedure from Ref.25. In short, as-synthesised UCNPs in chloroform were diluted to a concentration of 0.1 mg/ml. The nominal fraction of the PMAO monomer units per UCNPs’ surface varied between n = 7–300 MAO/nm2. First, a chloroform solution of PMAO polymer (100 mg/ ml) was admixed to the diluted UCNPs dispersion in a 50­ml flask and stirred for 2 h. Then chloroform solution of BHMT (50 mg/ml) was added with the ratio of 0.5–30 BHMT molecules/nm2 (i.e., MAO:BHMT = 10:1) and mixed for 30 min. After mixing, chloroform was evaporated using a rotary evaporator. The solid residual was re-dis- Average equivalent Coated Nominal MAO Sample name Comparative diameter of the core polymer fraction (no./nm2) UCNPs@... study UCNPs (nm) 300 PB-300 69 ± 3 PMAO-BHMT 105 20 PB-105 PB-20 52 ± 2 52 ± 2 7 PB-7 52 ± 2 DLS: 210 P-210 52 ± 2 Effect of the polymer nominal fraction on PMAO 105 20 P-105 P-20 52 ± 2 52 ± 2 the colloidal stability 7 P-7 52 ± 2 DLS: PMAO-BHMT 150 PB-150 69 ± 3 Long-term colloidal PMAO 150 P-150 67 ± 5 stability (4 months) 150 PB-150 67 ± 5 PMAO-BHMT 105 20 PB-105 PB-20 52 ± 2 52 ± 2 7 PB-7 52 ± 2 Chemical stability: dissolution in PBS at 150 P-150 67 ± 5 physiological PMAO 105 20 P-105 P-20 52 ± 2 52 ± 2 conditions 7 P-7 52 ± 2 persed in 20 ml of water with 0.5 ml of 1M NaOH and son-icated for 30 min. The dispersion was first filtered with 0.45 µm pore size filters and later centrifuged (15294 rcf for 30 min). The sedimented coated UCNPs were dispersed in de­ionised water. The samples were labelled as UCNPs@PB-n. An additional set of samples was prepared by coating the UCNPs with PMAO omitting the crosslinking with BHMT. All other parameters were kept the same as above. These samples are labelled as UCNPs@P-n. Firstly, we determined the optimal concentration of as-synthesised UCNPs for the coating procedure, which was 0.1 mg/ml. Secondly, we identified a side reaction when the polymers were used in too large excess. In such a case, the polymers also precipitated homogeneously, not only on the UCNPs surfaces. Therefore, we were lowering the nom­inal surface density of PMAO from approximately 300 MAO/nm2 (as in Ref.25) down to 7 MAO/nm2. This optimi­sation step was done for both types of coatings, UCNPs@ PB-n and UCNPs@P-n. A list of the coated samples used in different comparative studies is given in Table 1. For comparison, a batch of bare UCNPs was pre­pared by stripping the surface oleate ligands from the as-synthesised UCNPs. The process was done with a known procedure in conc. HCl.26 2. 3. Characterisation As-synthesised and polymer-coated UCNPs were an-alysed with a transmission electron microscope (TEM, Jeol 2100). Size distribution of the as-synthesised UCNPs was determined from their surface area as an equivalent diame­ter using the DigitalMicrograph software (Gatan Inc.). A minimum of 250 particles per sample were accounted for statistics. The crystalline structure of the as-synthesised UCNPs was verified with an X-ray diffractometer (XRD, PANalytical X’Pert pro) using CuKa1 irradiation. The UC fluorescence emission of the UCNPs dispersions was in­duced with a focused beam from a diode laser emitting 2 W of light at 980 ± 2 nm, and analysed using a compact diffrac­tion spectrometer (Ocean FX Vis-NIR, Ocean Optics) in the 400–900 nm spectral range. Zeta-potential behaviour vs pH was measured in 0.1 mg/ml aqueous dispersions of bare and coated UCNPs with Anton Paar LitesizerTM 500. The pH value was adjusted with 0.1 or 1 M solutions of HCl or NaOH. Hydrodynamic sizes of these aqueous dispersions were measured with dynamic light scattering (DLS, Fritsch, Analysette 12 DynaSizer). The presented results are statisti­cal averages from five measurements with a time interval of 30 s. DLS and zeta potential were also measured in the dis­persions of coated UCNPs (0.1 mg/ml) in PBS (both with LitesizerTM 500, Anton Paar). 2. 4. Chemical Stability Studies Bare and polymer-coated UCNPs were aged under physiological conditions for 3 days. Aqueous dispersions of all samples were filtered beforehand (see Materials Sec­tion) to eliminate any potential aggregates that could affect the results. 0.1 mg/ml of bare or coated UCNPs were dis­persed in PBS and aged in a thermostatic water bath at 37 °C for 3 days. For comparison, selected samples were also aged at a concentration of 5 µg/ml. After 3 days, the dis­persions were cooled to room temperature. The solid frac­tion was sedimented in a centrifuge (3016 rcf for 5min). For complete removal of the smallest nanoparticles, the supernatant was subsequently ultrafiltered through a 30­kDa membrane. The dissolved fluoride was determined in the filtrates potentiometrically with Orion 960 Autochem­istry System, equipped with a temperature sensor and a ion-selective electrode (ISE, Orion 96-09).27 Three sam­ples were analysed per batch, each one at least in duplicate. For comparison with previous studies, the dissolved fluo­ride was expressed as the molar fraction of the released fluoride (XF), calculated by considering the nominal chemical composition of the as-synthesised NPs (Eq. S1 in Supporting Information). 3. Results and Discussion 3. 1. UCNPs with Amphiphilic Polymer Coatings All as-synthesized UCNPs have the typical shape of hexagonal discs, resulting from their hexagonal crystal structure (Figure 1). Most of the UCNPs lie flat on the sam­ple support, while some are oriented perpendicularly (en­circled in Figure 1a), showing that their thickness is approx­imately one half of their diameter. The UCNPs were relatively homogenous in size; however, the average sizes (i.e., diameters) varied between different synthesis batches (Figure S1 in Supporting Information). Therefore, all direct comparisons were performed on series of samples originat­ing from the same synthesis batch of the UCNPs (Table 1). TEM investigation revealed the presence of an amor­phous layer on the surfaces of all coated UCNPs. The PB coatings appeared thicker than the P coatings (Figure 2). Most of the coated UCNPs were well separated on the TEM-sample support, with a small fraction of (apparent) aggregates of 2–3 UCNPs (Figure S2 in Supporting Infor­mation). However, such aggregation may have been in­duced during the drying of the TEM samples and does not necessarly reflect the situation in the dispersion. In addi­tion, amorphous impurities were observed in the sample coated with the nominal fraction of 300 MAO/nm2. We assume that the impurities were homogeneously precipi­tated polymers, since only C and O were identified with EDXS analyses. This was a result of a highly excessive PMAO fraction, in addition to the limited solubility of BHMT in the reaction medium, i.e., chloroform. There­fore, only the samples with =150 MAO/nm2 were used in the subsequent studies. Figure 1. TEM image of the as-synthesised UCNPs (a) and their XRD pattern (b) with indices corresponding to space group P63/m. Some UCNPs lying perpendicularly to the sample support are encircled in panel (a). Figure 2. TEM image of a UCNP coated with: @PB-150 (a) and @P-150 (b). Arrows point at the amorphous surface layer. Successful incorporation of Yb3+ and Tm3+ into the NaYF4 crystal lattice was confirmed by the characteristic UC fluorescence spectra upon excitation at 980 nm, within the strong absorption band of the Yb3+ ion (Figure 3). The dominant peak centred at 804 nm corresponds to the tran­sition 3H4 . 3H6 of the Tm3+ ion, broadened by the Stark splitting of the Tm3+ ground-state manifold. The UC fluo­rescence in the visible region is significantly weaker in comparison. Nevertheless, emission peaks at 452, 477, 648 and 698 nm can be easily identified (see the inset) and as­signed to radiative transitions originating from the higher excited levels of the Tm3+ ions. In Figure 3, we can also see a decrease of the UC fluorescence intensities (normalised to the same UCNP mass concentration) upon stripping of the oleate ligands from the as-synthesised UCNPs (dispersed in chloro­form) and their transfer to water (bare UCNPs). This is a well-known effect, indicative of the surface quenching by the high-energy vibrations of the water molecules, fa-vouring non-radiative relaxation of the excited levels in Yb3+ and Tm3+ ions. However, the UC fluorescence spec­tra of the coated UCNPs in water dispersions demon­strate that both polymer coatings with a high nominal fraction of MAO (i.e., the @PB-150 and @P-150) provide some protection against the surface quenching. In both these cases, the dominant UC emission line (around 800 nm) is ~30% stronger than the bare UCNPs of the same diameter (67 nm). The zeta potential of the coated UCNPs is negative at the neutral pH range, and its absolute value decreases with the decreasing pH (Figure 4). This can be explained by deprotonation of surface carboxylic groups, resulting in the negative zeta potential at higher pH values. The car-boxylic groups are exposed at the UCNPs surface after the MAO anhydride ring opens in water (Scheme 1). Alterna­tively, the anhydride ring also opens to react with amine groups of BHMT, and the negative surface-charge density of UCNPs@PB is lower than that of UCNPs@P. The same trend was observed for other nominal fractions. Such ze­ta-potential behaviour is also an indication that PMAO is crosslinked with BHMT at the UCNPs surfaces, as con­cluded previously16 from infrared spectroscopy analysis. Figure 4. Zeta-potential dependence on pH of the polymer-coated UCNPs. The colloidal stability of differently coated UCNPs in water was assessed from DLS measurements of the coated UCNPs with an average core diameter of 52 ± 2 nm (Fig­ure 5a and b). The hydrodynamic sizes of the coated UCNPs were, in general, larger than the crystalline core, which can be attributed to the polymer coating and hydra­tion surface layer. The fractions with hydrodynamic sizes = 100 nm indicate some smaller aggregates (Figure S2b in Supporting Information). The absence of larger agglomer­ates was also in accordance with the transparency of all the dispersions, with the exception of a turbid dispersion of UCNPs@PB-7 containing some fraction of ~300 nm large NPs. Another exception was the UCNPs@P-105 disper­sion with a small fraction of ~250 nm sized NPs (see the arrow in Figure 5b), suggesting minor aggregation in this sample. In contrast, the dispersion of the UCNPs@PB-210 contained a significant fraction of ~35 nm sized NPs. These NPs, with the sizes lower than the as-synthesised core UCNPs, were homogeneously precipitated polymer NPs, also identified with TEM and EDXS. Long-term colloidal stability was examined for the coated UCNPs with 150 MAO/nm2 within 4 months (Fig-ure 5c). The UCNPs@PB-150 were, on average, larger than the UCNPs@P-150, but the hydrodynamic sizes of both samples were in the range of 1-2 UCNP core sizes (~70 nm) at all times. This demonstrates their long-term stabil­ity in water. We have also verified the colloidal stability of the coated samples in a physiological buffer, i.e., PBS, used in our chemical-stability studies of the UCNPs. We selected two colloidally stable UCNPs samples in water with the lowest and highest nominal polymer fraction, i.e., UCN­Ps@P-20 and UCNPs@PB-150. The hydrodynamic size of fresh dispersions of UCNPs@PB-150 in PBS (Figure 6) was comparable to that in water (Figure 5). However, it increased slightly in 3 days, indicating a slow aggregation of the UCNPs@PB-150 in PBS, whereas the aggregation of UCNPs@P-20 in PBS was more significant. After 3 days, a noticeable fraction of aggregates with hydrodynamic sizes of several microns was detected. The colloidal instability can be ascribed to the relatively low absolute value of the zeta potential, i.e., around –10 mV in the fresh PBS disper­sions, compared to much higher negative zeta-potential values in water (Figure 4). In addition, the decreasing ze­ta-potential value with time (>50% in 3 days) indicates some changes in the UCNPs surface chemistry. A TEM inspection of dried PBS dispersions (older than 1 month) did not reveal any significant morphological changes of the coated UCNPs (see example in Figure S3 in Support­ing Information). This result demonstrates that the phos­phate ions from PBS did not destroy the studied coatings at room temperature. Note that characteristic fibrous de­composition product of bare UCNPs in PBS was observed with TEM even at room temperature,15 which was not the case in this study. The observed limited colloidal stability of the coated UCNPs in PBS is not critical for their use, since other aqueous media can be used for their storage. 3. 2. Chemical Stability of UCNPs Our dissolution study represents the most rigorous chemical test of the UCNPs chemical stability under simu­lated physiological conditions. Namely, the fastest dissolu­tion of UCNPs was so far observed in PBS.7,12,20 The effect of polymer coatings on the UCNPs dissolution was evalu­ated by comparing the bare and coated UCNPs aged under the same conditions (Table 2). No significant difference in the XF-values was observed if the coating was only PMAO or PMAO-BHMT. Both types of polymer coatings almost completely prevented the dissolution of UCNPs. Although differences were not significant, a trend of increasing pro­tecting efficiency in line with the increasing nominal MAO was found. Moreover, no significant difference in the dis­solution was found for differently large UCNP, i.e., be­tween ~50 and ~70 nm. Most of the analysed dispersions contained 0.1 mg/ ml of bare or coated UCNPs, which is about the maximum NPs concentration typically used in cytotoxicity and bio-compatibility studies (i.e., 1–100 µg/ml).28–30 It was report­ed previously that the dissolution of bare UCNPs is more significant for aqueous dispersion with lower concentra­tions of UCNPs.12 This was explained with the chemical equilibrium between the dissolved and crystalline fluo­ride: (1) The lower is the concentration of dispersed UCNPs, the larger fraction of UCNPs (i.e., larger XF) dissolves to reach the equilibrium in Eq. 1. The decomposition mecha­nism of UCNPs in PBS is not that simple because it is ac­companied by the precipitation of Y-phosphates: (2) Eq. 2 is a simplified equilibrium where we consider only one phosphate form. The concentration of all solutes in PBS (phosphates, NaCl, KCl) is very large and can be considered constant. Therefore, the fraction of dissolved fluoride should also increase with the decreasing UCNPs concentration in PBS as in pure water (Eq. 1). For the ver­ification, we analysed a couple of dispersions containing only 5 µg/ml UCNPs in PBS. Indeed, the XF-values of the Figure 6. Number-weighted hydrodynamic-size distribution of differently coated UCNPs in PBS; fresh (0d)) and 3-days old dispersions (3d). Core diameters of UCNPs ~ 50 nm. Panel (b) shows an enlarged section of panel (a) Table 2. Released fluoride concentration after ageing the aqueous dispersions bare or polymer-coated UCNPs (0.1 mg/ml) in PBS at pH 7.4 and 37 °C for 3 days: measured concentration (CF), molar fraction considering the total solid content (XF; Eq. S1 in Supporting Information). Size of core UCNPs CF XF Coating (nm) (µg/ml) (mol. %) none 67 ± 5 30 ± 1 83 ± 4 @PB-150 67 ± 5 0.19 ± 0.04 0.52 ± 0.04 @PB-105 52 ± 2 <0.1 <0.27 0.07 ± 0.05* 3.8 ± 2.9* @PB-20 52 ± 2 0.2 ± 0.1 0.6 ± 0.4 @PB-7 52 ± 2 0.33 ± 0.05 0.9 ± 0.1 @P-150 67 ± 5 0.4 ± 0.1 1.1 ± 0.3 @P-105 52 ± 2 <0.1 <0.27 @P-20 52 ± 2 0.20 ± 0.01 0.55 ± 0.02 0.03 ± 0.01* 1.4 ± 0.6* @P-7 52 ± 2 0.15 ± 0.03 0.41 ± 0.08 * dispersions with 5 µg/ml UCNPs UCNPs@PB-105 and @P-20 were larger for lower (5 µg/ ml) than for the higher UCNPs concentrations (0.1 mg/ml) (Table 2). This indicates that the hydrophobic surface layer is an efficient barrier to significantly slow down the disso­lution of UCNPs, but it does not prevent it completely. The XF values of the UCNPs@P and @PB are compa­rable to those obtained with UCNPs coated with a thick (~70 nm) silica shell and by far lower than with any other protective coatings tested at similar conditions.15,20,21 This means that our ~1 nm-thick polymer coating with the sol-vation layer of =25 nm (as estimated from the hydrody­namic size) provides for equivalent protection against the dissolution of UCNPs as the 70-nm silica shell.20 If the dissolution cannot be completely prevented, it is crucially important for in vivo applications to, at least, reduce the dissolution rate to such an extent that the con­centration of dissolved species remains insignificant until the UCNPs are excreted from the body. We estimate that 3 days would be the required time for their excretion after being used as contrast agents. The measured concentration of released fluoride (CF, Table 2) is the fluoride concentra­tion that would interact in vivo if the dissolution kinetics is assumed similar to the studied system. The studied poly­mer coatings suppress the dissolution of UCNPs to such an extent that the released fluoride (=0.33 µg/ml) would not exceed the acceptable daily intake of fluoride for hu­mans, ADI = 50 µg/kg body mass/day,11 even if 10 ml of the coated-UCNPs dispersion with a concentration of 0.1 mg/ml would be administered. Altough, we have not found any data on the toxicity of rare earths in humans, a rough estimation was done. If we consider complete disso­lution of studied UCNPs as in water (Eq. 1) with the disso­lution rate comparable to that in PBS, the released ~0.5 µg/ ml of rare-earth ions (Y3+, Yb3+ and Tm3+) from 10 ml of dispersion would not exceed the median lethal dose, LD50 = 500 mg/kg body mass determined in mice.31 So, we con­clude that the studied polymer coatings, PMAO and PMAO-BHMT, show a promise for safe application of UCNPs in biomedical diagnostics. Moreover, free carboxyl groups of PMAO can be subsequently functionalised for specific in vivo applications. 4. Conclusions We studied amphiphilic-polymer coatings made of poly(maleic anhydride-alt-octadec-1-ene) (PMAO) and bis(hexamethylene)triamine (BHMT) to improve the chemical stability of upconverting nanoparticles (UCNPs) based on NaYF4 codoped with Yb3+ and Tm3+ that decom­pose in aqueous phosphate solutions. The decomposition of UCNPs was followed by measuring the concentration of released fluoride phosphate-buffered saline (PBS) at phys­iological conditions. PMAO coating formed directly on the as-synthesised oleate-capped UCNPs. Hydrophobic chains of the amphiphilic PMAO and oleate formed a dif­fusion barrier for the solutes and solvent molecules. Con­sequently, the decomposition of the PMAO-coated UCNPs in PBS at physiological conditions was almost completely suppressed. Subsequent crosslinking of PMAO coating with BHMT resulted in a similarly low concentration of the released fluoride, which was for all samples =0.33 µg/ ml, i.e., well below the acceptable daily intake of fluoride for humans. We conclude that the chemical stability of UCNPs was achieved with the hydrophobic diffusion bar­rier and that the crosslinking of PMAO with BHMT on the UCNPs surface was not necessary. The PMAO-coated UCNPs can be subsequently bioconjugated to be suitable for diagnostic applications in vivo. Conflicts of Interest: The authors declare no conflict of interest. Supporting Infromation includes the calculation of the molar fraction of the released fluoride (Eq. S1), size-distribution graph of the as-synthesized UCNPs (Figure S1), TEM images of the coated UCNPs (Figure S2) and of the coated UCNPs after being aged in PBS (Figure S3). Acknowledgement The authors acknowledge the financial support from the Slovenian Research Agency (research core funding P2­0089, P1-0192, P1-0045, and P3-0067). We thank Center of Excellence in Nanoscience and Nanotechnology (CENN) for the use of TEM (Jeol 2100). 5. References 1. H. Guo, S. Sun, Nanoscale 2012, 4 (21), 6692–6706. DOI:10.1039/c2nr31967e 2. H. H. Gorris, U. Resch-Genger, Anal. Bioanal. Chem. 2017, 409 (25), 5875–5890. DOI:10.1007/s00216-017-0482-8 3. F. Vetrone, R. Naccache, A. Zamarrón, A. Juarranz de la Fuente, F. Sanz-Rodríguez, L. Martinez Maestro, E. Martín Rodriguez, D. Jaque, J. García Solé, J. A. 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DOI:10.1289/ehp.96104s185 Povzetek Nanodelci NaYF4, socasno dopirani z Yb3+ in Tm3+, izkazujejo fluorescenco z energijsko pretvorbo navzgor v bližnjem infrardecem in vidnem spektru. Tovrstne nanodelce z energijsko pretvorbo navzgor (NDEPN) lahko uporabljamo kot kontrastna sredstva v medicinski diagnostiki in bioloških testih. Pomanjkljivost NDEPN je omejena kemijska stabilnost v vodnih disperzijah, še posebej v fosfatnih raztopinah. Optimizirali smo zašcitne amfifilne polimerne prevleke na os­novi poli(maleinski anhidrid-alt-oktadec-1-en) (PMAO) in bis(heksametilen)triamin (BHMT) za izboljšavo kemijske stabilnost NDEPN pri simuliranih fizioloških pogojih. Morfologijo izhodnih in prevlecenih NDEPN smo analizirali s presevno elektronsko mikroskopijo. V vseh vzorcih smo izmerili intenzivno fluorescenco pri ~800 nm, znacilno za Tm3+. Koloidno stabilnost vodnih disperzij izhodnih in prevlecenih NDEPN smo ocenili z meritvami dinamicnega si­panja svetlobe in zeta potenciala. Raztapljanje NDEPN pri 37 °C v fosfatnem fiziološkem pufru smo spremljali s po­tenciometricnimi meritvami koncentracije raztopljenega fluorida. Ugotovili smo, da NDEPN lahko pred raztapljanjem zašcitimo s PMAO in s PMAO zamreženim z BHMT. Except when otherwise noted, articles in this journal are published under the terms and conditions of the Creative Commons Attribution 4.0 International License DOI: 10.17344/acsi.2022.7350 Scientific paper Adsorption Properties of Low-Cost Synthesized Nanozeolite L for Efficient Removal of Toxic Methylene Blue Dye from Aqueous Solution Neda Salek Gilani,1,* Salma Ehsani Tilami2 and Seyed Naser Azizi1 1 Analytical division, Faculty of Chemistry, University of Mazandaran, Postal code 47416-95447, Babolsar, Iran 2 Department of basic science, Farhangian University, Postal code 19989-63341, Tehran, Iran * Corresponding author: E-mail: salekgilani@gmail.com Received: 01-07-2022 Abstract In this study, nanozeolite L was applied for the removal of toxic methylene blue dye (MB) to evaluate its feasibility as an effective adsorbent. Synthesized nanozeolite L was characterized by Xray diffraction (XRD), Fourier transform infrared (FTIR), scanning electronic microscopy (SEM), Brunauer–Emmett–Teller (BET), and energy-dispersive Xray analysis (EDX) methods to determine its basic physicochemical properties. Batch adsorption studies were performed as a func­tion of pH, adsorbent dose, contact time, initial MB concentration, and temperature. The adsorption behavior of MB was fitted better by the Langmuir isotherm than by the Freundlich isotherm, and the maximum adsorption capacity of nanozeolite L was obtained 80.64 mg g–1. The negative values of Gibbs free energy change (.G°) and the positive value of the standard enthalpy change (.H°) affirmed that the adsorption process is spontaneous and endothermic. Based on these findings, nanozeolite L, with high surface area, great adsorption capacity, and low synthetic cost, can be an effective and economical adsorbent for MB removal. Keywords: Nanozeolite L; Hydrothermal synthesis; Methylene blue; Adsorption 1.Introduction The discharge of dye effluents from various indus­tries such as paper, textiles, leather, plastics, rubber, cos­metics, and food beverages to the environment, especially to the water system, is becoming a major concern because of their toxicity. The considerable release of dyes from these industries is a major problem of pollution, leading to severe damages to aquatic life. Dyes can consume the dis­solved oxygen necessary for aquatic life, and also some of them have direct toxicity to microbial populations and even can be toxic and carcinogenic to mammals.1–3 Methylene blue (MB) is a cationic aromatic dye and has several adverse effects on Homo sapiens, such as an increase in hypertension, nausea, diarrhea, headache, diz­ziness, and eye injuries. So it must be removed from waste­water before discharge into water bodies.4–6 Various physical and chemical methods such as co­agulation, sedimentation, ozonation, membrane separa­tion, photocatalysis, and adsorption have been used for the removal of dyes from wastewaters.7,8 Among these meth­ods, the adsorption process is preferred because of its high efficiency and cost-effectiveness.9,10 The selection of adsor­bent plays a significant role in determining its cost-effec­tiveness, and the search for efficient and low-cost adsor­bents such as natural, agricultural, and industrial byproduct waste is still underway.11–13 Clay materials such as bentonite, montmorillonite, kaolinite, and zeolite have received much attention due to their unique structural and surface features, which offer high chemical stability and specific surface area that conduces to high adsorption ca­pacities.14-22 Among these clays, zeolite has received the greatest consideration and recognition as an appropriate adsorbent. Among a large number of zeolites, zeolite L is one of the most interesting and versatile adsorbents. It contains one-dimensional (1D) channels running along the length of its hexagonal crystals. The unique configuration of its large pores with a diameter of 7.1 Å makes this kind of zeolite an excellent candidate for shape-selective catalysis, for the adsorption of various species such as ions, metals, and organic molecules and mass transport and/or occlu­sion.23,24 The reduction of zeolite size from the micrometer to the nanometer scale leads to unique properties such as decreased diffused path lengths and large external surfac­es, which are effective for improving the adsorption of or­ganic dye pollutants.25,26 Most nanozeolites are synthe­sized in the presence of costly organic structure-directing agents (OSDAs), but nanozeolite L could be synthesized in the absence of OSDAs, which is desirable due to economic considerations.27 The main objective of the present study is to evalu­ate the feasibility of using synthesized nanozeolite L as a cost-effective adsorbent for removing MB from aqueous solution. The effects of various adsorption parameters such as solution pH, adsorbent dose, reaction time, ini­tial pollutant concentration, and temperature on adsorp­tion of MB were investigated. Also, the adsorption iso­therm, kinetic, and thermodynamic were studied. This fundamental study would be useful for further applica­tion in designing a batch reactor for the treatment of dye-containing wastewaters coming out from various industries. 2. Experimental 2. 1. Chemicals and Apparatus Silicic acid, potassium hydroxide (= 85.0 %), and hy­drochloric acid (37%) were purchased from Merk, and aluminum foil and MB were purchased from Fluka. Xray diffraction (XRD) pattern was recorded with an Xray dif­fractometer (SHIMADZUXD-DL) using CuKa radiation (. = 1.5418 Å) at 35.4 kV and 28 mA with a scanning speed of 2. = 10° min–1. Fourier transform infrared (FTIR) spec­trum was recorded with an FTIR spectrometer (Tensor 27Bruker) at room temperature in the range of 400–1300 cm–1. Scanning electron microscopy (SEM; EM-3200, KYKY) was used to determine the crystallite size and mor­phology of the sample. Nitrogen adsorption-desorption isotherms were measured at 77 K using a Quantachrome NovaWin2 apparatus. For each set of experiments, the re­sidual MB concentrations in the supernatant were ana­lyzed using a T90+ -UV-vis spectrophotometer. The pH measurements were made with a pH meter, model 827 pH lab, Metrohm, Switzerland. 2. 2. Synthesis of Nanozeolite L In order to synthesize nonozeolite L, silica and alu­mina solutions were prepared by dissolving 20 mmol silic­ic acid and 2 mmol aluminum foil in potassium hydroxide solutions, respectively. Then, the silica solution was added dropwise to the alumina solution under vigorous stirring. The resulting aluminosilicate gel was transferred into a Teflon-lined stainless steel autoclave and heated at 170°C for 2 days. The resulting product was then centrifuged, washed with deionized water, and dried at 80 °C. 2. 3. MB Sorption Experiments Batch adsorption experiments were performed to in­vestigate the adsorption behavior of MB onto synthesized nanozolite L in aqueous solution at 25°C. The effect of pH was studied in the range of 2-10 and the pH values were adjusted using 0.1 M HCl and 0.1 M NaOH solutions. To investigate the effect of pH, 5 mg of nanozeolite L was add­ed to 10 mL of each MB solution (20 mg L–1) and the solu­tions were shaken for 30 minutes. To study the effect of adsorbent dosage, 10 mL of 20 mg L–1 MB solution at pH = 9 was shaken with 2–15 mg of nanozeolite L for 30 minutes. To study the kinetics of the adsorption process, MB solution containing 0.5 g L–1 of nanozeolite L at pH = 9 was vigorously agitated for different times in the range of 5–40 minutes. For adsorption isotherm studies, constant dosages of nanozeolite L (0.5 g L–1) were agitated with dif­ferent initial concentrations of MB in the range of 20–100 mg L–1 at pH = 9. Also, the temperature effect on adsorption was studied by mixing 5 mg of nanozeolite L with 10 mL of 40 mg L–1 MB solution at different temperatures ranged from 25 to 55 °C. At the end of the experiments, samples were centri­fuged and the residual concentrations of MB were deter­mined using UV-Vis spectrophotometer. The removal per­centage was determined from equation (1): (1) where Ci and Ce (mg L–1) are the concentrations of MB in initial solution and the aqueous phase after adsorption, re­spectively. Also, the adsorption capacity (qe, mg g–1) of nanozeolite L was calculated using the following equation: (2) where V (L) is the volume of the aqueous phase and m is the weight of adsorbent. 3. Results and Discussion 3. 1. Characterization of Nanozeolite L Figure 1a shows the XRD pattern of nanozeolite L. The presence of the characteristic peaks of nanozeolite L at 2. = 5.5°, 19.4°, 22.7°, 25.6°, 28.0°, 29.1°, and 30.7° in the diffraction pattern indicates that the pure phase of nanoze­olite L is formed.28 The FTIR spectrum (inset, Fig 1a) shows the characteristic vibrational bands of nanozeolite L in the range of 400–1300 cm–1. The broadband in the range 1008–1145 cm–1 shows the asymmetric stretching vibra­tions of O–T–O (T=Si, Al) tetrahedral. The adsorption band near 721 cm–1 belongs to the symmetric stretching vibrations of O–T–O, and the band near 765 cm–1 is relat­ed to the symmetric stretching vibrations of AlO4 tetrahe­dral. The band around 605 cm–1 is attributed to the vibra­tion of double six-membered rings (D6Rs), and the band near 476 cm–1 is assigned to the O–T–O bending vibra­tions.29 The size and surface morphology of synthesized nanozeolite L was revealed from its SEM image. The for­mation of spherical-like nanoparticles with an average di­ameter of about 20 nm could be observed in Figure 1b. The elemental analysis of the synthesized nanozeo-lite L was investigated through EDX studies, and the re­sults are represented in Figure 1c. As can be seen in the figure, the elemental content of synthesized nanozeolite consists of oxygen (O), aluminum (Al), silicon (Si), and potassium (K), with the weight percentages of 42.37, 27.48, 20.14, and 10.01. The textural properties of nanozeolite sample were investigated by N2 sorption analysis. The nitrogen adsorp­tion-desorption isotherms of nanozeolite L prepared sam­ple is shown in Fig 1d. It shows type IV isotherm with an H1 type hysteresis loop which is found in well-defined cy­lindrical pores or material agglomerates consisting of ex­tremely uniform particles.30 The results showed that the specific surface area of the prepared sample is 215 m2 g-1. Also, the pore volume of synthesized nanozeolite obtained 0.4 cc g-1, and its pore size distribution is centered at about 3.65 nm (inset, Figure 1d). 3. 2. Adsorption Studies 3. 2. 1. Effect of pH The initial pH plays an important role in the adsorp­tion process. The effect of solution pH on MB removal by the synthesized nanozeolite was investigated in the range of 2–10. As shown in Figure 2, the adsorbed MB increased Figure 1. (a) XRD pattern of synthesized nanozeolite L, Inset: FT-IR spectrum of nanozeolite L, (b) SEM image of nanoparticles of zeolite L, (c) EDX spectrum of nanozeolite L, (d) Nitrogen adsorption-desorption isotherms of nanozeolite L, Inset: pore size distribution of nanozeolite L with increasing solution pH and reached its maximum at pH = 9. It is probably due to the presence of H+ ions at lower pH, which will compete with cationic MB for ad­sorption sites. As pH increases, the zeolite surface is more negatively charged, which results in an increment of ad­sorption because of the decrease in the electrostatic repul­sion force between MB and zeolite surface.31,32 3. 2. 2. Effect of Adsorbent Dosage Adsorbent dosage is an important factor in the ad­sorption process because it defines the capacity of an ad­sorbent for a given initial concentration of the adsorbate. Figure 3 shows the effect of adsorbent dosage on the ad­sorption of MB. The figure shows that the optimum dos­age of nanozeolite L for the removal of MB is 5 mg. The further increase in the amount of MB removal was found to be negligible above a dose of 5 mg. The increase in the extent of MB removal with the adsorbent dose is at­tributed to the increase in the adsorbent surface area and thus the number of adsorption sites available for adsorp­tion.33 3. 2. 3. Effect of Contact Time and Kinetic Studies Figure 4a shows the effect of contact time on the ad­sorption process of MB on synthesized nanozeolite so-dalite. As can be observed in the figure, the dye removal efficiency was increased as the contact time increased until equilibrium.34 The adsorption equilibrium with nanozeo-lite sodalite was reached at 30 min. Figure 3. Effect of adsorbent dosage on MB adsorption by nanoze­olite L To evaluate the mechanism of adsorption, pseu-do-first-order and pseudo-second-order models were in­vestigated to find the best-fitted model for the experimen­tal data. When the adsorption process is diffusion-con­trolled, the kinetics follows the pseudo-first-order model, and when the adsorption process occurs through the chemisorption mechanism, the adsorption kinetics fol­lows the pseudo-second-order model. These models are expressed by the following equations:35,36 (3) (4) where qe and qt are the adsorption capacity (mg g–1) of the adsorbent at the equilibrium and at time t, respectively. Also, K1 (min–1) and K2 (g mg–1 min–1) are the rate constants of the pseudo-first-order and the pseudo-second-order adsorption and can be obtained from the log (qe–qt) versus t plot (Figure 4b) and t/qt versus t plot (Figure 4c), respectively. The obtained kinetic parameters of the models and coefficients of determination (R2) are presented in Table 1. From the R2 values, it can be deduced that the pseudo-sec­ond-order model fits the experimental data slightly better than the pseudo-first-order model. 3. 2. 4. Effect of MB Concentration and Isotherms Studies The effect of the initial concentration of MB on its removal percentage by nanozeolite L was investigated, and the results are presented in Figure 5a. The results show that as the initial dye concentration increases, the removal per­centage decreases due to the consumption of the available active adsorption sites on the adsorbent.37 Table 1. Kinetic parameters of pseudo-first-order model and pseu-do-second-order model for MB adsorption by nanozeolite L Models Parameters values pseudo-first-order K1 (min–1) qe, cal (mg g–1) R2 0.0907 12.50 0.9236 pseudo-second-order K2 (g mg–1 min–1) qe, cal (mg g–1) R2 0.0140 39.84 0.9989 Experimental data qe,exp (mg g–1) 38.49 The adsorption isotherm studies are important for distinguishing the nature of adsorption. The adsorption isotherm experimental data were analyzed with Langmuir and Freundlich models. Langmuir isotherm is used for the monolayer ad­sorption on a homogenous surface and is expressed as fol-lows:38 (5) where Ce is the dye concentration at the equilibrium (mg L–1); qe is the equilibrium adsorption capacity (mg g–1); qmax is the maximum monolayer uptake capacity of the ad­sorbent and KL is the Langmuir equilibrium constant (L mg–1). qmax and KL can be obtained from the plot of Ce/qe versus Ce (Figure 5b). Freundlich isotherm is used to describe the multilayer adsorption equilibrium on the heterogeneous surface and is mathematically described by the following equation:39 (6) where KF is the Freundlich constant related to adsorbent capacity, and n is the heterogeneity factor which indicates the adsorption intensity. The values of KF and n can be ob­tained from ln qe versus ln Ce plot (Figure 5c). The Langmuir and Freundlich isotherm constants, along with the correlation coefficients, are presented in Ta­ble 2. As can be seen in the table, the obtained correlation coefficient value is better for Langmuir isotherm, and the equilibrium adsorption capacity (qmax, cal) obtained from the Langmuir model is close to the experimentally ob­served saturation capacity (qmax,exp). So, it could be con­cluded that the equilibrium data for MB adsorption onto nanozeolite L gave a better fit to the Langmuir model. 3. 2. 5. Comparison with Other Adsorbents Among several adsorbents reported in the literature for the removal of MB from aqueous solutions, some of them are summarized in Table 3. It can be observed that the adsorption capability of nanozeolite L is greater than Figure 5. (a) Effect of initial concentration on MB adsorption by nanozeolite L, (b) Langmuir isotherm for MB adsorption by nano-zeolite L and (c) zeolite Freundlich isotherm for MB adsorption by nanozeolite L most of those reported for other adsorbents. This can be attributed to a higher amount of adsorption sites of nano-zeolite L and con.rms that it can be used as a promising sorbent for MB removal from its aqueous solution. Table 2. Isotherm parameters for MB adsorption by nanozeolite L Isotherm Parameters values Langmuir qmax, cal (mg g-1) KL (L mg-1) R2 80.64 0.43 0.9989 Freundlich KF ( mg g-1( L mg -1) 1/n) nF (mg g–1) R2 37.71 5.28 0.9735 Experimental data qmax,exp (mg g–1) 78.00 3. 2. 6. Effect of Temperature and Thermodynamic Studies In this study, the effect of temperature on MB adsorp­tion was investigated in the range of 298–328 K. The results (Figure 6) showed that the percentage removal increased with the increment of temperature. This can be attributed to the increase of dye molecule mobility with temperature. As the reaction temperature in the solution increas­es, the number of molecules which have enough energy to undergo an interaction with active sites at the surface in­creases.40,41 Table 3. Comparison of maximum adsorption capacities of various adsorbents for MB Adsorbent qm (mg g–1) Reference Kaolinite 45.60 42 Magnetic multi-wall carbon nanotube Walnut shells 15.87 51.55 43 44 Freeze–dried agarose gel Zeolite A 10.40 64.80 45 46 Cortaderia selloana flower spikes ZSM-5 47.90 8.67 47 48 Algerian palygorskite powder (Chit/AILP-Kao) nanocomposite Fly ash based spheres 57.50 99.01 79.70 49 50 51 Sodium dodecyl benzenesulfonate modified ZSM-5 15.68 48 Rice husk 40.59 52 Zeolite/Ferrite Nickel/Alginate nanocomposite 54.05 53 Nanozeolite L 80.64 This work The Increase of percentage removal with tempera­ture demonstrates that the adsorption process is endother­mic. Thermodynamic parameters (.G°, .H°; .S°) values were calculated using the following equations: (7) (8) where R (8.314 J mol–1K–1) is the universal gas constant, T (K) is the absolute temperature, and Kc is the ratio of MB concentration adsorbed on the adsorbent at equilibrium and MB concentration remaining in solution at equilibri­um. The values of .H° and .S° can be obtained from the slope and intercept of ln kd versus 1/T plot, respectively. The obtained values of thermodynamic parameters are tabulated in Table 4. .G° values obtained negative, indi­cating that the adsorption process is thermodynamically feasible and spontaneous at room temperature. The positive .S° value for MB adsorption onto nano-zeolite L is due to increasing randomness at the solid-solu­tion interface during the adsorption process. The Values of .G° are negative, confirming that the adsorption process is thermodynamically feasible and spontaneous in nature. The obtained positive value of .Hº reveals the endothermic nature of the adsorption process, and the calculated positive value of .S° is related to the increase of randomness at the solid-solution interface during the adsorption process. Figure 6. Effect of temperature on MB adsorption by nanozeolite L Table 4. Thermodynamic parameters for MB adsorption by nano-zeolite L Temperature .G° .H° .S° (K) Kc (kJ mol–1) (kJ mol–1) (J K–1 mol–1) 298 2.880 –2.621 –26.256 96.66 308 3.998 –3.549 318 5.446 –4.481 328 7.690 –5.563 4. Conclusion Uniform nanoparticles of zeolite L were synthesized via a hydrothermal approach. Then the feasibility of using synthesized nanozeolite L as an adsorbent for MB removal was investigated. The removal efficiency was optimized at a reaction time of 30 min at pH 9 and an adsorbent dose of o.5 g l–1. The experimental adsorption isotherm data were best fit­ted with the Langmuir model with a maximum sorption capacity of 80.64 mg g–1. The better R2 showed that the kinetics of the system could be conveniently modeled by the pseudo-second-order model, indicating that the rate controlling step is a chemisorption mechanism. The thermodynamic studies represent that the adsorption process is endothermic, feasible, and spontaneous in na­ture. Based on these evidences, nanozeolite L with high adsorption capacity and low preparation cost can be used as an efficient and cost-effective adsorbent for the removal of MB from wastewater. For example, using this zeolite to separate MB from wastewater of dye and textile factories can be applicative; also the ability of this zeolite can be ex­amined to separate other industrial dyes wastewater. 5. References 1. V. K. Gupta, A. Mittal, L. Krishnan, J. Mittal, J. Colloid. Interf. Sci. 2006, 293, 16–26. DOI:10.1016/j.jcis.2005.06.021 2. R. Han, Y. Wang, W. Zou, Y. Wang, J Shi, J. Hazard. Mater. 2007, 145, 331–335. DOI:10.1016/j.jhazmat.2006.12.027 3. E. N. El-Qada, S. J. Allen, G. M. 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DOI:10.1016/j.ijbiomac.2018.05.012 DOI:10.1016/j.procbio.2021.05.013 Povzetek Nanozeolit L je bil uporabljen kot adsorbent z namenom dolocitve njegove ucinkovitosti za odstranjevanje strupene­ga barvila metilen modro (MB). Sintetizirani nanozeolit L je bil okarakteriziran z rentgensko difrakcijo (XRD), infra­rdeco Fourierjevo transformacijo (FTIR), elektronsko mikroskopijo (SEM), Brunauer-Emmett-Teller analizo (BET) in energijsko-disperzivno rentgensko spektroskopijo (EDX) za dolocitev njegovih osnovnih fizikalno-kemijske lastnosti. Adsorpcija je bila preucevana v odvisnosti od pH, kolicine adsorbenta, kontaktnega casa, zacetne koncentracije MB in temperature. Adsorpcijsko obnašanje MB je bilo bolje opisano z Langmuirovo izotermo kot s Freundlichovo, najvecja adsorpcijska zmogljivost nanozeolita L pa je bila dosežena pri 80,64 mg g–1. Negativna vrednost Gibbsove proste energi­je (.G°) in pozitivna vrednost standardne spremembe entalpije (.H°) sta potrdili, da je proces adsorpcije spontan in endotermen. Na podlagi teh ugotovitev je nanozeolit L z veliko površino, veliko adsorpcijsko zmogljivostjo in nizkimi stroški sinteze lahko ucinkovit in ekonomicen adsorbent za odstranjevanje MB. Except when otherwise noted, articles in this journal are published under the terms and conditions of the Creative Commons Attribution 4.0 International License DOI: 10.17344/acsi.2022.7358 Scientific paper Synthesis, Characterization and Thermal Degradation Kinetics of a New Pyrazole Derived Methacrylate Polymer, Poly(1,3-Diphenyl-1H-Pyrazol-5-Yl Methacrylate) Adnan Kurt1,* and Murat Koca2 1 Department of Chemistry, Faculty of Science and Arts, Adiyaman University, Adiyaman, Turkey 2 Department of Pharm. Chemistry, Pharmacy Faculty, Adiyaman University, Adiyaman, Turkey * Corresponding author: E-mail: akurt@adiyaman.edu.tr Received: 03-09-2022 Abstract Since the behavior and properties of macromolecular pyrazole derived polymers differ from their small molecules, such polymers are in the class of well-defined functional polymers due to the fact that the pyrazole ring contains two p-bonds as well as two hetero atoms in its structure, and this feature makes them important in the fields of scientific and techno­logical applications. In present study, therefore, we synthesized a new pyrazole derived methacrylate monomer, 1,3-di­phenyl-1H-pyrazol-5-yl methacrylate, from the reaction of 1,3-diphenyl-5-pyrazolone with methacryloyl chloride in the presence of triethylamine. After that, its homopolymerization was carried out by free radical polymerization method at 60 °C initiated with benzoyl peroxide. Spectral characterizations were achieved by 1H-NMR and FTIR spectroscopies. The kinetics of thermal degradation of the new polymer, poly(1,3-diphenyl-1H-pyrazol-5-yl methacrylate), poly(DPPMA), were investigated by thermogravimetric analysis (TGA) at different heating rates. The initial decomposition temperature of the polymer changed from 216.3 °C to 243.5 °C depending on the increasing heating rate. The thermal decomposition activation energies in a conversion range of 7–19% were 79.45 kJ/mol and 81.56 kJ/mol by the Flynn–Wall–Ozawa and Kissinger methods, respectively. Thermodegradation mechanism of the poly(DPPMA) were investigated in detail by us­ing different kinetic methods available in the literature such as Coats-Redfern, Tang, Madhusudanan and Van Krevelen. Among all these methods, the best result was obtained for Coats-Redfern method (E = 90.93 kJ/mol) at the optimum heating rate of 15 °C/min for D1 mechanism that is a one-dimensional diffusion type deceleration mechanism. Keywords: Pyrazole derived polymer; synthesis and characterization; thermal degradation kinetics; activation energy; reaction mechanism 1.Introduction As is known, briefly, pyrazoles (or 1H-pyrazol) (1) are compounds in the class of heteroaromatic molecules containing two nitrogen atoms in the quintuple ring sys­tems. When this five-membered lactam ring contains a ketonic group and two nitrogen atoms in its structure, it is called pyrazolone (or 1H-pyrazol-5(4H)-one) (2). There is also a tautomerization in the pyrazolone ring and, in that case, the resulting compound is named as pyrazolol (or 1H-pyrazol-5-ol) (3).1,2 The studies on the pyrazole/pyrazolones have gained importance recently, especially because of the broad phar­macological properties of these compounds. Some of these features are cytotoxic,3 anti-inflammatory,4 antimicrobial,5 antioxidant,6 antiviral,7 oral hypoglycemic activity,8 anal-gesic,9 etc. In addition, these compounds are widely used in the preparation of herbicides,10 liquid crystals,11 dyes,12 thermally stable polymers.13,14 Since pyrazolones have dif­ferent biochemical and physicochemical properties, new scientific research programs have been developed in the synthesis of these compounds.1 In this context, pyrazolo­nes are traditionally prepared from the reaction of be-ta-keto esters with hydrazine derivatives in an acidic envi­ronment at high temperatures.15 In addition, the so­lid-phase synthesis reaction,16 two-step reaction of benzo­yl hydrazones with silyl enolates in catalytic medium,17 microwave synthesis method,18 solvent-free medium reac­tion14 are also available in literature. Some of the features as above stated generally include the synthesis and application of these compounds in the form of small molecules. However, pyrazole/pyrazolone chemistry is also important for polymers. The behavior and properties of macromolecular pyrazole-derived polymers differ from these small molecules, and therefore, such poly­mers are in the class of well-defined functional polymers.19 The most important factor causing this is that the pyrazole ring contains two p-bonds as well as two hetero atoms in its structure. Thus, the electro-optical properties and thermal properties of polymers containing pyrazole ring comes to the fore.13,19–24 These polymers can be used in the semicon­ductor class thanks to their good optical properties such as refractive index, absorption, optical band gap, which are important for the electro-optical parts such as various di­odes and transistors.22–24 On the other hand, the presence of such a hetero ring in the polymer main chain, as well as the partial delocalization of the p -bonds, cause side group interactions between polymer chains and on the polymer main chains. This significantly restricts the movement of polymer chains and also delays the thermal degradation of polymers. Therefore, these structural behaviors allow the preparation of thermally stable polymers.19–21 There are some studies in the literature investigating the thermal properties of pyrazole-derived polymers. One of these is re­ported by Connell et al, in that study, thermally stable poly­pyrazoles have been prepared by using the reactions of bis-hydrazines with acetylenic ketones and esters.20 Moore and Mehta have developed a new way to synthesize poly­pyrazole by reacting bis-chloro vinylidene cyanides with diamines. They have succeeded in increasing the thermal stability of the synthesized polypyrazoles using the vinylic nucleophilic substitution reaction.21 Free radical addition polymerization is a well-known polymerization method that has been studied in detail. This method has been one of the most useful and beneficial areas of chemistry ever discovered. It is widely used in scientific and technological fields. It has been one of the most pre­ferred polymerization methods in almost every period and platform. The free radical polymerization method is highly tolerant of functional groups in monomers and polymeriza­tion can occur in a wide variety of environments.25 Free rad­ical polymerization takes place in three different stages: initi­ation, propagation and termination.26 In addition to chemical initiators such as organic peroxides, azo com­pounds, redox initiators as initiator system, radical polymer­ization can also be initiated with different physical factors such as heat, light, high energy, electrochemical method, etc.27 In addition, radical polymerization is a very effective polymerization method in the polymerization of olefins, sty­rene and its derivatives, acrylamides, acrylates, methacry­lates and their derivatives, and many other vinylic mono­mers, including unsaturated alkene chemistry.28 On the other hand, there are different polymerization techniques in polymer chemistry and they are widely used for the purpose. Suspension and emulsion polymerizations have been used in important industrial processes for years. Apart from these, step growth polymerization techniques, ionic addition, co­ordination, ring opening, electrochemical polymerization etc. have been used for years.25 In recent years, controlled polymerization techniques such as atom transfer radical po­lymerization (ATRP), degenerative transfer (DT), reversible addition fragmentation chain transfer processes (RAFT), nitroxide mediated polymerization (NMP), stable free radi­cal polymerization (SFRP) have been developed to control the molecular weight distribution.29 Generally, in these methods, since special reaction conditions and reagents are required, the application area is relatively limited, costly and mostly preferred in the synthesis of specific polymers. How­ever, some factors such as ease of application, cheapness, low number of reagents (monomer and initiator), etc. make free radical polymerization more advantageous and common.30 As mentioned above, most of the reported studies on pyrazole/pyrazolone derivatives are for the synthesis of small molecules using different organic synthesis reactions and some applications. Within our knowledge of the liter­ature, the studies on the synthesis of polymers derived from the pyrazole molecule, especially the methacrylate polymers containing it as a side group, is quite limited. Un­fortunately, the study investigating the thermal degrada­tion kinetics of pyrazole-derived methacrylate polymers is not seen in the literature. In order to fill this lack of litera­ture, we aim the synthesis and spectral characterization of a new pyrazole-derived methacrylate polymer, poly(1,3­diphenyl-1H-pyrazol-5-yl methacrylate), poly(DPPMA), in the present study. The thermal behavior of the polymer is determined using the thermogravimetric analysis meth­od (TGA). In addition, the thermal decomposition kinet­ics of the polymer have been investigated in detail by using different kinetic methods available in the literature. Kinet­ic parameters such as the activation energy and thermal decomposition mechanism are reported. 2. Experimental 2. 1. Materials Ethyl benzoylacetate, phenylhydrazine, methacry­loyl chloride, triethylamine (TEA), benzoyl peroxide (BPO), acetone, acetic acid, chloroform, ethyl alcohol and tetrahydrofuran (THF) were purchased from Sigma-Al­drich. Benzoyl peroxide before use in the polymerization as initiator was crystallized in ethanol. 2. 2. Synthesis of 1,3-diphenyl-5-pyrazolone Ethyl benzoylacetate (1.921 g, 10 mmol), phenyl hy­drazine (1.081 g, 10 mmol), ethanol (25 ml) and catalytic Scheme 1. Homopolymerization of 1,3-diphenyl-1H-pyrazol-5-yl methacrylate monomer amount of acetic acid were added into a 50 ml single-necked reaction flask and refluxed for 12 h on a magnetic stirrer using a method similar to that previously reported.1 After the mixture was cooled to room temperature, it was precip­itated in water. The product was then recrystallized in etha­nol, and dried. The yield (1) was calculated as 94%. FTIR (cm–1): 3067–3044 (aromatic C-H stretching), 2956–2911 (aliphatic C-H stretching), 1699 (pyrazolone C=O stretching). 1H-NMR (600MHz, CDCl3):d(ppm) = 7.4–8.1 (mul­tiple, 10H, phenyl ring protons), 3.8 (singlet, 2H, pyra­zolone ring protons) 2. 3. Synthesis of 1,3-diphenyl-1H-pyrazol-5­yl Methacrylate 1,3-diphenyl-5-pyrazolone (2.3627 g, 10 mmol), methacryloyl chloride (1.0453 g, 10 mmol), triethylamine (1.0119 g, 10 mmol) and acetone (50 ml) were added into a 100 ml single-necked reaction flask and stirred for 24 h at room temperature. The crude product was precipitated in water. It was crystallized again in ethanol. The yield (2) was calculated as 74%. FTIR (cm–1): 3085–3036 (aromatic C-H stretching), 2987–2903 (aliphatic C-H stretching), 1744 (methacrylate ester C=O stretching), 1635 (aliphatic C=C stretching), 1596 (aromatic C=C stretching), 1109 (-COC- stretching). 1H-NMR (600MHz, CDCl3):d(ppm) = 7.4–8.0 (mul­tiple, 10H, phenyl ring protons), 6.8 (singlet, 1H, pyrazol ring proton), 6.4 (singlet, 1H vinyl proton) 5.8 (singlet, 1 H, vinyl proton), 2.0 (singlet, 3H, methyl protons next to vinyl group) 2. 4. Synthesis of Poly(1,3-diphenyl-1H­pyrazol-5-yl methacrylate) The homopolymerization of 1,3-diphenyl-1H-pyra­zol-5-yl methacrylate monomer was carried out under conventional free radical polymerization conditions. For this purpose, monomer (1.000 g), benzoyl peroxide (0.0100 g, 1% w/w of monomer) as initiator and THF (3 ml) as solvent were added into a polymerization tube, re­spectively, and the mixture was dissolved. It was then passed through argon gas for about 15 minutes. The tube was closed and immersed in an oil bath preheated to 60 °C. After a 21 h polymerization time, the polymer mixture was cooled to room temperature, and then precipitated twice in excess ethanol. The resulting polymer (3) was filtered, and dried under vacuum overnight. The appropriate scheme for this synthesis is shown in Scheme 1. FTIR (cm–1): 3095–3030 (aromatic C-H stretching), 2996–2909 (aliphatic C-H stretching), 1764 (methacrylate ester carbonyl stretching), 1595 (aromatic C=C stretch­ing), 1072 (-COC- stretching) 1H-NMR (300MHz, CDCl3):d(ppm) = 7.7–6.8 (phe­nyl ring protons), 6.6 (pyrazol ring proton), 1.8 and 0.8 (methylene and methyl protons in the polymer main chain, respectively). 2. 5. Characterization Techniques The spectral characterizations were performed with a Perkin Elmer Spectrum 100 FTIR spectrometer with an attenuated total reflectance accessory and also with a high resolution Bruker Avance III HD 600 MHz NMR Spec­trometer (its 300 MHz version used for homopolymer). NMR measurements were taken at room temperature us­ing deuterated chloroform solvent and tetramethylsilane standard. Molecular weight and molecular weight distri­bution of polymer was determined by gel permeation chromatography (GPC) a Malvern Viscotek GPCmax sys­tem equipped with a refractive index detector. The eluting solvent was tetrahydrofuran (THF) and calibrated by us­ing polystyrene as standard. A Perkin Elmer Differential Scanning Calorimeter (Pyris 6 DSC) was used to deter­mine the glass transition temperature of poly(DPPMA) in the temperature range of 30–210 °C at a heating rate of 5 °C/min under a nitrogen atmosphere (100 ml/min). TGA analysis was performed on a Seiko SII 7300 TG/DTA thermobalance for obtaining the TG/DTG curves. Poly­mer samples were added to aluminum pans and then sub­mitted to a controlled temperature program from ambient temperature to 500 °C in nitrogen flow of 25 mL/min. The samples were heated at various heating rates of 5, 10, 15 and 20 °C/min, respectively, in order to determine the ki­netic parameters of the thermal decomposition of poly­mer. 3. Results and Discussion Figure 1a shows the FTIR spectrum of 1,3-diphe-nyl-5-pyrazolone compound. The most characteristic ab­sorption bands are observed at the frequencies of 3067– 3044 cm–1 (aromatic C-H stretching), 2956–2911 cm–1 (aliphatic C-H stretching), 1699 cm–1 (pyrazolone C=O stretching). FTIR spectrum of 1,3-diphenyl-1H-pyra­zol-5-yl methacrylate monomer is shown in Figure 1b where the aromatic and aliphatic C-H stretchings are ab­sorbed at 3085–3036 cm–1 and 2987–2903 cm–1, respec­tively. The other important bands are recorded at 1744 cm–1 for methacrylate ester C=O stretching, 1635 cm–1 for aliphatic C=C stretching, 1596 cm–1 for aromatic C=C stretching, 1109 cm–1 for -COC- stretching. Figure 1c shows the FTIR spectrum of poly(1,3-diphenyl-1H-pyra­zol-5-yl methacrylate). In this figure, the most significant change is that the signal observed at 1635 cm–1 character­istic for C=C stretching in the vinyl group is lost due to polymerization. The disappearance of this signal is the most obvious evidence that polymerization has taken place, so it can be easily detected by FTIR.31 The character­istic absorptions are also attributed to aromatic C-H stretching (3095–3030 cm–1), aliphatic C-H stretching (2996–2909 cm–1), methacrylate ester carbonyl stretching (1764 cm–1), aromatic C=C stretching (1595 cm–1) and -COC- stretching (1072 cm–1). 1H-NMR spectra of all compounds are shown in Fig­ure 2. Among them, Figure 2a is recorded for 1,3-diphe-nyl-5-pyrazolone in which the multiple peaks in the range of 7.4–8.1 ppm are due to the phenyl ring protons. The sin­glet observed at 3.8 ppm is for the protons in the pyrazolone ring. Figure 2b is also recorded for 1,3-diphenyl-1H-pyra­zol-5-yl methacrylate monomer. As a result of tauteriza­tion in pyrazolone ring and then bonding of the methacryl group to it, the protons in the pyrazole ring decreased to one proton, which is resonated in the low chemical shift region at 6.8 ppm as a singlet. The location of the protons in the phenyl ring did not change significantly and sig­naled at 7.4–8.0 ppm. Two singlet at 5.8 and 6.4 ppm, which are new signals proving that the methacrylate group is in the structure, are attributed to methylene protons in the vinyl group. In addition, the singlet corresponding to 3 protons at 2.0 ppm is characteristic for the methyl protons adjacent to the vinyl group. 1H-NMR spectrum of poly(1,3­diphenyl-1H-pyrazol-5-yl methacrylate) homopolymer is shown in Figure 2c. The 5.8 ppm and 6.4 pm signals, the two most important characteristic signals for the mono­mer, are disappeared due to polymerization. These signals are shifted to 1.8 ppm as a new signal group and appeared as methylene protons in the polymer main chain. Methac­rylate methyl protons are also shifted to 0.8 ppm. The sig­nal at 6.6 ppm is due to the proton in the pyrazol ring, and the 7.7–6.8 ppm signals are also due to the protons in the phenyl ring. Figure 3 shows the chromatogram output of the gel permeation chromatography (GPC). The number – aver­age molecular weight of the pyrazole derived poly(1,3-di­phenyl-1H-pyrazol-5-yl methacrylate) polymer is mea­sured to be 45800 and its molecular weight distribution (polydispersity) is 3.24. Since the free radical polymeriza­tion method is used, the measured polydispersity value is relatively moderate and in the acceptable range for this method. As seen in the literature, a precise molecular weight and distribution control cannot be achieved in the free radical polymerization process. This is because all polymer chains cannot be started at the same time, and undesirable events such as chain termination, chain trans­fers, etc. also occur simultaneously. Thus, very large and small chains may form during polymerization and the chain distribution may often wide (polydispersity = 2).32 In addition, the molecular mass of present DPPMA mono­mer is 304.342 g/mol, and thus the degree of polymeriza­tion (DP) is calculated as 150. Figure 3. GPC chromatogram of poly(1,3-diphenyl-1H-pyrazol-5­yl methacrylate) The glass transition temperature of poly(1,3-diphe­nyl-1H-pyrazol-5-yl methacrylate) was determined by us­ing differential scanning calorimetry technique (DSC) and its thermogram is illustrated in Figure 4. As can be seen from that thermogram, the glass transition temperature of poly(1,3-diphenyl-1H-pyrazol-5-yl methacrylate) was measured to be 158.1 °C. Similar results to this measured value are seen in the literature for pyrazole derived hetero­cyclic polymers. Wang and Cheng synthesized a series of 3,5-disubstituted pyrazole polymers and found that the glass transition temperatures of these polymers varied in the range of 147 °C–158 °C.33 Thermal analysis of poly(1,3-diphenyl-1H-pyrazol-5-yl methacrylate) was performed by thermogravimetric analysis (TGA). The thermal decomposition process was followed on a temperature range of ~25–500 °C in nitro­gen gas atmosphere in order to detect the possible data of decomposition phases. Therefore, a controlled heating program at different rates of 5, 10, 15 and 20 °C/min was applied to this process. TGA thermograms recorded from these heatings are given in Figure 5 comparatively. In general, at these heating conditions, the degrada­tion of the poly(1,3-diphenyl-1H-pyrazol-5-yl methacry-late) took place in three stages. The first one, with up to 20% weight loss at ~215–300 °C usually caused by volatile hydrocarbons, the second decomposition stage is about be­tween 300–380 °C with up to 70% weight loss, and the third one is ~ 380–450 °C with up to 93% weight loss (according to 10 °C/min used as reference). These results are in good agreement with the thermal behavior of pyrazole polymers. Wang and Cheng reported 3,5-disubstituted polypyrazoles synthesized by click polymerization procedure were de­composed at ~290 °C about 10% of their weight losses.33 Some thermal data obtained in the thermal decomposition of the polymer, such as the initial decomposition tempera­ture, the final decomposition temperature, the temperature at 50% mass loss, the % decomposition values at different temperatures, the % residual value, etc. are determined. These values are summarized in Table 1. As can be seen from the Table 1, the initial degradation temperatures of the polymer were calculated as 216.3, 230.9, 238.8 and 243.5 °C at heating rates of 5, 10, 15 and 20 °C/min. As ex­pected, there is a positive correlation between heating rate increase and thermal stability. Similar results for thermal degradation of heterocyclic polymers have been reported in our previous publications and also by different research­ers.34–42 For example, Kurt et al.37 reported the degradation temperatures of an isocoumarin derived polymer changed in a positive direction, which increased 256.6 °C to 286.1 °C by increasing heating rate from 5 to 20 °C/min. Meng and friends investigated the thermal degradation kinetics of a polyimide polymer containing 2,6-benzobisoxwwwwazole units and reported the onset decomposition temperatures increased with increase of heating rates.40 A typical kinetic process for thermal decomposition of polymeric materials can be given by the following equa-tion:43 (1) where f(a) is the differential function of a kinetic model, A is the pre-exponential factor, E is activation energy, R is the ideal gas constant (8.314 J/mol K), T is the absolute tem­perature (K), a is the extent of reaction and t is the time. The integral function of a kinetic model, g(a), can be expressed as: Reaction Tinitial Tfinal T%50 %Weight loss %Weight loss %Weight loss %Residue rate (ºC/min) (°C) (°C) (°C) at 300 °C at 350 °C at 400 °C at 500 °C 5 216.3 431.6 346.3 24.4 54.5 86.9 5.1 10 230.9 445.2 360.7 18.9 39.5 78.8 7.2 15 238.8 454.2 369.5 15.0 32.2 73.7 7.7 20 243.5 463.7 374.8 13.4 29.1 71.9 5.9 (2) where ß is the heating rate and Tp is the peak temperature (K). Thermal degradation of polymers mostly proceeds via a sigmoidal type mechanism or a deceleration type mechanism.43 Different versions of these mechanisms are stated in Table 2. These g(a) functions allow the determi­nation of thermal degradation mechanism of polymer by thermogravimetry method.44 Besides, derivative thermo-gravimetry (DTG) method is also used to determine the thermal decomposition mechanisms of polymers and to determine the physical or chemical interaction types during decomposition. Therefore, kinetic parameters of thermal degradation can be determined based on TG/ DTG data. These kinetic parameters are highly dependent on the calculation methods due to the different solutions of Equation 2. There are many methods in which the ap­parent activation energies are determined based on the data obtained from TGA curves recorded at different heating rates. Here, we used some kinetic methods name­ly, Flynn-Wall-Ozawa, Kissinger, Coats-Redfern, Tang, Madhusudanan and Van Krevelen method in order to de­termine kinetic parameters and decomposition mecha­nism for the degradation of poly(1,3-diphenyl-1H-pyra­zol-5-yl methacrylate). Of the kinetic methods mentioned, Flynn-Wall-Oza­wa45,46 and Kissinger47 methods do not depend on the re­action mechanism and the degree of reaction. Therefore, it is used to calculate the thermal degradation activation en­ergy of the polymer.37,40,43 The Flynn-Wall-Ozawa equa­tion is expressed as: (3) According to this equation, in order to calculate the activation energy, log ß values versus (1000/T) values are plotted and the E value is calculated from the slope of the obtained line. For this, the decomposition temperatures corresponding to 7%, 9%, 11%, 13%, 15%, 17% and 19% conversions at different heating rates were measured and these values were plotted for each conversion as shown in Figure 6. As can be seen from the figure, the lines of all con­versions in an almost parallel order. In addition, the linear regression value of each line is also a high level. This in­creases the reliability of the calculations. The activation energy value corresponding to each conversion mentioned above was calculated separately. The results are given in Table 3. According to the table, activation energy values varied between the lowest value 75.43 kJ/mol and the highest 85.82 kJ/mol in the studied conversion range. The mean thermal decomposition activation energy value of poly(1,3-diphenyl-1H-pyrazol-5-yl methacrylate) accord­ing was calculated as 79.45 kJ/mol. The closest activation Table 2. Algebraic expressions for g(a) for the most frequently used mechanisms of solid state processes Symbol g(a) Solid state processes Sigmoidal curves A2 A3 A4 [-ln(1-a)]1/2 [-ln(1-a)]1/3 [-ln(1-a)]1/4 Nucleation and growth (Avrami equation 1) Nucleation and growth (Avrami equation 2) Nucleation and growth (Avrami equation 3) Deceleration curves R1 R2 R3 D1 a [1-(1-a)1/2] [1-(1-a)1/3] a2 Phase boundary controlled reaction (One-dimensional movement) Phase boundary controlled reaction (contraction area) Phase boundary controlled reaction (contraction volume) One-dimensional diffusion D2 D3 D4 (1-a)ln(1-a)+a [1-(1-a)1/3]2 (1-2/3a) (1-a)2/3 Two-dimensional diffusion Three-dimensional diffusion (Jander equation) Three-dimensional diffusion (Ginstling-Brounshtein equation) F1 -ln(1-a) Random nucleation with one nucleus on the individual particle F2 F3 1/ (1-a) 1/ (1-a)2 Random nucleation with two nuclei on the individual particle Random nucleation with three nuclei on the individual particle Table 3. Activation energies of poly(1.3-diphenyl-1H-pyrazol-5-yl methacrylate) obtained by Flynn-Wall-Ozawa method a (%) E (kJ/mol) R 7 85.82 0.9996 9 82.71 0.9990 11 80.16 0.9985 13 75.43 0.9840 15 76.23 0.9965 17 76.75 0.9956 19 79.06 0.9951 Mean 79.45 energy value to that was reached at a conversion of 19%, with a value of 79.06 kJ/mol. The activation energy of thermal decomposition of poly(1,3-diphenyl-1H-pyrazol-5-yl methacrylate) was also determined by the Kissinger method. This method, like the Flynn-Wall-Ozawa method, does not depend on the reaction mechanism and order. This method is ex­pressed by following equation: (4) where n is the reaction order, Tmax is the temperature at the maximum reaction rate, amax is the maximum conversion at Tmax. According to this method, ln(ß/T2 ) versus max 1000/Tmax values are plotted and the activation energy is determined from the slope of this line. Figure 7 shows these plots. The Tmax values required to determine the acti­vation energy according to the Kissinger method were measured from the DTG curves. These temperatures were 245.12, 262.04, 273.98 and 281.59 °C for the rates of 5, 10, 15 and 20 °C/min, respectively. Accordingly, the activation energy value was determined as 81.56 kJ/mol with a high linear regression (R = 0.9990) using the Kissinger method. When the results are examined, an energy difference of only 2.11 kJ/mol is detected among the energy values of these integral methods. This result also shows that there is a very good agreement between the methods used and the reliability of the results is high. There are some kinetic methods in which the differ­ent sigmoidal and deceleration type g(a) functions given in Table2 are analyzed with different approaches in deter­mining the thermal degradation mechanism of the poly­mer. One of these methods is the Coats-Redfern method.48 This method is used the below equation: (5) From the fitting ln[g(a)/T2] vs. 1000/T plots, the ac­tivation energy corresponding to different g(a) functions is obtained at constant heating rates. Another method to determine thermodegradation mechanism is Tang meth­od,49 and which used: (6) The plots of ln[g(a)/T1.89466100] vs. 1000/T give a straight line and from its slope, E can be calculated for each g(a) function. Madhusudanan method50 is another one, and which is expressed as: (7) E values for each g(a) mechanisms can be calculated from the slope of ln[g(a)/T1.921503] vs. 1000/T plots. As a final method, the Van Krevelen method51 is proposed by the following equation: (8) where Tr corresponds the reference temperature which is taken as the temperature of the maximum reaction rate obtained by derivative thermogravimetry. E is defined from a linear fitting of log g(a) vs. logT plots for every g(a) mechanisms. In general, each activation energy value calculated for the g(a) mechanisms in different states and for the heating rates is compared with the Flynn-Wall-Ozawa and Kissing­er methods to determine the thermal degradation mecha­nism of the polymer. Because, as is known, these two meth­ods are independent of the reaction mechanism and order. The activation energies, obtained from the other methods, closest to Flynn-Wall-Ozawa and Kissinger methods are determined. The corresponding g(a) mechanism is accept­ed as the thermal decomposition mechanism of the pro­ Table 4. Activation energies of poly(1.3-diphenyl-1H-pyrazol-5-yl methacrylate using Coats-Redfern Method Mechanism Heating Rate 5 °C/min 10 °C/min 15 °C/min 20 °C/min E (kJ/mol) R E (kJ/mol) R E (kJ/mol) R E (kJ/mol) R A2 20.57 0.9659 19.02 0.9719 17.43 0.9825 18.22 0.9858 A3 10.78 0.9445 9.63 0.9506 8.50 0.9661 8.99 0.9730 A4 5.88 0.8977 4.94 0.8968 4.04 0.9157 4.38 0.9351 R1 46.17 0.9729 43.56 0.9783 40.79 0.9866 42.39 0.9888 R2 48.04 0.9751 45.34 0.9803 42.48 0.9882 44.13 0.9902 R3 48.67 0.9758 45.95 0.9809 43.05 0.9887 44.72 0.9907 D1 101.16 0.9776 96.26 0.9824 90.93 0.9894 94.24 0.9912 D2 103.61 0.9787 98.60 0.9834 93.15 0.9902 96.54 0.9919 D3 106.15 0.9799 101.03 0.9845 95.45 0.9910 98.91 0.9926 D4 104.46 0.9791 99.41 0.9838 93.92 0.9905 97.33 0.9921 F1 49.95 0.9772 47.17 0.9822 44.20 0.9896 45.92 0.9915 F2 –1.08 0.9731 –1.75 0.9845 –2.35 0.9744 –2.24 0.9603 F3 6.66 0.9961 5.65 0.9908 4.65 0.9648 4.99 0.9590 cess. The heating rate corresponds to the optimum heating rate.37,43 The activation energy values calculated for each g(a) function for different heating rates using the above ki­netic methods are given in Tables 4–7. Conversion percent­ages for this process are between 7% and 19%. When these tables were analyzed in detail, it was determined that the degradation of polymer progressed through the Dn mecha­nism, which is a common degradation mechanism for all methods. Because, the activation energy values calculated for the Dn mechanisms (deceleration-type dimensional dif­fusions) are the closest to the Flynn-Wall-Ozawa and Kissinger methods. In addition, linear regression values determined for Dn mechanisms are relatively high. In order to see the results more clearly, the activation energy values corresponding to the Dn mechanisms calculated for all methods are also summarized in Table 8. Table 4 shows the results obtained for the Coats Red-fern method. Compared to other methods and their Dn mechanisms, especially for the D1 mechanism, the activa­tion energy value at the heating rate of 15 °C is 90.93 kJ/ mol. This value is very close to the values obtained from Flynn-Wall-Ozawa (E = 79.45 kJ/mol) and Kissinger (E = 81.56 kJ/mol) methods, and the results are quite compati­ble. According to the results calculated by the Coats Red-fern method, it can be said that the thermal degradation mechanism of poly(1,3-diphenyl-1H-pyrazol-5-yl meth-acrylate) proceeds strongly through the D1 mechanism, deceleration type one-dimensional diffusion mechanism. Activation energy and linear regression values calcu­lated according to the Tang method are given in Table 5. As can be seen from this table, the closest result to Fly-nn-Wall-Ozawa and Kissinger methods among all g(a) mechanisms is obtained for D1 mechanism at 15 °C heat­ing rate, which is E = 91.29 kJ/mol. The results obtained from Tang method also support the Coats Redfern meth­od and increase the reliability of their results. Table 5. Activation energies of poly(1.3-diphenyl-1H-pyrazol-5-yl methacrylate using Tang Method Table 6. Activation energies of poly(1.3-diphenyl-1H-pyrazol-5-yl methacrylate using Madhusudanan Method Mechanism Heating Rate 5 °C/min 10 °C/min 15 °C/min 20 °C/min E (kJ/mol) R E (kJ/mol) R E (kJ/mol) R E (kJ/mol) R A2 20.57 0.9659 19.02 0.9719 17.43 0.9825 18.22 0.9858 A3 10.78 0.9445 9.63 0.9506 8.50 0.9661 8.99 0.9730 A4 5.88 0.8977 4.94 0.8968 4.04 0.9157 4.38 0.9351 R1 46.17 0.9729 43.56 0.9783 40.79 0.9866 42.39 0.9888 R2 48.04 0.9751 45.34 0.9803 42.48 0.9882 44.13 0.9902 R3 48.67 0.9758 45.95 0.9809 43.05 0.9887 44.72 0.9907 D1 101.16 0.9776 96.26 0.9824 90.93 0.9894 94.24 0.9912 D2 103.61 0.9787 98.60 0.9834 93.15 0.9902 96.54 0.9919 D3 106.15 0.9799 101.03 0.9845 95.45 0.9910 98.91 0.9926 D4 104.46 0.9791 99.41 0.9838 93.92 0.9905 97.33 0.9921 F1 49.95 0.9772 47.17 0.9822 44.20 0.9896 45.92 0.9915 F2 –1.08 0.9731 –1.75 0.9845 –2.35 0.9744 –2.24 0.9603 F3 6.66 0.9961 5.65 0.9908 4.65 0.9648 4.99 0.9590 Mechanism Heating Rate 5 °C/min 10 °C/min 15 °C/min 20 °C/min E (kJ/mol) R E (kJ/mol) R E (kJ/mol) R E (kJ/mol) R A2 20.90 0.9670 19.36 0.9729 17.78 0.9833 18.58 0.9864 A3 11.11 0.9479 9.98 0.9541 8.86 0.9690 9.36 0.9752 A4 6.22 0.9081 5.29 0.9096 4.40 0.9287 4.75 0.9447 R1 46.47 0.9733 43.88 0.9786 41.11 0.9868 42.72 0.9890 R2 48.34 0.9755 45.66 0.9806 42.80 0.9884 44.46 0.9904 R3 48.97 0.9762 46.26 0.9812 43.37 0.9889 45.05 0.9908 D1 101.40 0.9778 96.52 0.9826 91.21 0.9895 94.52 0.9912 D2 103.86 0.9789 98.87 0.9836 93.43 0.9903 96.81 0.9919 D3 106.39 0.9800 101.29 0.9846 95.72 0.9911 99.18 0.9926 D4 104.70 0.9793 99.68 0.9839 94.19 0.9906 97.60 0.9922 F1 50.25 0.9775 47.48 0.9825 44.53 0.9898 46.24 0.9917 F2 –0.73 0.9423 –1.39 0.9747 –1.99 0.9635 –1.86 0.9427 F3 6.99 0.9965 6.00 0.9918 5.01 0.9693 5.36 0.9640 Table 7. Activation energies of poly(1.3-diphenyl-1H-pyrazol-5-yl methacrylate using Van Krevelen Method Mechanism Heating Rate 5 °C/min 10 °C/min 15 °C/min 20 °C/min E (kJ/mol) R E (kJ/mol) R E (kJ/mol) R E (kJ/mol) R A2 24.39 0.9808 22.93 0.9849 21.46 0.9910 22.34 0.9924 A3 14.82 0.9808 13.80 0.9849 12.79 0.9910 13.36 0.9924 A4 10.04 0.9808 9.24 0.9849 8.46 0.9910 8.86 0.9924 R1 49.40 0.9779 46.79 0.9824 44.15 0.9889 45.85 0.9906 R2 51.22 0.9794 48.53 0.9837 45.79 0.9900 47.55 0.9915 R3 51.84 0.9798 49.12 0.9841 46.35 0.9903 48.13 0.9918 D1 103.11 0.9779 98.04 0.9824 92.85 0.9889 96.31 0.9906 D2 105.51 0.9789 100.33 0.9833 95.02 0.9896 98.55 0.9912 D3 107.99 0.9798 102.69 0.9841 97.25 0.9903 100.87 0.9918 D4 106.34 0.9792 101.11 0.9836 95.76 0.9899 99.32 0.9914 F1 53.09 0.9808 50.31 0.9849 47.48 0.9910 49.29 0.9924 F2 3.26 0.9994 2.75 0.9990 2.26 0.9965 2.44 0.9956 F3 10.82 0.9994 9.95 0.9990 9.08 0.9965 9.49 0.9956 Table 8. Activation energies for Dn mechanisms of all methods Method Mechanism 5 °C/min 10 °C/min 15 °C/min 20 °C/min E (kJ/mol) E (kJ/mol E (kJ/mol E (kJ/mol Coats-Redfern D1 D2 D3 D4 101,16103,61106,15 104,46 96,26 98,60 101,03 99,41 90,9393,1595,4593,92 94,24 96,54 98,91 97,33 Tang D1 D2 D3 D4 101,47103,93106,46 104,77 96,60 98,94 101,36 99,75 91,2993,5195,8094,27 94,60 96,89 99,26 97,68 Madhusudanan D1 D2 D3 D4 101,40103,86106,39 104,70 96,52 98,87 101,29 99,68 91,2193,4395,7294,19 94,52 96,81 99,18 97,60 Van Krevelen D1 D2 D3 D4 103,11105,51 107,99 106,34 98,04 100,33 102,69 101,11 92,8595,0297,25 95,76 96,31 98,55 100,87 99,32 Table 6 shows the results obtained by the Madhusu­danan method in which activation energy is found to be E = 91.21 kJ/mol and linear regression is R = 0.9895 for the D1 mechanism at a heating rate of 15 °C/min. These values are also in good agreement with the Flynn-Wall-Ozawa and Kissinger methods. In addition to the results obtained from the above kinetic methods, the latest Van Krevelen method is used. The results determined by this method are summarized in Table 7. Similar results to other meth­ods are also available for this method. Accordingly, the activation energy (E = 92.85 kJ/mol) value and linear re­gression (R = 0.9889) value are close to Flynn-Wall-Oza­wa (E = 79.45 kJ/mol) and Kissinger (E = 81.56 kJ/mol) methods. These results show that there is a very good agreement between all the kinetic methods used and also the degradation mechanism proceeds through the D1 mechanism, one-dimensional diffusion-type deceleration mechanism. 4. Conclusions A new pyrazole derived methacrylate polymer poly(1,3-diphenyl-1H-pyrazol-5-yl methacrylate) poly(DP­PMA) was synthesized and spectrally characterized. Then, it was tested for determining the thermal characteristics by TGA method at various heating rates. The initial decompo­sition temperature of the polymer changed from 216.3 °C to 243.5 °C depending on the increasing heating rate. The thermal decomposition activation energies in a conversion range of 7–19% were 79.45 and 81.56 kJ/mol by the Flynn– Wall–Ozawa and Kissinger methods, respectively. Thermo-degradation mechanism of the poly(DPPMA) were investi­gated in detail by using different kinetic methods available in the literature. The best result was obtained for Coats-Red-fern method (E = 90.93 kJ/mol) at the optimum heating rate of 15 °C/min for D1 mechanism, one-dimensional diffusion type deceleration mechanism. Acknowledgements We wish to thank the Adiyaman University Scientific Research Projects Unit for financially supporting this studywith project number (ADYÜBAP/ FEFMAP/2021-0002). 5. References 1. M. I. Marzouk, G. H. Sayed, M. S. Abd ElHalim, S. Y. Man-sour, Eur. J. Chem. 2014, 5, 24–32 DOI:10.5155/eurjchem.5.1.24-32.870 2. L. Kumar, C. Thakur, V. Sharma, Int. J. Res. 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Povzetek Obnašanje in lastnosti polimerov, pridobljenih iz makromolekularnega pirazola, se razlikujejo od njihovih osnovnih molekul. Temu ustrezno takšni polimeri spadajo v razred dobro opredeljenih funkcionalnih polimerov zaradi dejstva, da pirazolni obroc vsebuje dve p-vezi in dva heteroatoma v svoji strukturi, kar jih dela pomembne na podrocju znan­stvenih in tehnoloških aplikacij. V tej študiji je bil tako sintetiziran nov metakrilatni monomer, pridobljen iz pirazola, 1,3-difenil-1H-pirazol-5-il metakrilata, prek reakcije 1,3-difenil-5-pirazolona z metakriloil kloridom v prisotnosti trie­tilamina. Za tem je bila izvedena njegova homopolimerizacija z metodo polimerizacije prostih radikalov pri 60 °C, zace­ta z benzoil peroksidom. Produkt je bil okarakteriziran z 1H-NMR in FTIR spektroskopijo. Kinetika termicne razgradn­je novega polimera, poli(1,3-difenil-1H-pirazol-5-il metakrilata), poli(DPPMA), je bila raziskana s termogravimetricno analizo (TGA) pri razlicnih hitrostih segrevanja. Zacetna temperatura razgradnje polimera je v obmocju od 216,3 °C do 243,5 °C, odvisno od narašcajoce hitrosti segrevanja. Aktivacijski energiji termicnega razpada v obmocju pretvorbe 7–19 % sta bili 79,45 kJ/mol po Flynn–Wall–Ozawa in 81,56 kJ/mol po Kissingerjevi metodi. Mehanizem termicnega razpada poli(DPPMA) je bil podrobno raziskan z razlicnimi kineticnimi metodami, ki so na voljo v literaturi, kot so Coats-Redfern, Tang, Madhusudanan in Van Krevelen. Med vsemi omenjenimi metodami je bil najboljši rezultat dosežen pri Coats-Redfernovi metodi (E = 90,93 kJ/mol) pri optimalni hitrosti segrevanja 15 °C/min za mehanizem D1, opredeljen kot enodimenzionalni zaviralni mehanizem difuzijskega tipa. DOI: 10.17344/acsi.2022.7359 Scientific paper iFIT: An Automated Web Tool for Determining Enzyme-kinetic Parameters Based on the High-curvature Region of Progress Curves Boštjan Petric, Marko Golicnik and Aljoša Bavec* University of Ljubljana, Institute of Biochemistry and Molecular Genetics, Faculty of Medicine, Vrazov trg 2, SI-1000 Ljubljana, Slovenia * Corresponding author: E-mail: aljosa.bavec@mf.uni-lj.si Received: 02-16-2022 Abstract The area where a progress curve exhibitsmaximum curvature contains the most information about kinetic parameters. To determine these parameters more accurately from progress curves, we propose an iterative approach that calculates the area of maximum curvature based on an estimate of kinetic parameters and then recalculates the parameters based on time-concentration data points within this area. Based on this algorithm, we developed a computer script called iFIT as a free web application at http://www.i-fit.si. The benefits of working with iFIT are that it decreases the importance of initial substrate concentration and the impact of certain side reactions on the final calculated kinetic parameters. Keywords: Progress curves; integrated Michaelis-Menten equation; Lambert W 1.Introduction The classical way to determine enzyme-kinetic pa­rameters, such as the Michaelis constant Km and limiting rate Vmax, was via initial-velocity-based approaches, such as the Michaelis-Menten (MM) diagram and its linearized derivations, e.g., the Lineweaver-Burk diagram. In recent decades, analyzing entire progress curves has mostly su­perseded the former approach as a simple way of deter­mining kinetic parameters with greater accuracy and pre­cision and using fewer measurements.1 While a common problem of analyzing initial velocities is a shortage of ex­perimental data points, progress curves often have too many data points, which may counterintuitively decrease the quality of the fitted parameters. It is well understood that information about kinetic parameters is not equally encoded in all parts of a progress curve. Once the curve reaches its plateau, recording addi­tional time-concentration points does not provide any fur­ther information about Km or kcat. Fitting a model function onto a curve with a long plateau might result in a model curve that fits perfectly onto the measured plateau, but at the expense of the area of maximum curvature (see Figure 1). Similarly, for progress curves at high substrate concen­tration relative to Km, the initial part of the progress curve will be almost linear (zero-order), and similar consider­ations apply to it as to the plateau. The most information about kinetic parameters can be extracted from the prog­ress curve’s area of maximum curvature.2 To ensure that the model function fits well onto the area of maximum curvature, different weights can be as­signed to different areas of the progress curve, or parts of the curve (e.g., the plateau) can be manually removed. However, to avoid accusations of tampering with raw data, any such method for optimizing progress curves must be clearly defined, universally applicable, and based on sound mathematical principles. We here propose such a method, based on the theoretical work of Stroberg and Schnell2. They published an equation that calculates the area of maximum curvature of a progress curve from already known kinetic parameters. Based on this, we developed an iterative approach for calculating the area without known kinetic parameters. Furthermore, we developed a comput­er script to automatically perform the iterative process. 2. Experimental 2. 1. Study Design Our study was designed only for the development of simple and quick methodology for determination of se­rum paraoxonase 1 (PON1) kinetic characteristics. We utilized a leftover routine blood sample of a healthy blood-donor. Since the biological material used in this re­port has been obtained from a leftover specimen, and the sample was not used for any other particular study, in­formed consent from volunteers and ethical approval was unnecessary because the sample was no longer traceable. 2. 2. Methods The blood sample was collected in a heparin tube, immediately centrifuged at 2200 g, 4 °C for 10 min, and the plasma was removed and stored at –80 °C until mea­surement. Enzyme activity measurements were performed as in Golicnik and Bavec.3 Briefly, the measurements were conducted at room temperature, in a buffer consisting of 50 mM Tris and 1 mM CaCl2, pH = 8. The substrate was dihydrocoumarin, prepared as a 25 mM stock solution in methanol. Each reaction had a total volume of 2 mL and was performed in a 1 cm cuvette. For each measurement, 20 uL of substrate stock solu­tion (final concentration: 250 uM) and 10 uL of plasma were added to 1970 uL of buffer. Substrate was added last, after which we started the measurement. Absorbance at 270 nm was measured every second until after the prog­ress curve had clearly reached its plateau. Afterwards, the progress curve was analyzed using iFIT, which is explained in detail in the main text of the present article. 2. 3. Data Analysis Stroberg and Schnell introduce the concept of tQ, i.e., the length of time during which the progress curve is at its maximum curvature. tQ depends on Km, Vmax, and initial substrate concentration (S0), according to Equation 1: (1) When an entire progress curve has been measured, and the extinction coefficient for the product is known, S0 can be easily calculated. However, calculating Km and Vmax from a progress curve is not trivial, even if we are only in­terested in an estimate. Hence, using tQ for an improved way to calculate Km results in a chicken-and-egg problem, which can be solved with an iterated approach. We start by applying the integrated MM equation (Equation 2) to the entire progress curve to acquire estimates for Km and Vmax. We then use these estimates to calculate tQ and subse­quently analyze only the tQ-bound region of the progress curve again with the model function to acquire more pre­cise estimates of Km and Vmax. We continue this process until the calculated tQ interval, i.e., the number of experi­mental time-concentration data points selected by the al­gorithm, is the same in two successive iterations. Several approaches for calculating kinetic parame­ters directly from progress curves have been proposed. Briefly, it is possible to (1) model the enzymatic reaction with a system of differential equations (an example of such a program is Dynafit);4 (2) treat each derivative of the progress curve with respect to time as an initial velocity and plot these “initial velocities” onto the standard MM diagram;5 or (3) use an integrated version of the MM equa­tion. We decided to use the latter: a numerical approxima­tion by Golicnik (Equations 2 and 3):6 (2) where (3) Golicnik’s approximation is based on an integrated MM equation (based on the Lambert Omega function) that was published by Schnell and Mendoza (Equation 4).7 (4) 3. Results and Discussion Performing the entire iterative procedure manually would be extremely time-consuming. Therefore, we devel­oped a computer script in Python, provisionally referred to as iFIT. The script takes a progress curve as its input, asks the user for an initial selection of an area of the curve (not necessary) and an extinction coefficient, and auto­matically calculates estimates for substrate concentration, baseline, and Vmax. The user then inputs an estimate for Km, which is required by the integrated MM equation to start the fitting procedure. iFIT begins the iterative process of calculating tQ from kinetic parameters and then recal­culating kinetic parameters from the part of the curve de­scribed by tQ. The area of the curve that is being fitted can expand, contract, or move left or right on the x-axis be­tween successive iterations. When two successive iterations produce the same se­lection of time-concentration data points as the result, it is output by the script as the final result, including calculated values for Km, Vmax, [S]0, and the baseline. If iFIT does not converge to a tQ value after 100 iterations (usually this oc­curs when the script oscillates between two values of tQ), the procedure is terminated without a final result. Addi­tionally, iFIT always draws all the intermediate graphs as well as the final graph, with both the data points and mod­el curve displayed, so that the user can visually check whether the final fit is indeed sensible. An example of an initial and final graph is displayed in Figure 1. More de­ a) b) c) d) Figure 1: The fit of kinetic progress curve data by iFIT for human PON1. a) The entire curve (blue) before fitting with a model function, with the area of maximum curvature shown in the shaded window. b) The area of maximum curvature (blue) of the progress curve in a) and the best-fit curve calculated with Equation 2 (red) after the final iteration of iFIT. The calculated value of Km is 6.70 µM. c) The entire progress curve from a) (blue) fitted with the model function (red) after the first iteration of iFIT; the area of maximum curvature is shown in the shaded window and is the same as in a). The calculated value of Km is 17.33 µM. d) The area of maximum curvature zoomed in after the entire curve (blue) was fitted with the mod­el function (red). It is apparent from the comparison between b) and d) that iFIT can produce model functions which fit much more closely to the area of maximum curvature. tailed information on how to use the iFIT web application is available in the Supplementary material. iFIT has two main requirements. 1) The progress curve must be smooth, without a substantial amount of noise. Even if each part of the curve can be fitted well with the integrated MM equation, noise may cause iFIT to os­cillate between two or more different values of tQ (and, consequently, two or more different output values of Km). 2) The initial substrate concentration must not be sub­stantially below Km. In such cases, iFIT will conclude that the area of maximum curvature lies before the beginning of the progress curve, i.e., at a “negative” time interval. Since such intervals do not contain time-concentration data points, iFIT will be unable to continue the calcula­tion. The same thing will happen if we try to input an exponential progress curve, e.g., from a first-order reac­tion. Conversely, it is not a problem for iFIT if the initial substrate concentration is significantly above Km, which is advised against in conventional enzyme-kinetic guidelines. As long as there are enough time-concentration data points in the high-curvature region of the progress curve, it does not matter how many other points lie outside of this region. It also does not matter if the progress curve’s plateau is very long, noisy, or deviates from a straight line; iFIT will simply not consider these points in the final result. A related advantage of iFIT is that it can decrease the influence of certain side reactions on the final calculated value of Km. During a MM reaction with a low Km and an [S]0 sufficiently above Km, the rate of the reaction will slowly decrease as substrate is being consumed. If we per­form the same reaction with a lower-affinity enzyme, the reaction rate will decrease more quickly as substrate is be­ing consumed. The example of this is a first-order reaction, such as spontaneous substrate hydrolysis, where the reac­tion rate will decrease even more rapidly with respect to substrate concentration. This means that the side reactions of either an impure sample, containing an unwanted en­zyme that catalyzes the same substrate (but with a higher Km), or a substrate that undergoes first-order spontaneous hydrolysis, will have the smallest impact on the total reac­tion just before the plateau of the progress curve, i.e., in the area of highest curvature. In such cases, fitting a model function on the entire progress curve produces a poorer fit and less accurate output kinetic parameters than fitting the same model function on only the high-curvature region using iFIT (see Figure 1). Many methodological articles on enzyme kinetics conclude with recommendations regarding proper experi­mental design. The value of iFIT, however, is that it renders precise experimental design less important. Using prog­ress curves instead of initial velocities minimizes the im­portance of ensuring precise initial substrate concentra­tion or immediately starting the measurement, as long as the product’s extinction coefficient is known. An addition­al advantage of using iFIT is that we do not need to worry about substrate concentration at all. The script will trim any progress curves with excessive substrate concentration down to size, and iFIT can immediately notify that the substrate concentration is too small by calculating a tQ in­terval that falls outside of the measured progress curve. When measuring progress curves, we must only ensure that the total absorbance of the solution does not exceed the functional range of the instrumentation being utilized. Apart from reducing noise, it is also helpful to record as many time-concentration data points per unit of time as possible when measuring progress curves for iFIT. Espe­cially when Km is low and Vmax is high, tQ will be short as well, i.e., only a small part of the progress curve will end up being fitted by the model function. If this part of the curve contains only a few data points, the resulting fit might be less accurate. For enzymatic reactions where the integrated MM equation cannot be applied to progress curves, iFIT cannot be applied as well. This includes equilibrium reactions (i.e reaction that are not irreversible), reactions which involve cosubstrates that are not present sufficiently in excess, re­actions with enzymes that degrade substantially over the course of the reaction, and reactions with strong product inhibition. If progress curves cannot be measured at all be­cause substrates and products have the same spectral properties, then iFIT obviously cannot help us either. Obviously, we cannot know whether a previously unstudied enzyme-substrate pair exhibits any of the above properties and whether it follows Michaelis-Menten kinet­ics at all. iFIT is primarily valuable not as a tool for study­ing novel enzymes or analysing one-time measurements, but as an accessory for routine kinetic parameter determi­nation. Researchers often wish to determine Km for a known enzyme-substrate pair on a number of samples, e.g. medical samples from different patients. In such cases, a simple and routine tool for progress curve analysis like iFIT is vastly preferable to initial-velocity calculations. However, for poorly understood enzymes, we recommend first comparing several approaches for kinetic parameter determination (including initial velocities) and making sure that we are indeed dealing with simple MM kinetics before settling for iFIT for routine work. 4. Conclusions Utilizing Stroberg & Schnell’s equation to improve the determination of kinetic parameters does not require a scripted approach or the integrated MM equation as part of the iterative algorithm. However, the script that we have developed is quick and easy to use. As it is based on the integrated MM equation, complex differential-equa­tion-based models, such as those behind Dynafit or ENZO, are unnecessary.4,8 At the same time, on a set of data with a purified recombinant enzyme, iFIT has been shown to compare favorably with Dynafit, the initial velocities ap­proach, and the integrated MM equation without data point removal.9 Using iFIT at http://www.i-fit.si (last ac­cessed 16. February 2022), any research group that deter­mines MM kinetic parameters from progress curves can solve the problem of unwanted data points at the begin­ning or end of progress curves. Acknowledgements This study was supported by the Slovenian Research Agency (grant P1-170). The authors thank Marjan Kužnik for his kind help with experimental work. Potential conflict of interests There are none. CRediT authorship contribution statement Boštjan Petric: Conceptualization, Formal analysis, Investigation, Writing – original draft, Writing – review & editing, Visualization. Marko Golicnik: Conceptualiza­tion, Methodology, Software, Formal analysis, Investiga­tion. Aljoša Bavec: Conceptualization, Methodology, Software, Formal analysis, Investigation, Writing – review & editing. 5. References 1. B. Choi, G. Rempala, J. K. Kim. Sci Rep. 2017, 7(1), 17018. DOI:10.1038/s41598-017-17072-z 2. W. Stroberg, S. Schnell, Biophys. Chem. 2016, 219, 17–27. DOI:10.1016/j.bpc.2016.09.004 3. M. Golicnik and A. Bavec, J Enzyme Inhib Med Chem. 2020, 7. S. Schnell, C. Mendoza, J. Theor. Biol. 1997, 187, 207–212. 35, 261–264. DOI:10.1080/14756366.2019.1695792DOI:10.1006/jtbi.1997.0425 4. P. Kuzmic, Anal. Biochem. 1996, 237, 260–273. 8. S. Bevc, J. Konc, J. Stojan, M. Hodošcek, M. Penca, M. Prapro­DOI:10.1006/abio.1996.0238tnik, D. Janežic, PLoS ONE 2011, 6(7), e22265. 5. S.L. Yun, C.H. Suelter, Biochim. Biophys Acta 1977, 480(1), DOI:10.1371/journal.pone.0022265 1–13. DOI:10.1016/0005-2744(77)90315-19. B. Petric, M. Golicnik, A. Bavec, Molecules 2022, 27(4), 1306. 6. M. Golicnik, Biochem. Mol. Biol. Educ. 2011, 39(2), 117–125. DOI:10.3390/molecules27041306 DOI:10.1002/bmb.20479 Povzetek Obmocje, kjer je krivulja casovnega poteka nastajanja produkta encimske reakcije najbolj ukrivljena, vsebuje najvec informacij o kineticnih parametrih. Za natancnejšo dolocitev teh parametrov iz krivulj casovnega poteka nastajanja pro-dukta predlagamo iterativni pristop, ki izracuna obmocje najvecje ukrivljenosti na podlagi ocene kineticnih parametrov in nato ponovno izracuna parametre na podlagi obmocja najvecje ukrivljenosti. Na podlagi tega algoritma smo razvili racunalniški program iFIT kot brezplacno spletno aplikacijo na naslovu http://www.i-fit.si. Prednosti dela z iFIT so, da se zmanjša pomen zacetne koncentracije substrata in vpliv nekaterih stranskih reakcij na koncne izracunane kineticne parametre. Except when otherwise noted, articles in this journal are published under the terms and conditions of the Creative Commons Attribution 4.0 International License DOI: 10.17344/acsi.2022.7370 Scientific paper Ibuprofen Loaded Electrospun Polymeric Nanofibers: A Strategy to Improve Oral Absorption Dibya Sundar Panda,1 Nabil K. Alruwaili,1 Kalpana Swain2 and Satyanarayan Pattnaik2,* 1 Pharmaceutics Department, College of Pharmacy, Jouf University, Sakaka, Kingdom of Saudi Arabia 2 Talla Padmavathi College of Pharmacy, Warangal, India * Corresponding author: E-mail: drsatyapharma@gmail.com Mobile +91-9849337607 Received: 01-21-2022 Abstract The poor aqueous solubility of candidate drugs has presented a great challenge to formulation scientists for their effective oral delivery. Poor solubility is often associated with poor dissolution behavior and, subsequently, poor bioavailability for those drugs when intestinal absorption is dissolution rate limited. In the present study electrospun polymeric na­nofibers were developed to address the poor aqueous solubility of ibuprofen, a Biopharmaceutic Classification System (BCS) class-II drug. Hydrophilic spinnable polymers like polyvinyl pyrrolidone were deployed as a carrier system for the fabrication of nanofibers. The electrospinning parameters like flow rate, voltage, and spinneret to collector distance were optimized. The fabricated ibuprofen-loaded nanofibers were characterized using scanning electron microscopy and differential scanning calorimetry. Drug release studies and ex vivo intestinal absorption studies were also carried out. The nanofiber-based platform significantly improved in vitro absorption of ibuprofen compared to pure ibuprofen crystals. Keywords: Electrospinning; Nanomedicine; Bioavailability; Absorption; Ibuprofen. 1.Introduction Effective delivery of active ingredients (APIs) to the target site at the desired concentration and rate is of para­mount importance in systemic drug therapy. Poorly solu­ble drug candidates continue to pose challenges to their optimal administration and have therefore drawn the at­tention of researchers around the world.1–5 Poor water sol­ubility of drug molecules often limits gastrointestinal ab­sorption and hence oral bioavailability, leading to therapeutic failure. The aggressive research efforts to pro­vide solutions to drug insolubility problems are evident from the abundance of literature available. Based primarily on the physicochemical properties of the active ingredients, a diverse strategy for active ingredi­ent delivery has been proposed to increase the solubility and/or the rate of dissolution of such molecules.6 These fas­cinating techniques include chemical modification,7 co-crystals,8 micro- / nanonization,3,5,6 polymorphic improve-ments,9 lipid-based systems,6,10 micellar solubilization in­cluding self-emulsifying drug delivery systems,11 inclusion complexes,12–15 amorphous solid dispersion/solution16–18 etc. In addition, nanotechnology has provided solutions for improving the solubility and/or bioavailability of active in­gredients.19–23 Among all of these available techniques, strategies involving the development of amorphous solid drug products have received much attention in the recent past due to the tremendous improvement in the solubility of drug candidates under this approach.3,19 Electrospinning is widely used by pharmaceutical researchers to deliver a wide variety of active ingredients to treat various disease states. The very efficient amorphization effect of electros-pinning is based on the immediate evaporation of the sol­vent, which leads to a solid solution of the active ingredient in the polymer matrix.24 Amorphous solid dispersions based on electrospun fibers can retain an incorporated ac­tive substance in the amorphous physical form for longer periods due to their homogeneous distribution of active substances within the matrix and the possibility of inhibit­ing molecular mobility, which leads to impaired devitrifica­tion. In an attempt to improve the dissolution of ibuprofen, a co-axial solid core spinneret-based electrospinning meth­od was adopted and the dissolution was reportedly im­proved drastically.25 The objective of the study was mostly to characterize the fast dissolution of the cargo molecule. In the present study, attempts were made to explore a convenient and efficient electrospinning approach to amorphizing ibuprofen (a poorly soluble drug candidate) to improve its oral absorption. 2. Materials and Methods 2. 1. Materials Ibuprofen was obtained as a gift sample from Cipla Ltd (Mumbai, India). Polyvinylpyrrolidone K30 (CAS Number-9003-39-8; PVP K30, MW= about 40,000 g/mol), and ethanol (analytical grade) were obtained from Sig-ma-Aldrich Corp (India). All other chemicals used were of analytical grade and procured locally. 2. 2. Fabrication of Nanofibers The ibuprofen loaded PVP-based nanofibers and only PVP nanofibers were prepared by electrospinning equipment (Super ES 2; E-Spin Nanotech, India). The na­nofibers were electrospun from 8% (w/v) PVP solution with or without 2% (w/v) ibuprofen at an applied DC volt­age of 12 kV. The working solution was initially stirred magnetically for 2–3 h to completely dissolve PVP in eth­anol. Subsequently, ibuprofen was added to the polymer solution and stirred for another 1 h to completely dissolve the drug. This solution was then electrospun at a flow rate of 2.0 ml/h using a syringe pump. The spinneret-to-collec­tor distance was fixed at 15 cm and a plate collector was used for the collection of the nanofibers. The spinning pa­rameters were optimized after the trial batches. 2. 3. Scanning Electron Microscopy The fiber morphology and diameter were analyzed using a scanning electron microscope (SEM). Samples were coated with 20 nm of gold under a vacuum using a sputter coater. All micrographs were taken at an accelera­tion voltage of 5 kV. The secondary electrons were detected using an Everhart-Thornley detector. 2. 4. X-ray Diffraction Studies The samples of nanofibers and raw ibuprofen were assessed for crystallinity using an X-ray diffractometer (Model: SEIFERT, C-3000, Germany) using Nickel-filtered CuKa radiation (k = 1.54 A°). The voltage and current were 35 kV and 30 mA, respectively, and smoothed 95. Measurements were carried out in the angular range from 5° to 40° (2.) using step sizes 0.05 and 0.25 s per step. 2. 5. Differential Scanning Calorimetry The solid-state properties of the raw ibuprofen and fabricated nanofiber samples were studied using differen­tial scanning calorimetry (DSC 822, Mettler Toledo). Each powdered sample (5 mg) was placed in an aluminum pan, sealed, and heated to 200 ° C at a heating ramp rate of 10 ° C / min under nitrogen gas (50 L/min). Before each meas­urement, the sample was allowed to equilibrate for 5 min at 30 ° C. Transition temperatures and enthalpy readings were automatically calculated using Mettler Toledo soft­ware for each peak. 2. 6. Drug Entrapment Efficiency (DEE) The drug loading efficiency was calculated using the following equation (Eq 1). DEE (%) = (Wm/Wa) × 100% (1) where Wm is the ibuprofen content measured in the elec­trospun nanofibers, and Wa is the ibuprofen added to the working fluid during the preparation. All tests were re­peated in triplicate and the mean is reported. The ibupro­fen content in the generated fibers was determined by dis­solving the fibers in ethanol. The solutions were then analyzed spectrophotometrically at 220 nm to assess the amounts of ibuprofen in each sample. 2. 7. In Vitro Dissolution and Solubility Studies Samples of pure ibuprofen drug (10 mg) and nano-fiber sample equivalent to 10 mg ibuprofen were subjected to dissolution studies in 900 ml of distilled water under sink conditions (USP paddle method: Thermonic, Camp­bell Electronics, Mumbai, India, at 100 rpm and 37 °C).19 At predetermined intervals, a sample of the solution was removed and filtered through a 0.45 µm filter, and the same amount of medium was replaced at the same temper­ature. The drug content in the withdrawn aliquots was spectrophotometrically analyzed at 220 nm (UV-160, Shi­madzu, Japan). The experimental points were the average of at least three repetitions. To assess the solubility of ibu­profen in studied dissolution media, an excess of ibupro­fen was added to distilled water and stirred for 24 hours. Later the suspension was centrifuged and the supernatant was filtered using Whatman filter paper. Further, the drug concentration was measured spectrophotometrically at 220 nm. 2. 8. Ex Vivo Intestinal Permeation Studies The ex vivo intestinal permeation studies were per­formed as per a previously reported method by the au­thor.19 The goat’s small intestine was collected from a local slaughterhouse for the study, kept in buffer fluid (Krebs- Ringer solution), and used immediately without storage for a prolonged period. The tissue sample was properly cleaned to separate the mesentery, rinsed with the buffer, and then cut into different sections. Each section was everted on a Teflon rod and fixed on its location using a thread. The setup consisted of an intestinal holder which was a cylindrical glass vessel connected to a “U” glass tube whose one portion was represented by the intestine. Intes­tine holders (four in one set) filled with buffer fluid repre­sented the receiver environment (4 × 12 mL) and the hold­er was placed in the donor environment. Both the receiver and donor phases were continuously aerated to keep the intestine cells alive during experimentation. At regular in­tervals of time, after the beginning of the permeation test, 4 mL of the receiver phase were sampled from each intes­tine holder and replaced with pure buffer, for a time dura­tion of 120 min. The concentration of ibuprofen in each of the four liquid phases sampled was estimated using a UV spectrophotometer (UV-160, Shimadzu, Japan). 2. 9. Statistical Analysis The performance of raw ibuprofen and ibupro­fen-loaded nanofibers in the in vitro dissolution studies and the ex vivo intestinal permeation studies were analyz­ed following a t-test at an overall significance level of 0.05 using Sigma Stat software (Sigma Stat 3.5, SPSS Inc, Chica­go, IL). 3. Results and Discussions Hydrophilic and water-soluble polymers have been widely exploited by formulation scientists to enhance the solubility of poorly soluble drugs.19, 23–26 It is supposed that these polymers act as antinucleating agents and stabi­lizers inhibiting the devitrification of amorphized drugs.6 Moreover, nanotechnology-based drug delivery approach­es have gained a lot of momentum providing viable solu­tions for optimal therapeutic delivery of poorly soluble drug candidates.27, 28 In the present work, PVP was used for the preparation of nanofibers due to its inherent prop­erties such as outstanding physiological compatibility, and satisfactory solubility in water along with other organic solvents. The solvent plays a vital role in the successful preparation of electrospun nanofibers. The solvent should dissolve the drug conveniently while keeping the electros­pinnability of polymer solutions intact. Amongst several solvents screened for solubilization of ibuprofen and PVP, ethanol was found to be suitable. 3. 1. Scanning Electron microscopic Investigation The formation of nanoscopic fibers was confirmed using SEM (Fig. 1). The nanofiber diameter, as revealed by SEM, fell in the approximate range of 450–500 nm. The SEM images revealed no visible surface deposition of drug crystals indicating molecular dispersion of the loaded ibu­profen in the PVP matrices. On the other hand, SEM im­ages of pure ibuprofen revealed distinct needle-shaped crystals. 3. 2. X-ray Diffraction Studies X-ray diffraction studies of the prepared samples were carried out to assess the polymorphic transitions (if any) that might have been taken place in ibuprofen when Fig 1. Scanning electron microscopy of (A) raw ibuprofen, (B) ibuprofen loaded PVP nanofibers, and, (C) PVP nanofibers. formulated as nanofibers. The X-ray diffraction patterns of ibuprofen alone, as a physical mixture with the ma-trix-forming polymer and ibuprofen, loaded nanofibers are depicted in Fig. 2. The XRD pattern of ibuprofen alone exhibited intense peaks of high-intensity reflections to the interplanar distances 13.5, 7.2, 5.3, 4.7, and 4.0 Å at 6.3°, 12.4°, 15.9°, 18.2°, and 22.5° (2.), respectively which reveal its crystalline nature. However, the pattern of ibupro­fen-loaded nanofibers showed broad and diffuse maxima peaks which may be attributed to the amorphization of ibuprofen in the nanofiber samples. Furthermore, the physical mixture (PM) samples prepared by physically mixing ibuprofen and PVP, exhibited the retained crystal­line properties of ibuprofen. It has been well documented that the amorphous state of drug substances possesses many advantages including enhanced solubility, improved wettability, and increased dissolution rate when compared to its crystalline counterpart.29–30 3. 3. Differential Scanning Calorimetry (DSC) The DSC thermograms of samples are in agreement with the results of XRD studies (Fig. 3). Ibuprofen alone showed a sharp endothermic peak at 79.8 °C correspond­ing to its melting point confirming its crystalline nature. The physical mixture sample retained the melting endo-therm of ibuprofen in the DSC studies. However, the peak associated with ibuprofen melting was significantly affect­ed (small peak) in the nanofiber samples indicating its sig­nificant amorphization. Thus, the results of DSC studies were in agreement with the XRD analysis. Fig 3. DSC thermograms of the studied samples indicate sharp melting endotherms at the melting point of ibuprofen in raw ibu­profen (Ibuprofen) and physical mixture (PM) samples. Ibupro­fen-loaded nanofiber sample (IBU-PVP-NF) lacks a sharp endo­thermic peak. PVP nanofiber sample (PVP) exhibited no endothermic peak indicating its amorphous nature. 3.4. Drug Loading Efficiency Drug loading in drug delivery systems is considered an important parameter when assessing the suitability of the drug carrier systems. The methods of drug loading in poly­meric nanofibers are diverse and include blending (the drug is dissolved/dispersed in polymer solution), surface modifi­cation (drugs are conjugated to the nanofiber surfaces), co­axial process (co-electrospinning of drug solution as core and polymer solution as a sheath), etc. The present study adopted the blending method for loading ibuprofen. The ibuprofen loading efficiency in the fabricated electrospun nanofibers was found to be as high as 93.42 ± 4.21%w/w. 3. 5. In Vitro Dissolution Studies For BCS II drug candidates like ibuprofen, dissolu­tion is the rate-limiting step for oral absorption. In such, a scenario, improving the dissolution of the drug has been an ideal strategy for its effective oral delivery. The raw ibu­profen (Ibu) dissolution in distilled water was found slow and incomplete (55.72± 4.82%). Moreover, the solubility of raw ibuprofen in distilled water was also found very poor (10.23 ±1.44 mcg/ml). The dissolution profile of the studied samples (Fig. 4) revealed a significant improve­ment (p < 0.05; t-test) in drug release from the nanofiber samples (Ibu-PVP-NF) when compared to native raw ibu­profen (Ibu). The improved dissolution of ibuprofen (88.96±3.22%) from the nanofiber samples may be due to the amorphous state of the drug.31,32 In our previous re­port, a similar improvement in dissolution was observed while attempting co-processing with hydrophilic polymers like hydroxypropyl methylcellulose.19 Fig 4. The dissolution profile of the raw ibuprofen (Ibu) and nano-fiber (Ibu-PVP-NF) samples. 3. 6. Ex vivo intestinal permeation studies As a surrogate for in vivo bioavailability studies, we have carried out ex vivo intestinal permeation studies to confirm dissolution data. The study conducted for two hours revealed a significant (p < 0.05) improvement in the rate and extent of permeation of ibuprofen from nanofiber samples (equivalent weight of nanofiber containing 50 mg of ibuprofen) when compared to raw ibuprofen samples (50 mg) (Fig. 5). Raw ibuprofen (Ibu) exhibited a lower percentage of permeation (34.66 ± 5.27 %). However, the nanofiber samples exhibited significantly (p > 0.05) higher intestinal permeation (79.83± 7.29 %). Since, intestinal permeation of ibuprofen is dissolution rate limited, the present approach to enhance the dissolution of ibuprofen using nanofiber technology successfully improved the ex vivo intestinal permeation of the cargo. Fig 5. Ex vivo intestinal permeation pattern of raw ibuprofen (Ibu) and nanofiber (Ibu-PVP-NF) samples. 4. Conclusion Hydrophilic polymer electrospun nanofibers for oral delivery of ibuprofen are reported with enhanced dissolu­tion and ex vivo permeation. The results are very promis­ing with the existence of ibuprofen in an amorphous state in the nanofiber matrix evident from the DSC and XRD investigations. The apparent absence of crystalline traces of ibuprofen in the nanofiber samples may be due to the antinucleating properties of PVP. The study has provided enough supporting data for further preclinical investiga­tions. Conflict of Interest The authors declare no conflict of interest. The au­thors alone are responsible for the content and writing of the article. Acknowledgment This work was funded by the Deanship of Scientific Research at Jouf University, Kingdom of Saudi Arabia un­der grant No. DSR-2021-01-03135. 5. References 1. S. Mallick, S. Pattnaik, K. Swain, P. De, Drug Dev Ind Pharm 2007, 33, 535–541. DOI:10.1080/03639040601050130 2. S. S. Hota, S. Pattnaik, S. 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Ghoneim, PLoS ONE 2021, 16, e0245482. DOI:10.1371/journal.pone.0245482 30. S. Mallick, S. Pattnaik, K. Swain, P. De, A. Saha, G. Ghoshal, A. Mondal, Eur J Pharm Biopharm 2008, 68, 346–351. DOI:10.1016/j.ejpb.2007.06.003 31. N. J. Babu and A. Nangia, Crystal Growth & Design 2011, 11, 2662–2679. DOI:10.1021/cg200492w 32. A. Ziaee, S. O’Dea, A. Howard-Hildige, L. Padrela, C. Potter, J. Iqbal, A.B. Albadarin, G. Walker, E.J. O’Reilly, Int J Pharm 2019, 572, 118816. DOI:10.1016/j.ijpharm.2019.118816 Povzetek Slaba topnost zdravilnih ucinkovin v vodi predstavlja velik izziv za farmacevtske tehnologe, ki pripravljajo formulacije za ucinkovito peroralno dostavo. Slaba topnost je pogosto povezana z oteženim raztapljanjem in posledicno nizko bio-loško uporabnostjo tistih ucinkovin, pri katerih je crevesna absorpcija omejena s hitrostjo raztapljanja. V tej študiji so z elektrostatskim sukanjem razvili polimerna nanovlakna s ciljem izboljšanja vodotopnosti ibuprofena, zdravila II. razreda po biofarmacevtskem klasifikacijskem sistemu. Hidrofilne polimere, kot je polivinil pirolidon, ki se lahko sukajo, so uporabili kot nosilni sistem za izdelavo nanovlaken. Optimizirali so parametre elektrostatskega sukanja, kot so pretok, napetost in razdalja med šobo in zbiralnikom. Izdelana nanovlakna z vkljucenim ibuprofenom so ovrednotili z vrsticno elektronsko mikroskopijo in diferencno dinamicno kalorimetrijo. Izvedli so tudi raziskave sprošcanja ucinkovine in absorpcije v ex vivo pogojih. Platforma na osnovi nanovlaken je bistveno izboljšala in vitro absorpcijo ibuprofena v primerjavi s cistimi kristali ibuprofena. Except when otherwise noted, articles in this journal are published under the terms and conditions of the Creative Commons Attribution 4.0 International License DOI: 10.17344/acsi.2022.7427 Scientific paper Combined Pharmacophore Modeling, 3D-QSAR, Molecular Docking and Molecular Dynamics Study on Indolyl-aryl-sulfone Derivatives as New HIV1 Inhibitors Mebarka Ouassaf,1,* Faizan Abul Qais,2 Salah Belaidi,1,**Mohamed Bakhouch,3 Ahmed Said Mohamed4 and Samir Chtita5 1 Group of Computational and Medicinal Chemistry, LMCE Laboratory, University of Biskra, BP 145 Biskra 707000, Algeria 2 Department of Agricultural Microbiology, Faculty of Agricultural Sciences, Aligarh Muslim University, Aligarh, UP-202002, India 3 Laboratory of Bioorganic Chemistry, Department of Chemistry, Faculty of Sciences, Chouaïb Doukkali University, El Jadida, Morocco 4 Institut de Recherche Médicinale, Centre d’Etude et de Recherche de Djibouti, Djibouti 5 Laboratory of Analytical and Molecular Chemistry, Faculty of Sciences Ben M’Sik, Hassan II University of Casablanca, B.P 7955, Casablanca, Morocco * Corresponding author: **E-mail: s.belaidi@univ-biskra.dz *E-Mail: nouassaf@univ-biskra.dz Received: 02-22-2022 Abstract The present study deals with the in silico of 45 indolyl-aryl-sulfones known as anti-HIV1. The data were collected from recent previously reported inhibitors and divided into a sub-set of 33 compounds as the training set and the remaining 12 compounds were kept in the test set. The selected pharmacophore–ADRRR–yielded a statistically significant 3D-QSAR model containing high confidence scores (R2 = 0.930, Q2 = 0.848, and RMSE = 0.460). The predictive power of the estab­lished pharmacophore model was validated with an external test (r2 = 0.848). A systematic virtual screening workflow shows an enrichment factor and has revealed a high predictive power. Then the model was used to screen the filtered PubChem database mapping all chemical features of model pharmacophore. The recognized hits were further assessed by in silico ADMET studies. Molecular dynamics also used to explore the stability of obtained complexes. Finally, these selected compounds are probably to become a good lead molecule for the development of effective anti-HIV-1 drugs. Keywords: Indolyl-aryl-sulfone, HIV-1 inhibitor, Pharmacophore, 3D-QSAR, Molecular Docking, Molecular Dynam­ics. 1.Introduction Therefore, persons having HIV can lead healthy and pro- AIDS is one of the most destructive of human im-ductive lives.3 mune system pandemic in the world, caused by human Most of the drugs designed and licensed have been immunodeficiency virus infection (HIV).1 It continues to classified as Nucleoside Reverse Transcriptase inhibi-be a critical global public health concern, 1.5 million peo-tors (NRTI’s), Non-nucleoside Reverse Transcriptase ple were newly infected with HIV in 2020, and around 38 Inhibitors (NNRTI’s), Protease Inhibitors (PI’s), Fusion million HIV-infected persons are estimated to be dealing Inhibitors, HIV integrase strand transfer inhibitors and with it to date.2 Unfortunately, there is no effective treat-Inhibitors–CCR5 co-receptor antagonist.4 Protease is an ment for HIV infection. Luckily, available antiretroviral important factor for viral maturation within the HIV life drugs are used to control the proliferation of the virus. cycle.5,6 The HIV protease is a homodimeric aspartyl pro­tease and each monomer consists of 99 amino acid resi­dues with a catalytic Asp at position 25. At nine processing sites, the main structural component of HIV-1 is the Gag polyprotein. HIV-1 protease cleaves polyprotein precur­sors Gag and Gag-Pol encoded by the HIV-1 virus genome to create mature active proteins.7 Gag-Pol is incorporated into virions via interactions with the Gag precursor Pr­55gag. The protease (PR) incorporated into Gag-Pol me­diates proteolytic processing of both Pr55gag and Gag-Pol during or shortly after release of viral particles from cells. Since efficient viral incorporation of Gag-Pol depends on interaction with Pr55gag through its N-terminal Gag do­main, prevention of premature Gag cleavage may attenuate Gag-Pol packaging deficiencies associated with enhance­ment of the PR cleavage.8 The vital role of HIV protease in viral maturation makes it a popular drug design target; there are 10 FDA-ap­proved HIV protease inhibitors, namely: Saquinavir, Indi­navir, Ritonavir, Nelfinavir, Amprenavir, Fosamprenavir, Lopinavir, Atazanavir, Tipranavir, and Darunavir. The FDA-approved HIV protease inhibitors have structural similarities and a similar binding pattern, which might ex­plain some of the protease inhibitor-related adverse effects such as dyslipidaemia, hyperglycaemia, and body-fat dis­tribution. It is possible to optimize the chemical structure of HIV protease inhibitors to avoid side effects.9,10 The computer-aided drug design CADD approach has played a crucial role in the search and optimization of potential lead compounds with a substantial benefit in time and expense; it has been used during different phases of drug discovery: target identification, validation, molec­ular design, and interactions of drug candidates with tar­gets of interest.11,12 Pharmacophores are a set of methods related to QSAR: they produce 3-dimensional arrangements of func­tional group that are required for activity.13,14 A well-developed pharmacophore model may be used to design novel and more active molecules, such pharmacophore models are also the starting point for 3D-QSAR analysis, and can allow quantitative predictions. In the very early stages of the drug development process, the use of 3D pharmacophore models will potentially anticipate unwanted side effects and thereby reduce the probability of late failure of drug candidates.15 Docking simulations are widely used to screen a li­brary of compounds rapidly and to identify new drug leads employing a simple model. Docking simulations are also useful for lead enhancement using more detailed models to analyze the atomic interactions between inhibitors and target macromolecules.16 In order to take a forward step for prediction and guidance of more effective drug, we have utilized state of the art techniques in drug design for the development of a three-dimensional pharmacophore model using a data­set of indolyl-aryl-sulfone derivatives from literature. We have used also a comprehensive approach involving vir­tual screening-based pharmacophore modeling, molecu­lar docking and Molecular Dynamics (MD) simulations to identify potential HIV1 inhibitors. The studied com­pounds were consequently analyzed for ADMET proper­ties and were found to be potential drug-like candidates that can effectively bind to the HIV protease enzyme. Taken together the specifics of the current study could provide important insights needed for the produc­tion of next-generation of inhibitors that could theoreti­cally reduce the function of HIV protease. 2. Materials and Methods 2. 1. At a Set Preparation In vitro biological data of a series of 45 indolyl-ar­yl-sulfones as anti-HIV-1 were collected from literature.17 the observed anti-HIV-1 activity was represented as EC50 and converted into logarithmic scale pEC50 = –logEC50 (µM). It is essential to examine the structures of the mole­cules in the data set before starting molecular modeling. For that, the 3D-structures of the 45 inhibitors were prepared using the builder panel in Maestro 12.0 and were generated for all ligands with LigPrep.18 Partial atomic charges were ascribed and possible ionization states were generated at a pH equal to 7.0. The OPLS3e force field was used to optimize and to produce low energy conformer of the ligand.19,20 En­ergy minimization was performed with OPLS3e force fieldtill root mean square mean deviation (RMSD) of 0.01 Å was attained. The so-prepared ligands were used to generate pharmacophore and to build QSAR model. 2. 2. Generation of Pharmacophore Model A pharmacophore describes the arrangement of mo­lecular or functional group’s characteristics that a ligand must contain in order to produce a given biological re­sponse. Pharmacophore models are developed to identify new compounds that meet the requirements of the phar­macophore, which could have the high probability to be biologically active. Often, such pharmacophore models are the starting point for 3D-QSAR analysis.21 Each compound structure was represented by a set of points in 3D space that correspond to different chemical features, which help the compound to bind with the target receptor non-covalently. The data set was created by assign­ing pEC50 > 8.6 as active and pEC50 < 6.5 as inactive to the threshold. Inactive compounds can be used to screen the hy­pothesis because they do not provide an explanation for the activity. However, they give signals of the inactive function. Six pharmacophore features defined the chemical features of the ligands: H-bond Acceptor (A), H-bond Do­nor (D), hydrophobic group (H), negatively charged group (N), positively charged group (P) and Aromatic Ring (R). The consistency of each alignment is calculated by: (1) the vector score, the average cosine of the angles generated in Waals non-hydrogen atom models in each pair of struc-the aligned structures by the corresponding pairs of vector tures (3); the site score which is the degree to which site characteristics (acceptors, donors and aromatic rings); (2) points in the alignment are applicable; (4) The final score, the volume score that is based on the overlap of Van der (5) and the function-survival score. Table 1. Various substituents attached to basic structure of indolyl-aryl-sulfones. N° Structural features XY ZW 1 SO2 H NH2 Cl 2 S H OEt H 3 S 2-NH2 OEt Cl 4 S 2-NH2-5-Cl OEt Cl 5 SO2 H OEt H 6 SO2 2-NH2-5-Cl OEt H 7 SO2 2- NH2-5-Cl OEt Cl 8 S H NH2 H 9 S 2-NH2-5-Cl NH2 H 10 S H NH2 Cl 11 S 2-Me NH2 Cl 12 S 4-F NH2 Cl 13 S 4-Cl NH2 Cl 14 S 4-iPr NH2 Cl 15 S 4-tBu NH2 Cl 16 S 3,5-Me2 NH2 Cl 17 S 2,6-Cl2 NH2 Cl 18 S 2-NH2-5-Cl NH2 Cl 19 SO2 H NH2 H 20 SO2 2-NH2-5-Cl NH2 H 21 SO2 2-Me NH2 Cl 22 SO2 3-Me NH2 Cl 23 SO2 4-Me NH2 Cl 24 SO2 4-F NH2 Cl 25 SO2 4-Cl NH2 Cl 26 SO2 4-iPr NH2 Cl 27 SO2 4-tBu NH2 Cl 28 SO2 2,4 Me2 NH2 Cl 29 SO2 3,5-Me2 NH2 Cl 30 SO2 2,6-Cl2 NH2 Cl 31 SO2 2-NH2-5-cl NH2 Cl 32 SO2 3,5-Me2 NH2 Br 33 SO2 3,5-Me2 NH2 COMe 34 SO2 3,5-Me2 NH2 CH(OH)Me 35 S H NHNH2 Cl 36 S 4-Me NHNH2 Cl 37 S 4-F NHNH2 Cl 38 S 4-Cl NHNH2 Cl 39 SO2 H NHNH2 H 40 SO2 H NHNH2 Cl 41 SO2 4-Me NHNH2 Cl 42 SO2 4-F NHNH2 Cl 43 SO2 4-Cl NHNH2 Cl 44 SO2 3,5-Me2 NHNH2 Cl 45 SO2 2-NH2-5-Cl NHNH2 Cl 2. 3. Building 3D-QSAR Model QSAR modeling was performed using the selected hypothesis by dividing randomly the data set into training set (60%) and test set (40%). This phase presents two op­tions for the alignment of the 3D-structure of molecules: pharmacophore-based alignment and atom-based align­ment. In this study, the selected 45 compounds from the chemical dataset were used to develop an atom-based 3D-QSAR model based on previously developed pharma­cophoric maps as a backbone with a default grid space of 1 Å via partial least-square (PLS) regression.22 2. 4. Model Validation Validation is a critical aspect of pharmacophore design, particularly when the model is constructed for predicting molecular activity in external test series.23 the intensity of the defined pharmacophore hypotheses was internally validated by statistical parameters, squared coefficient of correlation (R2) and the ratio of variance (F). Validation on chemicals was not used in the model development, the so-called external validation, is par­ticularly important in the context of using QSAR models for the prediction of new data in virtual screening.24 The approach demonstrated by Golbraikh and Tropsha, in 200025 and Roy and al., 200826 was used to evaluate the predictive potential of the current QSAR model. Further, the best hypothesis selected was validated by enrichment studies using the decoy test. More than 1000 decoy test set compounds retrieved from the PubChem database, were taken to evaluate the predictive power of the built model27 and were taken to evaluate some parameters, such as: En­richment Factor (EF), Robust Initial Enhancement (RIE), Receiver Operating Characteristic (ROC) and Boltz­mann-Enhanced Discrimination of ROC (BEDROC). These parameters were used to benchmark the reliability of the model and for the accurate ranking of compounds.28 2. 5. Virtual Screening of PubChem Database In pharmaceutical research, computational screen­ing of databases has become incredibly popular. Based on biological structures, virtual screening uses comput­er-based methods to discover new ligands.29,30 the aim of virtual screening, in this work, is to detect potential leads to anti-HIV with various scaffolds and high inhibitory ac­tivity. To identify inhibitors of PR HIV, we have screened the PubChem database31 by searching compounds hav­ing more than 80% similarity instead of compound that have the most fitness score (compound 40, Table 1). All PubChem drug-like compounds (459926) were filtered by Canvas’s property filter utility to pick compounds with low-dimensional properties similar to the anti-HIV com­pounds. We used the following property filters: AlogP = 1, AlogP = 5, HBA = 2, HBA = 3, HBD = 1, HBD = 3, MW = 250, MW = 500, Num rings = 3, Num rings = 5, Polar = 45, Polar = 60, RB = 3 and RB = 5. As a result, Fig. 1. Schematic representation of the methods followed in the current study. 5575 compounds were obtained and mounted as a series of decoys. Each compound must fit a minimum of four sites and a distance of 2.0 Å matching between sites. After the pharmacophore-based screening according to the fitness score, the top 230 hits were selected for molecular docking analysis. Figure 1 represents the schema of the methods followed in the current study. 2. 6. Molecular Docking Analysis The top 230 compounds were selected for molecu­lar docking analysis based on the pharmacophore model following virtual screening. X-ray crystal structure of wild type HIV-1 protease in complex with GRL-09510 (PDB ID: 5v4y) were obtained from the Protein Data Bank PDB.32 The protein structure was preprocessed using the Protein Preparation Wizard,33 available in Schrödinger suite 2021­2, by eliminating crystallographic water molecules (water molecules without H-bonds), inserting all missing side chain atoms, and pH 7.0 corresponding hydrogen bonds, taking into account the necessary ionization states for the residues of both acid and basic forms of the amino acid. Finally, energy minimization (up to 3.0 Å RMSD val­ue) was performed using OPLS-2005 force field after the assignment of charge and protonation state. In fact, it was minimized to alleviate the steric clashes between the resi­dues due to the addition of hydrogen atoms. The active site was defined with a radius of 10 Å around the ligand pres­ent in the crystal structure and a grid was generated at the center of gravity of the active site for docking. All studied compounds were docked into the catalytic pocket of the Protease protein (PDB-ID: 5v4y) using Grid-Based Ligand Docking with Energetics34 with default parameters. Final­ly, the docking results were analyzed using Biovia Discov­ery Studio 4.5.12 (Dassault Systèmes 2018).35 2. 7. Analyzing ADMET ADMET (Absorption, Distribution, Metabolism, Elimination, Toxicity) analysis is important in drug de­sign. These properties were calculated using the QikProp module36 of Schrodinger suite for assessing the drug abil­ity and to filter the ligand molecules at an early stage of identifying the new inhibitors. Toxicity is the degree to which a substance can dam­age an organism or substructure of the organism. The pre­dictions of toxicity of the compounds are essential to re­duce the cost and labor of a drug’s preclinical and clinical trials. The toxicity evaluation was performed also using the ProTox platform.37 It gives predicted toxicity values, cy­totoxicity, mutagenicity, carcinogenicity, immunotoxicity and LD50 values of selected compounds. 2. 8. Molecular Dynamics Simulations Two compounds showing highest binding affin­ity towards HIV-1 protease were selected for Molecu­lar Dynamics (MD) simulation studies. The molecular docked complexes with lowest binding energy were used as initial point for the MD simulations performed using Gromacs-2018.1 packages with amber99sb-ILDN force field.38,39 The protein alone and their complexes with li­gands (11630770 and 55868948) were solvated in triclin-ic-boxed using TIP3P water model. Each structure was neutralized using counter chlorine ions. The topology of both ligands was prepared using antechamber packages in Amber Tools 19.40 For the removal of weak Van der Waals contacts; each system was minimized using the steepest descent minimization. The systems were then equilibrated for NVT using V-rescale thermostat for 1 ns at 300 K tem­perature followed by NPT equilibration using Parrinel-lo-Rahman barostat at 1.0 bar for 1 ns.41,42 MD simulation of each system was carried out for 100 ns and the trajec­tories were recorded at 10 ps inervals. Each trajectiry was sunjected to PBC (periodic boundary conditions) correc­tions before analysis. All calculations except MM-PBSA was done uisng Gromacs utilities. MM-PBSA calculation was performed for the calculation of vriuous binding en­ergies the ligands with HIV-1 protease.43 3. Results and Discussions 3. 1. Pharmacophore Modeling Our work is focused on the identification of new compounds with potential antiviral activity anti-HIV-1. To fulfill the objective, a ligand-based pharmacophore model was built using previously reported inhibitors, with a different combination of pharmacophoric features, 920 pharmacophore hypotheses have been produced. Its vector, volume, sites survival score, and the num­ber of matches measured the quality of each hypothesis. Table 2 represents the different scoring parameters for best hypothesis. The best fitted Model ADRRR1 with the Table 2. Different parameter scores of the generated hypothesis ADRRR1. Survival score Site Vector Volume Bedroc Matches ADRRR1 6.067 0.944 0.977 0.917 0.843 5 ADRRR2 6.063 0.695 0.922 0.613 0.843 5 ADRRR3 6.062 0.705 0.919 0.620 0.844 5 ADRRR4 6.061 0.711 0.922 0.619 0.843 5 ADRRR5 6.061 0.681 0.932 0.622 0.844 5 highest survival score (6.067) and site score (0.944) consist five-point hypothesis one hydrogen acceptors, one hydro­gen donor, and three ring group. The spatial arrangement of the best pharmacophore hypothesis, ADRRR1 with their distance between the five-pharmacophore features is shown in figure 2 and tables S1-S2. 3. 2. 3D-QSAR Model The previously developed pharmacophore hypothe­sis ADRRR1 was used to build an atom-based 3D-QSAR with the phase program.44 Based on the training set mole­cules for the chosen hypothesis, the pharmacophore mod­el, that is statistically significant, was created through par­tial least-square (PLS) regression. The partial least-squares factor has been raised to five, as there is a gradual improve­ment in the model’s predictive power and statistical signif­icance until the fifth factor. A statistically significant 3D-QSAR model was ob­tained using this pharmacophore hypothesis with a strong correlation coefficient (R2 = 0.929) and a high Fisher ratio (F = 57) for the training set. The predictive power of the developed model was also found to be important, verified by the high value of the coefficient of cross-validated cor­relation (Q2 = 0.848) and Pearson’s R (0.926) for the test set. The plots between the observed and the predicted ac­tivities were made for both the training and test sets (Fig­ure 3). The higher values of R2and Q2 in the training and test sets, respectively, are clearly indicated by the points lying extremely near to the best-fit line. Fig. 2. (A) Pharmacophore model ADRRR1 interstice angles in (°) unit between the pharmacophoric points and (B) Pharmacophore model ADRRR1 interstice distances in Å unit. Fig. 3. The plot of the correlation between the experimental and predicted activity of based indolyl aryl sulfones inhibitors using pharmacoph­ore-based QSAR model of training and test set. 3. 3. Model Validation All the external validation results were above the threshold values for the various parameters presented in table S3. The squared correlation coefficient values be­tween the observed and predicted values of the test set compounds (r2) and (r2 0), respectively, were observed and the model had satisfied the requirement of the term (r2 - r2 0)/r2. This was in agreement with a previous study reported by Golbraikh and co-workers, which states that the value (r2 - r2 0)/r2 exhibits less than 0.1. In case of good external prediction, predicted values will be very close to observed activity values. Therefore, r2 value will be very near to r2 0 value. In the best case r2 will be equal to r2, whereas in m the worst-case r2 value will be zero, including values of m r2 < 0.6 indicate these models are useless for external m predictivity. In the present study r2 value of the model is m acceptable (Table S3). This developed model passed all the Golbraikh and Tropsha criteria for the acceptability of the model. The screening results were evaluated by an enrich­ment factor at the top 1% of the ranked database (EF1) and are summarized in table S4. reported by Golbraikh and co-workers, which states that the value (r2 - r2 0)/r2 exhibits less than 0.1. In case of good external prediction, predicted val­ues will be very close to observed activity values. There­fore, R2 value will be very near to R20 value. In the best case r2 will be equal to r2, whereas in the worst-case r2 mm value will be zero, including values of r2 < 0.6 indicate m these models are useless for external predictivity. In the present study r2 value of the model are acceptable (Ta­ m ble S3). This developed model passed all the Golbraikh and Tropsha criteria for the acceptability of the model. The screening results were evaluated by an enrichment factor at the top 1% of the ranked database (EF1) and are sum­marized in table S4. The enrichment factor (EF) of this screening proto­col was calculated to be 13.012, which indicated that se­lected model has 13 times more stability to identify active molecules than inactive. Visualization of the validation was presented in ROC analysis to show how effectively the pharmacophore mod- Table 3. 3D-QSAR PLS statistical results of the selected Pharmacophore model ADRRR1. ID PLS factors SD R2 F P RMSE Q2 Pearson-R ADRRR1 1 0.650 0.722 39 4.52 10–9 0.600 0.742 0.869 2 0.509 0.835 49 1.78 10–11 0.620 0.724 0.865 3 0.425 0.889 56 5.83 10–13 0.490 0.826 0.914 4 0.404 0.903 50 7.10 10–13 0.530 0.799 0.904 5 0.351 0.930 57 4.30 10–14 0.460 0.848 0.925 All the external validation results were above the threshold values for the various parameters presented in table S3. The squared correlation coefficient values be­tween the observed and predicted values of the test set compounds (r2) and (r2 0), respectively, were observed and the model had satisfied the requirement of the term (r2 - r2 0)/r2. This was in agreement with a previous study els distinguished between active and inactive compounds (Figure 4). Sensitivity (in other words, true positive rate, recall, hit rate) and specificity (in other words, true nega­tive rate) are general indices to show the predictive power of a validated model and is indicated by the area under the curve. The area under the curve (AUC) was calculated as 0.8157. Fig. 4. (A) Mapping of the active compounds onto the pharmacophore. (B) Mapping of inactive compounds on to the pharmacophore. Thus, we can conclude that our model is not ran­domly classified. Considering area under the ROC curve, it is statistically significant from those obtained by random classifier (Area = 0.5). The result revealed that in 1% of the total database the generated model screened 28 decoys and five active compounds overall while in the top 2% it was able to get 21 decoys and 8 active compounds hits, with an enrichment factor of 19. The detailed results are given in table S5. 3. 4. Contour Plot Analysis Contour plot analysis was conducted at spatial loca­tions of the system to interpret and understand the distinct vital pharmacophoric criteria. Positive and negative activ­ity coefficients of different properties are described in the map, including (a) donor hydrogen bond, (b) hydrophobic/ non-polar and (c) ionizable negative properties. The blue cubes show their individual positive contribution, and the red cubes reflect the negative contribution (Figure 5). 3. 4. 1. H-Bond Red region near and around position X and W in­dicates that the substitutions at these positions by groups having more hydrogen bond donor property is unfavorable to anti-HIV activity for example in compounds 3 and 5. The blue cubes around the position Z suggests that the presence of a donor substitution (e,g N, O, P, or S) at this position may favor the formation of H bond interac­tion. Almost all the compounds containing Sulfur Diox­ide (e, g Compound 1) were found to have better activity profile in comparison with the ones with Sulfur atom (e, g Compound 8). 3. 4. 2. Hydrophobic Another significant component that affects the an-ti-HIV activity is the hydrophobic character. In figure 5B, the contour map for hydrophobic characteristics displays blue cubes highly distributed proximal to the R9, R10 and R8 regions of indolyl-aryl-sulfones. This result reveals that the multiple rings R8, R9, and R10 of the indolyl-aryl-sul­fones may enhance the hydrophobicity, and might play a major role in its higher activity. The presence of red cubes at W position of phenyl ring directly attached to the Cl group indicates that hydrophobic groups are unfavorable at this position. This assumption is supported by the low activity of Cl substituted compounds when compared to their unsubstituted derivatives. This is evident while com­paring the compounds 6 with 7 and 32. 3. 4. 3. Negative In contour plot of compound 25 (Figure 5C), the pres­ence of red cubes at para position X indicates that the pres­ence of electron withdrawing groups is undesirable at this position. This is evident while comparing the compounds 2 (X: S) with 5 (X: SO2). On the contrary, the presence of blue cubes at para position of W indicated the preference of elec­tron withdrawing groups at this position. This is explained by the significant anti-HIV activity of compounds with para halogen substitution (30, 35 and 40) (pEC50= 8.70, 7.82 and 7.60) in the order of Br > COMe > CHOHMe. Fig. 5. QSAR model visualized in the context of favorable and unfavorable effects in compound: (A) hydrogen bond donor, (B) hydrophobic/ non-polar and (C) negative ionizable properties. 3. 5. Identifying Novel Inhibitors The created five-site pharmacophore hypothesis, ADRRR-1, was used to identify new inhibitors with a new scaffold that corresponds to the 3D-QSAR model’s predicted molecular properties. Lead-like compounds from PubChem have been used to obtain new inhibitors, which could potentially target the HIV protease receptor. There was a total of 5575 hits compounds found from the PubChem datasets was similar 80% for most active com­pound of indolyl-aryl-sulfones composes. Pharmacophore model was used to identify the molecules that satisfy the hypothesis. Pharmacophore pre-filtering with ADRRR-1 hypothesis reduced initial 233 hits. 3. 6. Virtual Docking Screening Molecular docking simulations for the selected set of hits were performed using the Glide (Grid-Based Ligand Docking with Energetics) program available in Schröding­er 2021. The 3D structures were prepared using the Maes­tro LigPrep module. This module generates possible 3D conformations for each ligand with various ionization states at pH 7.0 ± 2.0. The docking screening process was conducted in two steps: (i) Glide/SP is performed for 230 ligands accelerated docking simulations; (ii) Glide/XP docking has chosen the top-docking ligands for more de­tailed analyses. The scores of docking studies Glide/SP are shown in the (Table S5). The key residues involved in substrate binding, in­cluding Asp25 Gly27 Ala28 Asp29 Asp30 Thr31 Val32 Ile47 Gly48 Gly49 Ile50 Gly51 Gly52 Phe53 Ile54 Leu76 Thr80 Pro81 Val82 Ile84, the structural analysis using the X-ray crystallographic data of PR complexed with GRL­09510 (8FM) showed that the P2-Crwn-THF of GRL­09510 forms a strong hydrogen-bond network with the backbone atoms of Asp 29 and Asp 30. Validation of the docking process was done by dock­ing of the compounds Nelfinavir and co-crystallized ligand 8FM ((3S,3aR,5R,7aS,8S)-hexahydro-4H-3,5-methano-furo[2,3-b] pyran-8-yl [(2S,3R)-3-hydroxy-4-{[(4-meth­oxyphenyl) sulfonyl] (2-methylpropyl) amino}-1-phenylbu­tan-2-yl] carbamate) at the active site of the target. Whereas, Nelfinavir is one of many protease inhibitors currently available, used to limit viral replication and improve im­mune function in people with HIV infection.45 The docking complex and binding interactions of Nelfinavir with HIV-1 protease are given in (Figure S1) with binding affinity –5.88 Table 4. Docking scores of the selected hits using GLIDE module. kJ/mol. This interaction is favored by the formation of the H-bond and non-hydrophobic interactions. The H-bonds are supported by the amino acids, Asp29 and Asp25 with the active site of protease and salt bridge interaction with Asp25, whereas pi-alkyl stacking with Val82 and Ile54. By comparing the docked energy of all the molecules studied, it is noteworthy that six bonds have better energy scores than Nelfinavir, knowing that the energy value for the reaction of the indicated molecule is –5.88 kcal/mol (Table 4). The top-scored hit molecule identified is PubChem 11560933 with a binding energy of –7.55 kcal/mol. The second top-scored hit molecule is PubChem 11654778 with a binding energy of –7.227 kcal/mol and the third top-scored hit molecule is PubChem 11710411 with an average binding energy of –6.655 kcal/mol. The molecular structure of top-scored compounds can be seen in table 5. Visualization of the docking results revealed that all the ligands adopted a very similar orientation in the ac­tive site. The nitrogen atoms with amide groups are ori­ented to the two aspartic acids as shown in figure 6. All of them form H-bond, while their large hydrophobic groups were often orientated to the main hydrophobic site, which distinguishes the PR active site. Moreover, the literature was mentioned that the catalytic triad Asp-Thr-Gly that is where the ligand binds determines the active site of the enzyme.46,47 Simplifying the docking results for the compounds, we have taken the 2D representative ligands; the bind­ing mode of the most active compound CID 11560933 is shown in table S13. In its binding mode, the Diamino Hex-anoyl amino fragment is observed to be inserted deeply in the cavity, interacting with Gly27 Asp29 Ala28 and Val32 through H-bond and pi-H contacts, respectively. In ligand 11654778, the two oxygen of the pentane diamide have shown strong hydrogen bonding acceptor interaction with aspartate (residue number 29 and 30) was also mapped on HBA features on ADRRR1 pharmacoph-ore model. Whereas the nitrogen atom of the carboxamide group in all the compounds shows H-bonds with Gly27 are observed explaining why the H-donor is beneficial for activity in the pharmacophore 3D-QSAR model. It should be noted that favorable interactions of hydrophobic type are observed between the indole rings and the residues Ala28, Ile54 and Val32; we can say that the aromatic ring was important for binding and stability of ligand with the HIV-1 PR active site complex. Compound Names CID Xp score Glid E model Compound Names Xp score Glid E model CID 11560933 –7.554 –56.274 CID 55868948 –6.615 –45.144 CID 11654778 –7.227 –53.648 CID 11567743 –6.454 –45.532 CID 11710411 –6.655 –48.052 CID 11630770 –6.449 –47.750 Nelfinavir. –5.882 –51.263 MF8 –4.851 –48.632 Table 5. Details of virtual screening compounds. Structure CID Molecular formula PubChem IUPAC Molecular mass name (g/mol) 11567743 C22H26N4O2S 6-[[(2-amino-3-methylpentanoyl)amino] 410.5 methyl]-3-phenylsulfanyl-1H-indole-2­carboxamide 11560933 C22H27N5O2S 6-[(2,6-diaminohexanoylamino)methyl]-3­425.5 phenylsulfanyl-1H-indole-2-carboxamide 11654778 C21H23N5O3S 2-amino-N-[(2-carbamoyl-3 phenylsulfanyl­425.5 1H-indol-6 yl)methyl]pentanediamide 55868948 C20H21ClN4O2 5-chloro-N-[[3-[[2-(dimethylamino)acetyl] 384.9 amino]phenyl]methyl]-1H-indole-2­carboxamide 11630770 C16H15N3OS 6-(aminomethyl)-3-phenylsulfanyl-1H­297.4 indole-2-carboxamide 11710411 C19H20N4O2S 6-[(2-aminopropanoylamino)methyl]-3­368.5 phenylsulfanyl-1H-indole-2-carboxamide One of the most important characteristics of the HIV-1 protease is that all amino acids of the active site are hydrophobic except for hydrophilic aspartic acids (Asp25).48 Due to these HIV-1 protease active site char­acters, the hits are considered a good inhibitor of the ac­tivity of HIV-1 protease because its hydrophobic surface provides strong Van der Waals interaction between hits and HIV-1 protease active site, which are beneficial to ac­tivity. This supports the proposed model pharmacophore ADRRR1 as it consists of three rings. The compounds CID 11560933, CID 11654778, CID 11710411, CID 55868948, CID 11567746, and CID 11630770 make good interaction with HIV-1 protease by forming hydrogen bonds, hydrophobic interactions and non-bonding interaction with catalytic residues such as Asp30, Thr80, Gly27, Asp29, Ile54 and Ile84 at the active site cavity of HIV protease. These interactions systems have the lowest total energies. According to this study, the type and spatial location of the hit compounds agree perfectly with the pattern of enzyme inhibitor interac­tions identified from Nelfinavir. In future, optimality of the compounds should be confirmed experimentally and Fig. 6. The orientation of HIV 1 PR inhibitors in the active site and hydrogen and hydrophobic surface compared its binding modes with a number of HIV-1 pro­tease. 3. 7. Analyzing Absorption, Distribution, Metabolism and Excretion (ADME) For additional validation purpose, The QikProp module of Schrodinger is a quick, accurate, and easy-to-use to describe absorption, distribution, metabolism and elimination results listed in table 6. The aqueous solubility plays an essential role in the bioavailability of the candidate. The aqueous solubility pa­rameter (QPlog S) of the test entities was assessed and the compounds were also found to be in the permissible range (< 0.5) (Table 6). One of the essential factors to be studied concern­ing the absorption of the drug molecule is also intestinal absorption or permeation, which was further supported by the expected permeability of Caco-2 cells (QPPCaco). The estimation of the test compounds for Caco-2 cell per­meability the compounds CID 55868948 shows excellent results, predicting strong intestinal absorption. The ether compounds have poor permeability across the gut–blood barrier. The parameters of the brain/blood partition coef­ficient (QPlogBB) define the drug’s ability to pass through the blood-brain barrier, which is important for ADME to investigate drug performance. The QikProp descriptor for blood/brain partition coefficient QPlogBB has shown reliable prediction for all the test compounds and refer­ence drugs. Predictions related to skin permeability (Kp), showed that these parameters for the active analogs fall within the standard ranges normally observed for drugs (Table 6). In addition, four out of the six compounds, shown to be more than 25% human oral absorption. Hu­man Ether-a-go-go Related Gene (hERG) parameter is used to determine the potential cardiac toxicity of the com­pounds. The hERG encodes a potassium ion (K+) channel that plays a role during systolic and diastolic activities of the heart. The blockage of hERG K+ channels can lead to cardiac arrhythmia. All the six compounds have logIC50 (hERG) values less than the acceptable range for the block­age of hERG K+ channels (logIC50 (hERG) < -5); however, Table 6. Estimated physicochemical and pharmacokinetic parameters by QikProp. Compound QPlogs QPPCaco QplogHEGd Qlog BB Percent human Metabolism Logkp Names CID oral absorption 11560933 –2.474 0.822 –4.316 –3.024 7.540 7 –7.518 11654778 –0.446 1.018 –3.709 –2.655 8.635 7 –7.374 11710411 –3.067 15.627 –5.325 –1.665 40.552 5 –5.837 55868948 –4.648 211.95 –7.225 –0.459 86.524 4 –4.064 11567743 –3.223 25.586 –5.525 –1.540 50.499 5 –5.302 11630770 –3.231 64.203 –5.923 –0.929 65.168 4 –5.479 QPlogS is the predicted aqueous solubility, log S: S in moles/l is the concentration of the solute in a saturated solution that is inequilibrium with the crystalline solid; QPPCaco is the predicted apparent Caco-2 cell permeability in nm/s; Caco-2 cells are a model for the gut–blood barrier Recom­mended values QPPCaCo = <25 POOR, >500 great,; QplogHEGd: Predicted IC50 value for blockage of HERG K+ channels QploghEGd concern below -5. QPlog BB is the predicted brain–blood partition coefficient; Percent Human-OralAbsorption = <20 POOR, >80great; QPlogBB = -3.0 to 1.2; *Recommended values – As per the guidelines given in Schrodinger’s Maestro software suite manual the values are near the borderline. All the six compounds were predicted to possess non-blocker to hERG channel (Table 6). An estimated number of possible metabolic re­actions has also been predicted by QikProp and used to determine whether the molecules can easily gain access to the target site after entering the blood stream. The com­pounds have the most elevated QPlogP values. A number of likely metabolic responses of the compounds are in the range of 4-7. The In silico ADMET results revealed that the top six of Mpro inhibitors are virtually safe and active. 3. 8. Prediction of Toxicity The computational prediction of toxicities was based on 5 different targets linked to adverse drug-reactions. The hepatotoxicity, carcinogenicity, mutagenicity and cytotox­icity of the compounds were predicted. It was found that 6 compounds have shown no toxicity. The LD50 has been also predicted, the obtained re­sults have shown that compounds 11630770 and 5586948 present a LD50 of 650 and 1000 mg/kg, respectively, as well as class four of toxicity. The other compounds show moderate toxicity with a LD50 value of 200 mg/kg and class 3 of toxicity. Table 7. Toxicity prediction of the selected compounds to be more stable during simulation competed to 5v4y HIV-1 protease alone. However, 5v4y HIV-1 protease 55868948 complex showed more variations with some spikes in RMSD. The RMSD average of both complexes was statistically insignificant (p-value < 0.05) with respect to 5v4y HIV-1 protease alone. The RMSD analysis of CID 55868948 alone revealed that these variations were due to the fluctuations in RMSD of the ligand (CID 55868948). The fluctuation in structures was assessed by calculat­ing the root mean square fluctuation (RSF) of Ca of 5v4y HIV-1 protease in the absence and the presence of ligands (Figure 8A). As evident from data, the RMSF of most of the residues of 5v4y HIV-1 protease alone was found to be less than 0.2 nm. A similar fluctuation was recorded for 5v4y HIV-1 protease CID 55868948 complex where most of the fluctuation in most of residues was below 0.2 nm. However, there was comparatively more fluctuations in Ca atoms of 5v4y HIV-1 protease CID 55868948 complex. The RMSF of each atom of both ligands (CID 11630770 and CID 55868948) was also calculated (Figure 8B). The RMSF value of the atoms of both ligands varied from their respective initial values indicating that the ligands exhib­ited dynamical shift from its initial position in the binding region. Compound Names CID Hepatotoxicity Carcinogenicity Mutagenicity Cytotoxicity LD50 mg/Kg 11567743 Inactive Inactive Inactive Inactive 200 11560933 Inactive Inactive Inactive Inactive 200 11654778 Inactive Inactive Inactive Inactive 200 55868948 Inactive Inactive Inactive Inactive 625 11630770 Inactive Inactive Inactive Inactive 1000 11710411 Inactive Inactive Inactive Inactive 200 3. 9. Molecular Dynamics Simulations To further obtain the insights regarding the inter­action of the ligands (CID 11630770 and CID 55868948) with 5v4y HIV-1 protease, Molecular Dynamics Simula­tion was performed. The docked complex was used as ini­tial conformations for MD simulations. 3. 9. 1. Analysis of RMSD and RMSF The initial analysis of MD simulation was performed by calculating the root-mean square deviations (RMSD) with respect to their respective backbone of initial struc­ture to assess the stability of the 5v4y HIV-1 protease and complexes under physiological conditions. The RMSD of 5v4y HIV-1 protease and complexes is shown in figure 7 and listed in table 8. The RMSD of 5v4y HIV-1 protease alone showed some variations initial time till 20 ns then it became stable for entire simulation period. The average RMSD of was found to be 0.290 ± 0.047 nm. The RMSD of 5v4y HIV-1 protease CID 11630770 complex was found 3. 9. 2. Assessment of Rg, SASA and Energies The mass-weighted root mean square distance of a collection of atoms from their common center of mass is Fig. 8. Root mean square .uctuation (RMSF) of HIV-1 protease in the absence and presence of 11630770 and 55868948. (B) The average RMSF value of each atom of ligands during the MD simulation. defined as radius of gyration (Rg). The Rg is also consid­ered as indicator of the stability of proteins or their com­plexes during MD.49 Generally, compact or globular pro­teins exhibit lesser variations in their Rg compared to the expanded form of proteins (5V4Y). The changes in Rg of 5v4y HIV-1 protease alone and in complex with ligands are shown in figure 9A. The Rg of 5v4y HIV-1 protease alone and 5v4y HIV-1 protease CID 11630770 complex was found to be stable during entire simulation period with negligible variations. On contra­ry, the Rg of 5v4y HIV-1 protease CID 55868948 complex showed relatively more variations that may be due to the dynamic behavior of the ligand. The average RMSD of HIV-1 protease alone, 5v4y HIV-1 protease CID 11630770 complex, and 5v4y HIV-1 protease CID 55868948 complex was found to be 1.278 ± 0.021, 1.265 ± 0.016, and 1.301 ± 0.027 nm, respectively. These values were statistically insig­nificant with respect to the control (HIV-1 protease alone). Solvent accessible surface area (SASA) of proteins is also taken into account while studying the stability of pro­teins during MD simulation.50 SASA of HIV-1 protease in the absence and the presence of ligands over simulation time is presented in figure 9B. The average SASA of HIV­1 protease alone, HIV-1 protease CID 11630770 complex, and HIV-1 protease CID 55868948 complex was found to be 64.318 ± 2.388, 63.049 ± 2.194, and 65.833 ± 1.995 nm2, respectively. The negligible variations in SASA of these structures further confirm their stable nature under phys­iological conditions. Further verification of the stability of HIV-1 pro­tease and complexes was performed by calculating the physicochemical parameters such as potential ener­gy and total energy (Figure S2). The straight line with negligible fluctuations in potential energy and total energy shows that the system reached equilibrium and remained stable during the entire simulation period.51 The RMSD, Rg and SASA values of both complexes showed statistically insignificant variations with respect to the potential and total energies HIV-1 protease alone (Table 8). Fig. 9. (A) Radius of gyration (Rg) of 5v4y HIV-1 protease in the absence and presence of CID 11630770 and CID 55868948 as a function of simu­lation time. (B) Solvent accessible surface area (SASA) of HIV-1 protease in the absence and presence of CID 11630770 and CID 55868948 as a function of simulation time. Table 8. Average RMSD, Rg and SASA of HIV-1 protease alone, and their complexes calculated over 10000 frames of 100 ns MD simulation. Parameters Proteins/complexes Values RMSD (nm) HIV-1 protease only 0.290 ± 0.047 HIV-1 protease 11630770 complex 0.246 ± 0.031 HIV-1 protease 55868948 complex 0.386 ± 0.105 11630770 only 0.096 ± 0.030 55868948 only 0.191 ± 0.060 Rg (nm) HIV-1 protease only 1.278 ± 0.021 HIV-1 protease 11630770 complex 1.265 ± 0.016 HIV-1 protease 55868948 complex 1.301 ± 0.027 11630770 only 0.342 ± 0.003 55868948 only 0.520 ± 0.033 SASA (nm2) HIV-1 protease only 64.318 ± 2.388 HIV-1 protease 11630770 complex 63.049 ± 2.194 HIV-1 protease 55868948 complex 65.833 ± 1.995 11630770 only 05.227 ± 0.195 55868948 only 06.869 ± 0.244 Potential energy (kcal/mol) HIV-1 protease only –6.360 ± 0.013 HIV-1 protease 11630770 complex –6.366 ± 0.014 HIV-1 protease 55868948 complex –6.358 ± 0.014 Total energy (kcal/mol) HIV-1 protease only –5.096 ± 0.018 HIV-1 protease 11630770 complex –5.101 ± 0.018 HIV-1 protease 55868948 complex –5.092 ± 0.018 RMSD: Root-mean square deviation, Rg: Radius of gyration, SASA: Solvent accessible surface area. 3. 9. 3. Analysis of Hydrogen Bonds and Secondary Structure The interaction of ligands with HIV-1 protease was studied by calculating the hydrogens bond profiles for 10000 frames of the MD simulation (Figure 10A). The average number of hydrogen bonds between CID 11630770 and HIV-1 protease was found to be 2.437 ± 1.050. Lesser number of average hydrogens binds was between CID 55868948 and HIV-1 protease. The hy­drogen bond existence was also calculated for both lig­ands. As observed, the hydrogen bond existence be­tween CID 11630770 and HIV-1 protease was found over the entire simulation period. However, the hydro­gen bond existence between CID 55868948 and HIV-1 protease showed some breaks as the MD simulation progressed. The effect of binding of the ligands on the secondary structure of HIV-1 protease was studies by calculating the average secondary structure of all frames of the respec­tive trajectories (Figure 10B). The coil, ß-sheet, bends, and turns in HIV-1 protease alone was found to be 22.34, 49.03, 11.18, and 11.55% respectively. These secondary structural components were insignificantly altered in the presence of both ligands. However, there was some incre­ment in a-helix, which increased from 4.81% to 5.27% due to binding of 55868948 Table 9. Binding free energy (kcal mol–1) for the interaction of pro­ 3. 9. 4. Calculation of Binding Energies and tein with 11630770 and 55868948 ligands using MMBSA analysis. Identification of Key Residues Involved in Binding The detailed investigation of the various binding energies involved in the interaction of both ligands with HIV-1 protease was done using MM-PBSA calculation. Usually in protein-ligand interactions, non-covalent inter­actions are predominant. The forces include hydrophobic forces, hydrogen bonds, electrostatic interactions, Van der Waals force. Each of these forces contributes either pos­itively or negatively to the overall binding energy.52 The MM-PBSA binding energies were calculated by extracting 100 frames from the entire MD simulation trajectories at uniform intervals (Table 9). In the binding of 11630770 to HIV-1 protease, electrostatic interactions were most prominent followed by Van der Waals interactions. The in­teraction of 55868948 with HIV-1 protease was mostly fa­vored by Van der Waals forces. Additionally, there was also small contribution of SASA energy in the overall binding of both leads. However, polar solvation energy impaired the interaction of both ligands with HIV-1 protease. The overall binding energy for 11630770 and 55868948 were found to be -7.067 ± 0.509 and -7.218 ± 2.080 kcal/mol respectively. From MM-PBSA calculations, the binding energies of all residues can be calculated. The polar, a polar and total binding energy contribution of the key residues of HIV-1 protease in the interaction is presented in figure 10 B. Glu-21, Asp-25, Asp-29, Asp-30, Glu-34, Glu­35, Asp-60, Glu-65, Ile-84, and Phe-99 were the major contributor to overall binding energy in interaction of 11630770 with HIV-1 protease. Similarly, Glu-21, Asp­25, Asp-29, Asp-30, Glu-34, Glu-35, Ile-50, Asp-60, Glu­65, Pro-81, Val-82, Phe-99 of HIV-1 protease contribut­ed maximally in the binding of 55868948 to the protein. It is interesting to note that polar energy of some key residues contributed negatively towards the total bind­ing process. Type of energy Ligands 11630770 ligand 55868948 ligand .EvdW –24.258 ± 0.533 –20.163 ± 1.340 .Eele –45.156 ± 1.261 –3.787 ± 1.817 .EPSE 66.022 ± 1.583 19.443 ± 1.997 .ESSASA –3.650 ± 0.046 –2.701 ± 0.163 .EBE –7.067 ± 0.509 –7.218 ± 2.080 .EvdW: Van der Waal energy, .Eele: Electrostatic energy, .EPSE: Polar solvation energy, .ESASA: Solvent accessible surface area en­ergy and .EBE: Binding energy. 3. 9. 5. Principal Component Analysis Principal component analysis (PCA) is the standard statistical procedure used for the investigation of large-scale motion in protein, which is performed by reducing the dimensionality of data set without losing important in­formation, which is characterized by eigenvectors.53 PCA was done to assess the differences in the .exibility param­eters between the HIV-1 protease alone and complexes. Using PCA analysis, a set of eigenvectors and eigenvalues were projected (Figure 12A). HIV-1 protease alone and HIV-1 protease 11630770 complex occupied larger con­formational space compared to HIV-1 protease 55868948 complex. These observations denote the presence of more structural stability HIV-1 protease 11630770 complex than HIV-1 protease 55868948 complex. Moreover, the free energy landscapes for the pro­tein alone and both complexes were plotted to decipher the variations in the protein folding patterns (Figure 13). Variations in the projection of free energy were recorded where alone energetically favorable and relatively stable was compared to both the complexes. The observations show that the binding of ligands partly perturbed the con­formation of HIV-1 protease. To gain further insights into the conformational transition of HIV-1 protease, the lowest energy minima structures were extracted. The Ramachandran plots were made for the energy minima structures of HIV-1 protease alone and in complex with the ligands (Figure 12B). The phi (f) and psi (.) angles for HIV-1 protease alone were found to be –82.31 and 93.60, respectively. The f and . an­gles for HIV-1 protease 11630770 complex were obtained as –85.15 and 91.30, respectively. Similarly, f and . angles for HIV-1 protease CID 55868948 complex were recorded as –85.14 and 84.72, respectively. In both the complexes, remarkable variations in the dihedral angles with respect Donor (HBD), Hydrogen-Bond Acceptor (HBA), hydro­phobic and aromatic groups, which may be responsible for the HIV1 inhibition. Both generated pharmacophore model was validated for its quality to identify new reliable chemical compounds. The validation procedure included two methods: test set validation, and decoy set validation. Based on the 3D-QSAR model ADRRR we have selected hits from the PubChem database. Initial screened com­pounds were passed through several criteria including the range of activity of training set, fitness score more than 2.5 and comparison of dock score and binding energy with Nelfinavir to reach the potential compounds. Docking into Fig. 12. (A) Principal component analysis (PCA) of HIV-1 protease in the absence and presence of 11630770 and 55868948. (B) Ramachandran plot of the energy minima of HIV-1 protease in the absence and presence of 11630770 and 55868948. Fig. 13. Free energy landscape plot of (A) HIV-1 protease alone (B) HIV-1 protease 11630770 complex and (C) HIV-1 protease 5586894 complex. to HIV-1 protease alone were observed, indicating the structural transition in the presence of ligands. 4. Conclusion In this study, a five-featured (ADRRR) pharmacoph-ore model for the HIV1 inhibitors was developed. 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F. A. Qais, T. Sarwar, I. Ahmad, R. A. Khan, S. A. Shahzad, F. M. Husain, Int. J. Biol. Macromol. 2021, 169, 143–152. DOI:10.1016/j.ijbiomac.2020.12.096 50. B. Rath, F. Abul Qais, R. Patro, S. Mohapatra, T. Sharma, Bi-oorg. Med. Chem. Lett. 2021. 41, 128029. DOI:10.1016/j.bmcl.2021.128029 51. P. V. Coveney, S. Wan, Chem. Phys. 2016, 18, 30236–30240. DOI:10.1039/C6CP02349E 52. S. Siddiqui, F. Ameen, T. Kausar, S. M. Nayeem, S. Ur Reh-man, M. Tabish, Spectrochim. Acta A, 2021. 249, 119296. DOI:10.1016/j.saa.2020.119296 53. S. Siddiqui, F. Ameen, I. Jahan, S. M. Nayeem, M. Tabish. New J. Chem. 2019, 43, 4137–4151. DOI:10.1039/C8NJ05486J Povzetek Ta raziskava obravnava in silico 45 indolil-aril-sulfonov, znanih kot anti-HIV1. Podatki so bili zbrani iz nedavnih pred­hodno prijavljenih zaviralcev in razdeljeni na podskupino 33 spojin, namenjenih nizu preizkušanj, preostalih 12 spo­jin pa je bilo shranjenih v namen testnega niza. Izbrani farmakofor –ADRRR– je podal statisticno pomemben model 3D-QSAR, ki vkljucuje visoke ocene zaupanja (R2 = 0,930, Q2 = 0,848 in RMSE = 0,460). Napovedna moc uveljavljenega modela farmakofora je bila potrjena z zunanjim testom (r2 = 0,848). Sistematicen navidezni potek dela je pokazal visok faktor uporabnosti in razkril visoko moc napovedovanja. Nato je bil model uporabljen za pregled filtrirane baze podat­kov PubChem in oznacil vse kemijske znacilnosti modelne farmakofore. Ustrezni zadetki so bili dodatno ocenjeni z in silico ADMET študijami. Poleg tega se molekularna dinamika lahko uporablja tudi za raziskovanje stabilnosti dobljenih kompleksov. V koncni fazi bodo te izbrane komponente lahko postale dobra zacetna molekula za razvoj ucinkovitih ucinkovin proti HIV-1. Except when otherwise noted, articles in this journal are published under the terms and conditions of the Creative Commons Attribution 4.0 International License DRUŠTVENE VESTI IN DRUGE AKTIVNOSTI SOCIETY NEWS, ANNOUNCEMENTS, ACTIVITIES Vsebina Porocilo o delu v letu 2021 ..................................................................................................... S51 Koledar važnejših znanstvenih srecanj s podrocja kemije in kemijske tehnologije ......... S56 Navodila za avtorje .................................................................................................................. S60 Contents Report for 2021 ........................................................................................................................ S51 Scientific meetings – Chemistry and chemical engineering................................................ S56 Instructions for authors .......................................................................................................... S60 S50 POROCILO PREDSEDNIKA SLOVENSKEGA KEMIJSKEGA DRUŠTVA O DELU DRUŠTVA V LETU 2021 Tudi v letu 2021 je bilo društvo aktivno na številnih podrocjih. Izvajali smo redne letne aktivnosti, pri katerih je bil glavni poudarek na rednem izdajanju društvene re-vije Acta Chimica Slovenica (ACSi) ter organizaciji naj­vecjega letnega dogodka društva, konference »Slovenski kemijski dnevi 2021«. Rednim aktivnostim so se pridružile tudi aktivnosti ob 70. letnici društva. Tako smo 15. februarja organizirali online slovesnost ob 70. letnici društva, kjer so lahko clani in drugi vabljeni ter zainterersirani poslušalci prisostvovali dvema vabljenima predavanja. Prvo predavanje z naslo­vom Z živim srebrom do srebra – Od alkimije do kemije in slovenski prispevek k postopku, ki je spremenil svet, je izvedel prof. dr. Alojz Demšar, drugo, z naslovom Racu­nalnik in kemija – Zaveznika v boju proti raku pa prof. dr. Urban Bren. Dogodek smo popestrili s prikazom vošcil, ki so nam jih namenila sorodna društva po svetu ter sloven-ske partnerske inštitucije. Ob tej priložnosti se zahvaljujem prof. dr. Zorki Novak Pintaric za odlicno vodenje dogodka, hkrati pa zahvala tudi podjetjem Krka d.d., Novo mesto, Kemomed, Aquafil, Primalab in Mettler Toledo za sponzor­sko podporo pri organizaciji slovesnosti. Za 23. september nacrtovana slavnostna akademija ob 70. letnici društva in podelitev castnih ter zaslužnih clanstev ter priznanj društva je bila zaradi Covid-19 razmer preložena na leto 2022. Za namen 70. letnice je bila oblikovana tudi nova graficna podoba, ki smo jo prikazali na plaketi«70 let«, ro­ll-upih, power point predlogah in vabilih. 23. junija 2021 smo izvedli redni obcni zbor društva, kjer smo izvolili nove vodstvene organe društva, castne, zaslužne clane ter prejemnike priznanj, glasovali pa smo tudi o spremembah in dopolnitvah statuta ter združitvi Sekcije za analizno kemijo in Spektroskopsko sekcijo v skupno sekcijo. Slovenski kemijski dnevi 2021 so bili organizirani v Portorožu, v Kongresnem centru Grand hotela Bernardin, in sicer v dneh od 22. do 24. septembra 2021. Program-skemu in organizacijskemu odboru je predsedoval znan. svet. dr. Albin Pintar, skupaj s clani odbora v zasedbi prof. dr. Romana Cerc-Korošec, prof. dr. Zorka Novak Pintaric, prof. dr. Darja Lisjak, doc. dr. Matic Lozinšek, prof. dr. Ma-tjaž Valant, dr. Silvo Zupancic in Marjana Gantar Albreht. Na konferenci je bilo predstavljenih preko 200 pri­spevkov v obliki predavanj in posterjev, kar je najvec v zadnjih petih letih. Delo je potekalo plenarno in v treh vzporednih sekcijah. Udeleženci konference, bilo jih je 305 iz Slovenije in sedmih drugih držav, so bili zelo zado­voljni s kakovostjo znanstvenih in strokovnih prispevkov ter družabnim programom srecanja. Na konferenci je so-delovalo tudi 19 razstavljalcev laboratorijske in procesne opreme. Sponzorji dogodka so bili Kemijski inštitut, Cin­karna, AquafilSLO, Kemomed, Kobis, Krka, Laboratori­jum, Merck, Mettler Toledo, Optik Instruments, Primalab in Vigor. Objavili smo zbornik povzetkov konference, ki je dostopen na USB kljucu ter na voljo v NUK-u in strokov­nih knjižnicah po Sloveniji. Plenarni predavatelji na konferenci so bili prof. dr. Barbara Malic (Institut »Jožef Stefan«, Ljubljana), dr. Ute Resch-Genger (Federal Institute for Materials Research and Testing, Berlin, Nemcija) in dr. Lukas Palatinus (In­stitute of Physics of the Czech Academy of Sciences, Pra­ga, Ceška). Poleg treh plenarnih predavanj so udeleženci poslušali šest »keynote« vabljenih predavanj, ki so jih iz­vedli izr. prof. dr. Kristina Žužek Rožman (Institut »Jožef Stefan«, Ljubljana), izr. prof. dr. Griša Mocnik (Univerza v Novi Gorici in Institut »Jožef Stefan«, Ljubljana), prof. dr. Irena Kralj Cigic (Fakulteta za kemijo in kemijsko tehno­logijo, Univerza v Ljubljani), dr. Sebastijan Kovacic (Ke­mijski inštitut, Ljubljana), prof. dr. Maja Leitgeb (Fakulteta za kemijo in kemijsko tehnologijo, Univerza v Mariboru) in dr. Igor Mihelic (Melamin d.d., Kocevje). Ob zakljucku konference smo že tradicionalno po­delili nagrade doktorskim študentom za najboljša preda­vanja in posterske predstavitve. Financni del podeljenih nagrad je sponzoriral Kemijski inštitut. V letu 2021 smo v reviji Acta Chimica Slovenica (ACSi) izdali 4 številke revije, v katerih je bilo skupaj ob-javljenih 94 originalnih znanstvenih clankov na skupno 1026 straneh z dvokolonskim tiskom. Clanki pokrivajo vsa podrocja kemije, kemije materialov in kemijskega in biokemijskega inženirstva. Od objavljenih clankov so bili štirje tako imenovani „Feature Articles“ (FA) in eden „Re­view Articles“ (RA). Vsi clanki so objavljeni na spletu in so prosto dostopni. Poleg tega so objavljeni tudi v vec podat­kovnih bazah. Vseh clankov, ki so bili leta 2020 oddani v uredniški sistem, je bilo 705, kar pomeni, da jih je bilo na koncu sprejetih okoli 13%. Clani Slovenskega kemijskega društva in avtorji clankov prejmejo elektronsko obvestilo o izidu nove številke. Reviji ACSi se vztrajno viša faktor vpliva. Za leto 2020 je faktor vpliva zrastel na 1,735 (iz 1, 1,223 v letu 2019). Tako je revija napredovala iz cetrte­ga v tretji kvartil. Sprememb uredniške politike in s tem vsebine revije ni bilo, zato v letu 2022 nadaljujemo po isti poti. V društvenih vesteh smo objavili seznam diplomskih, magistrskih in doktorskih del FKKT UL, FKKT UM, po­diplomskega študijskega programa »Znanosti o okolju« in Fakultete za znanosti okolju, UNG v letu 2020. Objavili smo tudi letna porocila sekcij. V letu 2021 so društvene vesti obsegale 122 strani. Na društvenih straneh sta bila poleg ostalih novic objavljena zanimiv strokovni clanek avtorja dr. M. Dolinarja in predstavitev nove znanstvene monografije avtorjev dr. J. Faganelija ter dr. A. Maleja. Zahvaljujem se tudi vsem inštitucijam, ki so v letu 2021 financno podprle izdajanje revije Acta Chimica Slo­venica. Te so Fakulteta za kemijo in kemijsko tehnologijo Univerze v Ljubljani, Fakulteta za kemijo in kemijsko teh­nologijo Univerze v Mariboru, Kemijski inštitut in Inšti-tut »Jožef Stefan«. Sponzorji revije so bili z objavo oglasa Krka d.d., Novo mesto, Donau Lab d.o.o. Ljubljana, Helios Domžale, d.o.o. in Sandoz. V letu 2021 smo nadaljevali z aktivnostmi za prido­bivanje novih clanov. Medse smo jih privabili 22, od tega 19 študentov. Za komunikacijo s clani smo pogosteje upo­rabljali Facebook in Twitter omrežje ter jih obvešcali o do-godkih po elektronski pošti. Clani Slovenskega kemijskega društva so bili aktivni tudi na podrocju mednarodnega sodelovanja. Predvsem je potrebno omejiti clanstvo društva v mednarodnih zdru­ženjih IUPAC, ECTN, IUCr, EURACHEM, EuChemS, EFCE, EPF, ECA in EFCATS. Izpostaviti gre tudi sodelovanje pri organizaciji kon­ference 9th European variety in university chemistry edu­cation conference (EUROVARIETY 2021), ki je je organi­zirala Pedagoška fakulteta UL, pod okriljem organizacije EuChemS in Komisije za izobraževanje pri Slovenskem kemijskem društvu Društvo se je v letu 2021 uspešno prijavilo na Javni razpis ARRS za sofinanciranje delovanja v mednarodnih znanstvenih združenjih v letu 2021, kjer smo bili uspešni pri vseh oddanih vlogah. prof. dr. Ksenija Kogej, glavna urednica ACSi dr. Albin Pintar, predsednik organizacijskega odbora konference Slovenski kemijski dnevi dr. Peter Venturini, predsednik društva Porocilo o delovanju in aktivnostih Mariborske podružnice v letu 2021 Mariborska podružnica se je v letu 2021 usmerila v izpolnitev ciljev, ki si jih je zastavila v preteklem letu. V Portorožu smo se udeležili konference Slovenski kemijski dnevi, kjer smo predsedovali pri razlicnih sekci­jah, sodelovali smo kot pr edavatelji in kot predstavniki prispevkov na posterjih ter v komisiji za ocenjevanje štu­dentskih del. Skrb Mariborske podružnice je tudi stalno izobraže­vanje clanov. V ta namen smo organizirali strokovna pre­davanja in razne seminarje, na katerih so predavali prizna­ni tuji in domaci strokovnjaki. Predavanja so pokrivala pomembna podrocja teoreticne in uporabne kemije, ke­mijske in procesne tehnike ter kemijskega izobraževanja. Gostili smo Dr. Bojana Kulcarja iz Leka Lendava, ki je predstavil podjetje in svoje izkušnje prenosa znanja iz maticne fakultete v industrijsko okolje. Podobno predsta­vitev je izvedel dr. Žan Zore iz Etola (Celje). Na predlog Fakultete za kemijo in kemijsko tehnolo­gijo Univerze v Mariboru je naziv castni doktor prejel prof. dr. Rafiqul Gani za izjemne dosežke in zasluge pri razvoju in delovanju Fakultete za kemijo in kemijsko tehnologijo Univerze v Mariboru. S svojim delom je prispeval vecanju ugleda naše clanice. Gostili smo prof. dr. Kathleen Aviso in prof. dr. Mi­chael Angelo Promentilla (Univerza De La Salle, Filipini), ki sta izvedla 2-tedensko predavanje na temo optimiranja procesov. Aktivno smo sodelovali tudi pri mednarodnih pole-tnih šolah. V mesecu juliju je na naši fakulteti potekala mednarodna polenta šola na temo visokotlacnih tehnolo­gij: ‘ESS-HPT 2021’ The Eurpean Summer school in High Pressure Technology, ki jo je organiziral naš Laboratorij za separacijske procese in produktno tehniko v sodelovanju s Tehnološko fakulteto v Gradcu. Matjaž Finšgar Porocilo Sekcije za kristalografijo za leto 2021 V letu 2021 se je epidemija COVID-19 delno umiri-la, zato so bili izvedena nekatera znanstvena srecanja, na primer kongres Mednarodne kristalografske zveze (In­ternational Union of Crystallography) avgusta v Pragi. Ta kongres, kot tudi vecina drugih kristalografskih srecanj je bil izveden hibridno, kar je omogocilo sodelovanje pred­stavnika kristalografske sekcije Slovenskega kemijskega društva, da je sodeloval na zasedanjih skupšcin obeh kri­stalografskih združenj (Mednarodne kristalografske zve­ze in Evropskega kristalografskega združenja (European Crystalloographic Association) v katerih smo slovenski kristalografi polnopravni clani. Nekatera druga srecanja (na primer Evropske kon­ferenca o praškovni difrakciji in tudi Hrvaško-slovensko kristalografsko srecanje) pa so bila ponovno preložena za eno leto v upanju, da bo mogoca normalna izvedba v živo. Ne glede na preložitev, je bilo potrebnega nekaj dela in truda, da smo za obe srecanji obdržali plenarne preda­vatelje, sponzorje in se dogovorili s kongresnimi centri in hoteli za nove termine pod podobno ugodnimi pogoji. Ve-cino dela za oba dogodka so sicer opravili hrvaški kolegi, ker se bosta oba dogodka odvijala na Hrvaškem, smo pa clani sekcije, ki sodelujemo v organizacijskem odboru, po­magali kolikor je bilo potrebno. V casu pisanja tega porocila je že objavljeno, da bo letos v zacetku junija v Šibeniku na klasicen nacin z ude­ležbo v živo, izpeljana Evropska konferenca o praškovni difrakciji v Šibeniku, ki se je bo udeležilo tudi nekaj slo­venskih raziskovalcev. Prav tako je objavljeno, da bo Hrva­ško-slovensko kristalografsko srecanje izvedeno klasicno letos od 8. do 11. septembra v Porecu. Prijave se še zbirajo, vsekakor pa racunamo tudi na tradicionalno dobro ude­ležbo slovenskih raziskovalcev. prof. dr. Anton Meden Porocilo Analizne sekcije za 2021 v okviru Slovenskega kemijskega društva Osnovna dejavnost sekcije za Analizno kemijo v okviru Slovenskega kemijskega društva je organiziranje mednarodnih in domacih znanstvenih ter strokovnih sre-canj, predavanj domacih in tujih strokovnjakov ter izvedba razlicnih delavnic, seminarjev in simpozijev. Clani sekcije pa sodelujejo tudi znotraj delovnih skupin Eurachem in drugih združenj v evropskem prostoru (DAC, FECS) in tako pomembno prispevajo k prepoznavnosti Slovenskega kemijskega društva. Na delo sekcije za Analizno kemijo v letu 2021 je bi-stveno vplivala epidemija Sars-Covid 19, tako so planirane aktivnosti potekale na spletu, veliko dogodkov pa je bila odpovedanih ali prestavljenih. Takšno usodo je doletelo tudi tradicionalno mednarodno srecanje podiplomskih študentov in njihovih mentorjev YISAC (Young Investiga­tors Seminar on Analytical Chemistry), ki bi moralo po­tekati junija 2021 na Univerzi v Lodzu na Poljskem. Ker srecanja ni bilo mogoce organizirati je obveljala dokoncna odpoved in izvedba v letu 2022. Med redkimi aktivnostmi sekcije, ki so bile izvedene, kot obicajno, velja izpostaviti 27. jubilejno konferenco »Slovenski kemijski dnevi 2021«, ki se je odvijala septembra 2021 v Portorožu. Posebej vzpodbudna je aktivna udeležba mlajših kolegov iz razlic­nih institucij, ki so na zavidljivem nivoju predstavili raz­nolike in zanimive raziskave na podrocju spektroskopije, kromatografije, elektrokemije, materialov in okolja. V prihodnje želimo v sekcijo aktivno vkljuciti mlaj­še kolege in nadaljevati z organizacijo domacih ter tujih srecanj, predavanj in konferenc. Posebej želimo okrepiti povezovanje in prenos znanja iz univerzitetnih in razisko­valnih laboratorijev v industrijo. prof. dr. Mitja Kolar Porocilo Sekcije mladih kemikov za leto 2021 Glavni namen sekcije mladih kemikov je vzposta­vitev vezi med študenti, mladimi raziskovalci, mladimi doktorji, znanstveno-raziskovalnimi inštitucijami, ke­mijsko in farmacevtsko industrijo ter pedagoškimi de­lavci v šolah in fakultetah. Sekcija mladih kemikov je clanica v European Young Chemists’ Network (EYCN) v okviru EuChemMS in International Young Chemists Network (IYCN) v okviru IUPAC, tako da je ena njenih pomembnejših nalog tudi povezovanje mladih kemikov in delovanje na skupnih projektih v mednarodnem merilu. V letu 2021 smo se udeležili generalne skupšcine združenja IYCN, ki je potekala v spletni obliki in na kateri se je izvolilo vodstvo v naslednjem mandatnem obdob­ju. Prav tako je bila v pripravah novembrska organizaci­ja ekskurzije v Krko, kjer smo združili moci s Študentsko organizacijo Fakultete za kemijo in kemijsko tehnologijo Univerze v Ljubljani. Na žalost je bila zaradi ukrepov pove­zanih z epidemijo ekskurzija prestavljena v pomlad 2022. dr. Sara Drvaric Talian Porocilo Sekcije za keramiko za leto 2021 Evropska keramicna organizacija (ECerS) je svojo osrednjo bienalno konferenco in razstavo zadnjic organi­zirala leta 2019 v Torinu v Italiji. Leta 2021 je bilo srecanje predvideno v Dresdnu v Nemciji, ki pa je bilo zaradi težav s pandemijo COVID-19 prestavljeno na leto 2022, ko se bo pod imenom Ceramics in Europe združilo tri keramicne konference, ECerS XVII, 9. Mednarodno konferenco o ke­ramiki (International Ceramic Conference, ICC) in konfe­renco Electroceramics XVIII. Srecanje bo potekalo v Kra­kovu na Poljskem od 10. do 14. julija 2022, njeno organizacijo pa podpirajo Evropsko keramicno združenje, Mednarodna keramicna zveza (ICF), Fraunhofer IKTS in Elektrokeramicna mreža društva ECerS. Pod okriljem ECerSa in njene mreže Europe Makes Ceramics (EMC) je bilo v letu 2021 izvedeno srecanje fo­ruma mladih keramicarjev za aditivno proizvodnjo (The young Ceramists Additive Manufacturing Forum). Sreca­nje je potekalo preko spleta 29. oktobra 2021 z 80 udele­ženci, pokrivalo pa je tematike tehnologije, materialov in aplikacij ter oblikovanja in programske opreme. ECerS je za leto 2021 podelil sledece nagrade: – Stuijts nagrada (raziskovalki / raziskovalcu, ki pripada državi clanici društva, za izjemne prispevke v keramicni znanosti, tehnologiji in izobraževalni dejavnosti ali proi­zvodnji): prof. Jérôme Chevalier, National Institute of Applied Sciences of Lyon, Francija. – Richard Brook nagrada (raziskovalki / raziskovalcu iz države zunaj Evrope za izjemen prispevek k keramicni znanosti ali tehnologiji ter za izboljšanje razumevanja in sodelovanja v mednarodni keramicni skupnosti): prof. Bikramjit Basu, Indian Ceramic Society. – Nagrada »Mlad znanstvenik / Mlada znanstvenica« (raz­iskovalki / raziskovalcu, mlajši od 40 let, zaradi izjemnih prispevkov k keramicnim znanostim): Izr. prof. Ondrej Jankovský, Institute of Chemical Technology, Praga, Ce­ška republika. – Babini nagrada (raziskovalki / raziskovalcu za izjemen tehnicni prispevek k razvoju evropske keramicne indu­strije): Franco Stefani, System Logistic SpA, System Ce­ramics SpA, Laminam SpA, Italija. Hermina Hudelja, doktorandka iz Odseka za na­nostrukturne materiale Instituta »Jožef Stefan«, se je ude­ležila tekmovanja mladih keramikov ECerS »Student Spe­ech Contest«. Tekmovanje je bilo izpeljano v okviru 14. bienalne konference za mlade znanstvenike s podrocja keramike (»14th ECerS Conference for Young Scientists in Ceramics«), 20.-23. oktobra 2021, v Novem Sadu v Srbiji. Na tekmovanju svoje rezultate predstavijo mladi znanstve­niki, kot predstavniki držav pridruženih clanic ECerSa. Zmagal je predstavnik Avstrije, Maximilian Staudacher iz Montanuniversität Leoben, Avstrija. Drugo mesto je za­sedla predstavnica iz Velike Britanije, Else Ellermann iz University of Cambridge. Tretja je bila predstavnica Belgi­je, Chloé Goutagny iz Belgian Ceramic Research Centre, Belgija. Tekmovalo je 18 predstavnic in predstavnikov pridruženih clanic. V letu 2021 je bila s strani Young Ceramists Networ­ka (YCN) izpeljana spletna delavnica YCN 2021 »Pitch me your idea«, v platformi Gather Town, med 15. in 17. juni­jem. Na delavnici je sodelovalo 77 udeležencev iz 29. držav. 24 udeležencev je sodelovalo v tekmovanju kratkih predstavitev svojih raziskovalnih idej. Zmagal je Vojtech Necina iz praške University of Chemistry and Technology, Ceška. Drugo mesto je zasedel Moritz Weiß iz Karlsruher Institut für Technologie, Nemcija. Tretja je bila Oana-An­dreea Condurache iz Odseka za elektronsko keramiko In-stituta »Jožef Stefan«. V letu 2021 je na mestu predsednika društva ECerS dr. Francis Cambier, Belgian Ceramic Research Centre, Belgija, zamenjal prof. Jon Binnerja, University of Birmin­gham, Velika Britanija, ki je društvo vodil med letoma 2019 in 2021. V okviru Divizije za elektroniko Ameriškega kera­micnega društva je v letu 2021 izr. prof. Matjaž Spreitzer iz Odseka za raziskave sodobnih materialov Instituta »Jožef Stefan« postal tajnik divizije omenjenega društva. Izr. prof. Matjaž Spreitzer in izr. prof. 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All the listed authors should have agreed on the content and the corresponding (submitting) au­thor is responsible for having ensured that this agree­ment has been reached. The acceptance of an article is based entirely on its scientific merit, as judged by peer review. There are no page charges for publishing articles in ACSi. The authors are asked to read the Author Guidelines carefully to gain an overview and assess if their manuscript is suitable for ACSi. Additional information  Citing spectral and analytical data  Depositing X-ray data Submission material Typical submission consists of: • full manuscript (PDF file, with title, authors, ab­stract, keywords, figures and tables embedded, and references) • supplementary files – Full manuscript (original Word file) – Statement of novelty (Word file) – List of suggested reviewers (Word file) – ZIP file containing graphics (figures, illustra­tions, images, photographs) – Graphical abstract (single graphics file) – Proposed cover picture (optional, single graphics file) – Appendices (optional, Word files, graphics files) Incomplete or not properly prepared submissions will be rejected. Submission process Before submission, authors should go through the checklist at the bottom of the page and prepare for submission. Submission process consists of 5 steps. Step 1: Starting the submission • Choose one of the journal sections. • Confirm all the requirements of the checklist.  Additional plain text comments for the editor can be provided in the relevant text field. Step 2: Upload submission • Upload full manuscript in the form of a Word fi­le (with title, authors, abstract, keywords, figures and tables embedded, and references). Step 3: Enter metadata • First name, last name, contact email and afliation for all authors, in relevant order, must be provided. Corresponding author has to be selected. Full po­stal address and phone number of the correspon­ding author has to be provided.  Title and abstract must be provided in plain text. • Keywords must be provided (max. 6, separated by semicolons).  Data about contributors and supporting agencies may be entered.  References in plain text must be provided in the relevant text filed. Step 4: Upload supplementary files • Original Word file (original of the PDF uploaded in the step 2)  List of suggested reviewers with at least five re­viewers with two recent references from the field of submitted manuscript must be uploaded as a Word file. At the same time, authors should declare (i) that they have no conflict of interest with suggest­ed reviewers and (ii) that suggested reviewers are experts in the field of the submitted manuscript.  All graphics have to be uploaded in a single ZIP file. Graphics should be named Figure 1.jpg, Figure 2.eps, etc.  Graphical abstract image must be uploaded separately  Proposed cover picture (optional) should be up­loaded separately.  Any additional appendices (optional) to the paper may be uploaded. Appendices may be published as a supplementary material to the paper, if accepted. • For each uploaded file the author is asked for addi­tional metadata which may be provided. Depending of the type of the file please provide the relevant title (Statement of novelty, List of suggested re­viewers, Figures, Graphical abstract, Proposed cov­er picture, Appendix). Step 5: Confirmation • Final confirmation is required. Article Types Feature Articles are contributions that are written on Editor’s invitation. They should be clear and concise summaries of the author’s most recent work written with the broad scope of ACSi in mind. They are intend­ ed to be general overviews of the authors’ subfield of research but should be written in a way that engages and informs scientists in other areas. They should con­tain the following (see also general guidelines for arti­ cle structure below): (1) an introduction that acquaints readers with the authors’ research field and outlines the important questions for which answers are being sought; (2) interesting, novel, and recent contributions of the author(s) to the field; and (3) a summary that presents possible future directions. Manuscripts should normally not exceed 40 pages of one column format (font size 12, 33 lines per page). Generally, experts who have made an important contribution to a specific field in recent years will be invited by the Editor to contrib­ute a Feature Article. Individuals may, however, send a proposal (of no more than one page) for a Feature Article to the Editor-in-Chief for consideration. Scientific articles should report significant and inno­vative achievements in chemistry and related scienc­es and should exhibit a high level of originality. They should have the following structure: 1. Title (max. 150 characters), 2. Authors and afliations, 3. Abstract (max. 1000 characters), 4. Keywords (max. 6), 5. Introduction, 6. Experimental, 7. Results and Discussion, 8. Conclusions, 9. Acknowledgements, 10.References. The sections should be arranged in the sequence gen­erally accepted for publications in the respective fields and should be successively numbered. Short communications generally follow the same order of sections as Scientific articles, but should be short (max. 2500 words) and report a significant as­pect of research work meriting separate publication. Editors may decide that a Scientific paper is catego­rized as a Short Communication if its length is short. Technical articles report applications of an already described innovation. Typically, technical articles are not based on new experiments. Preparation of Submissions Text of the submitted articles must be prepared with Microsoft Word. Normal style set to single column, 1.5 line spacing, and 12 pt Times New Roman font is recommended. Line numbering (continuous, for the whole document) must be enabled to simplify the re­ viewing process. For any other format, please consult the editor. Articles should be written in English. Correct spelling and grammar are the sole responsibility of the author(s). Papers should be written in a concise and succinct manner. The authors shall respect the ISO 80000 standard [1], and IUPAC Green Book [2] rules on the names and symbols of quantities and units. The Système International d’Unités (SI) must be used for all dimensional quantities. Graphics (figures, graphs, illustrations, digital imag­es, photographs) should be inserted in the text where appropriate. The captions should be self-explanatory. Lettering should be readable (suggested 8 point Arial font) with equal size in all figures. Use common pro­grams such as MS Excel or similar to prepare figures (graphs) and ChemDraw to prepare structures in their final size. Width of graphs in the manuscript should be 8 cm. Only in special cases (in case of numerous data, visibility issues) graphs can be 17 cm wide. All graphs in the manuscript should be inserted in relevant places and aligned left. The same graphs should be provid­ed separately as images of appropriate resolution (see below) and submitted together in a ZIP file (Graphics ZIP). Please do not submit figures as a Word file. In graphs, only the graph area determined by both axes should be in the frame, while a frame around the whole graph should be omitted. The graph area should be white. The legend should be inside the graph area. The style of all graphs should be the same. Figures and illustrations should be of sufcient quality for the printed version, i.e. 300 dpi minimum. Digital images and photographs should be of high quality (minimum 250 dpi resolution). On submission, figures should be of good enough resolution to be assessed by the refer­ ees, ideally as JPEGs. High­resolution figures (in JPEG, TIFF, or EPS format) might be required if the paper is accepted for publication. Tables should be prepared in the Word file of the pa­per as usual Word tables. The captions should appear above the table and should be self-explanatory. References should be numbered and ordered se­quentially as they appear in the text, likewise meth­ods, tables, figure captions. When cited in the text, reference numbers should be superscripted, follow­ing punctuation marks. It is the sole responsibility of authors to cite articles that have been submitted to a journal or were in print at the time of submission to ACSi. Formatting of references to published work should follow the journal style; please also consult a recent issue: 1. J. W. Smith, A. G. White, Acta Chim. Slov. 2008, 55, 1055–1059. 2. M. F. Kemmere, T. F. Keurentjes, in: S. P. Nunes, K. V. Peinemann (Ed.): Membrane Technology in the Chemical Industry, Wiley­VCH, Weinheim, Ger­many, 2008, pp. 229–255. 3. J. Levec, Arrangement and process for oxidizing an aqueous medium, US Patent Number 5,928,521, date of patent July 27, 1999. 4. L. A. Bursill, J. M. Thomas, in: R. Sersale, C. Collela, R. Aiello (Eds.), Recent Progress Report and Discus­sions: 5th International Zeolite Conference, Naples, Italy, 1980, Gianini, Naples, 1981, pp. 25–30. 5. J. Szegezdi, F. Csizmadia, Prediction of dissociation constant using microconstants, http://www. che­maxon.com/conf/Prediction_of_dissociation _con­stant_using_microco nstants.pdf, (assessed: March 31, 2008) Titles of journals should be abbreviated according to Chemical Abstracts Service Source Index (CASSI). Special Notes • Complete characterization, including crystal structure, should be given when the synthesis of new compounds in crystal form is reported.  Numerical data should be reported with the number of significant digits corresponding to the magnitude of experimental uncertainty.  The SI system of units and IUPAC recommen­dations for nomenclature, symbols and abbrevia­tions should be followed closely. Additionally, the authors should follow the general guidelines when citing spectral and analytical data, and depositing crystallographic data.  Characters should be correctly represented throughout the manuscript: for example, 1 (one) and l (ell), 0 (zero) and O (oh), x (ex), D7 (times sign), B0 (degree sign). Use Symbol font for all Greek letters and mathematical symbols.  The rules and recommendations of the IUBMB and the International Union of Pure and Applied Chemistry (IUPAC) should be used for abbreviation of chemical names, nomenclature of chemical com­pounds, enzyme nomenclature, isotopic compounds, optically active isomers, and spectroscopic data.  A confict of interest occurs when an individual (author, reviewer, editor) or its organization is in­volved in multiple interests, one of which could pos­sibly corrupt the motivation for an act in the other. Financial relationships are the most easily identifi­able conflicts of interest, while conflicts can occur also as personal relationships, academic competi­tion, etc. The Editors will make effort to ensure that conflicts of interest will not compromise the evaluation process; potential editors and reviewers will be asked to exempt themselves from review process when such conflict of interest exists. When the manuscript is submitted for publication, the authors are expected to disclose any relationships that might pose potential conflict of interest with respect to results reported in that manuscript. In the Acknowledgement section the source of fund­ing support should be mentioned. The statement of disclosure must be provided as Comments to Editor during the submission process.  Published statement of Informed Consent. Research described in papers submitted to ACSi must adhere to the principles of the Declaration of Helsinki (http://www.wma.net/e/policy/ b3.htm). These studies must be approved by an appropriate institutional review board or commit­tee, and informed consent must be obtained from subjects. The Methods section of the paper must include: 1) a statement of protocol approval from an institutional review board or committee and 2), a statement that informed consent was obtained from the human subjects or their representatives.  Published Statement of Human and Animal Rights.When reporting experiments on human subjects, authors should indicate whether the procedures followed were in accordance with the ethical standards of the responsible committee on human experimentation (institutional and na­tional) and with the Helsinki Declaration of 1975, as revised in 2008. If doubt exists whether the research was conducted in accordance with the Helsinki Declaration, the authors must explain the rationale for their approach and demonstrate that the institutional review body explicitly ap­proved the doubtful aspects of the study. When reporting experiments on animals, authors should indicate whether the institutional and national guide for the care and use of laboratory animals was followed. • To avoid conflict of interest between authors and referees we expect that not more than one referee is from the same country as the corresponding au-thor(s), however, not from the same institution.  Contributions authored by Slovenian scientists are evaluated by non-Slovenian referees.  Papers describing microwave-assisted reac­tions performed in domestic microwave ovens are not considered for publication in Acta Chimica Slovenica.  Manuscripts that are not prepared and submit­ted in accord with the instructions for authors are not considered for publication. Appendices Authors are encouraged to make use of supporting in­formation for publication, which is supplementary ma­terial (appendices) that is submitted at the same time as the manuscript. It is made available on the Journal’s web site and is linked to the article in the Journal’s Web edition. The use of supporting information is particular­ ly appropriate for presenting additional graphs, spectra, tables and discussion and is more likely to be of interest to specialists than to general readers. When preparing supporting information, authors should keep in mind that the supporting information files will not be edited by the editorial staff. In addition, the files should be not too large (upper limit 10 MB) and should be provided in common widely known file formats to be accessible to readers without difculty. All files of supplementary materials are loaded separately during the submission process as supplementary files. Proposed Cover Picture and Graphical Abstract Image Graphical content: an ideally full-colour illustration of resolution 300 dpi from the manuscript must be proposed with the submission. Graphical abstract pic­tures are printed in size 6.5 x 4 cm (hence minimal resolution of 770 x 470 pixels). Cover picture is print­ ed in size 11 x 9.5 cm (hence minimal resolution of 1300 x 1130 pixels) Authors are encouraged to submit illustrations as can­ didates for the journal Cover Picture*. The illustration must be related to the subject matter of the paper. Usually both proposed cover picture and graphical ab­ stract are the same, but authors may provide different pictures as well. * The authors will be asked to contribute to the costs of the cover picture production. Statement of novelty Statement of novelty is provided in a Word file and submitted as a supplementary file in step 4 of sub­mission process. Authors should in no more than 100 words emphasize the scientific novelty of the present­ed research. Do not repeat for this purpose the con­tent of your abstract. List of suggested reviewers List of suggested reviewers is a Word file submitted as a supplementary file in step 4 of submission pro­cess. Authors should propose the names, full afliation (department, institution, city and country) and e­mail addresses of five potential referees. Field of expertise and at least two references relevant to the scientif­ic field of the submitted manuscript must be provid­ed for each of the suggested reviewers. The referees should be knowledgeable about the subject but have no close connection with any of the authors. In addi­ tion, referees should be from institutions other than (and countries other than) those of any of the authors. Authors declare no conflict of interest with suggested reviewers. Authors declare that suggested reviewers are experts in the field of submitted manuscript. How to Submit Users registered in the role of author can start sub­ mission by choosing USER HOME link on the top of the page, then choosing the role of the Author and follow the relevant link for starting the submission process. Prior to submission we strongly recommend that you familiarize yourself with the ACSi style by browsing the journal, particularly if you have not submitted to the ACSi before or recently. Correspondence All correspondence with the ACSi editor regarding the paper goes through this web site and emails. Emails are sent and recorded in the web site database. In the correspondence with the editorial ofce please provide ID number of your manuscript. All emails you receive from the system contain relevant links. Please do not answer the emails directly but use the embed­ded links in the emails for carrying out relevant actions. Alternatively, you can carry out all the ac­tions and correspondence through the online system by logging in and selecting relevant options. Proofs Proofs will be dispatched via e-mail and corrections should be returned to the editor by e­mail as quick­ly as possible, normally within 48 hours of receipt. Typing errors should be corrected; other changes of contents will be treated as new submissions. Submission Preparation Checklist As part of the submission process, authors are required to check off their submission’s compliance with all of the following items, and submissions may be returned to authors that do not adhere to these guidelines. 1. The submission has not been previously published, nor is it under consideration for publication in any other journal (or an explanation has been provid­ed in Comments to the Editor). 2. All the listed authors have agreed on the content and the corresponding (submitting) author is re­sponsible for having ensured that this agreement has been reached. 3. The submission files are in the correct format: manuscript is created in MS Word but will be sub­mitted in PDF (for reviewers) as well as in orig­ inal MS Word format (as a supplementary file for technical editing); diagrams and graphs are cre­ ated in Excel and saved in one of the file formats: TIFF, EPS or JPG; illustrations are also saved in one of these formats. The preferred position of graphic files in a document is to embed them close to the place where they are mentioned in the text (See Author guidelines for details). 4. The manuscript has been examined for spelling and grammar (spell checked). 5. The title (maximum 150 characters) briefly ex­plains the contents of the manuscript. 6. Full names (first and last) of all authors together with the afliation address are provided. Name of author(s) denoted as the corresponding author(s), together with their e­mail address, full postal ad­dress and telephone/fax numbers are given. 7. The abstract states the objective and conclu­sions of the research concisely in no more than 150 words. 8. Keywords (minimum three, maximum six) are provided. 9. Statement of novelty (maximum 100 words) clearly explaining new findings reported in the manuscript should be prepared as a separate Word file. 10. The text adheres to the stylistic and bibliographic requirements outlined in the Author guidelines. 11. Text in normal style is set to single column, 1.5 line spacing, and 12 pt. Times New Roman font is recommended. All tables, figures and illustrations have appropriate captions and are placed within the text at the appropriate points. 12. Mathematical and chemical equations are provided in separate lines and numbered (Arabic numbers) consecutively in parenthesis at the end of the line. All equation numbers are (if necessary) appropri­ately included in the text. Corresponding numbers are checked. 13. Tables, Figures, illustrations, are prepared in cor­rect format and resolution (see Author guideli­nes). 14. The lettering used in the figures and graphs do not vary greatly in size. The recommended lettering size is 8 point Arial. 15. Separate files for each figure and illustration are prepared. The names (numbers) of the separate files are the same as they appear in the text. All the figure files are packed for uploading in a single ZIP file. 16. Authors have read special notes and have accor­dingly prepared their manuscript (if necessary). 17. References in the text and in the References are correctly cited. (see Author guidelines). All ref­erences mentioned in the Reference list are cited in the text, and vice versa. 18. Permission has been obtained for use of copy­righted material from other sources (including the Web). 19. The names, full afliation (department, institution, city and country), e­mail addresses and referenc­es of five potential referees from institutions other than (and countries other than) those of any of the authors are prepared in the word file. At least two relevant references (important recent papers with high impact factor, head positions of departments, labs, research groups, etc.) for each suggested re­viewer must be provided. Authors declare no con­flict of interest with suggested reviewers. Authors declare that suggested reviewers are experts in the field of submitted manuscript. 20. Full-colour illustration or graph from the manu­script is proposed for graphical abstract. 21. Appendices (if appropriate) as supplementary material are prepared and will be submitted at the same time as the manuscript. 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ActaChimicaSlovenica ActaChimicaSlovenica Soft hydrophilic nano and microgels are attractive polymer constructs with several potential applications; they are deformable and, in some cases, biocompatible. Gel particles prepared of a thermoresponsive polymer either swell or collapse with changing temperature. Poly(N­vinylcaprolactam) gel particles may be synthesized through precipitation polymerization or (mini)emulsion polymerization. A new approach is the polymerization induced self-assembly (page 251). Year 2022, Vol. 69, No. 2