Chemical Sciences for the New Decade. Volume 3: Computational, Education, and Materials Science Aspects [3] 9783110783599

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Ponnadurai Ramasami (Ed.) Chemical Sciences for the New Decade

Also of interest Chemical Sciences for the New Decade Volume : Organic and Natural Product Synthesis Ponnadurai Ramasami (Ed.),  ISBN ----, e-ISBN ----

Chemical Sciences for the New Decade Volume : Biochemical and Environmental Applications Ponnadurai Ramasami (Ed.),  ISBN ----, e-ISBN ----

Chemical Sciences in the Focus Volume : Pharmaceutical Applications Ponnadurai Ramasami (Ed.),  ISBN ----, e-ISBN ----

Chemical Sciences in the Focus Volume : Green and Sustainable Processing Ponnadurai Ramasami (Ed.),  ISBN ----, e-ISBN ----

Chemical Sciences in the Focus Volume : Theoretical and Computational Chemistry Aspects Ponnadurai Ramasami (Ed.),  ISBN ----, e-ISBN ----

Physical Sciences Reviews e-ISSN -X

Chemical Sciences for the New Decade Volume 3: Computational, Education, and Materials Science Aspects Edited by Ponnadurai Ramasami

Editor Prof. Dr. Ponnadurai Ramasami Computational Chemistry Group, Department of Chemistry, Faculty of Science, University of Mauritius, Réduit 80837, Mauritius E-mail: [email protected]

ISBN 978-3-11-078359-9 e-ISBN (PDF) 978-3-11-078364-3 e-ISBN (EPUB) 978-3-11-078368-1 Library of Congress Control Number: 2022940908 Bibliographic information published by the Deutsche Nationalbibliothek The Deutsche Nationalbibliothek lists this publication in the Deutsche Nationalbibliografie; detailed bibliographic data are available on the internet at http://dnb.dnb.de. © 2022 Walter de Gruyter GmbH, Berlin/Boston Cover image: sorbetto/DigitalVision Vectors/Getty Images Typesetting: TNQ Technologies Pvt. Ltd. Printing and binding: CPI books GmbH, Leck www.degruyter.com

Preface of the Book of Proceedings of the Virtual Conference on Chemistry and its Applications (VCCA-2021). A virtual conference on chemistry and its applications (VCCA-2021) was organized online from 9th to 13th August 2021. The theme of the virtual conference was “Chemical Sciences for the New Decade”. There were 197 presentations for the virtual conference with 400 participants from 53 countries. A secured platform was used for virtual interactions of the participants. After the virtual conference, there was a call for full papers to be considered for publication in the conference proceedings. Manuscripts were received and they were processed and reviewed as per the policy of De Gruyter. This book, volume 3, is a collection of the fourteen accepted manuscripts covering computational, education, and materials science aspects. Korkmaz et al. synthesised, characterized propan-2-one O-((2,5-dichlorophenyl) sulfonyl) oxime and complemented the experimental work using molecular docking. Eno and co-workers investigated on the photovoltaic properties of novel reactive azobenzoquinoline using experimental and theoretical methods. Chinda and Chinda examined the awareness level of social networking site and how it was applied for learning in an era of Corona virus by chemistry students in tertiary institutions in Rivers State Nigeria. A conversation on the quartic equation of the secular determinant of methylenecyclopropene was summarised by Ramasami. Tetteh focused on the modification of kaolinite/muscovite clay for the removal of Pb(II) ions from aqueous media. Lim and Choong designed molecules that could be potentially use to prevent the recognition of SARS-CoV-2 RBM with hACE2. Das et al. conducted a computational study of CunAgAu (n=1-4) clusters and used the DFT based descriptors for analysis and discussion. Abdul-Hammed and co-workers studied the anti-breast cancer activities of isolated phytochemicals present in Annona muricata using a computer-aided drug design approach with a view in identifying lead compounds that could be developed into useful drugs. Martinez et al. discussed the development of an online assessment system to evaluate knowledge on chemical safety and security. Ouma optimized the Cr(VI) adsorption parameters on magnetite (Fe3O4) and manganese doped magnetite (MnxFe(3-x)O4) nanoparticles. Mammino elaborated on the spontaneity of chemical reactions with a focus on the challenges with handling the concept and its implications. Mammino also studied the conformational preferences and intramolecular hydrogen bonding patterns of tetraflavaspidic acid which is a tetrameric acylphloroglucinol. Louis and co-workers synthesised and characterised, 4-chloro-6(phynylamino)-1,3,5-triazin-2-yl)amino-4-(2,4-dichlorophynyl)thiazol-5-yl-diazenyl)phynyl as potential SARS-CoV-2 agent and they also used theoretical methods. Narod and

https://doi.org/10.1515/9783110783643-201

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Preface

Narrainsawmy provided the educators’ reflections on the teaching and learning of theperiodic table of elements at the upper secondary level. I hope that these chapters of this volume 3 will add to literature and they will be useful references for researchers. To conclude, VCCA-2021 was a successful event and I would like to thank all those who have contributed. I would also like to thank the Organising and International Advisory committee members, the participants and the reviewers. Prof. Ponnadurai Ramasami Computational Chemistry Group, Department of Chemistry, Faculty of Science, University of Mauritius, Réduit 80837, Mauritius E-mail: [email protected]

Contents Preface V List of contributing authors

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Adem Korkmaz, Lydia Rhyman and Ponnadurai Ramasami 1 Synthesis, characterization, DFT and molecular docking studies of acetone 1 O-((2,5-dichlorophenyl)sulfonyl) oxime 1 1.1 Introduction 2 1.2 Materials and methods 2 1.2.1 General 1.2.2 The synthesis of the acetone O-((2,5-dichlorophenyl)sulfonyl) 2 oxime 3 1.2.3 DFT study 3 1.2.4 Molecular docking study 4 1.2.5 ADME study 4 1.3 Results and discussion 8 1.4 Conclusions 10 References Ededet A. Eno, Hitler Louis, Tomsmith O. Unimuke, Ernest C. Agwamba, Anita T. Etim, Justina I. Mbonu, Henry O. Edet, ThankGod Egemoye, Kayode A. Adegoke and Umar S. Ameuru 2 Photovoltaic properties of novel reactive azobenzoquinolines: experimental 13 and theoretical investigations 14 2.1 Introduction 15 2.2 Experimental 16 2.2.1 Computational details 17 2.3 Results and discussion 17 2.3.1 Synthetic Aspects 19 2.3.2 Quantum descriptors frontier molecular orbital (FMO) 2.3.3 Density of states (DOS) and energy gap of dye and titanium oxide 21 (TiO2) 23 2.3.4 Molecular orbital composition analysis 24 2.3.5 Hole electron analysis 30 2.4 Conclusions 31 References

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Chinda Worokwu and Kechinyere Chinda 3 Social media and learning in an era of coronavirus among chemistry students 37 in tertiary institutions in Rivers State 38 3.1 Introduction 39 3.2 Types of learning 3.2.1 Factors militating the use of social media in teaching and learning during 42 the (COVID-19) 43 3.2.2 Shift from classroom learning to virtual learning 43 3.3 Finance 43 3.4 Mental health 44 3.5 Assessment and evaluation 44 3.5.1 Review of empirical literature 45 3.6 Statement of the problem 45 3.7 Aim of the study 46 3.8 Research questions 46 3.9 Hypotheses 46 3.10 Methodology 47 3.11 Results 49 3.12 Discussion 51 3.13 Conclusion 51 3.14 Suggestions 52 References Ponnadurai Ramasami 4 A conversation on the quartic equation of the secular determinant of 55 methylenecyclopropene 55 4.1 Background 56 4.2 Act 1 57 4.3 Act 2 63 4.4 Conclusions 63 References Samuel Tetteh, Albert Ofori, Andrew Quashie, Sirpa Jääskeläinen and Sari Suvanto 5 Modification of kaolinite/muscovite clay for the removal of Pb(II) ions from 65 aqueous media 65 5.1 Introduction 66 5.2 Materials and methods 66 5.2.1 Materials 67 5.2.2 Activation of the clay samples 67 5.2.3 Characterization 67 5.2.4 Adsorption studies

Contents

5.3 5.3.1 5.3.2 5.3.3 5.3.4 5.3.5 5.4

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Results and discussion 67 67 Characterization 70 FTIR Spectroscopy 72 Adsorption kinetics 75 Adsorption isotherm 78 Adsorption kinetics 79 Conclusions 79 References

Theam Soon Lim and Yee Siew Choong 6 In silico design of ACE2 mutants for competitive binding of SARS-CoV-2 83 receptor binding domain with hACE2 83 6.1 Introduction 84 6.2 Methodology 85 6.3 Results and discussion 89 6.4 Conclusions 89 References Shayeri Das, Prabhat Ranjan and Tanmoy Chakraborty 7 Computational study of CunAgAu (n = 1–4) clusters invoking DFT based 93 descriptors 93 7.1 Introduction 95 7.2 Computational details 96 7.3 Results and discussion 96 7.3.1 Electronic properties and DFT based descriptors 98 7.4 Conclusions 98 References Misbaudeen Abdul-Hammed, Ibrahim Olaide Adedotun, Karimot Motunrayo Mufutau, Bamidele Toheeb Towolawi, Tolulope Irapada Afolabi and Christianah Otoame Irabor 8 Antibreast cancer activities of phytochemicals from Anonna muricata using 103 computer-aided drug design (CADD) approach 103 8.1 Introduction 105 8.2 Materials and methods 105 8.2.1 Preparation of target receptor 105 8.2.2 Identification and validation of active site 105 8.2.3 Preparation of ligands and geometric optimization 105 8.2.4 Analyses of drug-like compounds and ADMET profiling 8.2.5 Oral bioavailability screening and prediction of activity spectra for 106 substances (PASS) 106 8.2.6 Molecular docking protocol

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8.3 8.3.1 8.3.2 8.3.3 8.3.4 8.3.5 8.3.6 8.4

Contents

Results and discussion 106 106 Macromolecular target elucidation and active-site validation Analyses of absorption, distribution, metabolism, excretion, and toxicity 107 (ADMET) of studied compounds 113 Drug-likeness analyses Molecular modeling analysis (molecular docking and molecular 113 interactions) 117 Oral-bioavailability radar analyses 118 Prediction of biological activities of the selected compounds 119 Conclusions 119 References

Imee Su Martinez, Daniel Ashok Maria Innasi and Rohan P. Perera 9 Development of an online assessment system to evaluate knowledge on 123 chemical safety and security 123 9.1 Introduction 123 9.1.1 Chemical safety and security 124 9.1.2 The role of the OPCW in chemical safety and security 9.1.3 The significance of an e-based questionnaire in chemical safety and 125 security monitoring 9.1.4 Brief background of web-based chemical safety and security 125 exams 127 9.2 Methodology 127 9.2.1 Developing the software 128 9.2.2 Scripting language 129 9.2.3 Creating the question pool or database 131 9.3 Results and analyses 9.3.1 Description of the developed eQchemSS program and installation in the 131 OPCW website 133 9.3.2 User feedback 134 9.4 Conclusions 135 References Linda Ouma, Agnes Pholosi and Martin Onani 10 Optimizing Cr(VI) adsorption parameters on magnetite (Fe3O4) and manganese 137 doped magnetite (MnxFe(3-x)O4) nanoparticles 137 10.1 Introduction 139 10.2 Experimental 139 10.2.1 Materials 139 10.2.2 Methods 140 10.3 Results and discussions

Contents

10.3.1 10.3.2 10.4

Characterization 140 Adsorption optimization 145 Conclusions 146 References

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Liliana Mammino 11 The spontaneity of chemical reactions: challenges with handling the concept 149 and its implications 149 11.1 Introduction 149 11.1.1 The perception of physical chemistry as a difficult area 151 11.1.2 Key steps in the presentation of the spontaneity concept 152 11.2 The context and the approaches 157 11.3 The spontaneity concept in chemical thermodynamics 157 11.3.1 Defining the concept and providing illustrative examples 160 11.3.2 Entropy – an elusive physical quantity? 162 11.3.3 The spontaneity criteria 163 11.4 The spontaneity concept in electrochemistry 163 11.4.1 Definitions and their implications 166 11.4.2 The interpretation of experimental information 168 11.5 Discussion and conclusions 171 References Liliana Mammino 12 Conformational preferences and intramolecular hydrogen bonding patterns 175 of tetraflavaspidic acid BBBB – a tetrameric acylphloroglucinol 175 12.1 Introduction 178 12.2 Computational details 178 12.3 Results and analyses 178 12.3.1 Atom numbering and naming of conformers 185 12.3.2 Conformational preferences and energetics 188 12.3.3 Characteristics of the intramolecular hydrogen bonds 193 12.3.4 Other molecular properties 193 12.4 Discussion and conclusions 194 References Ededet A. Eno, Hitler Louis, Tomsmith O. Unimuke, ThankGod C. Egemonye, Stephen A. Adalikwu, John A. Agwupuye, Diana O. Odey, Abu Solomon Abu, Ishegbe J. Eko, Chukwudubem E. Ifeatu and Tabe N. Ntui 13 Synthesis, characterization, and theoretical investigation of 4-chloro6(phenylamino)-1,3,5-triazin-2-yl)asmino-4-(2,4-dichlorophenyl)thiazol197 5-yl-diazenyl)phenyl as potential SARS-CoV-2 agent 198 13.1 Introduction 199 13.2 Experimental and computational details

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13.2.1 13.2.2 13.3 13.3.1 13.3.2 13.3.3 13.3.4 13.3.5 13.3.6 13.3.7 13.3.8 13.4

Contents

Experimental 199 200 Computational details 200 Result and discussion 200 Quantum chemical descriptors 202 Aromaticity index 204 Conceptual density functional theory (CDFT) 205 Natural bond orbital (NBO) Analysis Atomic dipole moment corrected Hirshfeld (ADCH) charge 206 NMR analysis 207 Theoretical ADMET prediction (PKSCM) 209 Molecular docking 211 Conclusions 212 References

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Fawzia Narod and Vickren Narrainsawmy 14 Educators’ reflections on the teaching and learning of the periodic table of 217 elements at the upper secondary level: a case study 217 14.1 Introduction 218 14.2 Context 219 14.3 Rationale 219 14.4 Aims of the study 220 14.5 Research questions 220 14.5.1 Sub-questions 220 14.6 Literature review 221 14.7 Methodology 221 14.7.1 Research design 222 14.7.2 Sampling and participants 222 14.7.3 Data collection 222 14.7.4 Analysis of data 222 14.8 Results and discussions 227 14.9 Conclusions 228 14.10 Recommendations 229 References Index

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List of contributing authors M. Abdul-Hammed Computational Biophysical Chemistry Laboratory Department of Pure and Applied Chemistry Ladoke Akintola University of Technology P.M.B. 4000 Ogbomoso Nigeria E-mail: [email protected] Abu Solomon Abu Computational and Bio-Simulation Research Group and Department of Marine Biology Faculty of Biology Sciences University of Calabar Calabar Nigeria Stephen A. Adalikwu Computational and Bio-Simulation Research Group University of Calabar Calabar Nigeria Ibrahim Olaide Adedotun Computational Biophysical Chemistry Laboratory Department of Pure and Applied Chemistry Ladoke Akintola University of Technology P.M.B. 4000 Ogbomoso Nigeria Kayode A. Adegoke Department of Chemical Sciences University of Johannesburg Johannesburg South Africa Tolulope Irapada Afolabi Computational Biophysical Chemistry Laboratory Department of Pure and Applied Chemistry Ladoke Akintola University of Technology P.M.B. 4000 Ogbomoso Nigeria

https://doi.org/10.1515/9783110783643-202

Ernest C. Agwamba Computational and Bio-Simulation Research Group University of Calabar Calabar Nigeria; and Department of Chemical Sciences Clifford University Owerrinta Abia State Nigeria John A. Agwupuye Computational and Bio-Simulation Research Group and Department of Pure and Applied Chemistry Faculty of Physical Sciences University of Calabar Calabar Nigeria Umar S. Ameuru Department of polymer and Textile Engineering Ahmadu Bello University Zaria Nigeria Tanmoy Chakraborty Department of Chemistry and Biochemistry School of Basic Sciences and Research Sharda University Greater Noida 201310 India E-mail: [email protected] Kechinyere Chinda Department of Educational Foundation Faculty of Education Rivers State University Nkpolu Oroworukwo Port Harcourt Rivers State Nigeria

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Yee Siew Choong Institute for Research in Molecular Medicine Universiti Sains Malaysia Penang Malaysia E-mail: [email protected]

Chukwudubem E. Ifeatu Computational and Bio-Simulation Research Group University of Calabar Calabar Nigeria

Shayeri Das Department of Mechatronics Engineering Manipal University Jaipur Dehmi Kalan 303007 India and Department of Electrical Engineering Ideal Institute of Engineering Kalyani, Nadia West Bengal, 741235 India

Daniel Ashok Maria Innasi National Authority for the Implementation of Chemical Weapons Convention in Sri Lanka Ministry of Industry and Commerce No. 73/1, Galle Road Colombo Sri Lanka

Henry O. Edet Computational and Bio-Simulation Research Group University of Calabar Calabar Nigeria ThankGod Egemoye Computational and Bio-Simulation Research Group University of Calabar Calabar Nigeria Ishegbe J. Eko Department of Polymer and Textile Engineering Ahmadu Bello University Zaria Kaduna Nigeria Ededet A. Eno Department of Pure and Applied Chemistry University of Calabar Calabar Nigeria Anita T. Etim Computational and Bio-Simulation Research Group University of Calabar Calabar Nigeria

Christianah Otoame Irabor Computational Biophysical Chemistry Laboratory Department of Pure and Applied Chemistry Ladoke Akintola University of Technology P.M.B. 4000 Ogbomoso Nigeria Sirpa Jääskeläinen Department of Chemistry University of Eastern Finland P. O. Box 111 Fi-80101 Joensuu Finland Adem Korkmaz Faculty of Health Science MuşAlparslan University Mush Turkey E-mail: [email protected] Theam Soon Lim Institute for Research in Molecular Medicine Universiti Sains Malaysia Penang Malaysia Hitler Louis Department of Pure and Applied Chemistry University of Calabar Calabar Nigeria E-mail: [email protected]

List of contributing authors

Liliana Mammino University of Venda Thohoyandou South Africa E-mail: [email protected] Imee Su Martinez Institute of Chemistry National Science Complex University of the Philippines-Diliman Quezon City, 2100 Philippines E-mail: [email protected] Justina I. Mbonu Department of Chemistry Federal University of Petroleum Resources Efurun Efurun Delta State Nigeria Karimot Motunrayo Mufutau Computational Biophysical Chemistry Laboratory Department of Pure and Applied Chemistry Ladoke Akintola University of Technology P.M.B. 4000 Ogbomoso Nigeria Fawzia Narod Department of Science Education Mauritius Institute of Education Réduit Mauritius E-mail: [email protected] Vickren Narrainsawmy Department of Science Education Mauritius Institute of Education Réduit Mauritius Tabe N. Ntui Computational and Bio-Simulation Research Group University of Calabar and Department of Chemistry Faculty of Physical Sciences Cross River University of Technology Calabar Nigeria

Diana O. Odey Computational and Bio-Simulation Research Group University of Calabar and Department of Biochemistry Faculty of Physical Sciences Cross River University of Technology Calabar Nigeria Albert Ofori Department of Chemistry School of Physical Sciences University of Cape Coast Cape Coast Ghana Martin Onani Department of Chemistry University of the Western Cape Private Bag X17 Bellville 7535 South Africa Linda Ouma Department of Chemistry University of the Western Cape Private Bag X17 Bellville 7535 South Africa; and Department of Science Technology and Engineering Kibabii University P. O. Box 1699 Bungoma 50200 Kenya E-mail: [email protected] Rohan P. Perera Organization for the Prohibition of Chemical Weapons Johan de Witlaan 32, 2517 JR The Hague The Netherlands

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Agnes Pholosi Department of Chemistry Vaal University of Technology Private Bag X021 Vanderbijlpark 1900 South Africa

Sari Suvanto Department of Chemistry University of Eastern Finland P. O. Box 111 Fi-80101 Joensuu Finland

Andrew Quashie Sanitation Environmental Management Division Institute of Industrial Research C.S.I.R. Cape Coast Ghana

Samuel Tetteh Department of Chemistry School of Physical Sciences University of Cape Coast Cape Coast Ghana E-mail: [email protected]

Ponnadurai Ramasami Department of Chemistry Computational Chemistry Group Faculty of Science University of Mauritius Reduit 80837 Mauritius; and Department of Chemical Sciences Center for Natural Product Research University of Johannesburg Doornfontein Campus Johannesburg 2028 South Africa Prabhat Ranjan Department of Mechatronics Engineering Manipal University Jaipur Dehmi Kalan 303007 India E-mail: [email protected] Lydia Rhyman Department of Chemistry Computational Chemistry Group Faculty of Science University of Mauritius Reduit 80837 Mauritius; and Department of Chemical Sciences Center for Natural Product Research University of Johannesburg Doornfontein Campus Johannesburg 2028 South Africa

Bamidele Toheeb Towolawi Computational Biophysical Chemistry Laboratory Department of Pure and Applied Chemistry Ladoke Akintola University of Technology P.M.B. 4000 Ogbomoso Nigeria Tomsmith O. Unimuke Computational and Bio-Simulation Research Group University of Calabar Calabar Nigeria Chinda Worokwu Department of Chemistry Faculty of Natural and Applied Sciences Ignatius Ajuru University of Education P.M.B 5047 Rumuolumeni Port Harcourt Rivers State Nigeria E-mail: [email protected]

Adem Korkmaz*, Lydia Rhyman and Ponnadurai Ramasami

1 Synthesis, characterization, DFT and molecular docking studies of acetone O-((2,5-dichlorophenyl)sulfonyl) oxime Abstract: Acetone O-((2,5-dichlorophenyl)sulfonyl) oxime was prepared from 2,5-dichlorophenylsulfonyl chloride and acetone oxime using triethylamine. The compound was characterized using 1H NMR and 13C NMR spectra. Molecular docking was performed with the compound and cholinesterase enzymes. The average affinity of the compound with the acetylcholinesterase and butyrylcholinesterase was calculated at −7.46 ± 0.14 and −6.70 ± 0.00 kcal/mol, respectively. The density functional theory method was also used to complement the experimental study. The findings of this work might be useful towards the applications of the compound studied. Keywords: acetylcholinesterase; ADME; arylsulfonate; butyrylcholinesterase; DFT; molecular docking; oxime.

1.1 Introduction It is known that degenerative Alzheimer’s disease (AD) causes unconsciousness [1–6]. AD causes cognitive decline in the brain [7–9]. This common neurodegenerative disease affects many elderly population, and this number is expected to increase significantly as the aging human body weakens the immune system [10–12]. Acetylcholine, a neurotransmitter, is metabolically broken down by acetylcholinesterase [13–15]. Therefore, acetylcholinesterase inhibitors (AChEIs), which increases the level of acetylcholine, are often administered for the prevention of AD [16, 17]. One of the current treatment methods is to increase or restore the acetylcholine level. AChE inhibitors (galantamine, donepezil, and rivastigmine) have been used treatment of AD [18–20]. It is known that these drugs do not cure AD completely [21–23]. These drugs are used to stop the symptoms due to AD or to ensure that AD does not get worse [24, 25]. Therefore, researchers are working towards new compounds to prevent the initial progression of AD [26, 27].

*Corresponding author: Adem Korkmaz, Faculty of Health Science, MuşAlparslan University, Mush, Turkey, E-mail: [email protected]. https://orcid.org/0000-0002-0345-5794 Lydia Rhyman and Ponnadurai Ramasami, Department of Chemistry, Computational Chemistry Group, Faculty of Science, University of Mauritius, Reduit 80837, Mauritius; and Department of Chemical Sciences, Center for Natural Product Research, University of Johannesburg, Doornfontein Campus, Johannesburg 2028, South Africa As per De Gruyter’s policy this article has previously been published in the journal Physical Sciences Reviews. Please cite as: A. Korkmaz, L. Rhyman and P. Ramasami “Synthesis, characterization, DFT and molecular docking studies of acetone O-((2,5-dichlorophenyl)sulfonyl) oxime” Physical Sciences Reviews [Online] 2022. DOI: 10.1515/psr-2021-0230 | https:// doi.org/10.1515/9783110783643-001

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1 Cholinesterase inhibitory effect of the oxime by in silico application

Gwaltney et al. synthesized new sulfonate analogs and reported on their biological evaluation [28]. These compounds have been proven to be potent inhibitors of cell proliferation and tubulin polymerization. It was investigated naphtho[2,3-b]thiophen4(9H)-one and 9(10H)-anthracenone derivatives relating inhibition microtubule formation with in vitro tubulin polymerization by Zuse et al. [29]. Also, the coumarin sulfonate derivatives were performed for in vitro antiproliferative activities [30]. In another investigation, raloxifene sulfonate/sulfamate derivatives were evaluated to inhibition effect for nucleotide pyrophosphatase/phosphodiesterase-1 and -3 enzymes [31]. In a recent study, sulfonate derivatives were implemented for the inhibitory effect of nucleotide pyrophosphatases [32]. Su et al. designed myricetin derivatives containing sulfonate groups and studied their antibacterial properties [33]. In view of the above and the interests of some of us to study the molecular parameters of compounds [34–38], acetone O-((2,5-dichlorophenyl)sulfonyl) oxime was synthesized and characterized by using 1H NMR and 13C NMR. The molecular docking studies of the acetone O-((2,5-dichlorophenyl)sulfonyl) oxime and cholinesterase enzymes were carried out. In addition, we complemented the experimental work using ADME parameters and density functional theory (DFT) method. We, hereby, report our research findings.

1.2 Materials and methods 1.2.1 General The thermo scientific melting point was used to determine the melting point. The NMR spectrum was recorded with the Bruker DRX-400 spectrometer. Acetone oxime (98%), 2,5-dichlorobenzenesulphonyl chloride (98%), and triethylamine (TEA) (>99.5%) were used (Sigma-Aldrich, Merck). N,NDimethylformamide (DMF) was processed through 4Ǻ molecular sieve and vacuumed before use.

1.2.2 The synthesis of the acetone O-((2,5-dichlorophenyl)sulfonyl) oxime Acetone O-((2,5-dichlorophenyl)sulfonyl) oxime was obtained utilizing procedure available in the literature [39]. DMF (2 mL), acetonoxime (3.425 mmol), and triethylamine (TEA) (3.425 mmol) were placed in a 150 mL flask. The reaction vessel was dipped in an ice bath. 2,5-dichlorobenzenesulfonyl chloride (3.425 mmol) was carefully added to the reaction vessel equipped with a magnetic stirrer and stirring was done for 5–10 min. The product formation was monitored by thin layer chromatography (TLC). The reaction (Figure 1.1) was terminated at 60 min and 10 mL of ice water was added. It was observed that a white suspension solid was formed. The resulting product was vacuum filtered. Later, it was performed by drying in a desiccator. The resulting compound was crystallized from a mixture hexane:benzene (5:1). Yield: 58%; m.p.: 97–98 °C; 1H NMR spectrum (CDCl3), (ppm): 8.14 (d, J = 2.3 Hz, 1H, Ar-C-H), 7.57–7.48 (m, 2H, two units of Ar-C-H), 2.10 (s, 3H, –N=C(CH3) (CH3)), 1.95 (s, 3H, –N=C(CH3) (CH3)); 13C NMR spectrum (CDCl3), (ppm):166.18 (–N=C(CH3) (CH3)), 135.36 (Ar-C), 134.57 (Ar-C), 133.19 (Ar-C), 132.92 (Ar-C), 132.57 (Ar-C), 131.11 (Ar-C), 21.57 (–N=C(CH3) (CH3)), 17.12 (–N=C(CH3) (CH3)).

1.2 Materials and methods

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Figure 1.1: The synthetic route of the acetone O-((2,5-dichlorophenyl)sulfonyl) oxime.

1.2.3 DFT study Acetone O-((2,5-dichlorophenyl)sulfonyl) oxime was fully optimized in the gas phase using the DFT method employing the M06-2X functional [40] and the 6–311++G(d,p) basis set was used for all the atoms [41] in the gas phase. The optimization was followed by frequency computations using the same method and the structure was confirmed as a ground state by the absence of imaginary frequency. The highest occupied molecular orbital (HOMO) and lowest unoccupied molecular orbital (LUMO) energy values and HOMO-LUMO energy gap were also calculated. In order to compute the chemical shifts of acetone O-((2,5-dichlorophenyl)sulfonyl) oxime, the compound was optimized in chloroform using the M06-2X/6–311++G(d,p) method based on the polarizable continuum model (PCM) [42, 43]. The GaugeIncluding Atomic Orbital method [44] using the computed isotropic shieldings of the optimized TMS was then used to compute the chemical shifts of the optimized geometry in chloroform. All computations were carried out on Gaussian 16 software [45].

1.2.4 Molecular docking study The molecular docking study was carried out utilizing a reported procedure [39]. The docking analyzes of the acetylcholinesterase and butyrylcholinesterase with acetone O-((2,5-dichlorophenyl)sulfonyl) oxime were carried out utilizing AutoDock Vina 1.5.6 and UCSF Chimera to understand the interactions [46, 47]. It was downloaded the acetylcholinesterase (PDB ID: 4EY9) and butyrylcholinesterase (PDB ID: 4BDS) enzymes from the Protein Data Bank. The enzymes were used to upload the Chimera tool and cleaned all-nonstandard structures except for A chain. The optimization of the acetone O-((2,5-dichlorophenyl)sulfonyl) oxime was calculated Avogadro software [48]. The

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1 Cholinesterase inhibitory effect of the oxime by in silico application

optimized acetone O-((2,5-dichlorophenyl)sulfonyl) was used for loading into UCSF Chimera tool. The binding pocket coordinates of the compound was performed in the acetylcholinesterase (Center x, y, z: −14.11, −43.84, 27.70, Size x, y, z: 15.66, 15.66, 15.66) and butyrylcholinesterase (Center x, y, z: 132.99, 116.02, 41.21, Size x, y, z: 11.46, 11.46, 11.46). The binding pocket coordinates were submitted for docking calculations to Chimera tool software. Biovia Discovery Studio Visualizer was used for visualized interactions and PyMOL visualization software used as describing superimpose graph [49, 50].

1.2.5 ADME study ADME study of the acetone O-((2,5-dichlorophenyl)sulfonyl) oxime was carried out using SwissADME estimator [51] and the pharmacokinetics, drug similarity, medicinal chemistry, and physicochemical properties were evaluated.

1.3 Results and discussion The co-ordinates of optimized geometry of acetone O-((2,5-dichlorophenyl)sulfonyl) oxime in gas phase and in chloroform are provided in Tables SI1 and SI2, respectively (see Supplementary Data). The bond lengths and bond angles of the compound in the gas phase are collected in Table SI3, its dipole moment is 5.73 Debye and its HOMO–LUMO gap is 7.60 eV. A detailed vibrational analysis of the compound using VEDA is provided in Table SI4. The characterization of the acetone O-((2,5-dichlorophenyl)sulfonyl) oxime was performed by 1H NMR and 13C NMR spectrum (Figure SI1). The 1H signal of the aromatic proton appeared δ = 8.14 ppm as doublet, as well as the 2H aromatic proton signal was observed from δ = 7.57 to 7.48 ppm as multiplet. The two methyls (–N=C(CH3) (CH3)) proton was observed as singlets at δ = 2.10 and 1.95 ppm, separately. In the 13C NMR spectrum, a carbon signal (–N=C(CH3) (CH3)) of the compound determined at 166.18 ppm (Figure 1.2). The aromatic carbon signals of compound demonstrated as 135.36, 134.57, 133.19, 132.92, 132.57, and 131.11 ppm, separately. The aliphatic methyl

Figure 1.2: The superimpose graph of the predicted (blue) versus co-crystallized (green) binding modes of tacrine (a) and donepezil (b).

1.3 Results and discussion

5

Table .: Computed chemical shifts of H and C (ppm) along with the optimized geometry for atom labeling.

Atom labeling         

δ H (ppm)

Atom labeling

δ C (ppm)

. . . . . . . . .

        

. . . . . . . . .

peaks (–N=C(CH3) (CH3)) were exhibited at 21.57 and 17.12 ppm. The computed chemical shifts are summarized in Table 1.1. It is worth to highlight the good comparison between the experimental and computed chemical shifts. The inhibitory activities of the acetone O-((2,5-dichlorophenyl)sulfonyl) oxime with target enzymes (4EY7 and 4BDS) using previously applied experimental processes [39]. The docking protocol was built and the alignment was carried out with the predicted and co-crystallized ligand. The co-crystallized structures used were donepezil (1-benzyl-4-[(5,6-dımethoxy-1-ındanon-2-yl)methyl]pıperıdıne) for 4EY7 and tacrine for 4BDS. The root mean square deviation (RMSD) values were observed at 1.145 Å (Angstrom) for donepezil and 0.044 Å for tacrine (Figure 1.2). The standard deviations were calculated for the molecular docking study which were repeated three times with the acetone O-((2,5-dichlorophenyl)sulfonyl) oxime and enzymes. H-bond and 2D-structure interaction poses of acetone O-((2,5-dichlorophenyl) sulfonyl) oxime and donepezil with acetylcholinesterase are represented in Figure 1.3. The interaction of the acetone O-((2,5-dichlorophenyl)sulfonyl) oxime with acetylcholinesterase depicts carbon hydrogen bonding (TRP A:83) as a hydrogen bond (Table 1.2). It is also observed the hydrophobic interactions as π–Sigma (PHE A:329), as

6

1 Cholinesterase inhibitory effect of the oxime by in silico application

Figure 1.3: The H-bond and 2D-structure interaction poses of the acetone O-((2,5-dichlorophenyl) sulfonyl) oxime and donepezil with 4EY7.

Table .: Molecular docking study of acetylcholinesterase. Compound

Binding affinity Type of (kcal/mol) interactions

Acetone O((,-dichlorophenyl)sulfonyl) oxime

−. ± . Carbon–hydrogen bond π–sigma π–π stacked π–Alkyl

Donepezil

−. ± . Attractive charge Conventional hydrogen bond Carbon–hydrogen bond π–sigma π–π stacked Alkyl π–Alkyl

Residue information TRP A: PHE A: TRP A: TRP A:; TYR A:; PHE A:; TYR A:; HIS A: ASP A: PHE A:; TYR A: PHE A:; SER A: TYR A: TRP A:; TRP A:; TYR A:; HIS A: LEU A: TYR A:; TRP A:

1.3 Results and discussion

7

π–π stacked (TRP A:83), and as π–alkyl (TRP A:83, TYR A:328, TYR A:332, PHE A:329, HIS A:438). The interactions of donepezil with acetylcholinesterase exhibit the attractive charge (ASP A:71) as an electrostatic interactions. The interactions of the conventional hydrogen (PHE A:286 and TYR A:121) and carbon-hydrogen (PHE A:286 and SER A:284) bonds are noted as hydrogen bonding. It was determined that π–sigma interaction (TYR A:332), π–π stacked interactions (TRP A:83, TRP A:277, TYR A:332, and HIS A:438), alkyl interaction (LEU A:280), and π-alkyl interactions (TYR A:69 and TRP A:277) were observed as hydrophobic effects. The amino acids interactions of the TRP A:83, TYR A:332, and HIS A:438 were observed in the acetone O-((2,5-dichlorophenyl)sulfonyl) oxime like donepezil. The amino acid interaction distance of TRP A:83 is 3.731Å for donepezil and the interaction distance TRP A:83 of the acetone O-((2,5-dichlorophenyl)sulfonyl) oximeis 4.270Å. The interaction distances of the amino acid (TRP A:83) are considered close to each other. The average binding affinity of donepezil (−11.60 ± 0.00 kcal/mol) was found to be higher than the acetone O-((2,5-dichlorophenyl)sulfonyl) oxime (−7.46 ± 0.14 kcal/mol). The average binding affinity of the tacrine (−8.10 ± 0.00 kcal/mol) with butyrylcholinesterase was calculated with a higher value than acetone O-((2,5-dichlorophenyl) sulfonyl) oxime (−6.70 ± 0.00 kcal/mol) (Table 1.3). It was observed that the interactions of the acetone O-((2,5-dichlorophenyl)sulfonyl) oxime with butyrylcholinesterase were mainly hydrogen bonds (SER A:76; TRP A:79) (Figure 1.4). The interaction of the hydrogen bond builds strong interactions of protein–ligand [52]. The hydrogen bond in acetone O((2,5-dichlorophenyl)sulfonyl) oxime makes strong interaction of butyrylcholinesterase. In addition, the interactions of the π–π stacked (TRP A:82 and HIS A:438), alkyl (ALA A:328), and π–alkyl (TRP A:82; TRP A:430) were observed between tacrine and butyrylcholinesterase. It was found that the amino acid interactions of tacrine and the acetone O-((2,5-dichlorophenyl)sulfonyl) oxime with the butyrylcholinesterase enzyme were not similar. The estimated ADME parameters of acetone O-((2,5-dichlorophenyl)sulfonyl) oxime are listed in Table 1.4. These parameters indicate that the Lipinski rules [53] are satisfied. In terms of biological properties, it is preferred that the TPSA value should

Table .: Molecular docking study of butyrylcholinesterase. Compound Acetone O-((,-dichlorophenyl) sulfonyl) oxime Tacrine

Binding affinity Type of interactions (kcal/mol) −. ± . Carbon–hydrogen bond π–π stacked π–Alkyl −. ± . π–π stacked Alkyl π–Alkyl

Residue information SER A:; TRP A: TRP A: TRP A:; PHE A:; TYR A: TRP A:; HIS A: ALA A: TRP A:; TRP A:

8

1 Cholinesterase inhibitory effect of the oxime by in silico application

Figure 1.4: The H-bond and 2D-structure interaction poses of the compound with butyrylcholinesterase.

be less than 140 Å. The topological polar surface area (TPSA) of the acetone O-((2,5-dichlorophenyl)sulfonyl) oxime (64.11 Å) was found lower than 140 Å. The consensus lipophilicity (3.04) of the compound was found lower than 5. Thus, the compound has a strong lipophilicity character. The compound has a moderate water solubility which may affect its absorption and dispersion properties. However, it has high gastrointestinal absorption, it can penetrate the blood–brain barrier and the skin permeability is −5.95 cm/s.

1.4 Conclusions To summarize, a novel acetone O-((2,5-dichlorophenyl)sulfonyl) oxime was synthesized and characterized by 1H NMR and 13C NMR. The compound was found to be effective against cholinesterase enzymes but not as effective as an inhibitor compared to standard compounds (Donepezil and tacrine). The experimental work was complemented with DFT and the estimation of AMDE parameters. It was found that the compound complies with Lipinski rule, and its TPSA value is suitable for biological properties. Therefore, the findings from the current research might be helpful towards the uses and applications of the compound investigated.

Table .: ADME prediction of the acetone O-((,-dichlorophenyl)sulfonyl) oxime. Compound Acetone O-((,-dichlorophenyl)sulfonyl) oxime

M.W.

HBA

HBD

nROTB

TPSA

GI abs

BBB

.







.

High

Yes

Consensus log p/w

Solubility

Lipinski rule

.

Moderate

Yes

TPSA, topological polar surface area; BBB, blood-brain barrier; M.W., molecular weight; HBA, hydrogen bond acceptor; HBD, hydrogen bond donor; NROTB, number of rotatable bonds; GI abs, gastrointestinal absorption.

1.4 Conclusions

9

10

1 Cholinesterase inhibitory effect of the oxime by in silico application

Acknowledgments: LR and PR are grateful to the Centre for High Performance Computing (CHPC) for computational resources.

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Supplementary Material: The online version of this article offers supplementary material (https://doi. org/10.1515/psr-2021-0230).

Ededet A. Eno, Hitler Louis*, Tomsmith O. Unimuke, Ernest C. Agwamba, Anita T. Etim, Justina I. Mbonu, Henry O. Edet, ThankGod Egemoye, Kayode A. Adegoke and Umar S. Ameuru

2 Photovoltaic properties of novel reactive azobenzoquinolines: experimental and theoretical investigations Abstract: In this work, synthesis, characterization, DFT, TD-DFT study of some novel reactive azobenzoquinoline dye structures to elucidate their photovoltaic properties. The azobenzoquinoline compounds were experimentally synthesized through a series of reaction routes starting from acenaphthene to obtained aminododecylnaphthalimide and finally coupled with diazonium salts to get the desired azobenzoquinoline. Azo dye synthesized differ in the number of alkyl chains designated as (AR1, AR2, AR3, and AR4) which were experimentally analyzed using FT-IR and NMR spectroscopic methods. The synthesized structures were modelled for computational investigation using density functional theory (DFT) and timedependent density functional theory (TD-DFT) combined with B3LYP and 6-31+G(d) basis set level of theory. The results showed that the HOMO-LUMO energy gap was steady at approximately 2.8 eV as the alkyl chain increases, which has been proven to be within the material energy gap limit for application in photovoltaic. The highest intramolecular natural bond orbital (NBO) for the studied compounds is 27.60, 55.06, 55.06, and 55.04 kcal/mol for AR1, AR2, AR3, and AR4 respectively and the donor and acceptor interacting orbitals for the highest stabilization energy (E(2)) are LP(1)N18 and π*C16−O19 respectively. The photovoltaic properties in terms of light-harvesting efficiency (LHE), Short circuit current density (JSC), Gibbs free energy of injection (ΔGinj), open-circuit voltage (VOC) and Gibbs free energy of regeneration (ΔGreg) were evaluated to be within the required limit for DSSC design. Overall, the obtained theoretical

*Corresponding author: Hitler Louis, Department of Pure and Applied Chemistry, University of Calabar, Calabar, Nigeria, E-mail: [email protected] Ededet A. Eno, Department of Pure and Applied Chemistry, University of Calabar, Calabar, Nigeria Tomsmith O. Unimuke, Anita T. Etim, Henry O. Edet and ThankGod Egemoye, Computational and BioSimulation Research Group, University of Calabar, Calabar, Nigeria Ernest C. Agwamba, Computational and Bio-Simulation Research Group, University of Calabar, Calabar, Nigeria; and Department of Chemical Sciences, Clifford University, Owerrinta, Abia State, Nigeria Justina I. Mbonu, Department of Chemistry, Federal University of Petroleum Resources Efurun, Efurun, Delta State, Nigeria Kayode A. Adegoke, Department of Chemical Sciences, University of Johannesburg, Johannesburg, South Africa Umar S. Ameuru, Department of polymer and Textile Engineering, Ahmadu Bello University, Zaria, Nigeria As per De Gruyter’s policy this article has previously been published in the journal Physical Sciences Reviews. Please cite as: E. A. Eno, H. Louis, T. O. Unimuke, E. C. Agwamba, A. T. Etim, J. I. Mbonu, H. O. Edet, T. Egemoye, K. A. Adegoke and U. S. Ameuru “Photovoltaic properties of novel reactive azobenzoquinolines: experimental and theoretical investigations” Physical Sciences Reviews [Online] 2022. DOI: 10.1515/psr-2021-0191 | https://doi.org/10.1515/9783110783643-002

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2 Photovoltaic properties of novel reactive azobenzoquinolines

photovoltaic results were compared with other experimental and computational findings, thus, are in excellent agreement for organic solar cell design. Keywords: azo dyes; DFT; DSSC; energy gap; light-harvesting; photovoltaic; TD-DFT.

2.1 Introduction Designing safer chemical products to keep function while lowering toxicity is one of the 12 principles of green chemistry that has recently piqued interest [1–4]. Fossil fuels and greenhouse gas emissions are changing the climate dramatically, causing major concerns on every continent [5–7]. As a result, new systems based on clean and renewable sources such as solar and wind have gained increasing attention to assure access to affordable, reliable, sustainable, and modern energy for everybody while also becoming more energy-efficient [8, 9]. The development and understanding of lightdriven charge separation in molecular systems as a technique of converting and storing solar energy has received a lot of attention in recent years [10, 11]. Because of its high efficiency and low cost, dye-sensitized solar cells (DSSCs) have gained a lot of attention for converting sunlight into electricity [12–16]. There are two classes of dye sensitizers: those that contains metals and those that do not [13–17]. Due to their intensive chargetransfer absorption across the whole visible range and highly efficient metal-to-ligand charge transfer, transition metal coordination compounds are used as excellent sensitizers [18]. The power conversion efficiency (PCE) of these metal-based DSSCs has been reported to be as high as 13% for Zn porphyrin and 11.5 percent for polypyridyl ruthenium (Ru) complexes [19, 20]. Nonetheless, these metal-based dyes are detrimental to the environment, and their syntheses and purification processes are timeconsuming and costly thereby limiting their use in DSSCs [21, 22]. To surmount these difficulties, scientists are directing research efforts on metal-free organic dyes which are more flexible in solar cell design [23–26]. The PCE for these metal-free organic dyes has been reported to be as high as 16 percent [27]. Metal-free organic dyes have a number of advantages, one of which is that they are relatively simple and inexpensive to make. Furthermore, these dyes have intriguing qualities such as variable absorption and good photoelectrical properties [28–30]. A normal DSSC is built-up of four important parts: a fixed dye molecules that responds to sunlight, a metal oxide semiconductor with wide bandgap (usually TiO2), a redox electrolyte commonly an (I − /I −3 ) redox couple, a counter electrode (customarily a piece of glass coated with platinum) and a mesoporous metal oxide layer, which acts as photo-anode commonly developed from TiO2 nanoparticles [31–34]. Dyes are so crucial in raising the effectiveness of converting sunlight to electricity because the performance of DSSCs strongly depends on (i) how efficient the dye sensitizer can absorb from the spectrum of sun light; (ii) ability to transfer electron from the excited state of the dye to TiO2 (efficiency of the charge separation); and (iii) the chance

2.2 Experimental

15

transferring electron from the donor to the oxidized dye. These influences are connected to the electronic configuration of the dye in its ground and excited states [35]. In the last 20 years, computational chemistry has become one of the most potent techniques for creating extremely realistic models of molecular systems and materials. Computational chemistry research supported the development of dye-sensitized solar cells and helped to rationalize the link between chemical structure and device performance. Specifically, density-functional theory (DFT) and time-dependent DFT have been used widely to study the electronic and optical absorption properties of dye molecules (both isolated and adsorbed on the semiconductor (TiO2) surfaces), in order to select dyes featuring broad absorption spectra and favourable electron transfer from the photo-excited dye to the semiconductor [36, 37]. Heterocyclic azo dyes have been generally studied largely owing to their specific use for colouring textiles and plastics. Thiophene, pyrrole and azoles bearing azo chromophores have been employed for electronic applications such as optical switching, second harmonic generation and organic sensitized solar cells [38, 39]. Several studies and patents [40, 41] have been published in the last decade describing the use of azo dyes as sensors. Azo dyes have a unique combination of optical and electrical properties, as well as chemical stability and the ability to be processed in solution, making them ideal for DSSC applications [42, 43]. Nevertheless, there are a few studies of DSSCs that use azo dyes as sensitizers [44]. Alkyl side chains have a significant impact on the solubility, charge transport characteristics, and crystallinity of polymers, which are critical for ensuring miscibility of active materials and facile device construction of organic solar cells (OSCs). The influence of the alkyl side chain on solubility, which has been validated by literature reports, could also be useful in thin film morphology, charge mobility, and inter-chain packing [45–52]. A series of novel conjugated polymers with benzo[1,2-b:4,5-b′] dithiophene (BDT) as the electron-donating unit and a benzo[1,2-d:4,5-d′] bis(thiazole) (BBT) backbone as the electron-accepting unit have been developed in recent years using functional group and side-chain engineering to achieve maximum efficiency and gratifying properties [53–55]. This research aims to uncover the physicochemical features that govern the light absorption process in the azobenzoquinolines-based series, as well as the key parameters that influence the dyes’ DSSC sensitizer efficiency. As a result, the goal of this research is to create critical criteria for evaluating the relative efficacy of azobenzoquinolines dyes. These criteria might be used to forecast the efficiency of other dyes and, presumably, to design new, more efficient sensitizers, saving money and time in the process.

2.2 Experimental The synthetic procedure involve first the synthesis of the dye intermediate starting from 5-nitroacenaphthene, 4-nitronaphthalene-1,8-dicarboxylic anhydride, 4-nitro-N-dodecyl-1,8-naphthalimide,

16

2 Photovoltaic properties of novel reactive azobenzoquinolines

Scheme 2.1: Synthesis of (Z)-2-Dodecyl-6-((2-hydroxynaphthalen-1-yl)diazenyl)-benzo[de] isoquinoline-1,3(2H)-dione dye: (AR). 4-amino N-dodecyl-1,8-naphthalimide, and the final coupling to produce the dye: (Z)-2-Dodecyl-6((2-hydroxynaphthalen-1-yl)diazenyl)-benzo[de]isoquinoline-1,3(2H)—dione as shown in Scheme 2.1 using the method described by Louis et al., [24].

2.2.1 Computational details The geometry of the studied structure was fully optimized at the DFT/B3LYP/6-31+G (d,p) level of theory by employing Gaussian 16 and Gauss view 6.0.16 [56]. To ensure proper ground state geometry is attained, frequency calculation was equally computed on the DFT optimized geometry at the same level of theory. The computed vibrational properties agree in all cases to potential energy minima for which no imaginary frequencies are obtained. The synthesized structures namely: (Z)-2-Dodecyl-6((2-hydroxynaphthalen-1-yl) diazenyl)-benzo[de]isoquinoline-1,3(2H)-dione dye is designated as AR with R groups ranging from C8 to C14. The dyes AR1, AR2, AR3 and AR4 are obtained by varying the alkyl chain length as follows AR1 (C8), AR2 (C10), AR3 (C12) and AR4 (C14) respectively and their structures are presented in Figure 2.1. Time dependent density functional theory (TD-DFT) was utilized to obtain the first five singlet excitation for the studied structures using the Coulomb attenuated functional CAM-B3LYP in gas, water, ethanol, and chloroform while employing the polarizable continuum solvation model (CPCM). Molecular structural analysis was conducted with Multiwfn 3.7 (dev) software [57]. To investigate the inter-and intramolecular hyperconjugative interactions, Natural Bond Orbital

2.3 Results and discussion

17

Figure 2.1: Experimental FTIR Spectrum. (NBO) analysis was computed by employing the NBO 6.0 programme as entrenched in Gaussian 16 package [56]. The photovoltaic properties of the studied system were computed by utilizing both DFT and TD-DFT calculations. The properties are obtained with the following equations: ΔEL = ELUMO (dye) − ECB (Tio2 )

(2.1)

where ΔEL is the change in LUMO of the dye and the reference dye. ΔEHOMO is described as the difference of Eredox  of(I − /I −3 ) and EHOMO of the dyes. It is mathematically represented as: ΔEH = E redox (I − /I −3 ) − EHOMO (dye)

(2.2)

ΔEHL is the energy gap EHOMO and ELUMO of the dyes expressed as: ΔEHL = ELUMO − EHOMO

(2.3)

2.3 Results and discussion 2.3.1 Synthetic Aspects The dyes were synthesized from acenaphthene following literature and their structures were confirmed using FT-IR, 1H NMR, elemental analysis (CHN) and mass spectroscopy. The synthesized dyes were obtained as solids in reasonable yield. The dyes were stable towards air and moisture, soluble in chloroform, ethanol and ethanol plus a drop of HCl and other organic solvents [18]. The synthetic route of dye along with its proposed structure is depicted in Scheme 2.1.

18

2 Photovoltaic properties of novel reactive azobenzoquinolines

Table .: Experimental and theoretical values for vibrational energy distributional analysis of AR. Experimental wave number (cm−) Unscaled

Theoretical wave number (cm−) Scaled

       

       

PED Assignment (%) υOH() AsyυCH() AsyυCH() AsyυOC() AsyυCC() + υCC() AsyυCC() βHOC() υCC()

Raman activity . . . . . . . .

2.3.1.1 FT-IR From Table 2.1 and Figures 2.1 and 2.2, The bands at 3106–2947 and 2922–2919 cm−1 in the IR-spectra of the dyes have been assigned to C–H stretching vibration of the Ar─H, CH3─ and CH2─ groups. The signals at 1663–1646 cm−1 is attributed to C=O groups. The band at 1647–1604 cm−1 is indicative of the presence of C=C groups. The absorption at 1599–1579 cm−1 is attributed to –N=N– stretch, while the signal at 1388–1349 cm−1 is due to C–N. Figures 2.1 and 2.2 spectrum was plotted using Multiwfn software [57]. A scale factor of 0.977 was employed in the spectrum plotting since it is the most recommended value for B3LYP/6-31G(d) functional.

Figure 2.2: Theoretical FTIR Spectrum.

2.3 Results and discussion

19

Table .: Comparison of the experimental and theoretical HNMR result of AR. S/No           

Experimental (ppm) Theoretical (ppm) Assignment . .–. . . .–. . . . . . .

. Singlet, –CH protons at some distance from electronegative atoms (H) .–. Triplet, –CH protons of the alkyl chain (H – H) . Multiplet, –NH protons of aromatic ring (H) . Triplet, –CH protons of aromatic ring (H) . Duplet, –CH protons of aromatic ring (H) . Multiplet, –CH protons of aromatic ring (H) . Duplet, –CH protons of aromatic ring (H) . Multiplet, –CH protons of aromatic ring (H) . Duplet, –CH protons of aromatic ring (H) . Triplet, –CH protons of aromatic ring (H) – –

2.3.1.2 NMR analysis From Table 2.2 and Figure 2.3A and B, The 1H NMR spectra, the CH3─ and CH2─ protons of the aliphatic parts of the dye were observed at 0.82–0.86 and 1.22–1.74 ppm. The downfield signal at 4.03–4.17 ppm is due to N─CH2 protons of the aliphatic group. The Ar─H protons were observed at 6.55–8.91 ppm. The doublets at 6.7 and 7.9 ppm in the 1H NMR spectra of AR3 indicates that the diazo group is 1,4-disubstituted on the N, N-diethylaniline ring.

2.3.2 Quantum descriptors frontier molecular orbital (FMO) The distributions of the FMOs of dye fastened to TiO2 surface are relevant in investigating the photo, optical properties, and adsorption dye/TiO2 complexes for DSSCs. The power conversion efficiency (PCE), of solar cells, is connected to energy of the frontier molecular orbitals and their band gap energies. Egap predicts the driving force for exciton dissociation and open-circuit voltage VOC. This means that the electron transition corresponding to the excited state is in the direction from the dye molecule of the TiO2. The combination of the FMOs of the dye and the TiO2 is advantageous to a direct optical charge transfer from the dye to TiO2 surface [58]. A molecule with smaller energy gap generates more noticeable intramolecular charge transfer from the sidechain donor groups to the acceptor unit through the π-conjugated bridge and this widens the scope of absorption toward a longer wavelength and results a stronger photovoltaic performance [59, 60]. In DSSCs, organic dye acts as a photosensitizer and its significant function is to make the photo-electrode with large band gaps (such as TiO2) [16] to be responsive to sunlight. Following adhesion of the dye on the surface of

20

2 Photovoltaic properties of novel reactive azobenzoquinolines

Figure 2.3: Experimental (A) theoretica l (B) 1HNMR Spectrum.

the photo-electrode, the excited electron(s) upon reception of sunlight is pushed into the photo-electrode surface. For satisfactory variation and regulation of light energy leading to light-sensitized electron transfer from the dye to the semiconductor, the location of HOMO–LUMO of the dye and their energy level must match conduction band edge (CBE) level of the TiO2 semiconductor and iodine/iodide redox potential. Therefore, analyzing the charge distribution on frontier molecular orbitals especially

2.3 Results and discussion

21

Table .: Energies of the HOMO and LUMO and the select calculated parameters for global reactivity. Descriptors HOMO LUMO Eg (eV) HOMO(excited state) LUMO(excited state) –μ η σ ω

AR

AR

AR

AR

−. −. . −. −. . . . .

−. −. . −. −. . . . .

−. −. . −. −. . . . .

−. −. . −. −. . . . .

the charge-separation between virtual and occupied orbitals gives insight to the optical properties and electronic responses of the dyes as they are excited. The energies of the highest occupied molecular orbital (HOMO) and lowest unoccupied molecular orbital (LUMO) were obtained for compounds AR1 to AR4, recorded and used to obtain the selected global reactivity descriptors. These data are presented in Table 2.3, the energies of the HOMO and LUMO increase with an increase in the number of carbon atoms in the alkyl group substituent. However, AR4 is an exception to this trend as the HOMO – LUMO energy decrease instead of increasing. Likewise, the global reactivity descriptors ω, η, σ, and µ display the same trend. However, with its lowest LUMO energy value as compared to the other dyes in this study, AR4 is potentially the best electron acceptor while AR3 is the best electron donor due to its high HOMO value Figure 2.5. Assesses the similarities and differences between the HOMO−LUMO energy levels of the azo dyes. All dyes have greater LUMO energy levels than that of the starting point of the TiO2 conduction band (−4.04 eV) [61] and smaller HOMO energy levels than that of the frequently used I−/I3− electrolyte redox level (−4.94 eV) [62]; After examining the FMOs, we conclude that all the dyes under study can be considered as potential applicants for DSSCs.

2.3.3 Density of states (DOS) and energy gap of dye and titanium oxide (TiO2) From Figure 2.4, it can be said that the interaction of the dyes greatly reduces the energy gap and improves electron injection potential of TiO2, which in turn increases the light harvesting efficiency (LHE). At B3LYP the TiO2 indicated an Egap of 6.929 eV, but when TiO2 was interacted with AR1, the Egap reduces significantly to 1.115 eV. The Egap increased significantly with increase in the alkyl chain length from AR1(1.115 eV) to AR2 (2.310 eV), but the energy gap remains approximately equal with further increase of the

22

2 Photovoltaic properties of novel reactive azobenzoquinolines

Figure 2.4: Density of state (DOS) plot and energy gap (Egap) for TiO2 and TiO2-dyes interactions.

alkyl chain from AR2 (2.130 eV), AR3 (2.306) to AR4 (2.310 eV). But most importantly is the reduced energy gap when interacted with TiO2, which can be attributed to increase numbers of density of states as more peaks at the Fermi level as showed an increased in the occupied orbitals compared to virtual orbitals. This observation resulted to increase the energy of the occupied orbital as indicated in all the density of state plot.

2.3 Results and discussion

23

Figure 2.5: Frontier molecular orbital plots of the studied compounds.

2.3.4 Molecular orbital composition analysis Orbital composition, a key parameter in determining the distribution of the HOMO─ LUMO within a molecule, was also carried out. Several theoretical approaches are being used by different scientists to investigate and understand the MO composition, such as Mulliken-like methods (including Mulliken analyzer, Stout-Politzer SP analysis and CSquared population analysis) and Natural atomic orbital NAO [61]. Though, in this work, Mulliken partition was preferred as the method for determining the distribution and position of HOMO-LUMO in the molecules. The following compositions were obtained for the respective molecules. The percentages of core atoms were

24

2 Photovoltaic properties of novel reactive azobenzoquinolines

Table .: Percentages of core atoms from orbital composition analysis. Dye

HOMO %

LUMO%

AR

N–. C–.

AR

N–. C–.

AR

N–. C–.

AR

N–. C–.

N–. N–. C– C– N–. N–. C–. C–. N–. C–. C–. N–. N–. C–. C–.

obtained from the molecular orbital analysis of the FMOs of the dyes and the results are presented in Table 2.4. The HOMOs are mainly located on N,N –dimethylaniline ring, while the LUMOs are diffused over nitronaphthalene-1,8-dicarboxylic anhydride moiety of the molecules (see Figure 2.5). As a result, it may be argued that when an individual molecule is excited, electron density moves from the electron-donor moiety to the electron-acceptor moiety. Mulliken charge analysis yields results that are consistent with the molecular electrostatic potential map and support intermolecular interactions based on the electronegativity of the atoms in the entire molecular system.

2.3.5 Hole electron analysis From Table S1 in the supporting information, the overlapping integral of the holeelectron distribution can be used to identify a transition mode in a local excitation (LE) and the space between the centroid of the hole and electron is a pointer to the charge transfer of the present transition mode. Some of the parameters used to elucidate holeelectron excitation are; the Sr index which gives the overlap of the electron and hole, the D index which gives the overall magnitude of charge transfer length, the t-index which measures the separation extent of the hole and electron in charge transfer direction [63], the H index which reflects the breadth of the average distribution of hole and electron, the hole delocalization index (HDI), and the electron delocalization index (EDI). The hole electron Coulomb attractive energy is a very useful parameter for the

2.3 Results and discussion

25

exposition of the distance between the main distribution regions of the hole and electron. This is further necessary to properly place an inference on the photovoltaic property of a potential solar cell. We gave several descriptors in Table 2.3 based on Tian Lu’s hole-electron theory [64], by which the electron transition characteristics of all dyes can be quantified and even seen, to figure out which moieties play a key part during the optical absorption process. A wider distribution of hole and electron for AR1 is highest in water and ethanol which induces highly localized π / π* excitation type on the benzene ring as is seen in its high H and Sr values. In water, it has less coulomb attractive energy which is depicted in its higher D index. AR2 has a wider distance between the major distributions of hole and electron from its studies in water and ethanol. Consequently, chloroform is seen to enhance the dye AR2 such that it exhibits strong coulomb attractive energy, high localization of hole and electron, highly localized π / π* excitation and less wide distribution of hole and electron when compared with the other two solvents used for this study. For the dye AR3, the values of the H and Sr indices in all the solvents under study are equal inferring equal π / π* excitation and equal distribution of hole and electron. However, from the D index, it can be noted that AR3 in chloroform has a closer distance between the main distribution regions of hole and electron than in ethanol and water. Also, the delocalization of electrons in this dye is higher in ethanol and water which is shown by its high values of HDI and EDI. Subsequently, AR4 in chloroform exhibits the strongest coulomb attractive energy amongst the three solvents with its lesser value of D. It also shows higher EDI and HDI values indicating less delocalization. Comparing the dyes in the three solvents, chloroform appears to be a better solvent majorly as reflected in the stronger coulomb attractive energy which each dye displays in this solvent and probably better solubility. In the gaseous phase, S0 / S5 exhibits a very marked difference from the other excitation states by showcasing a CT excitation due to its all-positive values of t for each dye. However, with its smallest value of D, AR3 has the strongest coulomb attractive energy amongst the dyes under study. This combined with data gotten from the analysis of the dyes in the solvents shows that AR3 in chloroform presents better prospects than the other systems studied here. Both the hole and electron distributions in the series are quite comparable, hinting that the electron excitation transition of these dyes should be very similar, with distinct CT properties. Since the π-conjugated backbones are extended, their H values are large ranging from 5.77 to 17.26 Å. For the S0 / S1 excitation, the D indices are in the range 0.21–0.44 Å which are very small values since these are less than the half-length of a typical C─C bond. The Sr reaches 0.62 in water and ethanol for AR1, which is a large value implying that about half of the hole and electron has perfectly matched. from the Sr and D indices we can deduce that these are local excitations. Furthermore, combining the isosurface maps and quantitative data of hole−electron descriptors presented in Table S1 in the supporting information, we identified the characteristics of the five excitations as follows:

26

2 Photovoltaic properties of novel reactive azobenzoquinolines

S0 / S1: LE excitation of n / π* type on the azo group S0 / S2: CT excitation of n / π* type from the amino group towards the azo group S0 / S3: CT excitation of π / π* from nitronaphthalene-1,8-dicarboxylic anhydride moiety towards the azo group S0 / S4: π / π* LE excitation occurring on the naphthalene ring S0 / S5: n / π* CT excitation from oxygen atoms 19 and 20 towards the nitronaphthalene-1,8-dicarboxylic anhydride moiety 2.3.5.1 Density of states analysis To support the FMO diagram, the total density of states of the dyes were studied using CAMB3LYP/6-31G(d) in the gas phase Figure 2.2. The s orbitals is red, p orbitals is blue and d orbitals are red lines respectively in the TDOS spectra. According to the plot, the s and p orbitals are more pronounced with a range of −0.52 to −0.27 and −0.53 to −1.8 for the s and p orbitals respectively. The d orbital is not pronounced in any way. This suggests that the electron transfers from hole to electron are quite rapid as there are fewer orbitals for occupancy. The HOMO (valence band) on the x-axis are negative while the LUMO (conduction band) have positive values [49, 50]. The separation between the HOMO and the LUMO gives band gap [49]. This results from DOS well supports those obtained from FMO analysis for the dye under study (Figure 2.6).

Figure 2.6: Partial density of state (PDOS) of (A) AR1, (B) AR2, (C) AR3, (D) AR4.

27

2.3 Results and discussion

2.3.5.2 Natural bond orbital analysis NBO is a powerful tool for interpreting hyper conjugative interactions and electron density delocalization within molecules chemically. The energy differential between the interacting orbitals is directly proportional to the stability of orbital interaction. Thus, to quantify the molecular interactions, a Second-order perturbation interaction energy (E2) was applied as provided by Eq. (2.1). E (2) = nr

(F(i, j))2 E(j) − E(i)

(2.4)

Higher values for E(2) indicate intensive interactions between the electron donors and electron acceptors, which will result in a greater extent of conjugation. Depending on the types of orbitals, various intra and intermolecular interactions exist within a

Table .: Wavelength, frequency, major contributions, assignment and orbitals of each excitation type from UV analysis of the dyes. Excitation Type AR S–S S–S S–S S–S S–S AR S–S S–S S–S S–S S–S AR S–S S–S S–S S–S S–S AR S–S S–S S–S S–S S–S

Wavelength (nm)

Energy (eV)

Oscillator strength (f)

Major Contribution

Assignment

Orbital

. . . . .

. . . . .

. . . . .

. . . . .

π-π* π-π*

– – – – –

. . . . .

. . . . .

. . . . .

. . . . .

π-π* π-π*

– – – – –

. . . . .

. . . . .

. . . . .

. . . . .

π-π* π-π*

– – – – –

. . . . .

. . . . .

. . . . .

. . . . .

π-π* π-π*

– – – – –

28

2 Photovoltaic properties of novel reactive azobenzoquinolines

molecule. For instance, π / π* interactions result in π conjugation or resonance in the benzene ring. On the other hand, primary hyper conjugative interactions occur due to the different types of orbital overlaps such as σ / π*, π / σ* and secondary hyper conjugative interactions occur due to σ / σ* orbital overlaps [65]. From Table S2 in the supporting information, the highest stabilization energy is seen in the donor-acceptor relationship of LP(1)N18 / C16─O19 for all the dyes under study except for AR1 (C8) which has its highest stabilization energy in the LP(2)O20 / π*C16─N18 interaction. Dyes AR2, AR3 and AR4 are less stable than dye AR1 hence favouring electron transition more efficiently and faster, suggesting that the dyes with an alkyl group having 10 or more carbons in the chain would be better sensitizers in a DSSC device. 2.3.5.3 UV electron analysis The peak UV/vis absorption wavelength and the associated absorptivity are important indicators of the light-harvesting capacity of DSSC dyes. Table 2.5 lists the calculated absorption peaks, transition energies, and oscillator strengths (here we have considered only the states of f > 0 1). In the UV–Vis spectral region, the main absorption band was found to be the first excited state (So / S1 state) has been deduced to be local excitation of n / π* type on the azo group. For the higher excited state (So / S2), the absorption intensities are lower than in S1, and the maximum absorption peak is in the region 447.65 nm with oscillator strength, f = 1.4920, which is attributed to a charge transfer electron transition process from N of diethylamino group towards the azo group. Generally, the observed values for wavelengths in all solvents used have close similarity differing only in minute figures of 0.1–0.5 nm with the wavelength of the dyes in the gaseous medium being higher. Experimentally the dyes are observed to exhibit longer wavelengths in DMF (522 nm), ethanol + water (520 nm) than in water (510 nm) and chloroform (510 nm). This finding was consistent with previous research, which found that increasing the polarity of the solvents caused bathochromic alterations for various azo dyes. This is owing to the interaction of protic and aprotic solvents with the produced azo dyes, which have a lone pair of electrons on nitrogen, sulfur, and oxygen atoms [34, 66]. As can be seen in Table 2.5, dye AR1 has the highest value of wavelength with AR3 having the lowest. Also, the values of oscillator strength and energy are lowest in chloroform solution inferring better absorption properties of the dye in chloroform than in the other solvents. In summary, AR1 absorbs sunlight more efficiently than other molecules do, which may lead to higher PCE. 2.3.5.4 Photovoltaic properties The open-circuit voltage (VOC) is defined as the voltage under zero current condition this is equivalent to the difference between the redox potential of the electrolyte’s redox

2.3 Results and discussion

29

couple (I − /I −3 ) and the quasi-Fermi level of the semiconductor’s conduction band (TiO2), It is denoted by the following equation: VOC =

E CB + ΔCB kT nc E Redox + In( )− N CB q q q

(2.5)

where, ECB is the conduction band edge of TiO2, q is the unit charge, T is the absolute temperature, κ is the Boltzmann constant, nc is the number of electrons in the conduction band, NCB is the density of accessible states in the conduction band and Eredox is the redox potential of the electrolyte. ΔCB represents the shift of CB when the dyes are adsorbed and could be expressed as: ΔCB =

qμnormal γ ϵ0 ϵ

(2.6)

where μnormal represents the dipole moment of the individual dye molecule perpendicular to the surface of TiO2, and γ is the dye surface concentration, ε0 is vacuum permittivity and ε are the dielectric permittivity. The calculation of VOC can also be approximately obtained by the difference between ELUMO and ECB. It is used for this purpose because the studied dyes are singly not in the adsorbed state on TiO2. Therefore, calculations of nc and NCB. JSC can be mathematically represented as: J SC = ∫LHE(λ)ϕinject ηcollect dλ

(2.7)

LHE (λ) is the light-harvesting efficiency at maximum wavelength, ϕinject is the electron injection efficiency, and ηcollect is the charge collection efficiency. To obtain a high JSC, LHE and ϕinject should be as high as possible. The LHE can be denoted as: LHE = 1 − 10−f

(2.8)

where f is the oscillator strength of the dye corresponding to λmax, ϕinject is related to the thermodynamic driving force ΔGinject of electron injection from the excited states of dye to conductive band TiO2 ΔGinject (The free energy difference for electron injection) is mathematically represented as: Table .: Photovoltaic properties of the studied compounds. Dye

Edye (eV)

ΔGreg

VOC

AR AR AR AR

−. −. −. −.

. . . .

. . . .

30

2 Photovoltaic properties of novel reactive azobenzoquinolines

dye 2 2 ΔGinject = E dye∗ − E TiO + ΔE − E TiO CB ≈ E CB

(2.9)

where Edye* is the redox potential of the oxidized dye at excited state, Edye is the redox potential of the oxidized dye at ground state and ΔE is the lowest vertical excitation 2 energy E TiO CB is the energy of the conductive band edge of TiO2. The driving force for dye regeneration, ΔGreg is denoted by: ΔGreg = μ(I − /I −3 ) − E dye

(2.10)

A value of ΔGreg greater than 0.2 eV for an oxidized dye could be the efficient electron injection. To determine the value of JSC and the overall conversion efficiency (μ), the calculated values of VOC, f, LHE, λmax, ΔGinj, ΔGreg, ΔGcr are reported in Table 2.6. From Eq. (2.5), Dye’s with small energy band gap is beneficial to a red-shifted absorption spectrum and gives rise to more electrons and corresponds to an increase in nc and thus, the efficiency of VOC increases. In calculating ΔGreg, value of ECB for the TiO2 semiconductor is −4.00 eV [16, 34, 67–70]. For all the solvents ΔGinject is greater than 0.2 eV indicating that all the dyes in the four phases provide efficient electron injection with the value of ΔGinject for AR3 being highest and thus, providing greatest electron injection among others in the series. The values of the injection-free energy (ΔGinj), oxidation potential of the dye in the excited state (Edye*), light-harvesting efficiency (LHE) and open circuit efficiency (VOC) are presented in Table 2.6 for the solvents and gas phases in the excited state. Also, the oxidation potential energy of the dye in the ground state (Edye), Greg and open-circuit voltage (VOC) of the dyes at the ground state was calculated and computed in Table 2.6. The positive values of ΔGinj give the inference for all the dyes is indicative of nonspontaneity in the process of electron injection to the semiconductor. For all the solvent phases, dye AR3 has the highest oxidation potential energy in the excited state. Hence, AR3 is the most oxidizing dye. The short circuit current is influenced indirectly by the open circuit current. In this case, the values of the open-circuit current are directly proportional to the values of the injection-free energy. Comparing the dyes, AR3 has the highest value of VOC for both excited and ground states inferring that AR3 display higher electron injection ability. Between the solvents, the values of the open-circuit current followed the pattern: gas>chloroform>ethanol>water. Thus, in the excited state, AR3 in the gas phase followed by the same dye in chloroform would readily eject electrons to the conduction band of the semiconductor.

2.4 Conclusions It can be concluded that the theoretical and experimental result of UV–VIS, FT-IR and NMR agrees significantly and help support the structural characterization of the synthesized dyes. All dyes have higher LUMO energy levels than that of the base level of

References

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the TiO2 conduction band and lower HOMO energy levels than that of the most commonly employed I−/I3− electrolyte redox level, these findings support their viability to undergo electron injection and dye regeneration within a DSSC. Comparing the dyes in the three solvents, it can be said that chloroform has a stronger coulomb attractive energy which each dye, which enhanced its solubility. From DOS, the electron transfers from hole to electron are quite rapid as there are fewer orbitals for occupancy, which corroborate with the energy gap. It can also be that, the interaction with of the dyes with TiO2 remarkable reduce the energy gap which will eventually improve the electron injection parameters like the light harvesting efficiency and this change in energy gap increases with increase in carbons of the alkyl chain. The stabilization energy from NBO analysis for the dyes with an alkyl group with 10 or more carbons in the chain would be better sensitizers in a DSSC device due to better stable during electron transition from donor to acceptor site. It can be said that the increase in the alkyl side chain does not significantly alter the HOMO-LUMO energy gap but are within the material energy gap limit for photovoltaic application. The compounds. The photovoltaic properties indicated an efficient electron injection in gas phase, which defines their non-spontaneity in the process of electron injection to the semiconductor. Thus, in the excited state, the shorter chain in the gas phase and chloroform, the higher the oxidation potential energy, indicating an excellent light harvesting efficiency for organic solar cell. In DSSCs, these organic dye will acts as a photosensitizer and efficiently to sensitize the photo-electrode with large band gaps such as TiO2. Acknowledgment: The authors are thankful to all those who have supported this work in any way.

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Supplementary Material: The online version of this article offers supplementary material (https://doi. org/10.1515/psr-2021-0191).

Chinda Worokwu* and Kechinyere Chinda

3 Social media and learning in an era of coronavirus among chemistry students in tertiary institutions in Rivers State Abstract: The study examined the awareness level of social networking site and how it is applied for learning in an era of coronavirus by Chemistry students in tertiary institutions in Rivers State Nigeria. Three research questions and two hypotheses guided the study. The study adopted the analytic descriptive survey design. One hundred and sixty undergraduate students from Ignatius Ajuru University of Education (IAUE) and Rivers State University (RSU) were randomly selected for the study. The instrument for data collection was a researcher made questionnaire titled social media awareness and application for learning in COVID-19. The consistency coefficient of the instrument was determined by Pearson product moment as a measure of its stability over time. The reliability coefficient of the instruments was 0.95. The instrument was administered to the students online via their numerous class social media platforms. Mean, standard deviation and t-test were the statistical tools used for data analysis. The result reveals that students are aware of social media for learning such as Zoom, WhatsApp, Facebook etc. A second outcome of this investigation showed that there is poor application or used of social networking site for learning among Chemistry students in Rivers State owned universities, it was also found out that finance, travel restriction, assessment and evaluation, poor internet reception, availability of electricity and others were identified as challenges while mental health of student, lack of pre-class preparation and associating with real friends were not considered to be challenges to the use of social media for learning. Furthermore, there was no significant difference in the level of awareness between IAUE students and those of RSU in the use of social network site for learning. The extent to which students apply social media for learning does not depend on university type. Based on the above results, it was recommended among others that universities in Rivers State should endeavor to engage their students in training that will avail them the opportunity to apply or utilize effectively social media in their learning activities. Keywords: chemistry; era; learning and coronavirus; social media.

*Corresponding author: Chinda Worokwu, Department of Chemistry, Faculty of Natural and Applied Sciences, Ignatius Ajuru University of Education, P.M.B 5047 Rumuolumeni, Port Harcourt, Rivers State, Nigeria, E-mail: [email protected] Kechinyere Chinda, Department of Educational Foundation, Faculty of Education, Rivers State University, Nkpolu Oroworukwo, Port Harcourt, Rivers State, Nigeria As per De Gruyter’s policy this article has previously been published in the journal Physical Sciences Reviews. Please cite as: C. Worokwu and K. Chinda “Social media and learning in an era of coronavirus among chemistry students in tertiary institutions in Rivers State” Physical Sciences Reviews [Online] 2021. DOI: 10.1515/psr-2020-0093 | https://doi.org/10.1515/ 9783110783643-003

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3 Social media and learning in an era of coronavirus

3.1 Introduction Coronavirus disease 2019 also known as the (COVID-19) was started in Wuhan city of China in December 2019 [1]. Corona disease (COVID-19) is a contagious infection triggered by a newly revealed coronavirus of SARS CoV transmitted from civet cats to humans in China [2]. Coronavirus disease 2019 (COVID-19) is defined as the illness caused by a new coronavirus now called severe acute breathing syndrome coronavirus 2 (SARS-CoV-2) formerly called 2019 NCoV. These viruses are known as source to illness ranging from common cold to more severe diseases such as Middle East respiratory syndrome (MERS) and severe acute breathing syndromes (SARS) [3]. On 30th January 2020, WHO declared the novel coronavirus a Universal health emergency, it recommended certain standards to reduce the spread of the disease such as continuing simple hand and respirational hygiene and safe food cultural practice and preventing of close contact with any person having symptoms of breathing illness such as coughing and sneezing. The disease spread to over 177 countries in February thereby causing WHO on 11th of March, 2020 to declare COVID-19 a universal pandemic [2]. As at March 2020, it has infected over 722,435 persons resulting to about 722,435 deaths [4]. In order to contain the spread of the disease many countries introduced travel restrictions, ban on religious and social gathering, closure of bars gyms, and other public places. In addition, this development also led to the closure of schools in many countries. On 20th of March 2020, the Nigeria University Commission (NUC) issued circulars to Vice Chancellors to close university for a period of one-month effect from March 30, 2020. This directive was adhered to strictly on 21st March, 2020 by the Senate of Ignatius Ajuru University of Education (IAUE) and Rivers State University (RSU). They suspended all academic activities and school were closed down all students vacated the universities. The outbreak of COVID-19 has challenged the notion of two state owned Ivory tower in Rivers State preventing it from using the school timetable as planned by their senate. With disruption of academic calendar, the universities transitioned numerous courses and programs from face-to-face to virtual delivery mode through the use of social media site. Outbreak of COVID-19 has posed a challenge to learning globally. The closure of schools has disengaged students from learning process and other skill, values attitude and potentials associated with learning. There are also the challenges of finance where students who engage in small medium scale business on campus to support themselves are suffering both academic and financial deficiencies. The closure of schools has taken most youths to social vices, such as prostitution, cybercrime and cultism to collaborate with the statement that an idle mind is devil workshop. All these and other challenges can be minimized if learning was taking place.

3.2 Types of learning

39

Learning is a relatively permanent change in behavior due to again knowledge understanding or skill accomplished through experience, which may comprise study, instruction, observation or training [5]. For learning to take place there must be a change in behavior. The change must be through experience or practice (only adaptive changes and not necessary changes due to growth, maturation and disease). The change must be relatively permanent that is it must last for a fairly long time.

3.2 Types of learning I. Physical-sensory learning This can be referred to as psychomotor or sensory perceptive learning. It involves the development of skills, and those insights that lead to the efficient performance of physical activity. Other components of this type of learning include skill, know-how, and economic performance of activity, e.g., reaction time and speed safety measures and efficient alternatives. II. Intellectual/cognitive learning This refers to the acquisition of new information and understanding. The components of cognitive learning include assembling and analysis of facts and opinions. It also includes developing problem solving techniques or approaches. III. Affective learning This relates to the development of attitudes, emotions, feelings, as well as qualities of character and conscience, values, interest and appreciations that are internally consistent. The inter-relatedness of these three domains of learning is significant in the execution of an educational program. The teacher as a resource person provides the pupils with the opportunity to use their sensory, cognitive and affective processes to adjust adequately to a learning process. For effective learning to take place Ikegbunam [5] listed seven essential conditions necessary for effective learning to take place (i) Readiness—There has to be the mental maturity and appropriate background experience for the learning experience being undertaken. (ii) Motivation—Motivation is crucial to learning because it arouses, withstands, guides and controls the strength of learning effort. Some of the best motivational techniques are to provide for the learner’s desire for practical activities and creative use of his talents. (iii) Self-discovery and problem solving—Self-discovery may be accompanied by explaining, demonstrating, correcting the learner’s provisional trials; by organizing learning sequences and leading questions/cues which aid self-discovery learning.

40

3 Social media and learning in an era of coronavirus

(iv) Perception of effects—Knowledge of result of each goal-directed trial is essential for improvement and mastery. Through perception of effects, one is able to identify and correct his mistakes and at the same time confirm his correct knowledge. (v) Practice—Appropriate and well distributed practice improve memory and practical skills. Practice conditions should be made to resemble as much as possible the situation in which the skill or knowledge is required to be demonstrated. (vi) Transfer—Ability to apply previous learning to new problems is indicative of effective learning. Student should be aided to discover similarity of concepts in order to facilitate transfer, generalization, application, and integration of new learning with previous knowledge. (vii) Mental health—Effective learning is fostered under conditions of good mental health, emotional and social adjustment. The student who feels insecure or rejected either by his teacher, parents or classmates may develop neurotic anxiety and emotional maladjustment. In the center of this COVID-19 crisis, we are sure that fellow educators, like us, are pondering what we need to be organizing our students for effective learning to take place. Though students being able to gain information, and even study a practical skill, through a few connects on their phones, tablets and computers. We will need to reexamine the role of the educationalist in the lecture hall. This may mean that the part of educationalists will move to helping young people’s progress as contributing members of society through use of social media sites. Social media also known as social networking service (SNS) is a virtual platform that persons use to build united relation with other people who share similar personal or career interest activities, background or real life connection [6]. It also a form of electronic communication (such as websites for social networking and microblogging through which users create virtual communities to share information, concepts, private messages, and other content (such as videos). Similarly, social media is a catch-all term for a variety of interest presentations that allow users to interact with each other. It is internet based applications that help users to communicate freely with people of like minds. They believe that social network is possible in person especially in work place, universities, high schools and most popular online [7]. Social network site can be divided into three namely [8] 1. Entertaining social network services used primarily for meet people with existing friends (e.g., Facebook). 2. Interacting social network used primarily for nonsocial relational communication (e.g., a profession and occupation oriented site. 3. Social direction-finding network sites used principally for helping users to find specific material or educational resources (e.g., good reads for books).

3.2 Types of learning

41

In another development, Farema in Akainwor [9] divided social media into 10 namely: 1. Social relationship network—connect with people 2. Mass media sharing networks—share photos, videos and other media 3. Conversation forum—where news and ideas are shared 4. Book marking and content duration networks. It is used to learn, save and grant new content 5. Customer assessment network—find and assess businesses 6. Blogging and dissemination networks—print content online 7. Attention-based networks—share interest and leisure 8. Shopping networks—shop virtual 9. Business network—trade, goods and services 10. Secret social networks—communicate secretly The first recognizable social network site was created in 1997 [10]. It was used to transfer an information and make friends with other users. In 1999, the first blogging site became widespread creating a social media awareness that is still known. The six degree moved the internet into the era of blogging and instant messages. By the year 2000, more than 100 million people had access to the internet it became an avenue to chat, make friends, dating and discuss topics of interest. In 2003, the first social media surge LinkedIn was created, it was geared toward professionals who want to network with one another. During 2004–2006, a great revolution was made with social networking service (SNS) when Mark Zuckerberg in 2004 launched the Facebook a number one social media website that is currently used by billions of people. The face.com that was launched for just Harvard students was widely patronize thus Mark Zuckerberg released it to the whole world. Other social media that marked this era was Twitter created by Jack Dorsey, and Co. This has allowed over 500 million users to send tweets to other users. In 2010 and beyond, dozens of website was develop to provide social media services. Social media services such as Flickr, Foursquare, Instagram, Google buzz, Lopp, Blippy Groupon etc. were invented in the era. The major happening in this era was that social media became widely used and widespread as a means of commerce (marketing). Social media today consist of thousands of media platform all serving the same purpose of communication but slightly different in purpose. A user of social media platform decides on the purpose for use and the platform that will serve that purpose. Notwithstanding the popularity of social media sites for private use there are a small ratio of students who use them for educational purpose [14]. The use of social media for educational aim such as helping by undergraduate students with course work. The use of social media for teaching and learning such as blog provide students with more participating role in learning process therefore by the integration of blog for educational purpose should be adopted by lecturers.

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3 Social media and learning in an era of coronavirus

There are plethora of social network with various feature meant to suit different people. They include WhatsApp, Facebook QQ, we chat Qzone, Tumblr, Instagram, Pinterest, LinkedIn, Telegram, Reddit, Tagged Four square, Renten, Tagged, Badoo, My space, stumble upon, THE-DAYS, Kiwibox.com skyrock snapfish, flixster, care2, YouTube, class mates, my Heritage flickr Wikipedia etc. The various social network sites are used by millions of people on daily bases. These various social networks are patronized by mostly students and youths. Social media has introduced new method of teaching and conducting research and have brought into educational facilities for online learning and research collaboration [9]. Cassidy et al. [11] asserts that social network sites seize the attention of the students and then distract it towards nonacademic and wrong action including unnecessary conversation. Social media platforms have benefits for student for developing ideas opinion, share information, ask question and get feedbacks. A study conducted by Barikpe [12] revealed that undergraduate students use social media platforms for online publishing that creates opportunities for producing knowledge sharing, research building social networks and developing professional interaction with minimum difficulty. In the same vein Halie et al. [13] assert that social media is an academic platform utilize by instructors and students to build creative thinking, collaborate by posting ideas, sharing essay to peers. Therefore social platform allows undergraduate students to post class time, rules, assignment notification, suggested reading, and exercises, uses for class discussions, organizing seminars and to provide summaries of reading, write ideas to be based on merit, rather than origin, and ideas that are of quality fitter across the internet ‘virallike’ and across the blogosphere. Social media are interactive networks which provide information and communication technology (ICTs) to the modern society through the instrumentality of the internet and telecommunication gadgets. These international network (internet) its makes the network to expand and increase the possibilities for communicating with wider users across the globe. According to Omekwu et al. [14], it was about 38 years for radio to spread 50 million consumers, 13 years before television attracted the same number and four years for the internet to do so, but took Facebook only 12 months to gain 200 million users

3.2.1 Factors militating the use of social media in teaching and learning during the (COVID-19) These are some of the problems universities faced in using social media sites in teaching and learning during to the COVID-19 pandemic.

3.4 Mental health

43

3.2.2 Shift from classroom learning to virtual learning The change from classroom learning due to the coronavirus pandemic has so many challenges ranging from one nation to another. Virtual learning is new strategy in teaching and learning in most universities in Nigeria. Most schools do not have adequate structure or resources to enable virtual teaching with instant effect. Functioning at home was a difficult task for the lecturers as most of them do not have internet facilities. Courses such practical, music and art could not be taught online as we do not have the expertise to do so. The home front was another challenge especially the female folks who have to guide their children and perform other house chores, this made it difficult for us function effectively in the virtual teaching. Undergraduates who do not have access to computers and at home-based internet services were not part of virtual learning. There is a higher disconnection of students with learning needs (educational, economical, students with special learning needs) or person who special needs who may study without the assistance of their fellow students [15].

3.3 Finance Students from low income family find it difficult to participate in the online teaching due to financial constraint. Kakuchi [16] reported that many students in Japan have drop out of university studies for financial motives, after the country’s coronavirus crisis and restrictions due to the loss of job and problems with personal incomes. The scholars were faced with the challenges of laptop, android phones and internet facilities in order to join the online learning. The university also faced a lot of unexpected, expenses from the outbreak of the COVID-19 by bearing the cost of increase technology cost associated with moving to online classes [17].

3.4 Mental health COVID-19 pandemic has disturbed the life of several family around the globe with the death total of over 70,000,000 people, this has caused a lot of stress and anxiety which has led to harsh effect on knowledge and Mental health of students [18]. Undergraduates staying far from the home are not only concerned about their health care and learning but are also concerned about their families well-being [19]. The study carried out by www.a.zurem.com revealed that the amount of time spent online has health implication on the students. The use of social media for a long term decides the way the genes operate in the body and weaken body system and hormone levels, it also has affect the psychological health of students.

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3 Social media and learning in an era of coronavirus

3.5 Assessment and evaluation Feedback inform of assessment and evaluation are motivators in education. knowledge of result enhances the performance of student. With the outbreak of COVID-19 many universities are unable to assess or evaluate their students. The two state owned Universities used in this study suspended their semester examination, teaching practice and industrial training due to the challenges of online learning. Laboratory practical, test, gymnastics and performance test cannot be conducted online [20]. Students who cannot access to internet facilities were disadvantaged in the assessment procedure and this will affect their performance [21].

3.5.1 Review of empirical literature Williams and Adesope [22] investigated students’ attitude towards the use of social media sites for educational purpose. The study was carried out in Rivers State Nigeria. The research design was a descriptive research design; the sample size was 300 undergraduate students from three faculties in University of Port Harcourt. The instrument for data collection is a structured questionnaire titled undergraduate attitudes towards the use of social media site for learning process (UATUSMLP). The data obtained were analyzed using mean, ANOVA, Z-test and Scheffe’s model. The result showed that social media site are used for learning purposes in terms of fast development in awareness and evidence. Oye et al. [23] conducted a study on the awareness adoption and acceptance of ICT innovation in higher education institutions. The study was carried out in Jos Plateau State Nigeria. The sample size is 100 undergraduate students (57 males and 43 females). The instrument for data collection is a questionnaire titled UTAUT. The data collected were analyzed using simple percentage, Pearson moment correlation. The result of the studies shows that university ICT makes task more easily accomplished Fatokun [24] investigated the effect of social media sites on learner achievement and awareness in chemistry in the North–central geopolitical zone of Nigeria. The study adopted a cross-sectional survey design with 240 chemistry learners as the sample size, the result revealed that 60.8% of the students engage in social media for academic purposes. The respondents also revealed that social media has helps in increasing their achievement. The findings further reveal that Facebook is most frequently used among the respondents. The result of the disclose that students did not see social media site as an effective studying instrument and that has made most of the not adopting to modern technology and are not proficient on it usage. The attention of 67.5% respondents are enhanced when using social media site for educational purposes. The findings also reveal that addiction, poor time management and health issues as problem facing the usage of social media site in learning

3.7 Aim of the study

45

In the same Vein Omachonu and Akanya [25] asserts that most students using social media sites, spend much time to displace their learning period thereby affecting them negatively. Similarly Asemah, Okpanachi and Edegoh [26] found that social media has adverse effects on the educational achievement of chemistry undergraduates and the Facebook is the mostly frequently used social media. Priti-Bajpai [27] reveal that university students extensively used WhatsApp, Instagram, LinkedIn, Snapchat Google plus or YouTube for academic purpose and socializing. He further encourages university students to strike a place between the both purposes.

3.6 Statement of the problem In the era of coronavirus called COVID-19 learning has become difficult if not impossible among students of various institutions due to the closure or shutdown of school. That is face-to-face learning and teaching which is the primary mode of teaching and learning in various institutions have been hindered due to COVID-19. However, social media has been seen as an alternative means of transmitting knowledge skills and values into students without necessarily coming in contact with the students. The question remains that despite the importance of social media in aiding learning and teaching are students aware of this advantage social media has over face-to-face learning a pandemic situation like COVID-19. If the answer to this question is yes for instance another question due arise and that is the level of presentation of social media for learning by students. Also what could be the possible challenges of the use of social network site for learning in coronavirus era forms crux of this investigation hence the problem of the study is to determine the level of awareness and application of social media for learning by undergraduates students in tertiary institutions in Rivers State as well as the possible challenges associated with the aforementioned activities.

3.7 Aim of the study The purpose of this study was to determine the awareness and application of social media for learning in tertiary institutions in Rivers State. Specifically, the study seeks to: 1. determine the extent to which students are aware of social media for learning in Rivers State owned tertiary institutions. 2. determine the extent to which students apply social media for learning in Rivers State owned tertiary institutions. 3. determine the challenges of students’ in the use of social media sites for learning in Rivers State owned universities.

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3 Social media and learning in an era of coronavirus

3.8 Research questions The following research questions guided the study. 1. To what extent are students aware of social media for learning for Rivers State owned Universities? 2. To what extent do students apply social media for learning for Rivers State owned Universities? 3. What are the challenges of students’ in the use of social media site in learning in Rivers State Universities?

3.9 Hypotheses The following null hypotheses were formulated and tested at 0.05 level of significance. 1. There is no significant difference in the level of awareness between RSU students and those of IAUE in the use of social media for learning. 2. The extent to which students apply social media does not significantly depend on university types.

3.10 Methodology The study adopted the analytic descriptive design. Nwankwo [28] defines descriptive survey design as that study in which the researcher collects data from a great sample taken from a given people and designates certain structures of the sample as they are at the time of the investigation which are of interest to the investigator, though, without controlling any independent variables of the study. One hundred and 60 year two undergraduate Chemistry students of Ignatius Ajuru University of Education (IAUE) and Rivers State University (RSU) both in Rivers State, Nigeria, were randomly selected Three research questions and two null hypotheses guided the study. The instrument for data collection was a researcher made questionnaire named Social Media Awareness and Application for Learning in COVID-19 (SMAALC). To ensure validity, the instrument was given to three experts in the field of science education, educational technology and educational measurement and evaluation. The topic, aims, hypotheses were also presented to the experts for them to indicate the relevance of items in the instrument with stated objectives. Observations and comments made by the professionals were used to modify and correct the items where necessary. The reliability coefficient of the instrument was determined by Pearson product moment as a measure of its stability over time. The consistency coefficient of the instruments was 0.95. The instrument was administered to the students online via their various class social media

3.11 Results

47

platforms. Mean, standard deviation and t-test were the statistical tool used for data scrutiny

3.11 Results Research question 1: To what extent are students aware of social media for learning for Rivers State owned universities? Table 3.1 show a grand mean of 2.90 which is higher than the criterion means of 2.50. The result is that student are aware of social media for learning in Rivers State owned universities. This means that the level of consciousness of social media among Chemistry students at tertiary institutions in Rivers State can be described as high. Research question 2: To what extent do students apply social media for learning in Rivers State owned universities? The results in Table 3.2 showed that the grand mean was found to be 2.01, which is less than the benchmark mean of 2.50. Consequently, the application or use of social media for learning in Rivers State owned universities is said to be low.

Table .: Mean rating of responses on the awareness of social media by students. SN

Social media items

Mean

SD

. . . . . . . . . . . . . . . . . . . . .

Zoom Instagram WhatsApp Skype Blog YouTube Snapchat Imo LinkedIn Wikipedia My space Facebook Duo Google classroom Twitter Flicky E-mail go Google Viber Xender Grand mean

. . . . . . . . . . . . . . . . . . . . . .

. . . . . . . . . . . . . . . . . . . . .

Remark High High High High Low High High Low High High High High Low High Low High High High Low Low High High

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3 Social media and learning in an era of coronavirus

Table .: Mean rating of responses on the use of social media for learning by students. SN Item statements remark . . . . . . .

I use social media for my education purposes I can exchange ideas with the use of social media I watch experiments in YouTube Social media encourages me to meet with other researchers I use social media for online learning Social media helps me to do my assignments and other I share and get information from my class mates and university group via social media . I use social media in sending instructional video to my peers outside class time . The posting of instructional video and audio clips and images on blogs increase my motivation for social network . Social media makes online learning easier during lockdown periods Grand mean

Mean

SD

. . . . . . .

. . . . . . .

Low Low Low Low Low Low Low

. . Low . . Low . . Low . Low

Research question 3: What are the challenges of students’ in the use for social media for learning in Rivers State universities? Table 3.3 is the results on the issue of challenges students perceive to the confronting the usage of social media for learning at Rivers State owned universities. The data on the Table revealed that items 1, 3, 4, 5, 6, 7, 8, 9, 10, 12, 13, 14, 15, 16, 17, 18 and 20 all had mean values greater or equal to the criterion mean of 2.50, hence were considered to be some of the challenges students in tertiary institutions in Rivers State owned universities face in the usage of social media site for learning. While, items 2, 11, and 19 had mean values lower than 2.50, therefore, were not considered to be challenges. Hypothesis 1: There is no significant difference in the level of awareness between RSU students and those of IAUE in the use of social media for learning. Table 3.4, shows that the mean and standard deviation values for IAUE is 61.00 and 2.379, respectively, while those of RSU were 60.90 and 2.368. On further analysis with independent t-test, the calculated t(158) = 0.258, at df = 158, p (0.797) > 0.05 level of significance, i.e., not significant. Hence, the stated null hypothesis is accepted. The result is that there is no significant difference in the level of awareness between RSU students and those of IAUE in the use of social media for learning. Hypothesis 2: The extent to which students apply social media does not significantly depend on university types

49

3.12 Discussion

Table .: Mean rating of responses on the challenges of social media for learning by students. SN

Challenges

. . . . . . . . . . . . . . . . . . . .

Finance Mental health Travel restrictions Poor interest reception Assessment and evaluation Lack of immediate responses to students’ questions Home front Electricity Audio problems Poor voice quality Lack of pre-class preparation Less control over online teaching Lack of trained assistant to support online education platform Inability to connect to class Lack of computer literacy Limited number of people to connect Location of the student Lack of privacy Real friendship I am addicted to social media and this is a challenge

Mean

SD

. . . . . . . . . . . . . . . . . . . .

. . . . . . . . . . . . . . . . . . . .

Remark Agree Disagree Agree Agree Agree Agree Agree Agree Agree Agree Disagree Agree Agree Agree Agree Agree Agree Agree Disagree Agree

That affects my academic life.

Table 3.5, shows that the mean and standard deviation values for IAUE is 22.08 and 2.173, respectively, while, those of RSU were 22.15 and 2.105. On further analysis and independent t-test, the calculated t(158) = 0.192, and df = 158, p(0.848) > 0.05 level of significance, i.e., not significant. Hence, the stated null hypothesis is accepted. The result is that the extent to which students apply social media for learning does not significantly depend on university types.

3.12 Discussion The results of the study from Table 3.1 show that the students are aware of social media for learning in Rivers State owned universities. The level of awareness of social Table .: Independent t-test analysis on the level of awareness of social media by students. n

Mean

SD

IAUE



.

.

RSU



.

.

University

T

df

Sig

.



.

Decision type NS

NS, not significant, p > . level of significance; IAUE, Ignatius Ajuru University of Education; RSU, Rivers State University.

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3 Social media and learning in an era of coronavirus

Table .: Independent t-test analysis on the use of social media for learning by students. n

Mean

Sd

IAUE



.

.

RSU



.

.

University

T

df

sig

.



.

Decision type NS

NS, not significant, p > . level of significance; IAUE, Ignatius Ajuru University of Education; RSU, Rivers State University.

media among chemistry students in tertiary institution in Rivers State can be describe as high based on grand mean of 2.90. The students since they are youths will like to exert their energy on new technologies in other to be abreast with the time they found themselves. The result of this study is in agreement with Barikpe [12] that undergraduate students are aware of social media platforms and use them for online publishing that create opportunities for producing knowledge sharing, research building social networks and developing professional interactions with minimum difficulty. Similarly, William and Adesope [22], Oye, Aiahad and Abraham [23] affirms that students are aware of social media and it is use for educational purpose makes them to accomplish educational task easily. Results in Table 3.2 show that the level of usage of social media for learning at state owned universities is low with a grand mean of 2.01. The low level of attributed to what Omachonu and Akanya [25] asserts that most students use social media site and spend much period in which displace their learning time thereby affecting them negatively The result of the study disagrees with Oye et al. [23] affirms that students are aware of social media and it is use for educational purpose makes them to accomplish educational task easily. The results in Table 3.3 reveal that students are faced with enormous challenges in the use of social media site. The challenges namely finance, travel restriction, privacy, assessment and evaluation, poor internet reception addiction, home front, poor voice quality among others. The result agrees with Omekwu et al. [14] , Fatokun [24], Smalley [17], Sahu [20] and Alruwise et al. [21] that students and universities are face with a lot of unexpected challenges to cope with learning in this era of coronavirus. The result of the study disagree with Al-Rabiahab et al. [18], Zhai and Du [19] that sees mental health as one of the challenge in the use of social network site in the era of coronavirus pandemic. Furthermore, Table 3.4 also shows that there no significant difference in the level of awareness between RSU students and those of IAUE in use of social network site for learning. The no significant result is because students in the university in this era of coronavirus are faced with socials media as the only option that can be used for learning since the face-to-face learning is not available. Smalley [17] and Sahu [20] asserts the novel coronavirus has reposition the university education system there by

3.14 Suggestions

51

transitioning to online platform. Online instruction as the only option for instruction for this era has increased the students’ awareness of various social media platforms. The no significant difference in the results is also attributed to the educational benefits of these social media platforms as observed by Barikpe [12] that student Mahe use of the benefit of social media especially to deal with the unexpected incident of online education due to coronavirus. In Table 3.5 the extent to which students apply social media for learning does not significantly depend on university types. The students in the both university have low level of application for social media. This is due to inability of students to make use of social network site for learning. The result of the study is in agreement with Fatokun [24] affirms that most students do not see the social media has an effective tool for learning and has not made effort in adopting to the modern technology.

3.13 Conclusion The study investigated the effect of social media and learning in an era of coronavirus among Chemistry students in tertiary institution in Rivers State Nigeria The results revealed that students are aware of social media for learning such as Zoom, WhatsApp, Facebook etc. The outcome of this investigation also showed that there is poor application of social network site for learning among Chemistry students in Rivers State owned universities. Finance, travel restriction, assessment and evaluation, poor internet reception, availability of electricity and others were identified as challenges while mental health of student, lack of before class preparation and associating with real friends were not considered to be a challenges to the use of social network sites for learning. Extent to which students apply social media does not depend on university types. Furthermore, the level of awareness and application of social media for learning do not significantly depend on school type.

3.14 Suggestions The researchers made the following suggestions based on the findings; 1. University in Rivers State should endeavor to engage their students in training that will avail them the opportunity to apply or utilize effectively social media on their learning activities. 2. Chemistry undergraduates should cultivate and maximize the potential of the different social media for their academic benefits. 3. Chemistry lecturers should make sure that they use social media as an instrument for enhancing learning and encouraging the educational performance of undergraduates in the university through academic discussions, assignment can be

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posted there, even teachers can upload their lesson notes, they can also upload useful videos on various topics 4. Students should create an equilibrium between educational and noneducational activities on social network site by directing on the educational relevance of most sites instead of engaging in nonprofit surfing.

References 1. Chachrour M, Assi S, Bejjani M, Nasrallah AA, Salhab H, Fare MY, et al. A bibilometric analysis of COVID-19 research activity a call for increased output. Cureus 2020;12:e7357. 2. Cennimo JJ. Coronavirus disease 2019 (COVID 19). Available from: https//emedicine.medscape. com> 2 [Accessed 16 June 2020]. 3. World Health Organization (WHO). Question and answer/COVID-19) health top WHO EMBRO. Available from: www.emro.who.int>corona-virus [Accessed 15 June 2020]. 4. Coronavirus COVID-19 global cases by Centre for system Science and Engineering (CSSE) at Johns Hopkins university (JHU). Available from: https://coronavirus.jhu.edu/map/html [Accessed March 2020]. 5. Ikegbunam CI. Learning approaches. In Ivowi UMO, editor. Curriculum theory and practice, curriculum organization of Nigeria. CON: Curriculum Organisation of Nigerian; 2009. 6. Amichai-Hamburger T, Hayat T. Social networking. In Rossler P, editor. The international encyclopedia of media effects; 2017, https://doi.org/10.1002/978111883764.wbieme0170. 7. Adejoh MJ, Tse TRS. The impact of social networks on students academic achievement in biology in Benue state. In: Communication technology and STEM education 57th annual conference proceedings of STAN. Abuja Nigeria: Heinmenn Publishers; 2016:30–5. 8. Rouse M. Definition of social media. Available from: Whatis.com [Accessed 12 Dec 2017]. 9. Akaninwor GIK. Educational technology: theory and practice. Owerri: Odessa Educational Books Publishers; 2017. 10. Available from: https://www.history.com/this-day-in-history/facebook-launches-markzuckerberg. Facebook Launch [Accessed 20 June 2020]. 11. Cassidy E, Britsch J, Griffin G, Manlolovitz T, Shen I, Turney L. Higher education and emerging technologies: student usage preferences and lessons for library services. Ref User Service Q 2011; 50:380–91. 12. Barikpe B. Usage of blogs for educational purpose by undergraduate students [Unpublished M Ed Dissertation]. Port Harcourt: University of Port Harcourt; 2018. 13. Halic O, Lee D,Paulus T,Spence M. To blog or not blog: studies perceptions of Blog Effectiveness for learning in a college-level course. Internet High Educ 2010 13, 206–13. 14. Omekwu OC, Eke HN, Odoh NJ. The use of social networking site among the undergraduate students of University of Nigeria Nsukka. Available from: www unn.edu.ng [Accessed 20 Apr 2020]. 15. UNESCO. Coronavirus impacts education. Available from: htps://en.unesco.org/themes/ education emergence/coronavirus_ school_ closures. 16. Kakuchi S. COVID-19 hits student finance amidst call for wider reforms. University World News: The Global Window on High Education; 2020. 17. Smalley A. Higher education response to coronavirus (COVID-19). In: National Conference of State Legislatives. Washington; 2020.

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18. Al-Rabiaahab A, Temsahabc MH, Al-Eyadhy A. Middle East respiratory syndrome-corona virus (MERS-CoV) associated among Medical students at a University Teaching Hospital in Saudi Arabia. J Infect Public Health 2020;5:10–8. 19. Zhai Y, Du X. Mental health care for international Chinese students affected by the COVID-19 outbreak. Lancet Psychiatry 2020;7. https//doi.org/10.1016/s2215-0366(20)30089-4. 20. Sahu P. Closure of Universities due to Coronavirus Disease 2017 (COVID- 19)Impact on education and mental health of students and Academic staff. Cureusiz 2020;4:7541–50. 21. Alruwais N, Wills G, Wald M. Advantages and challenges of using e- Assessment. Int J Inf Educ Technol 2018;8:34–7. 22. Willams C, Adesope RY. Undergraduates’ attitude towards the use of social media for learning purposes. World J Educ 2017;6:90–5. 23. Oye ND, Aiahad N, Abrahim N. Awareness adoption and acceptance of ICT Innovation in higher education institutions. Int J Eng Res Afr 2017;4:1393–9. 24. Fatokun VK. Effect of social media on Undergraduate students Achievement and interest in Chemistry in the North Central geo-political zone of Nigeria. Int J Sci Technol Educ Res 2019;2:9–15. 25. Omachonu CG, Akanya J. Effect of social media on the Academic Achievement of student: A case of study of student of the Department of Arts education Kogi state University Anyigba Nigeria. Eur Am J 2018;14–23. https://doi.org/10.1183/13993003.00869-2018. 26. Asemah ES, Okpanachi RA, Edegoh LN. Influence of social media on the academic performance of the undergraduate student of Kogi State University Anyigba, Nigeria [unpublished B.Ed thesis]. 2013. 27. Priti-Bajpai M. Analyzing effect of social media and academic performance of University graduates. ICIEI Sociol 2018;10. https://doi.org/10.1145/3234825.3234830. 28. Nwankwo OC. A practical guide to research writing for student of research enterprise. Port Harcourt: University of Port Harcourt Press; 2016.

Ponnadurai Ramasami*

4 A conversation on the quartic equation of the secular determinant of methylenecyclopropene Abstract: The Hückel method (HM) is based on quantum mechanics and it is used for calculating the energies of molecular orbitals of π electrons in conjugated systems. The HM involves the setting up of the secular determinant which is expanded to obtain a polynomial which is to be solved. In general, the polynomial is one which may be factorized. However, in May 2020, students brought to my attention that the secular determinant of methylenecyclopropene could not be factorized completely. As a result of this, we used a combination of online tools, technology and visualization to calculate the roots of the secular determinant. This write-up, in a playwriting format, describes the conversation between the facilitator and the students. Keywords: conversation; Hückel method; methylenecyclopropene; quartic equation; secular determinant.

4.1 Background Professor Ponnadurai Ramasami joined the University of Mauritius [1] in August 2000; he leads the Computational Chemistry Group [2] and he has been teaching physical chemistry modules. In one of these modules, the final year of the three years BSc (Hons) Chemistry, there is a module namely physical chemistry III CHEM-3021 and one has to teach “The Hückel molecular orbital method [3, 4]”. I have taught this topic for several years and most of the students enjoyed same. After the lectures and tutorials, students are provided with assignment and usually, they do not have difficulties. On 20th March 2020, a curfew was installed in Mauritius due to COVID-19 [5] and thus, obviously the University was closed. However, we were requested to complete the remaining topics via online teaching/meeting. Thus, I arranged for online meetings with students using Google Meet [6] and the first meeting was held on 4th May 2020 which started with a summary of the last lecture we had at the University. The last lecture was to derive the secular determinant of ethene. After the summary, I was convinced that I could continue based on the “go ahead” of the students. Thus, we continued with setting up the secular determinants for 1,3-butadiene and

*Corresponding author: Ponnadurai Ramasami, Department of Chemistry, Computational Chemistry Group, Faculty of Science, University of Mauritius, Réduit 80837, Mauritius; and Department of Chemistry, University of South Africa, Private Bag X6, Florida, 1710, South Africa, E-mail: [email protected] As per De Gruyter’s policy this article has previously been published in the journal Physical Sciences Reviews. Please cite as: P. Ramasami “A conversation on the quartic equation of the secular determinant of methylenecyclopropene” Physical Sciences Reviews [Online] 2021. DOI: 10.1515/psr-2021-0215 | https://doi.org/10.1515/9783110783643-004

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4 Conversation on the quartic equation

Figure 4.1: Methylenecyclopropene.

cyclobutadiene. I ended the meeting by suggesting to the students to solve these two determinants and then try for methylenecyclopropene (Figure 4.1). In the next meeting, the students did not have any problem for 1,3-butadiene and cyclobutadiene but one of the students brought to my attention that she could not solve the polynomial for methylenecyclopropene. I noted the polynomial as “x4 − 4x2 + 2x + 1” and I informed the students that I would look into this issue and would revert in the next meeting. I got interested in this polynomial and this led to a memorable story in the COVID era which I would like to narrate as per the actual conversations I had with the students. The most interesting aspects are how we used a combination of online tools, technology and visualization to calculate the roots. PR = Ponnadurai Ramasami; S1 = Student 1; S2 = Student 2; SS = Many students.

4.2 Act 1 Curtain rises …… PR: Is everything okay for ,-butadiene and cyclobutadiene? SS: …. Yes …… PR: Could you try for methylenecyclopropene? SS: ……. Some responded, … there is an issue we need to discuss …. PR: … Okay fine …, one of you … go ahead and explain the issue ….? S: Sir, I obtained a polynomial “x − x + x + ” and I could not solve it ……. PR: Oh … really … this is interesting … how come! S: Yes, Sir PR: What about others who tried? … Did you obtain this polynomial? … Could you solve it? … SS: Yes, … this polynomial was obtained but … we could not solve it … PR: Okay …, I have taken note of it … and I informed the students I would look into this issue … S: Thank you Sir. ……. PR: The meeting will end today. Have a good day and thank you. … End of Act  … Curtain falls

4.3 Act 2

57

4.3 Act 2 In between Act 1 and Act 2 …. After the meeting PR investigated the issue and could not find a thorough discussion on the secular determinant of methylenecyclopropene although in some books and online academic sites, PR could find the roots were −2.170, −0.311, +1 and +1.481. PR explored the issue and obtained these roots using a combination of online tools, technology and visualization. These were explained to the students in the next meeting … Start of Act  … Curtain rises … PR: Good morning you all SS: …. Most replied good morning … PR: I have something interesting to share. I checked about the issue with the polynomial and I would like to explain to you three methods which I could use to obtain the roots. I hope that you will find these interesting … PR: Method , remember that you have the equation as x − x + x + . This is a quartic equation and you can have online tools to solve it. I used the Keisan online calculator [] and I shared the screen with the students, Figure ..

Figure 4.2: Keisan online calculator.

As I was entering the coefficients, SS2 unmuted the microphone … SS: Sir, coefficient of b should be zero … PR: … Sorry …, I changed accordingly, Figure ., ….

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4 Conversation on the quartic equation

Figure 4.3: Keisan online calculator with the coefficients of the quartic equation.

I clicked on “Execute” … to obtain the roots, Figure 4.4, ….

Figure 4.4: Keisan online calculator with the roots of the quartic equation.

4.3 Act 2

PR: SS: PR: PR:

S: PR: SS: PR:

59

Can you see that the solutions match with those I could find from the book …! This is indeed useful … … Okay good …. I will show you Method . … Question was targeted to S … Can you realize that when x = +, the polynomial goes to zero …, thus (x − ) is a factor of the polynomial … and … hence, we need to divide the polynomial with (x − ) to obtain the other factor? … After some thoughts … yes … yes Alternatively, you can plot the quartic polynomial and you will find out the curve meets the x-axis at four distinct points including x = +. Would you like to try this and show me later? … yes … yes … [indeed, they tried and showed this to me the following week] Okay, now that we know that (x − ) is a factor of the polynomial …, … we can divide the polynomial by (x − ). This was done and the factor resulted in x + x − x −  …. Oops … this cubic equation also cannot be factorized easily … no problem … let us go online again … this time … for cubic equation (Figure .) ….

Figure 4.5: Keisan online calculator with the coefficients of the cubic equation.

I clicked on “Execute” … to obtain the roots, Figure 4.6, ….

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4 Conversation on the quartic equation

Figure 4.6: Keisan online calculator with the roots of the cubic equation.

So the solutions are again 1 and those from the table online …… … Are you with me? … … Yes. … … May I go ahead … … Yes … Now after showing you these online tools, I will show you a combination of visualization and an approximate method to obtain the roots of the cubic equation. Remember that the factor (x − ) gives you one of the roots x = + easily …. PR: I plotted the cubic function for the range x = from − to + and shared the screen to show to the students (Figure .) … PR: SS: PR: SS: PR:

Figure 4.7: Plot of cubic function, x = from −4 to +4.

4.3 Act 2

61

So … Can you see … there are three roots? … SS: PR:

… Many replied yes … … I have a better view with the range x = from − to +, Figure . ….

Figure 4.8: Plot of cubic function, x = from −3 to +2.

PR: … Now I will use the approximate roots as x = +., −. and −. to obtain the accurate roots based on the Newton–Raphson method [] …. S: Excuse me Sir, which software did you use to plot the graph? PR: I used Microsoft Excel [] but remember that one can use other tools for plotting a graph. PR: … May I proceed … SS: … Yes … yes … PR: Okay, now that we know that (x − ) is a factor PR: … Okay … we will use again an online tool, PLANETCALC [], ……

I provided the cubic function with the first approximate root, x = −2.1, Figure 4.9…

Figure 4.9: PLANETCALC with the cubic function, approximate root x = −2.1 and the accurate root.

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4 Conversation on the quartic equation

PR: So the first root is x = −. … PR: I provided the second approximate root, x = −., Figure ., ….

Figure 4.10: PLANETCALC with the cubic function, approximate root x = −0.3 and the accurate root.

PR: So the second root is x = −. PR: I provided the second approximate root, x = +., Figure ., ….

Figure 4.11: PLANETCALC with the cubic function, approximate root x = +1.2 and the accurate root.

References

PR: PR: PR: SS PR:

63

So the third root is x = +. …. . … So you can see these three roots are in order with respect to the solutions in the book …. Did you understand? … Are you happy? … … Yes … yes … indeed we are … … Be sure that in the examinations, if ever the polynomial does not have factors which are easy to obtain, the roots will be given to you for further actions ….

…… PR: … These will conclude the meeting today, thank you very much, have a good day and I will see you tomorrow … SS: …Thanks … …. End of Act  … Curtain falls …

4.4 Conclusions This write up represents an actual “resume” of the conversations between PR and SS as part of online meetings to teach the HM. PR considers it memorable and would like to share with facilitators. PR hopes that the methods will be useful to facilitators while teaching the HM leading to polynomials which cannot be factorized easily! The effectiveness of these tools was tested by giving assignments involving polynomials which cannot be solved easily and it was clear from the answers that they could apply the techniques they were taught. Acknowledgments: The 2017 batch final year BSc (Hons) Chemistry students enrolled at the University of Mauritius. Miss Mary-Joyce Natacha Lourde (S1) for raising the issue and Mr Manishrao Moteeram (S2) for pointing the mistake while inputting one of the coefficients.

References 1. 2. 3. 4. 5. 6. 7. 8.

9. 10.

Available from: https://www.uom.ac.mu/. Available from: https://sites.uom.ac.mu/ccuom/. Hückel E. Zur Quantentheorie der Doppelbindung. Zeitschrift für Physik 1930;60:423–56. Yates K. Huckel molecular orbital theory. US: Academic Press; 1978. Available from: https://www.who.int/emergencies/diseases/novel-coronavirus-2019/events-asthey-happen. Available from: https://meet.google.com/. Available from: https://keisan.casio.com/exec/system/1181809416. Tetsuro Y. Historical developments in convergence analysis for Newton’s and Newton-like methods. In: Brezinski C, Wuytack L, editors. Numerical analysis: historical developments in the 20th century. North-Holland: Elsevier Science; 2001:241–63 pp. Available from: https://www.microsoft.com/en-us/microsoft-365/excel. Available from: https://planetcalc.com/7748/.

Samuel Tetteh*, Albert Ofori, Andrew Quashie, Sirpa Jääskeläinen and Sari Suvanto

5 Modification of kaolinite/muscovite clay for the removal of Pb(II) ions from aqueous media Abstract: Natural clay extracted from the Central Region of Ghana was used for this study. Energy dispersive X-ray and powder X-ray diffraction analysis showed the composition of the clay to be 67.5% kaolinite and 32.5% muscovite. The samples were successfully modified by H2SO4 and NaOH activation. They were also characterized by scanning electron microscopic and Fourier transformed-infrared spectrophotometric techniques. Batch adsorption studies revealed that the samples are effective adsorbents for the removal of Pb(II) ions from aqueous media. Factors studied include contact time, pH, effect of ionic strength and the mass of adsorbent. Generally, the alkali activated samples had the highest adsorptive capacity followed by the acid activated clay. The kinetics of the adsorption process fitted the pseudo-second order model and the adsorption isotherm conformed to the Langmuir as well as the Freundlich models. All the experiments were carried out at room temperature (303 K). Keywords: adsorption; clay; isotherm; kaolinite; muscovite.

5.1 Introduction Clay minerals have been the subject of numerous research as a result of their interesting physical and chemical properties [1, 2]. These aluminosilicate group of minerals are made up of polymeric layers of SiO4 tetrahedra linked to sheets of (Al, Mg, Fe) (O, OH)6 octahedra with generally platy morphology due to the atomic arrangements in the structure [3]. These materials are readily available, cheap and environmentally friendly with large surface area suitable for various applications including adsorption of heavy metals [4–6], dyes [7, 8], antibiotics [9, 10] and other organic compounds [11, 12]. They also provide suitable surfaces for catalysis [13]. In kaolinite clay the tetrahedral silica sheets are

*Corresponding author: Samuel Tetteh, Department of Chemistry, School of Physical Sciences, University of Cape Coast, Cape Coast, Ghana, E-mail: [email protected]. https://orcid.org/ 0000-0002-8989-6346 Albert Ofori, Department of Chemistry, School of Physical Sciences, University of Cape Coast, Cape Coast, Ghana Andrew Quashie, Sanitation Environmental Management Division, Institute of Industrial Research, C.S.I.R., Cape Coast, Ghana Sirpa Jääskeläinen and Sari Suvanto, Department of Chemistry, University of Eastern Finland, P. O. Box 111, Fi-80101 Joensuu, Finland As per De Gruyter’s policy this article has previously been published in the journal Physical Sciences Reviews. Please cite as: S. Tetteh, A. Ofori, A. Quashie, S. Jääskeläinen and S. Suvanto “Modification of kaolinite/muscovite clay for the removal of Pb(II) ions from aqueous media” Physical Sciences Reviews [Online] 2022. DOI: 10.1515/psr-2021-0145 | https://doi.org/10.1515/ 9783110783643-005

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bonded to octahedral alumina sheets in a 1:1 ratio linked together through the sharing of oxygen atoms in adjacent sheets [1]. Muscovite clay on the other hand, is a mica-type clay comprising of a sheet of octahedral sites sandwiched between two tetrahedral silica sheets in a 2:1 ratio. These layers are held together by interlocking potassium ions. The possible chemical variation in the clay structure results from the substitution of Al3+ and Si4+ with other cations such as Mg2+ and Fe3+ which introduces variable surface charges in the clay [3]. This property is important in ion exchange studies. The clay lattices also have important Si-OH and Al-OH groups which can be modified to improve the surface charge of the clay material. Pillaring, intercalation and other chemical modifications are some of the common techniques used [14–16] in these surface modifications. Numerous studies have been reported in the literature involving the acid treatment of clays, especially smectite, bentonite, montmorillonite, kaolinite and gluconite. The commonly used acids include sulfuric acid, hydrochloric acid, nitric acid acetic acid, and oxalic acid. These acid-treated clays have been shown to possess increased surface area, higher number of acid centers and modified functional groups with high porosity and have been successfully employed in the removal of heavy metals such as Cu(II), Cd(II), Ni(II), Hg(II) and Pb(II) from aqueous media. According to Slaty et al. [17], alkali activation leads to dehydroxlation and improved crystallinity with an increase in active centers [18] of these materials. However, not much has been reported on the use of alkali activated clay for the removal of heavy metals from aqueous media. In this study, the composition of natural clay extracted from the Central Region of Ghana was determined. These samples were modified by acid and alkali activation and the feasibility of using these materials for removing Pb(II) ions from aqueous media was investigated. Lead is one of the most toxic heavy metals which impart negative effects on human health. Human exposure to lead can lead to kidney damage, neurological defects and in some cases induced sterility [33]. According to the World Health Organization (WHO), approximately 143,000 persons die annually as a result of lead poisoning [34]. This heavy metal enters the environment through activities such as acid mine drainage and effluents released from industries such as petroleum refineries as well as battery, paint and glass manufacturing [35]. Adsorption conditions such as contact time, pH, initial Pb(II) concentration, ionic strength and the mass of adsorbent were also investigated. All the experiments were carried out at room temperature and the overall kinetics and adsorption isotherms determined.

5.2 Materials and methods 5.2.1 Materials Raw clay samples were collected (into polyethylene bags) from several parts of the Central Region of Ghana and transported to the laboratory for purification and activation. The method of purification is described elsewhere [19]. Sulfuric acid (H2SO4) and sodium hydroxide (NaOH) used for the activation were purchased from VWR chemicals and used without further purification.

5.3 Results and discussion

67

5.2.2 Activation of the clay samples For the acid activation, exactly 20 g of the purified sample was added to 250 mL of 2 M H2SO4 solution in an Erlenmeyer flask fitted with a condenser. The mixture was refluxed for 2 h and allowed to cool. The cold mixture was filtered through Whatman number 1 filter paper. The residue was then washed severally with double distilled water until the pH was approximately 7. The acid activated clay was then heated at 100 °C for four (4) hours to evaporate excess water after which it was calcined at 450 °C in air for another four (4) hours to eliminate all organic and volatile components. The base activation was similarly carried out using aqueous sodium hydroxide.

5.2.3 Characterization The purified and activated clay samples were characterized by powder X-ray diffraction (PXRD) (Bruker Advance D8 Powder Diffractometer, scan speed 5°/min, step increment 0.1, rotation 120 rpm, 2θ (5.015°– 69.965°)), Fourier transform infrared (FTIR) spectrophotometry (Bruker Vertex 70 spectrometer, KBr pellets, 400–400 cm−1), scanning electron microscopy (SEM) (Hitachi S-4800 scanning electron microscope, working distance around 8 mm, acceleration voltage 2.0 kV, magnifications 500, 1000, 5000, 10,000 and 20,000×), electron dispersive X-ray spectroscopy (EDS) (working distance 15 mm, acceleration voltage 10.0 kV).

5.2.4 Adsorption studies Batch adsorption studies were carried out at room temperature (30 °C) by slight modifications of the method reported elsewhere [19] as follows; exactly 0.5 g of clay (except in cases where the effect of the mass of adsorbent was studied) was added to 50 mL aqueous solution of Pb(II) ions in a 250 mL Erlenmeyer flask on a magnetic stirrer. The mixture was stirred for pre-determined time intervals. Approximately 5 mL samples of the solution were taken and centrifuged at 2000 rpm for 10 min and the Pb(II) ions remaining unadsorbed were analysed on a Shimadzu AA7000 atomic absorption spectrophotometer with air-acetylene flame. Experiments were repeated trice and the averages reported. The pH was appropriately adjusted using aqueous solutions of sodium hydroxide or nitric acid. The variables determined include (i) pH (from 2.0 to 6.0 unit intervals), (ii) interaction time (min):15, 30, 45, 60, 75, 90, 105, 120, 135, 150. Equilibrium adsorption studies were however carried out at 180 min, (iii) amount of clay (g): 0.50, 0.75, 1.00, 1.25, (iv) Pb(II) ion concentration (mg/L): 10, 20, 30, 40, 50.

5.3 Results and discussion 5.3.1 Characterization Energy dispersive X-ray spectroscopy (EDS) was used for the qualitative analysis of the constituent elements in the purified clay sample. As shown in Figure 5.1, the conspicuous elements were found to be oxygen (O), aluminium (Al) and silicon (Si). These elements make up the aluminosilicate structure of clay minerals [3]. Minor elements such as sodium (Na), magnesium (Mg) and potassium (K) whose cations balance the excess surface charge of clay materials were also found (despite the possible replacement of

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5 Modification of kaolinite/muscovite clay

Figure 5.1: EDS spectrum of the purified clay with SEM of the purified clay (A) the acid activated clay (B) and the base activated clay (C) samples.

Al3+ with Mg2+ in some clay minerals [20]) with some amount of carbon (C) which might have been introduced during sample preparation. As shown in Figures 5.1A–C, the SEM images of the respective purified clay (PC), acid activated clay (AAC) and alkali/base activated clay (BAC) samples show the characteristic platy morphology of kaolinite clay which is ideal for catalytic and adsorption studies. It is evident that base activation significantly increased the surface area and volume of the clay material as compared to acid activation. Generally, both activation processes increased the crystallinity of the samples relative to the nonactivated sample. Based on the elemental composition data obtained in Figure 5.1, Crystallographic Information Files (CIFs) corresponding to clay minerals of similar composition were downloaded from the Crystallographic Open Database and fitted to the experimental data using the MAUD software [21, 22]. As shown in Figure 5.2, peak fitting using a combination of Gaussian and Lorentzian peaks followed by Rietveld refinement [22] gave the composition of the purified clay mineral to be 67.5% kaolinite and 32.5% muscovite. This composition shows kaolinite as the dominant phase in the clay mineral. Figure 5.3 shows the powder X-ray diffraction spectra of the purified and activated clay samples. The width of the peaks confirms the crystallinity of samples observed in Figure 5.1A–C and this increases in the order PC < AAC < BAC showing that base activation slightly increase the crystallinity of the clay sample.

5.3 Results and discussion

69

Figure 5.2: XRD pattern for the phase analysis and quantification of the purified clay sample.

A characteristic peak corresponding to d(002) reflection of muscovite [23] can be observed at 2θ value of 8.9°. Also conspicuous in the spectra are the basal peaks of kaolinite clay corresponding to d(001) (2θ = 12.3°) d(002) (2θ = 24.8°), d(003) (2θ = 37.6°) and d(004) (2θ = 51.1°). According to Panda et al. [24], peaks corresponding to the 2θ value 34°–36°, 38°–42°, 45°–50° and 54°–63° may vary for kaolinites from different sources. Generally, the characteristic peaks became sharper upon alkali activation as the intensity of the peaks slightly reduced with acid treatment. A phenomenon which has been attributed to structural disorder which occurs as a result of acid treatment [24].

K

K

Purified clay Acid Activated Clay Base Activated Clay

M K

Intensity

K

M

K

K M

M

K

M

5

15

25

35

45

2 theta (deg) Figure 5.3: XRD patterns of the purified and activated clay samples.

55

65

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5 Modification of kaolinite/muscovite clay

5.3.2 FTIR Spectroscopy 5.3.2.1 The high wavenumber region (4000–3200 cm−1) Figure 5.4 shows the high energy vibrational spectra of the three samples. Three characteristic peaks at 3695, 3651 and 3620 cm−1 can be assigned to antisymmetric AlOH vibrations [24]. The peak at 3659 cm−1 results from in-plane symmetric stretching and the weak vibration at 3651 cm−1 can be assigned to out-of-plane stretching vibrations. The absorption at 3620 cm−1 is as a result of inner OH groups lying between the tetrahedral and octahedral sheets (Figure 5.5). Acid treatment was found to decrease the intensity of the inner hydroxyl group vibration as a result of the penetration of protons into the structure of the clay layers which results in dehydroxylation and subsequent leaching of the Al3+ ions from the octahedral sheets [25]. 5.3.2.2 The low wavenumber region (1200–400 cm−1)

% Transmittance

This region contains the low energy symmetric stretching and bending vibrations of the aluminosilicate structure. It is the source of important fingerprint vibrations which have been successfully employed to characterize clay minerals [26]. Figure 5.6 shows a series of bands which result from bending and stretching vibrations of the SiO4 tetrahedra and AlO6 octahedra of the phyllosilicate sheets [27]. The peaks at 1104 and

3651 3695

4000

3900

3800

3620

3700 3600 3500 Wavenumber (cm-1)

Purified Clay Acid Activated Clay Base Activated Clay

3400

3300

3200

Figure 5.4: High wavenumber region of the FTIR spectra of the purified and activated clay samples.

% Transmittance

5.3 Results and discussion

71

755 693

1032

912

1104

1200

1100

1000

472

541

900 800 700 Wavenumber (cm-1)

600

500

400

Figure 5.5: Low wavenumber region of the FTIR spectra of the clay samples.

2.1 2 1.9

qt (mg/g)

1.8 1.7 1.6 1.5 Purified Clay

1.4

Acid Ac vated Clay Base Ac vated Clay

1.3 1.2 15

35

55

75 95 Time (min)

115

135

155

Figure 5.6: Influence of reaction time on Pb(II) uptake by the clay samples (experimental conditions: clay 0.5 g, initial Pb(II) 20 mg/L: pH 6, temperature 303 K).

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5 Modification of kaolinite/muscovite clay

1034 cm−1 can be assigned to symmetric Si–O stretching vibrations. Generally, the positions and intensities of these peaks were not affected by either acid or alkali treatment showing the resilience of the SiO4 tetrahedral structure to either mode of activation. The peak at 912 cm−1 can be assigned to Al–Al–OH bending vibration of the aluminate sheets. The intensity of this peak reduced slightly upon acid activation as a result of the penetration of the protons into the clay structure which results in dealumination during acid treatment [24]. The Si–O–Al symmetric stretching vibrations appear as relatively weak peaks at 755 and 693 cm−1. Weak energy bending vibrations of the Si–O tetrahedra and Si–O–Al bond can also be ascribed to the bands at 472 and 541 cm−1, respectively. Generally, the positions of these vibrational peaks remain unchanged despite the reduction in intensity of the acid activated clay peaks which can be attributed to de-alumination of the clay minerals as a results of the acid treatment.

5.3.3 Adsorption kinetics 5.3.3.1 Effect of contact time The effect of contact time on the transportation of Pb(II) ions from the bulk of the solution onto the surface of the clay samples was investigated at 30 °C, using 0.5 g clay in 20 mg/L of 50 mL Pb(II) solution and at different time intervals. As shown in Figure 5.7, 2.1 2 1.9

qe (mg/g)

1.8 1.7 1.6 Purified Clay Acid Activated Clay Base Activated Clay

1.5 1.4 1.3 1.2 2

3

4

pH

5

6

7

Figure 5.7: Effect of pH on the uptake of Pb(II) ions by the clay samples (experimental conditions: clay 0.5 g, initial Pb(II) 20 mg/L, time 180 min, temperature 303 K).

5.3 Results and discussion

73

the adsorption process equilibrated after 30 min in all the samples studied. Generally, alkali activated clay was the better adsorbent with an adsorption rate of 2 mg Pb(II) ions per grams of clay as compared to the acid activated with approximately 1.7 mg/g. This differences in adsorption capacity can be attributed to the relatively negative surface charge of alkali activated clay samples [28] which have stronger affinity for the positive Pb(II) ions. For the purified clay, the estimated equilibrium adsorption rate was 1.5 mg/ g. Despite the fast equilibration time for the Pb(II) ions on the clay samples, a contact time of 180 min was chosen for the equilibrium studies. 5.3.3.2 Effect of pH Adsorption of metal ions from aqueous solutions is strongly dependent on the pH of the medium [4]. This property affects the solubility of the metal ions, the concentration of counter ions as well as the degree of ionization of the adsorbate during the adsorption process [27]. At low pH values where the concentration of H3O+ is high, there is competition between the metal ions and the hydroxonium ions for active sites on the surface of the adsorbent. This competition however decreases at pH ∼ 6 where there are fewer H3O+ ions in solution despite the fact that most metal ions precipitate as hydroxides at pH > 7. As shown in Figure 5.7, the equilibrium amount of Pb(II) ions adsorbed increased slightly for the purified and acid activated samples although marginally for the alkali activated clay. The adsorption capacity is in the order BAC > AAC > PC. This shows the superiority of the alkali activation of the adsorbent for the adsorption of Pb(II) ions. 5.3.3.3 Effect of ionic strength The ionic strength describes the effect of added electrolyte on the effective concentration of the adsorbate ions [29]. Figure 5.8 shows the effect of added KNO3 on the equilibrium amount of Pb(II) ions adsorbed. Generally, alkali activated and purified clay samples had the higher adsorption capacities with averages of 2 and 1.95 mg/g, respectively. The equilibrium amounts of Pb(II) ions adsorbed onto acid activated clay however decreased slightly from an initial value of 1.047 mg/g in 0.03 M KNO3 to 0.949 mg/g in 0.15 M KNO3. With the same counterion (NO3−(aq)) on both the adsorbate and the electrolyte, the adsorption rate is determined by the competition between K+ ions and the Pb(II) ions for the negatively charged sites (Si–O) and (Al–OH) on the clay surface [30]. The marginal changes in the equilibrium amounts of Pb(II) ions adsorbed suggests that the electrolyte has negligible effect on the adsorption of Pb(II) ions within the electrolyte concentration range (0.03–0.15 M KNO3) studied.

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5 Modification of kaolinite/muscovite clay

2.1

1.9

1.7

qe (mg/g)

1.5

1.3

1.1

0.9 Purified Clay Acid Ac vated Clay

0.7

Base Ac vated Clay 0.5 0.02

0.04

0.06

0.08

0.1

0.12

0.14

0.16

[KNO3], M

Figure 5.8: Effect of ionic strength on the uptake of Pb(II) ions by the clay samples (experimental conditions: clay 0.5 g, pH 6, initial Pb(II) 20 mg/L, time 180 min, temperature 303 K).

5.3.3.4 Effect of initial amount of clay The equilibrium amount of 20 mg/L Pb(II) ions adsorbed was also studied using different amounts (mass) of adsorbent. As shown in Figure 5.9, there was a general decline in the amount of Pb(II) ions adsorbed with increasing amount of adsorbent. Bhattacharyya and Gupta [4], working on the removal of Cu(II) ions by natural and acid-activated clays reported that a decrease in adsorption density with increasing amount of adsorbent could be due to the fact that, with small amount of adsorbent, the metal ions have easy access to the active sites which results in high qe values. Generally, the adsorption capacity for the alkali activated clay decreased sharply from an initial value of 1.98 mg/g (0.5 g adsorbent) to 0.80 mg/g (1.25 g) adsorbent. Similar changes can be observed for both the purified and acid-activated clay samples. These observations suggest that the Pb(II) ions may find it difficult to approach the active sites of the clay mineral as a result of overcrowding of the particles. 5.3.3.5 Effect of initial Pb(II) ion concentration The equilibrium amount of Pb(II) ions adsorbed unto 0.5 g adsorbent was investigated within the concentration range of 10–50 mg/L Pb(II) ions at 303 K. The results shown in

75

5.3 Results and discussion

2.1

1.9

qe (mg/g)

1.7

1.5

Purified Clay Acid Activated Clay Base Activated Clay

1.3

1.1

0.9

0.7

0.5

0.6

0.7

0.8

0.9 1 Mass of Clay (g)

1.1

1.2

1.3

Figure 5.9: Influence of amount of clay on the uptake of Pb(II) ions (experimental conditions: clay 0.5, 0.75, 1.00, 1.25 g, pH 6, initial Pb(II) 20 mg/L, time 180 min, temperature 303 K).

Figure 5.10 reveal that the adsorption capacity increases with the concentration of the Pb(II) ions. For the alkali activated clay, the equilibrium adsorption capacity increased from 1.65 mg/g for 10 mg/L Pb(II) ions to 5.72 mg/g for 50 mg/L Pb(II) ions. However, the percent adsorption (%) decreased from 16.60 to 14.44%. This is due to the fact that at lower concentrations, there is less competition of the metal ions for the available active sites which increases the adsorption rate. This phenomenon however changes with high concentration of the metal ions which leads to competition for the available active sites and a resultant decrease in the rate of adsorption. The general rate of adsorption was in the order of BAC > AAC > PC.

5.3.4 Adsorption isotherm The adsorption isotherms of Pb(II) ions for the three adsorbents were studied using the Langmuir and Freundlich models [31, 32]. According to the Langmuir model, the uptake of metal ions occurs as a monolayer on the surface of the adsorbent with negligible interaction between the adsorbed ions. The Langmuir equation is given as Ce 1 Ce = + qe K L qm qm

(5.1)

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5 Modification of kaolinite/muscovite clay

5.5

qe (mg/g)

4.5

3.5 Purified Clay Acid Ac vated Clay Base Ac vated Clay

2.5

1.5

0.5 10

20

30

Pb(II) (mg/L)

40

50

60

Figure 5.10: Influence of initial Pb(II) ion concentration on the uptake of Pb(II) ions by the clay samples (experimental conditions: clay 0.5 g, pH 6, initial Pb(II) 10, 20, 30, 40, 50 mg/L, time 180 min, temperature 303 K).

where Ce (mg/L) is the equilibrium concentration of the metal ions, qe (mg/g) is the equilibrium adsorption capacity, KL (L/mg) is the Langmuir equilibrium constant and qm is the maximum monolayer adsorption capacity. According to Eq. (5.1), the value of qm and KL can be estimated from the slope and intercept of the linear plot of Cqee against Ce. The Freundlich equation is given as 1 logqe = logK f + logC e n

(5.2)

where Kf is the Freundlich adsorption equilibrium constant and n is the Freundlich intensity factor. These parameters can be obtained from the respective intercept and slope of the linear plot of logqe against logCe as given in Eq. (5.2). Figure 5.11 shows the Langmuir plot of the adsorption of Pb(II) ions on all the adsorbents. The positive linear regression coefficients (r 2 = 0.98–0.99) clearly shows that these plots fit the monolayer adsorption model. As shown in Table 5.1, the value of the maximum monolayer adsorption capacity increased from 9.71 mg/g for the purified clay to 13.41 mg/g (27.6%) upon acid activation as compared to 20.66 mg/g (53.00%) after alkali activation. This increase in monolayer adsorption capacity results from the modification of the surface –OH groups and the increase in crystallinity (Figures 5.1 and 5.4) which influences the surface charge and the surface area.

77

5.3 Results and discussion

7.00

6.00

Ce/qe (g/L)

5.00

4.00 Purified Clay 3.00

Acid Ac vated Clay Base Ac vated Clay

2.00

1.00

0.00 0

5

10

15

20

25

30

35

Ce (mg/L)

Figure 5.11: Langmuir plots for the adsorption of Pb(II) ions onto the clay samples (experimental conditions: Clay 0.5 g, initial Pb(II) concentration 10, 20, 30, 40, 50 mg/L, pH 6, time 180 min, temperature 303 K). Table .: Langmuir and Freundlich coefficients of adsorption of Pb(II) ions onto the clay samples (Experimental conditions: clay . g, initial Pb(II) concentration , , , ,  mg/L, pH , time  min, temperature  K). Parameters

Langmuir coefficients

Freundlich coefficients

Clay adsorbent

−

qm (mg g ) KL (L mg−) r Kf (mg−/n L/n g−) n r

PC

AAC

BAC

. . +. . . +.

. . +. . . +.

. . +. . . +.

The enhanced effect of alkali activation is to deprotonate surface Si–OH and Al– OH groups in order to increase the electrostatic surface charge for the positively charged Pb(II) ions. Similar observations have been reported elsewhere [4]. Also shown in Table 5.1 are the Freundlich coefficients Kf and n. The r2 values of +0.99 for the adsorption of Pb(II) ions on all the adsorbents also points to the fact that the adsorption

78

5 Modification of kaolinite/muscovite clay

process follows the Freundlich model. In all instances, the value of n is greater than one, which indicates that the adsorption process was favorable [27]. Generally, the data from the studies fitted both isotherms very well. This observation supports the fact that most adsorption sites are generally non-uniform and nonspecific in nature [30] and there are different adsorption sites with different activation energies. The observed adsorption coefficients also support the conditions for favorable adsorption of Pb(II) ions unto the studied adsorbents.

5.3.5 Adsorption kinetics The kinetics of the adsorption process was investigated using the pseudo-second order equation: t 1 t = + qt k 2 qe 2 q e

(5.3)

where the equilibrium amount of Pb(II) ions adsorbed (qe) and the rate constant (k2) can be estimated from a plot of t/qt against t. According to Eq. (5.3), the adsorption process is predominantly dependent on the interaction between the clay (adsorbent) and the Pb(II) ions according to the equilibrium: Clay (adsorbent) +  Pb(II) ions (aqueous phase) → Clay...Pb(ii)

120.00

100.00

t/qt (g min/mg)

80.00

60.00

40.00

20.00

0.00 0

20

40

60

80

100

120

140

160

Time (min)

Figure 5.12: Second order plots for Pb(II) adsorbed onto the clay samples (clay 0.5 g, initial Pb(II) ion concentration 20 mg/L, pH 6, temperature 303 K).

79

References

Table .: Rate coefficients for second order plots of the adsorption of Pb(II) ions onto the clay samples. (Experimental conditions: clay . g, initial Pb(II) ion concentration  mg/L, pH , temperature  K). Clay adsorbent

PC AAC BAC

Second order coefficients k (g mg−min−)

qe (mg g−)

r

. . .

. . .

. . .

when all other factors are kept constant. The value of k2 qe 2 in Eq. (5.3) describes the initial adsorption rate as t → 0. As shown in Figure 5.12 the data set perfectly fits the pseudo-second order plot with r2 values of +1.00 in all instances. The rate constants, k2 (given in Table 5.2) decreased from 1.17 to 0.62 g mg−1 min−1 upon acid activation but increased to 1.86 g mg−1 min−1 when the clay was alkali activated. These observations support the earlier findings in Figures 5.6–5.10 that the adsorption of Pb(II) ions is more favorable on the alkali activated clay samples relative to the acid activated samples.

5.4 Conclusions Natural clay extracted from the Central Region of Ghana was successfully purified and characterized. This kaolinite/muscovite clay was modified by acid and base activation. These materials are effective for the removal of Pb(II) ions from aqueous media. Factors such as contact time, pH, initial Pb(II) ion concentration and mass of adsorbent have strong influence on the adsorption process although the effect of ionic strength has negligible influence. The agreement of the adsorption process to both Langmuir and Frendlich isotherm models suggests that the adsorption sites are non-uniform and nonspecific in nature. The adsorption process usually followed pseudo-second order kinetic showing that the adsorption of Pb(II) ions depends on the concentration of the adsorbent as well as the adsorbate.

References 1. Murray HH. Structure and composition of the clay minerals and their physical and chemical properties. Dev Clay Sci 2006;2:7–31. 2. Bergaya F, Lagaly G. General introduction: clays, clay minerals, and clay science. Dev Clay Sci 2006;1:1–18. 3. Ismadji S, Soetaredjo FE, Ayucitra A. Clay materials for environmental remediation, vol. 25. Berlin: Springer; 2015.

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4. Bhattacharyya KG, Gupta SS. Removal of Cu (II) by natural and acid-activated clays: an insight of adsorption isotherm, kinetic and thermodynamics. Desalination 2011;272:66–75. 5. Uddin MK. A review on the adsorption of heavy metals by clay minerals, with special focus on the past decade. Chem Eng J 2017;308:438–62. 6. Padilla-Ortega E, Leyva-Ramos R, Flores-Cano J. Binary adsorption of heavy metals from aqueous solution onto natural clays. Chem Eng J 2013;225:535–46. 7. Kausar A, Iqbal M, Javed A, Aftab K, Bhatti HN, Nouren S. Dyes adsorption using clay and modified clay: a review. J Mol Liq 2018;256:395–407. 8. Gürses A, Doğar Ç, Yalçın M, Açıkyıldız M, Bayrak R, Karaca S. The adsorption kinetics of the cationic dye, methylene blue, onto clay. J Hazard Mater 2006;131:217–28. 9. Polubesova T, Zadaka D, Groisman L, Nir S. Water remediation by micelle–clay system: case study for tetracycline and sulfonamide antibiotics. Water Res 2006;40:2369–74. 10. Premarathna K, Rajapaksha AU, Adassoriya N, Sarkar B, Sirimuthu NM, Cooray A, et al. Claybiochar composites for sorptive removal of tetracycline antibiotic in aqueous media. J Environ Manag 2019;238:315–22. 11. Okada T, Seki Y, Ogawa M. Designed nanostructures of clay for controlled adsorption of organic compounds. J Nanosci Nanotechnol 2014;14:2121–34. 12. Qu F, Zhu L, Yang K. Adsorption behaviors of volatile organic compounds (VOCs) on porous clay heterostructures (PCH). J Hazard Mater 2009;170:7–12. 13. Dasgupta S, Toeroek B. Application of clay catalysts in organic synthesis. A review. Org Prep Proced Int. 2008;40:1–65. 14. Bergaya F, Aouad A, Mandalia T. Pillared clays and clay minerals. Dev Clay Sci 2006;1:393–421. 15. Chiu C-W, Huang T-K, Wang Y-C, Alamani BG, Lin J-J. Intercalation strategies in clay/polymer hybrids. Prog Polym Sci 2014;39:443–85. 16. Pajak M. Adsorption capacity of smectite clay and its thermal and chemical modification for two anionic dyes: comparative study. Water Air Soil Poll 2021;232:1–18. 17. Slaty F, Khoury H, Wastiels J, Rahier H. Characterization of alkali activated kaolinitic clay. Appl Clay Sci 2013;75:120–5. 18. Kumar S, Panda AK, Singh R. Preparation and characterization of acids and alkali treated kaolin clay. Bull Chem React Eng Catal 2013;8:61–9. 19. Tetteh S, Quashie A, Anang MA. Purification, characterization, and time-dependent adsorption studies of Ghanaian muscovite clay. J Chem 2018. https://doi.org/10.1155/2018/6252913. 20. Zhou X, Liu D, Bu H, Deng L, Liu H, Yuan P, et al. XRD-based quantitative analysis of clay minerals using reference intensity ratios, mineral intensity factors, Rietveld, and full pattern summation methods: a critical review. Solid Earth Sci 2018;3:16–29. 21. Lutterotti L. Materials analysis using diffraction (MAUD) software. 22. Lutterotti L. MAUD tutorial-instrumental broadening determination. Trento: Dipartimento di Ingegneria dei Materiali, Università di Trento; 2006. 23. Ismail NHC, Bakhtiar NSAA, Akil HM. Effects of cetyltrimethylammonium bromide (CTAB) on the structural characteristic of non-expandable muscovite. Mater Chem Phys 2017;196:324–32. 24. Panda AK, Mishra BG, Mishra DK, Singh RK. Effect of sulphuric acid treatment on the physicochemical characteristics of kaolin clay. Colloids Surf. A: Physicochem. Eng. 2010;363:98–104. 25. Madejová J. FTIR techniques in clay mineral studies. Vib Spectrosc 2003;31:1–10. 26. Yan W, Liu D, Tan D, Yuan P, Chen M. FTIR spectroscopy study of the structure changes of palygorskite under heating. Spectrochim. Acta A: Mol. Biomol. Spectrosc. 2012;97:1052–7. 27. Jiang M-q, Wang Q-p, Jin X-y, Chen Z-l. Removal of Pb (II) from aqueous solution using modified and unmodified kaolinite clay. J Hazard Mater 2009;170:332–9.

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Theam Soon Lim and Yee Siew Choong*

6 In silico design of ACE2 mutants for competitive binding of SARS-CoV-2 receptor binding domain with hACE2 Abstract: The receptor binding motif (RBM) within the S-protein of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has been touted as one of the main targets for vaccine/therapeutic development due to its interaction with the human angiotensin II converting enzyme 2 (hACE2) to facilitate virus entry into the host cell. The mechanism of action is based on the disruption of binding between the RBM and the hACE2 to prevent virus uptake for replication. In this work, we applied in silico approaches to design specific competitive binders for SARS-CoV-2 S-protein receptor binding motif (RBM) by using hACE2 peptidase domain (PD) mutants. Online single point mutation servers were utilised to estimate the effect of PD mutation on the binding affinity with RBM. The PD mutants were then modelled and the binding free energy was calculated. Three PD variants were designed with an increased affinity and interaction with SARS-CoV-2-RBM. It is hope that these designs could serve as the initial work for vaccine/drug development and could eventually interfere the preliminary recognition between SARS-CoV-2 and the host cell. Keywords: hACE2 peptidase domain (PD); specific binders for SARS-CoV-2 receptor binding motif (RBM); structure-based rational design.

6.1 Introduction Since the first reported case on Coronavirus Disease 2019 (COVID-19) in the end of year 2019, the World Health Organization (WHO) has reported the extent of pandemic has already surpassed 5 million mortalities worldwide and nearly 300 million infections in just 2 years (https://www.who.int/data/; accessed date 7th January 2022). The causative agent of COVID-19, SARS-CoV-2, is closely related two other known pathogenic coronaviruses (CoV), i.e. the severe acute respiratory syndrome (SARS) and the Middle East respiratory syndrome (MERS). To date, no specific antivirals have been proven to effective in treating CoV related infections, thus emphasizing the important of vaccines and antiviral development for coronavirus disease management.

*Corresponding author: Yee Siew Choong, Institute for Research in Molecular Medicine, Universiti Sains Malaysia, Penang, Malaysia, E-mail: [email protected]. https://orcid.org/0000-0001-5067-2073 Theam Soon Lim, Institute for Research in Molecular Medicine, Universiti Sains Malaysia, Penang, Malaysia As per De Gruyter’s policy this article has previously been published in the journal Physical Sciences Reviews. Please cite as: T. S. Lim and Y. S. Choong “In silico design of ACE2 mutants for competitive binding of SARS-CoV-2 receptor binding domain with hACE2” Physical Sciences Reviews [Online] 2022. DOI: 10.1515/psr-2021-0136 | https://doi.org/10.1515/9783110783643-006

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6 In silico design of ACE2 mutants

The structural proteins of CoV are the envelope (E), membrane (M), nucleocapsid (N) and spike (S) proteins [1, 2]. The S-protein mediates the viral entrance into the host cell [3–11]. Upon the entry into host cell, CoV S-protein will first be cleaved into subunit S1 and S2 by the host proteases [12, 13]. The C-terminal domain (known as receptor binding domain; RBD) in the subunit S1 will be recognized by the host receptor. SARS-CoV and SARS-CoV-2 recognize human angiotensin converting enzyme II (hACE2) their receptor [4, 11, 14] with the later has stronger affinity with hACE2 [15–18]. The RBD region of SARS-CoV/-2, the receptor binding motif (RBM) is the anchor for the engagement with the N-terminal peptidase domain (PD; residues 19–63) of hACE2 [12, 16]. Therefore, the ability to inhibit binding of the RBM with the PD of hACE2 is seen as the best approach for vaccine and therapeutic designs. This could be possible by interfering with the complexation of S-protein with the host receptor to suppress viral infections. Majority of strategies are focused on developing antibodies against the RBD to afford a neutralizing effect against SAR-CoV-2. In this work, we aimed to design molecules that could be potentially used to prevent the recognition of SARS-CoV-2 RBM with hACE2. We propose the utilization of hCAE2 variants as an alternative to inhibit the binding of SARS-CoV-2 RBM with hACE2. The predicted single point mutation of hACE2 PD that can improve the binding affinity with RBM was used in the design of hACE2 PD variants. This structure-based rational design of PD variants has yielded three mutants. These designs are the first steps towards the development of the variants for analysis. The information from these designs can potentially facilitate the development of alternative hACE2 variants capable of disrupting the recognition of SARS-CoV-2 with the host cells.

6.2 Methodology The continuous interface residues of hACE2 PD (residues S19–Y83) obtained from PDB X-ray crystal structure (PDB id 6LZG [15]) were subjected to structure-based free energy of binding changes prediction caused by point mutations. Each residue was mutated by webservers Single Amino Acid Mutation related change of Binding Energy (SAAMBE) [19] and BeAtMiSiC [20]. The binding free energy changes upon point mutation with negative values indicated an improvement of the binding affinity within the complex and are marked as preferred mutations. The point mutations that contribute to at least 50% from the most negative binding free energies were taken into consideration for the design hACE2 PD variants. Sequence-based protein-protein binding affinity prediction by PPA-Pred2 (Protein–Protein Affinity Predictor) [21] was performed on the new hACE2 PD variants. The new designs (mutant type; MT) were also modelled using MODELLER v10.1 (23). Superimposition of the MTs with crystal structure was performed with PyMOL alignment tool [22] and the affinity of PD designs in complex with RBM was estimated again using PRODIGY [23]. The interactions between the MTs hACE2 PD with SARS-CoV-2-RBM were analysed and compared with the crystal structure (PDB ID 6LZG).

6.3 Results and discussion

85

6.3 Results and discussion In this work, we considered the design of peptide inhibitors to block the recognition of SARS-CoV-2-RBM with the actual human host receptor (hACE2 PD). Peptides as antimicrobial inhibitors have been reported for bacteria, fungi, parasites as well as viral infections [24–31]. Besides broad spectrum activity, the antimicrobial peptides also have lower chances of drug resistance development and possess immunomodulatory properties. Therefore, antimicrobial peptides have been considered as the alternative to conventional synthetic drugs [32–34]. Furthermore, taking advantage of the availability of the structure of hACE2 in complex with SARS-CoV-2 RBD that was recently solved [11, 15, 35], we utilized the continuous interface regions of the hACE2 PD (residues S19–Y83) and redesigned the regions as peptide inhibitors towards SARS-CoV2-RBM. In order to stop the entry of SARS-CoV-2 into the host cell, an inhibitor should bind better with SARS-CoV-2-RBM compare with wild type (WT) hACE2 PD as the actual receptor. Thus, in theory, a better binding affinity of the inhibitor should be able to block the recognition of SARS-CoV-2 with the host receptor. Hence, we hypothesized by using WT hACE2 PD as the initial scaffold, we should be able to design mutant type (MT) hACE2 PD with enhanced binding affinity with SARS-CoV-2-RBM than that of WT hACE2 PD. In this work, we first estimated the effects of hACE2 PD single point mutation on the affinity with SARS-CoV-2-RBM. To avoid possible bias results, two webservers (SAAMBE and BeAtMuSic) were used in the calculation of mutation effects. SAAMBE uses a combined knowledgebased approach of modified MM-PBSA terms to predict the free energy changes while BeAtMuSic is a coarse-grained predictor derived from the statistical potential to calculate the overall stability of a complex. Results from both SAAMBE and BeAtMuSic were used in this work. SAAMBE predicted the PD mutations at A25F, A36K/R, A46K, A65K/R, A71D and A80D have at least 50% of the most negative binding free energy compared with the WT (Figure 6.1a). It is also interesting to note that all mutations that were estimated to improve the affinity with SARS-CoV-2-RBM were resulted from alanine mutations. While BeAtMuSiC showed that PD mutation at S19C, Q24W, T27W, L29E, E35F, N49V, N61A and K68I will improve the affinity with SARS-CoV-2-RBM (Figure 6.1b). Among the single point mutation, SAAMBE predicted PD A46 have the most negative binding free energy changes upon a A46K mutation (Figure 6.1a) while BeAtMuSiC showed that Q24W has the best binding affinity with RBM (Figure 6.1b). The mutations that were at least 50% of the most negative binding free energy were taking into consideration in the design of new hACE2 PD variants (MT PD), therefore; it resulted in three variant designs for hACE2 PD. Two and one variants were resulted from SAAMBE and BeAtMuSiC estimation, respectively. Table 6.1 lists the mutations for hACE2 PD variants. There are six and eight PD point mutations from SAAMBE and BeAtMuSiC results, respectively.

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6 In silico design of ACE2 mutants

Figure 6.1: Single point mutation on the binding free energy affinity (ΔΔGbind) differences predicted by (a) SAAMBE [19], and (b) BeAtMuSiC [20] for hACE2 PD (residues S19–Y83). Red and blue denotes favourable and unfavourable mutation, respectively. Table .: The mutations (red bold font) of the new hACE PD designs (residues S–Y). hACE PD

Sequence

WT MTSB MTSB MTBM

STIEEQAKTFLDKFNHEAEDLFYQSSLASWNYNTNITEENVQNMNNAGDKWSAFLKEQSTLAQMY STIEEQFKTFLDKFNHEKEDLFYQSSLKSWNYNTNITEENVQNMNNKGDKWSDFLKEQSTLDQMY STIEEQFKTFLDKFNHEREDLFYQSSLKSWNYNTNITEENVQNMNNRGDKWSDFLKEQSTLDQMY CTIEEWAKWFEDKFNHFAEDLFYQSSLASWVYNTNITEENVQAMNNAGDIWSAFLKEQSTLAQMY

WT and MT denote for wild type and mutant type, respectively. SB and BM denote designs from the results of SAAMBE [] and BeAtMuSiC [], respectively.

The MT PD variants were then subjected to protein–protein binding affinity prediction to estimate the binding free energy of RBM-PD complex. The sequence-based binding affinity predicted was employed using the sequence of new PD variants. The sequence-based predicted binding affinity for RBM- WT hACE2 PD is −12.9 kcal/mol.

6.3 Results and discussion

87

The estimated binding free energy based solely on the primary sequence of RBM-MT is −13.8, −10.8 and −12.5 kcal/mol for RBM-MTSB1 (PD design #1 from the results of SAAMBE), RBM-MTSB2 (PD design #2 from the results of SAAMBE) and RBM-MTBM (PD design from the results of BeAtMuSic), respectively. However, considering that the three dimensional structure of a protein is important in the interaction with the receptor, we thus modelled the three MT PD and then the binding free energy was estimated again using structure-based protein-protein binding affinity prediction. The modelled structure of MT PD designs were first accessed on the quality of their folding using Ramachandran plot. All 62 residues in the three MT PD designs are within the most favoured regions, indicating good quality of modelled MT PD with acceptable main-chain ψ and ϕ torsional angles. Besides, the superimposition of MT PD variants with WT PD has root mean square deviation (RMSD) values of 0.2–0.6 Å, thus showing insignificant deviation from the WT (Figure 6.2). Therefore, the build models of MT PD variants can be used for further analysis. In order to model RBM-MT PD complex, the built MT PD variants were replaced with the initial RBM-WT PD complex. Further analysis was performed on the affinity of MT PD variants towards RBM. The binding free energy of RBM-MT PD complex based on the three dimensional structure were calculated. The tabulated results showed that all MT PD designs have better protein-protein affinity values compared with that of the WT

Figure 6.2: Superimposition of the wild type (WT) hACE2 PD (PDB ID 6LZG) with (a) mutant type (MT) MTSB1 PD, (b) MTSB2 PD and (c) MTBM PD. The calculated root mean square deviation (RMSD) of the WT with MT is only 0.3, 0.2 and 0.6 Å for MTSB1 PD, MTSB2 PD and MTBM PD, respectively. SB and BM denote designs by taking into consideration of the results from SAAMBE [19] and BeAtMuSiC [20]. Figure was prepared using PyMol [36].

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6 In silico design of ACE2 mutants

Table .: Structure-based binding affinity prediction by PRODIGY [] for the mutant type (MT) hACE PD designs compared with that of wild type (WT) hACE PD. SB and BM denotes designs from the results of SAAMBE [] and BeAtMuSiC [], respectively. RBM

hACE PD

Binding affinity (ΔG; kcal/mol)

WT

WT

−.

MTSB

−.

MTSB

−.

MTBM

−.

Interfacial contacts Apolar–apolar Charged–apolar Charged–charged Charged–polar Polar–apolar Polar–polar Apolar–apolar Charged–apolar Charged–charged Charged–polar Polar–apolar Polar–polar Apolar–apolar Charged–apolar Charged–charged Charged–polar Polar–apolar Polar–polar Apolar–apolar Charged–apolar Charged–charged Charged–polar Polar–apolar Polar–polar

Number of contacts                        

PD (Table 6.2). MTBM PD is the most favourable design amongst all MT PD. The binding free energy of RBM-MT PD variants has improved by the range of 0.5–1.1 kcal/mol. The interface contacts analysis showed that the affinity of RBM-WT PD is mainly contributed by polar–apolar and charged–apolar contacts. Charged–charged contacts contributed the least to RBM with either WT or MT PD designs. It is noted that there is an increased number of charged–apolar and apolar–apolar contacts between RBM and MT PD compared with that of WT PD. Besides, slight increase in the number of polar– apolar interactions for RBM-MT PD was also noticed. This might explain the improved binding affinity of MT PD designs with RBM. Culminating the results from the point mutation prediction, binding affinity estimation and interaction analysis of the new MT hACE2 PD designs, these MT PD variants could potentially be useful as a SAR-CoV-2 decoy target or extended as a peptide inhibitor for SAR-CoV-2. In addition, as hACE2 alleles have also been reported to

References

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influence the recognition with SARS-CoV-2 S-protein [37], therefore the new MT hACE2 PD designs could highlight the possible resistance against SARS-CoV-2 S-protein. Future work can be extended to in vitro testing of the MT hACE2 PD designs to validate the affinity of MT PD variants.

6.4 Conclusions The study on all respect on SARS-CoV-2 has been intensified since the declaration of COVID-19 as a global pandemic. In order to manage this global health emergency, the race for new vaccines and therapeutics is ongoing with an estimated 100 vaccine candidates and 170 therapeutic drugs currently in development. Since SARS-CoV-2 S-protein involves in the host receptor recognition for the host cell entry, hence exploit the potential of hCAE2 variants to inhibit the binding of SARS-CoV-2 receptor binding motif (RBM). In this work, we identified the mutations on hCAE2 N-terminal peptidase domain (PD) that could improve the binding affinity with RBM. We used wild type (WT) PD as the scaffold to design mutant type (MT) PD as potential peptide inhibitor for blocking the recognition of SARS-CoV-2 RBM with the actual receptor. The WT PD and its MT PD variants in complexed with RBM were modelled and compared their binding affinity. Results showed that the MT PD designs have were better binding free energy with RBM compared with that of WT PD. The computational calculation we applied in this work could be worth further study and might shred some lights for COVID-19 vaccine or drug development.

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Shayeri Das, Prabhat Ranjan* and Tanmoy Chakraborty*

7 Computational study of CunAgAu (n = 1–4) clusters invoking DFT based descriptors Abstract: Metallic clusters have shown potential uses in science and technology especially in the domain of photovoltaics, biomedical and catalysis. The noble metal based clusters like Cu, Ag, and Au exhibits notable structural, electronic and optical properties. In this work, we have examined physico-chemical behaviours of tri-metallic clusters CunAgAu (n = 1–4) by using density functional theory (DFT) technique. Conceptual DFT based descriptors of these clusters are calculated and analysed. HOMO–LUMO gap at n = 2, 3 and 4 are found as 1.667, 1.610 and 1.785 eV, respectively. It states that these clusters can be used in optoelectronic and photovoltaic devices. HOMO–LUMO energy gap, hardness and electronegativity of CunAgAu clusters exhibit an odd–even fluctuation behaviour with the cluster size, n. Molecular hardness of CunAgAu cluster shows linear relationship with energy gap whereas molecular softness exhibits an inverse relationship. Keywords: CuAgAu; density functional theory; electronegativity; HOMO–LUMO gap; metallic clusters.

7.1 Introduction In the past decade, metallic clusters especially transition metal based clusters have opened several new dimensions of research due to their large range of applications. These clusters have shown unique physical and chemical properties. Metallic clusters have diverse applications in various fields like semiconductors, nanotechnology, biological sciences etc. [1–5]. Cluster sizes vary depending on size, geometry and composition and it can be mono-metallic, bi-metallic or multi-metallic. Among these categories mono-metallic and bimetallic clusters have been extensively studied [6–13]. However, study on multi-metallic clusters is very limited because of complexities in

*Corresponding authors: Prabhat Ranjan, Department of Mechatronics Engineering, Manipal University Jaipur, Dehmi Kalan, 303007, India, E-mail: [email protected]; and Tanmoy Chakraborty, Department of Chemistry and Biochemistry, School of Basic Sciences and Research, Sharda University, Greater Noida, 201310, India, E-mail: [email protected]. https://orcid.org/ 0000-0002-3374-8125 (T. Chakraborty) Shayeri Das, Department of Mechatronics Engineering, Manipal University Jaipur, Dehmi Kalan, 303007, India; and Department of Electrical Engineering, Ideal Institute of Engineering, Kalyani, Nadia, West Bengal, 741235, India As per De Gruyter’s policy this article has previously been published in the journal Physical Sciences Reviews. Please cite as: S. Das, P. Ranjan and T. Chakraborty “Computational study of CunAgAu (n = 1–4) clusters invoking DFT based descriptors” Physical Sciences Reviews [Online] 2022. DOI: 10.1515/psr-2021-0141 | https://doi.org/10.1515/9783110783643-007

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7 Computational study of CunAgAu (n = 1–4) clusters

design, stability and configuration [14–16]. The multi-metallic clusters show high catalytic activity as well as selectivity [14–16] and also display promising uses in optical, electronic, magnetic and catalytic fields [17–28]. Multi-metallic clusters exhibit distinct physio-chemical properties and suitable for optoelectronic and nonlinear optical devices [21–28]. Belloni et al. [29] reported the metal cluster synthesis in solution and described the proportions and arrangement of the particles with distinctive attention to oligomers and nanometric-sized elements. Lai [30] studied on the structures of metallic clusters by using genetic algorithm and basin hopping technique. Authors reported that ground state configuration of metallic clusters obtained through both these methods are similar. Yang et al. [31] studied mono-metallic cluster surrounded by fullerene – YCN@Cs(6)-C82. Authors stabilized the cluster with only one metal cation within a carbon cage. Song et al. [32] performed DFT study on Pt and Au clusters for various shape, geometry and composition and also investigated reaction with these clusters with CO. It is observed that planar geometry is more suitable for Pt clusters however for higher number of Pt 3-dimensional structure is more preferred. They found that CO adsorption is weak at Au site as compared to Pt site. Gálvez-González et al. [33] reported DFT analysis of Re–Pt clusters. They observed that doping of Re in Pt clusters enhances the binding energy as well as HOMO–LUMO gap of overall system. Megha et al. [34] investigated hydrogen doped gold cluster by using DFT technique. They stated that ground state configuration of Au7H with point group C2V follow the similar structure as its parent Au8 cluster. Efremenko et al. [35] investigated the structure of PdnCum (m + n ≤ 6) where they found that Pd atoms tend to blend with copper at every possible combination whereas copper atoms favour to segregate from themselves at high content. Zanti et al. [36] stated that PdnAum clusters (n + m ≤ 14) exhibit geometry which predominantly depends on their composition. Authors observed that Pd-rich system favours three-dimensional geometry whereas Au-rich prefer planar geometry. Granja-DelRío et al. [37] investigated structural, magnetic and stability behaviour of Ni–Pd nanocluster by using DFT method. The lowest energy structure of mixed Ni–Pd clusters are obtained by placing Ni and Pd on all possible sites of the pure Ni and Pd clusters. Kobayash et al. [38] investigated AuAg nanoclusters experimentally. Authors found that addition of silver atoms results in the enhancement of electronic structures of gold clusters. Ag-Au nanocluster displays low response of circular dichroism as compared to their equivalent gold clusters. It is observed that accumulation of a diverse element improves the desired responses of metallic clusters, which is favourable for tri-metallic clusters [39]. Amarillas et al. [39] investigated stability and structures of tri-metallic clusters CulAgmAun (l + m + n = 6) clusters by using DFT technique. Authors described the binding energy and frontier orbitals of these clusters and shows that these clusters are having thermal as well as kinetic stability. Zhao et al. [40] investigated AuxPdyPtz (x + y + z = 7) clusters by using DFT. They found that system with maximum platinum atoms and less number of gold atoms display maximum binding energy. Pacheco-Contreras et al. [41] investigated AglAumPtn (l + m + n = 13, 19, 33, 38) clusters. Authors found that chances of structural

7.2 Computational details

95

deformation are high in the case of larger system i.e. clusters with 38 atoms. Cheng et al. [23] reported that segregation processes are unaffected by the size and composition in Ag–Cu–Au clusters. In the present work, tri-metallic CunAgAu, n = 1–4 clusters using DFT method is investigated. Lowest energy structure and low lying isomers of CunAgAu are optimized. The descriptors based on DFT are studied and discussed.

7.2 Computational details DFT is the most efficacious method in quantum mechanics to understand the electronic structures as well as to examine the various physical and chemical properties of metallic clusters. DFT technique is widely used in the domain of physics, chemistry, nano-science, biological science, etc. due to its ease of use and high accuracy [42–47]. In recent years, we have studied various systems of metallic clusters by using DFT methodology [12, 13, 48–53]. Computational software Gaussian 16 and Gauss View 6.0 is used for geometry optimization and modelling of tri-metallic CunAgAu, n = 1–4 [54]. For optimization, we have selected exchange correlation – local spin density approximation (LSDA) and basis set LANL2DZ [55–57]. Using Koopman’s theorem, ionization energy (I) and electron affinity (A) of CunAgAu clusters are calculated [58]: I = −εHOMO

(7.i)

A = −εLUMO

(7.ii)

Based on the value of ionization energy and electron affinity, the DFT based descriptors – molecular hardness (η), softness (S), electronegativity ( χ ) and electrophilicity index (ω) for tri-metallic CunAgAu system are obtained as follow: Electronegativity,  χ = −μ =

I+A 2

(7.iii)

where, μ: chemical potential Hardness,  η =

I−A 2

(7.iv)

1 2η

(7.v)

Softness,  S =

Electrophilicity index,  ω =

μ2 2η

(7.vi)

96

7 Computational study of CunAgAu (n = 1–4) clusters

Figure 7.1: Optimized structure of CunAgAu, (n = 1–4).

7.3 Results and discussion 7.3.1 Electronic properties and DFT based descriptors In this segment of the report, the electronic structures and DFT based descriptors – highest occupied molecular orbital (HOMO) – lowest unoccupied molecular orbital (LUMO) gap, molecular hardness, softness, electronegativity, electrophilicity index along with dipole moment of tri-metallic CunAgAu, n = 1–4 are computed. In the pursuit to find the ground state configuration, large number of isomers for CunAgAu clusters are optimized, however only ground state configuration for each system is presented in Figure 7.1. The molecular orbitals HOMO and LUMO have considerable role in metallic clusters [59–61]. According to the findings, charge transfer and bonding in chemical species are driven by HOMO and LUMO of both donor and acceptor [60]. HOMO–LUMO of cluster is also associated with the stability, it is reported that clusters with a smaller HOMO– LUMO gap have poorer stability than clusters with a wide HOMO–LUMO gap [12, 13]. HOMO–LUMO energy gap is the minimum energy a charge carrier needed to shift from an occupied to an unoccupied orbital [60, 61]. DFT based descriptors of tri-metallic CunAgAu (n = 1–4) clusters are shown in Table 7.1. Data reveals that maximum and minimum value of energy gap is obtained at n = 4 and n = 1 respectively. For n = 2, 3 and Table .: Physico-chemical properties of tri-metallic CunAgAu, (n = –) cluster. Systems

CuAgAu CuAgAu CuAgAu CuAgAu

HOMO– Hardness Softness Electronegativity LUMO energy (in eV) (in eV) (in eV) gap (in eV) . . . .

. . . .

. . . .

. . . .

Electrophilicity index (in eV)

Dipole moment (in Debye)

. . . .

. . . .

7.3 Results and discussion

97

4 HOMO–LUMO gap is found as 1.667, 1.610 and 1.785 eV respectively. It indicates that Cu2AgAu, Cu3AgAu and Cu4AgAu clusters can be used for photovoltaic and optoelectronic devices as their energy gap is in the required range [12, 13, 48, 51, 52, 57, 62]. The stability of a chemical system is determined by the molecular hardness and softness [60]. These factors are linked with the energy gap of clusters. Hardness and softness of CunAgAu cluster varies between 0.355–0.892 eV and 0.560–1.406 eV correspondingly. It shows that system at n = 4 is more stable whereas system at n = 1 is more reactive in the considered range. Electronegativity plays an irreplaceable role in charge transfer of chemical system [60,63,64]. Electronegativity of CunAgAu fluctuate from 4.437 to 4.953 eV. Cluster at n = 2 displays maximum value of electronegativity whereas at n = 3 shows minimum value. HOMO–LUMO energy gap, hardness and electronegativity of CunAgAu clusters displays an odd–even alteration behaviour with the cluster size, n. It indicates that cluster at n = 1 and 3 shows less values of these parameters in contrast with systems at n = 2 and 4. The alternation behaviour of HOMO–LUMO gap and electronegativity of CunAgAu with cluster size, n is lucidly displayed in Figure 7.2. The electrophilicity index mainly govern by ionization potential and electron affinity is an important parameter in metallic system [65]. Electrophilcity index of CunAgAu is witnessed between 12.229 and 28.887 eV. Maximum electrophilicity index is found at n = 1 while lowest value observed at n = 3. Dipole moment of CunAgAu is obtained between 1.759 and 2.143 Debye. Clusters at n = 4 and 3 shows the minimum and maximum value of dipole moment in this range. The cluster with uppermost HOMO–LUMO gap exhibits the least dipole moment. Based on computed data it can be concluded that all clusters except Cu4AgAu have higher separation of charge. 6.000

HOMO-LUMO gap (in eV)

Electronegativity (in eV)

1

3

5.000 4.000 3.000 2.000 1.000 0.000 2

4

Number of Cu atoms

Figure 7.2: Relationship among HOMO–LUMO gap with electronegativity.

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7 Computational study of CunAgAu (n = 1–4) clusters

7.4 Conclusions Computational study of tri-metallic clusters CunAgAu (n = 1–4) is conducted by DFT framework. The result reveals that HOMO–LUMO gap of CunAgAu fluctuates among 0.711–1.786 eV. At n = 2, 3 and 4, energy gap is found as 1.667, 1.610 and 1.785 eV, respectively. It indicates their suitability for optoelectronic and photovoltaic devices. System at n = 4 is more stable whereas at n = 1 more reactive in the considered range. HOMO–LUMO energy gap, hardness and electronegativity of CunAgAu clusters display an even–odd alteration behaviour with the cluster size, n. It shows that cluster at n = 1 and 3 shows less values of these parameters against their neighbour clusters at n = 2 and 4. Electronegativity of CunAgAu cluster is found maximum at n = 2 whereas least value is observed at n = 3. All the considered system shows dipole moment greater than two Debye, except Cu4AgAu. Maximum dipole is found for cluster Cu3AgAu. Acknowledgments: Shayeri Das and Dr. Prabhat Ranjan would like to acknowledge Manipal University Jaipur for providing research facilities and computational resources.

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Misbaudeen Abdul-Hammed*, Ibrahim Olaide Adedotun, Karimot Motunrayo Mufutau, Bamidele Toheeb Towolawi, Tolulope Irapada Afolabi and Christianah Otoame Irabor

8 Antibreast cancer activities of phytochemicals from Anonna muricata using computer-aided drug design (CADD) approach Abstract: Antibreast cancer activities of 131 phytochemicals from Annona muricata (Soursop) were investigated against human placental aromatase (PDB ID: 3S7S), a prominent target receptor in breast cancer therapy using computer aided-drug design approach. An antibreast cancer drug (tamoxifen) was used for comparison. The result of this work flourishes caffeoquinic acid (−8.4 kcal/mol), roseoside (−8.3 kcal/mol), chlorogenic acid (−8.2 kcal/mol), feruloylglycoside (−8.1 kcal/mol), citroside A (−8.0 kcal/mol), and coreximine (−7.8 kcal/mol), as probable inhibitors of human placental aromatase. This is due to their excellent binding affinities (ΔG), coupled with outstanding druglike, absorption, distribution, metabolism, excretion, and toxicity profiles, bioavailability and oral-bioavailability properties, and the interactions of important residues with the active pocket of human placental aromatase. All the results obtained were similar to that of the standards tamoxifen (−8.0 kcal/mol) but could be better when optimized. Thus, lead optimization, molecular dynamics, and in vivo investigations are thereby recommended on the identified potent compounds in the quest of developing new therapeutic agents against breast cancer. Keywords: ADMET profiling; Annona muricata; breast cancer; CADD; oralbioavailability.

8.1 Introduction The increase in mortality and decrease in life expectancy can be attributed to cancer [1]. According to the World Health Organisation, it accounts for nearly 10 million deaths in 2020 with 70% of deaths occurring in low- and middle-income countries [2]. Cancer is a

*Corresponding author: M. Abdul-Hammed, Computational Biophysical Chemistry Laboratory, Department of Pure and Applied Chemistry, Ladoke Akintola University of Technology, P.M.B. 4000, Ogbomoso, Nigeria, E-mail: [email protected]. https://orcid.org/0000-0002-5453-5858 Ibrahim Olaide Adedotun, Karimot Motunrayo Mufutau, Bamidele Toheeb Towolawi, Tolulope Irapada Afolabi and Christianah Otoame Irabor, Computational Biophysical Chemistry Laboratory, Department of Pure and Applied Chemistry, Ladoke Akintola University of Technology, P.M.B. 4000, Ogbomoso, Nigeria As per De Gruyter’s policy this article has previously been published in the journal Physical Sciences Reviews. Please cite as: M. Abdul-Hammed, I. O. Adedotun, K. M. Mufutau, B. T. Towolawi, T. I. Afolabi and C. O. Irabor “Antibreast cancer activities of phytochemicals from Anonna muricata using computer-aided drug design (CADD) approach” Physical Sciences Reviews [Online] 2022. DOI: 10.1515/psr-2021-0160 | https://doi.org/10.1515/9783110783643-008

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general term used for various diseases caused by an uncontrolled division of cells in a part of the body and invasion of nearby tissues. Cancers are of different types based on the affected part of the body. The most prevalent ones are brain cancer, lungs cancer, Hodgkin lymphoma, multiple myeloma, and breast cancer. Breast cancer has been a leading contributor to newly reported cancer cases in the world with 2.26 million cases reported in 2020 worldwide. It is also the 5th most common cause of cancer deaths as 685,000 deaths were reported in the same year [2]. In Nigeria, breast cancer is one of the leading causes of death, as shown in 28,380 new cases (22.7%), 14, 274 deaths (18.1%) and a 60,296 (5-year cumulative prevalence for all ages (59.31% per 100,000 cases of cancer) reported in 2020 by the Nation’s Cancer Registries [3]. There is an increased vigor in the fight against breast cancer and a day has been dedicated to the fight against this life-threatening disease globally. Estrogen is synthesized during the conversion of androstenedione and testosterone to estrone and estradiol by the human aromatase enzyme (a member of cytochrome P450) [4]. Breast cancer cells contain estrogen receptor (ER) α which becomes activated when estrogen binds to them. Thus, ER α is the major driver of approximately 75% of breast cancers cases and it is the major target of most antibreast cancer drugs. Patients have been exposed to endocrine therapy by the use of aromatase inhibitors and multiple ER targeting drugs such as tamoxifen, abemaciclib, anastrozole, exemestane, toremifene, fulvestrant, goserelin, and letrozole [4, 5]. However, as effective as these drugs are, there are side effects that limit the usage of the drugs in the fight against breast cancer. The side effects of tamoxifen (the most widely used drug) include increased bone or tumor pain, reddening around tumor site, hot flashes, nausea, excessive tiredness, dizziness, depression, headache, hair thinning, weight loss, stomach cramps, constipation, loss of sexual desire, vision problems, loss of appetite, unusual bleeding, yellowing of skin and eyes, fever, blisters, rash, swelling of lower legs, feet, ankles, arms, hands, throat, tongue, lips, face or eyes, thirst, muscle weakness, and restlessness. It may also increase the risk of developing other cancers including liver cancer [6]. Hence the need for an alternative therapy that can effectively modulate and competitively inhibit the binding of estrogen to the estrogen receptor. Interestingly, secondary plant metabolites have been extensively studied and have been found to have anti-oxidative and anti-inflammatory properties which can inhibit tumour initiation, promotion, and progression [7]. Annona muricata commonly called soursop or graviola in Spanish has been identified to have over 300 bioactive organic compounds. The antioxidant, anti-microbial, and DNA protective efficacy of the plant has been widely studied [8–10]. Anticancer activities of A. muricata crude extracts on breast cancer cell lines have also been studied in vivo and in vitro [11–13], but much has not been reported on the inhibitory potency of the isolated compounds from this medicinal plant against breast cancer prominent target enzymes. Hence, there is a need to study the anti-breast cancer activities of isolated phytochemicals present in A. muricata using a computer-aided drug design approach as this will aid in identifying

8.2 Materials and methods

105

lead compounds that can be developed into useful drugs. Therefore, this research work is aimed at identifying potent lead molecules from A. muricata that can effectively modulate estrogen receptors and inhibit human placenta aromatase towards the development of new safer, economical and effective antibreast cancer drugs.

8.2 Materials and methods 8.2.1 Preparation of target receptor The human placenta aromatase (PDB ID: 3S7S) was used as the target receptor in this research. The crystal structure was downloaded in (.pdb) format from the protein data bank (RCSB) (https://www.rcsb.org/pdb) with a resolution of 3.21 Å [14]. The human placental aromatase is a protein that is responsible for the catalysis of the biosynthesis of estrogens from the androgenic precursors which is responsible for breast cancer therefore inhibiting the aromatase is the target of potential inhibitors in the drug discovery and development process. The binding pocket of the initial inhibitors present in 3S7S was used in determining the binding parameters as −26.84, 12.60, and 58.96 for X, Y, and Z, respectively. The Ramachandran plot (Figure 8.2) of the protein shows the quality of the receptor understudy. This was assessed using the Discovery Studio Software 2019. Impurities including water molecules were removed from the protein to avoid interference with the active site of the target receptor during the virtual screening exercise.

8.2.2 Identification and validation of active site Binding pocket, amino acids, and all ligands interactions in the active site of 3S7S were determined using the computed atlas for surface topography of proteins (CASTp) (http://sts.bioe.uic.edu/castp/ index.html?2011) [15] and Biovia Discovery Studio (2019). The result obtained was compared and validated with the active sites and residues of exesmestane which was used as a coordinate for docking of the ligands to the active site, exemestane was used because it was found attached in the active site of 3S7S in the PDB database [14].

8.2.3 Preparation of ligands and geometric optimization One hundred and thirty-one phytochemicals isolated from soursop were used for this research and the standard drug used was tamoxifen, because it is a known aromatase inhibitor. The two-dimensional structures of all the isolated ligands and standard drugs were downloaded from the PubChem database (https://pubchem.ncbi.nlm.nih.gov/) and were converted to three-dimensional structures in (.pdb) format using Spartan 14 software. Conformer search was done using conformer distribution and molecular mechanic/MMFF, and the most stable conformers were obtained and optimized using density functional theory with B3LYP functional, and 6-31+G(d) as a basis set to obtain structures with the best equilibrium geometry.

8.2.4 Analyses of drug-like compounds and ADMET profiling The drug-likeness prediction of the ligands and the standards were examined using the molinspiration web server (https://www.molinspiration.com/) [16], while the absorption, distribution, metabolism,

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excretion, and toxicity (ADMET) properties were predicted using ADMET SAR2 web-server (http:// lmmd.ecust.edu.cn/admetsar2/) [17].

8.2.5 Oral bioavailability screening and prediction of activity spectra for substances (PASS) The oral bioavailability properties of the studied compounds were analyzed using the Swiss-ADME web tool (http://www.swissadme.ch/) while the biological activity of the studied phytochemicals was analyzed for their anticancer property using the PASS software (http://pharmexpert.ru/passonline/), [18].

8.2.6 Molecular docking protocol Molecular docking is an in-silico method that predicts the interaction of ligands with the active site of their target receptor (protein). It is one of the most frequently used structure-based drug design methods to estimate the most favorable binding modes and binding affinities of ligands with their receptor [19]. Molecular docking simulation was performed using PyRx software [20], each of the isolated ligands and standard drugs was docked in duplicates, and the mean average binding energies (ΔG) (kcal/mol) and standard deviation were obtained. Their respective inhibition constants (Ki) were calculated from their binding energies as shown in Eq. (8.1) below. Biovia discovery studio 2019 and PyMol software were used for the result analyses. K i = 10(B.E./ 1.366)

(8.1)

8.3 Results and discussion 8.3.1 Macromolecular target elucidation and active-site validation The choice of a target protein of high quality and validation of its active site is an indispensable operation in structure-based drug design. Human Placenta Aromatase (HPA) (PDB: 3S7S) (Figure 8.1), a member of cytochrome P450s enzymes is responsible for the biosynthesis of estrogen that binds and activates the estrogen receptor (ER) in breast cancer cells, leading to about 75% of breast cancer incidents [4, 14]. Thus, human aromatase is a principal target of most aromatase inhibitors in breast cancer therapeutic studies. In this research, the selected target (HPA) is a well-defined x-ray crystallographic structure with 3.21 Å resolution, a heme group, and a single polypeptide chain of 503 amino acid residues. The standard geometry of HPA accounted for 0.43 and 0.59 bond length and bond angle z-scores respectively, the values were significantly lower compared to (>5) value which is generally considered as an outlier [14]. Detailed inspection of the torsion angles (protein backbone) of the selected target via Ramachandran plot (Figure 8.2) revealed a 91% favored region with only 0.9% Ramachandran outlier. HPA has a cell constant values of a = 140.626 (α = 90), b = 140.626 (β = 90), and c = 119.024 (γ = 120), and R-value free, work, and

8.3 Results and discussion

107

Figure 8.1: The crystal structure of human placental aromatase (HPA) (PDB ID: 3S7S) in complex with its inhibitors.

observed of (0.256, 0.221, 0.223) [14]. The important residues in the active site of HPA reported by Ghosh and his co-workers which were validated using active site validation software (CASTp and Biovia Discovery Studio) include Arg115, Arg192, Ile133, Phe134, Trp224, Ile305, Ala306, Met374, Val372, Val370, Val313, Leu372, Ser478, Phe221, His480, Glu483, and Asp309. The aforementioned information qualifies HPA as a quality protein of target in this study.

8.3.2 Analyses of absorption, distribution, metabolism, excretion, and toxicity (ADMET) of studied compounds Investigating the ADMET properties of a potential drug candidate is an important operation in drug design and development. It helps to identify promising lead with recommended safety and efficacy properties and to avoid experimental time and resources wasted on undesired ligands in the laboratory [21, 22]. In this study, the ADMET profile of 131 bioactive compounds (ligands) from A. muricata (soursop) were

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8 Anti-breast cancer activities of phytochemicals Anonna muricata

Figure 8.2: The crystal structure of human placental aromatase (HPA) (PDB ID: 3S7S) in complex with its inhibitors. Glycine = triangles, squares = proline, all other residues = circles.

evaluated. 82 of the ligands (66 acetogenins, 9 megastigmanes, 1 alkaloid, 5 phenolic, 2 flavonoids triglycerides, and 1 cyclopeptide) passed the ADMET test, while others failed and were discarded due to their high toxicity rate. The detailed ADMET analysis is shown in Tables 8.1 and 8.2. As observed in Tables 8.1 and 8.2, the selected ligands except for a few have positive human intestinal absorption (HIA+) and blood–brain barrier (BBB+), this shows that the ligands understudy have good absorption potential in the intestine and can cross the blood–brain barrier easily in the central nervous system. The

Ligands

L L L L L L L L L L L L L L L L L L L L L L L L L L L

S/N

                          

. (+) . (+) . (+) . (+) . (+) . (+) . (+) . (+) . (+) . (+) . (+) . (+) . (+) . (+) . (+) . (+) . (+) . (+) . (+) . (+) . (+) . (+) . (+) . (+) . (+) . (+) . (+)

. (+) O. (+) . (+) O. (+) . (+) . (+) . (+) . (+) . (+) . (+) . (+) . (+) . (+) . (+) . (+) O. (+) O. (+) . (+) . (+) . (+) O. (+) O. (+) O. (+) . (+) . (+) . (+) . (+)

. (−) . (−) . (−) . (−) . (−) . (−) . (−) . (−) . (−) . (−) . (−) . (−) . (−) . (−) . (−) . (−) . (−) . (−) . (−) . (−) . (−) . (−) . (−) . (−) . (−) . (−) . (−)

− − − − − − − − − − − − − − − − − − − − − − − − − − −

− − − − − − − − − − − − − − − − − − − − − − − − − − −

− − − − − − − − − − − − − − − − − − − − − − − − − − −

C − − − − − − − − − − − − − − − − − − − − − − − − − − −

D

A − − − − − − − − − − − − − − − − − − − − − − − − + − −

− − − − − − − − − − − − − − − − − − − − − − − − − − −

B (+/−)

A

C

AS (log S)

BBB (+/−)

HIA (+/−)

Extn.

Metabolism (CYP Inhibitors)

Absorption and Distribution

Table .: ADMET profile of the  selected acetogenins.

− − − − − − − − − − − − − − − − − − − − − − − − − − −

AM III III III III III III III III III III III III III III III III III III III III III III III III III III III

AOT

Toxicity

− − − − − − − − − − − − − − − − − − − − − − − − − − −

EI − − − − − − − − − − − − − − − − − − − − − − − − − − −

EC

− − − − − − − − − − − − − − − − − − − − − − − − − − −

hI

− − − − − − − − − − − − − − − − − − − − − − − − − − −

C

8.3 Results and discussion

109

Ligands

L L L L L L L L L L L L L L L L L L L L L L L L L L L L

S/N

                           

. (+) . (+) . (+) . (+) . (+) . (+) . (+) . (+) . (+) . (+) . (+) . (+) . (+) . (+) . (+) . (+) . (+) . (+) . (+) . (+) . (+) . (+) . (+) . (+) . (+) . (+) . (+) . (+)

O. (+) . (+) . (+) . (+) . (+) . (+) . (+) . (+) O. (+) . (+) . (+) O. (+) O. (+) . (+) . (+) . (+) . (+) . (+) . (+) . (+) . (+) . (+) O. (+) . (+) . (+) . (+) . (+) . (+)

Absorption and Distribution

Table .: (continued)

. (−) . (−) . (−) . (−) . (−) . (−) . (−) . (−) . (−) . (−) . (−) . (−) . (−) . (−) . (−) . (−) . (−) . (−) . (−) . (−) . (−) . (−) . (−) . (−) . (−) . (−) . (−) . (−)

− − − − − − − − − − − − − − − − − − − − − − − − − − − −

− − − − − − − − − − − − − − − − − − − − − − − − − − − −

− − − − − − − − − − − − − − − − − − − − − − − − − − − − −

− − − − − − − − − − − − − − − − − − − − − − − − − −

Metabolism (CYP Inhibitors) − − − − − − − − − − − − − − − − + − − − − − − − − − − −

− − − − − − − − − − − − − − − − − − − − − − − − − − − −

Extn. − − − − − − − − − − − − − − − − − − − − − − − − − − − − III III III III III III III III III III III III III III III III III III III III III III III III III III III III

Toxicity − − − − − − − − − − − − − − − − − − − − − − − − − − − −

− − − − − − − − − − − − − − − − − − − − − − − − − − − −

− − − − − − − − − − − − − − − − − − − − − − − − − − − −

− − − − − − − − − − − − − − − − − − − − − − − − − − − −

110 8 Anti-breast cancer activities of phytochemicals Anonna muricata

L L L L L L L L L L L

          

. (+) . (+) . (+) . (+) . (+) . (+) . (+) . (+) . (+) . (+) . (+)

. (+) . (+) . (+) . (+) . (+) . (+) . (+) . (+) . (+) O. (+) O. (+)

Absorption and Distribution . (−) . (−) . (−) . (−) . (−) . (−) . (−) . (−) . (−) . (−) . (−)

− − − − − − − − − − −

− − − − − − − − − − −

− − − − − − − − − − −

− − − − − − − − − − −

Metabolism (CYP Inhibitors) − − − − − − − − − − −

− − − − − − − − − − −

Extn. − − − − − − − − − − − III III III III III III III III III III III

Toxicity − − − − − − − − − − −

− − − − − − − − − − −

− − − − − − − − − − −

− − − − − − − − − − −

BBB = blood brain barrier, HIA = human intestinal absorption, AS = aqueous solubility, Extn. = excretion, B = biodegradation (+/−) biodegradable (+), non-biodegradable (−) AM = Ames mutagenesis (+/−) acute toxicity (+), non-toxic (−) AOT = acute oral toxicity, EC = eye corrosion, EI = eye irritation, hI = human either-a-go-go inhibition, C= carcinogenicity, L = muricin J, L = annopentocin A, L = isoannonacin--one, L = longifolicin, L = muricahexocin A, L = muricin C,L = Cis-goniothalamicin, L = annomutacin, L = annohexocin, L = muricin E, L = annonacinone, L = annopentocin B, L = annocatacin A, L = murihexocin A, L = annonacin--one, L = xylomaticin, L = Cis-uvariamicin I, L = muricatetrocin A, L = annomontacin, L = annomuricin C, L = corossolin, L = gigantetrocin A, L = muricapentocin, L = Cis solamin, L = murisolin, L = Cisannomontacin, L = isoannonacin, L = muricatetrocin B, L = muricatocin A, L = Cis-uvariamicin IV, L = goniothalamicin, L = muricin I, L = annonacin, L = annonacin A, L = gigantetrocin B, L = javoricin, L = Cis-pantellin, L = muricin D, L = muricatocin C, L = muricatenol, L = Cis-annonacin--one, L = gigantetrocin, L = annoreticuin--one, L = Cis-annonacin, L = annocatalin, L = corossolone, L = Cis-corosolone, L = muricin H, L = arianacin, L = murihexocin C, L = muricin A, L = muricin K, L = muricatocin B, L = murihexocin B, L = annomuricine E, L = corepoxylone, L = annomuricin B, L = donhexocin, L = muricin M, L = muricin N, L = cohibin D, L = cohibin C, L = muricin B, L = annomuricin A, L = Cis-reticulatacin--one, L = muricatacin.

Ligands

S/N

Table .: (continued)

8.3 Results and discussion

111

Ligands

. (+) . (−) . (−) . (+) . (+) . (+) . (+) . (+) . (+)

. (+) . (+) . (+) . (−) . (+) . (−) . (+)

. (+) . (+)

. (+)

. (+)

. (+)

. (+) . (+) . (+) . (+) . (+) . (+) . (+) . (+) . (+)

. (−) . (−) . (−) . (−) . (−) . (−) . (+)

. (−) . (−)

. (+)

. (−)

. (−)

. (−)

. (−)

. (−)

. (−) . (−)

. (−) . (−) . (−) . (−) . (−) . (−) . (−)

. (−) . (−) . (−) . (−) . (−) . (−) . (−) . (−) . (−)

−

−

+

− −

− − − − − − −

− − − − − − − − −

−

−

−

− −

− − − − − − −

− − − − − − − − −

−

−

−

− −

− − − − − − −

− − − − − − − − −

C

+

−

+

− −

− − − − − − −

− − − − − − − − −

D

+

−

+

− −

− − − − − − +

− − − − − − − − −

A

−

−

−

− −

− − − − − + −

− − − − − − − − −

B (+/−)

A

C

AS (log S)

BBB (+/−)

HIA (+/−)

Extn.

Metabolism (CYP inhibitors)

Absorption and distribution

−

−

−

− −

− − − − − − −

− − − − − − − − −

AM

−

III

III

III III

III III III III IV III III

III III III III III III III III III

AOT

Toxicity

III

−

−

− +

− − − − + + +

− − − + + − − − −

EI

−

−

−

− −

− − − − − + −

− − − − − − − − −

EC

−

−

−

− −

− − − − − − −

− − − − − − − − −

hI

−

−

−

− −

− − − − − − −

− − − − − − − − −

C

BBB = blood brain barrier, HIA = human intestinal absorption, AS = aqueous solubility, Extn. = excretion B = biodegradation (+/−) biodegradable (+), non-biodegradable (−) AM = Ames mutagenesis (+/−) acute toxicity (+), non-toxic (−) AOT = acute oral toxicity, EC = eye corrosion, EI = eye irritation, hI = human either-a-go-go inhibition, C = carcinogenicity, L = roseoside, L = citroside A, L = annoinoside, L = +(−)-epiloliolide, L = loliolide, L = vomivoliol, L = annoinol B, L = annoinol A, L = blumenol, C, L = dicaffequinic acid, L = dihydrokampferol-hexoside, L = caffeoquinic acid, L = feruloylglycoside, L = caffeic acid, L = P-cumaric acid, L = P-cumaricacidmethylester, L = chlorogenic acid, L = gallic acid, L = coreximine, L = annomuricatacin A, SD = tamoxifen.

Megastigmane  L  L  L  L  L  L  L  L  L Phenolics  L  L  L  L  L  L  L Flavonoids  L  L Alkaloid  L Cyclopeptide  L Standard drug  SD

S/N

Table .: ADMET profile of selected megastigmanes, phenolic, flavonoids, alkaloids, cyclopeptide and the standards.

112 8 Anti-breast cancer activities of phytochemicals Anonna muricata

8.3 Results and discussion

113

dissolution potential of the ligands and the standard drug understudy as observed in their aqueous solubility (log S) values fall within the (−1 to −5) acceptable solubility value for the ligand of good quality [21], thus the selected ligands and standard possessed finer absorption and distribution properties. However, the absorption and distribution rate of these ligands can be improved during the lead optimization stage. All the 66 ligands selected are noncarcinogenic, non-AMES toxic, and displayed type III acute oral toxicity level (i.e. slightly toxic) which could be improved to type IV (non-toxic) during the ADMET lead optimization stage of the drug discovery [23]. The metabolic activities of the selected ligands as potential drug candidates were assessed using microsomal enzymes (cytochrome P450 inhibitors. The CP450s help to catalyze various reactions involved in the drug metabolic activities, thus a dependable drug candidate is expected to be a non-inhibitor of the microsomal enzymes. Interestingly, most of the ligands evaluated do not inhibit the CYP450 inhibitors. Evaluating the inhibitory potential of a drug against human ether a-gogo (hERG2) is an important factor to be considered when choosing a promising lead. Its inhibition may affect the potassium ion channel of the myocardium which may result in its blockage, and subsequently, make the heart malfunction and can cause sudden death [24]. The selected ligands are non-heRG2 inhibitors. In summary, the ADMET properties of all the selected ligands are outstanding, and thus they could have been subjected to other chemo-informatics screening to identify the best ligands for target-ligand screening.

8.3.3 Drug-likeness analyses Assessment of drug-like properties and physicochemical parameters of the potential drug candidate is an indispensable exercise in lead discovery [25, 26]. It aids in predicting the oral-bioavailability of the drug and determining its permeability and rate of absorption across the biological membrane [27]. According to Lipinski rule of five (RO5), the acceptable molecular mass (MW) of a drug-like compound should not exceed ≤500 g mol−1, while hydrogen bond donor (HBD) and hydrogen bond acceptor (HBA) should be ≤5 and ≤10, respectively. The octanol-water partition coefficient (log P) of a drug-like ligand should be ≤5 and no more than one violation allowed [28]. 66 ligands from ADMET analyses were subjected to drug-likeness screening. However, only 23 (Table 8.3) of the 66 ligands screened passed the RO5 test. They displayed either 0 or 1 violations of the rule as compared to the standard drug (Tamoxifen) with 2 violations of the rule.

8.3.4 Molecular modeling analysis (molecular docking and molecular interactions) Molecular docking and dynamic techniques are widely used methods for evaluating the interactions and inhibiting potential of a drug-like compound against a known target [26, 29]. To obtain reliable docking results, the docking exercise was carried out

114

8 Anti-breast cancer activities of phytochemicals Anonna muricata

Table .: Drug-likeness analyses of megastigmanes, phenolic, flavonoids, alkaloids, cyclopeptides, and the standard (tamoxifen). Compounds Muricin J Muricin K Muricin M Muricin N Muricatacin Roseoside Citroside A Annoionoside (+)-Epiloliolide Loliolide Vomifoliol Annoinol B Annoinol A Blumenolic C Caffeoylquinic acid Ferulolylglycoside Caffeic acid P-Coumaric acid P-Coumaric methylester Chlorogenic acid Gallic acid Coreximine Tamoxifen

Molecular weight (MW)

RO violation

Hydrogen bond donor (HBD)

Hydrogen bonds acceptor (HBA)

log P

. . . . . . . . . . . . . . . . . . .

                  

                  

                  

. . . . . −. −. −. . . . . . . −. −. . . .

. . . .

   

   

 −.  .  .  −.

in duplicate and the mean and standard deviation were calculated for each of the 23 compounds. However, the results of the best 6 compounds obtained from the docking exercise were reported in Table 8.4. As clearly observed, the binding affinities of the selected compounds are −8.4 kcal/mol, −8.3 kcal/mol, −8.2 kcal/mol, −8.1 kcal/ mol, −8.0 kcal/mol, and −7.8 kcal/mol for caffeoquinic acid, roseoside, chlorogenic acid, feruloylglycoside, citroside A, and coreximine, respectively, with caffeoquinic acid having the best binding affinity. A comparison of the results above with the binding affinity of the standard drug (tamoxifen −8.0 kcal/mol), it is evident that caffeoquinic acid, roseoside, chlorogenic acid, and feruloylglycoside have better binding potential than the tamoxifen. Furthermore, as reported by Onawole 2017, a relationship exists between AutoDock Vina docking score and inhibition constant value (Ki) (Eqn. (8.1)), the lower the binding affinity, the lower the (Ki) value and more the inhibiting potential [30]. All the six (6) compounds and standard selected (Table 8.4) have excellent (Ki) values. Also, a careful check through the docking results (Table 8.4)

Caffeoquinic acid

Roseoside Chlorogenic acid

Feruloylglycoside

Citroside A

Coreximine

Tamoxifen

.

. .

.

.

.

.

−. ± . Thr (. Å)

−. ± . Arg (., . Å), Arg (., . Å), ARG (., . Å), TRP (. Å), Ile (. Å), Ile (. Å), Met (. Å) −. ± . Thr (., . Å), Ala (. Å) −. ± . Arg (. Å), Gly (. Å), Arg (. Å), Arg (. Å), Mrg (. Å), Ala (. Å), Leu (. Å), −. ± . Pro (. Å), Phe (. Å), Arg (. Å), Arg (., ., . Å), ARG (. Å), Trp (. Å), Ile (. Å) −. ± . Met (. Å), Arg (. Å), Arg (.å), Gly (. Å), Ala (., . Å) −. ± . Nill

Binding affinity (ΔG), SS receptor amino acid forming H-bond kcal mol− with ligands (H-bond distance, Å)

*The binding affinity values are the mean standard deviation (mean ± SD) of two determinations.

Ligands

S/ N

Ile, Leu, Ser, Trp, Ala, Ala Ile,Trp,Phe, Ala, Val,Val, Arg, Cys

Phe, Trp, Leu, Ser, Val, Ala

Arg, Ala, Ala, Cys

Phe, Trp, Leu, Cys, Val Met

Arg, Ala, Ala, Ile

Electrostatic/hydrophobic interactions

Table .: Drug-likeness analyses of megastigmanes, phenolic, flavonoids, alkaloids, cyclopeptides, and the standard (tamoxifen).

.

.

.

.

. .

.

Inhibition constant (Ki), µM

8.3 Results and discussion

115

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8 Anti-breast cancer activities of phytochemicals Anonna muricata

shows that the selected compounds bind with the active pocket of the target, thereby forming conventional hydrogen bonds, and other electrostatic and hydrophobic interactions, and thus could be probable inhibitors of human placenta aromatase. It is worthy of mentioning that, the best six compounds identified as probable anti-breast cancer agents are isolated compounds from the leaf of the A. muricata. Various submissions from pieces of literature have confirmed the cytotoxic potential of crude extracts prepared from the leaf of this medicinal plant. As reported by Gavamukulya, Nawwar and their co-workers [31, 32] extracts from the leaf of A. muricata were more toxic to cancer cell lines than normal cell lines, and also more viable to noncancerous cells lines and thus induce healing. Similarly, the protective potential of A. muricata leaves administered against DNA damage and cell proliferation of breast tissues of mice induced by 7,12-dimethyl benzene anthracene (DMBA) has also been reported [33]. These confirmed the therapeutic potentials and protective effects of the aboveidentified phytochemicals isolated from the leaf of A. muricata against the development of breast carcinogenesis. Figure 8.3 shows the 2D representations of the interactions of the identified lead compounds with the active pocket of the target enzyme. However, the level of interaction and the inhibiting efficiency of the selected

Figure 8.3: The binding mode and molecular interactions of the selected lead. (a) Caffeoquinic acid, (b) roseoside, (c) chlorogenic acid, (d) feruloylglycoside, (e) citroside A, (f) ceroximine, (g) tamoxifen.

8.3 Results and discussion

117

compounds could be improved when their pharmacophores are modified in the lead optimization stage of drug design and development.

8.3.5 Oral-bioavailability radar analyses Figure 8.4 shows the oral-bioavailability RADAR of the identified lead. Close examination of the RADAR shows the oral-bioavailability properties of the identified compounds [16]. The favorable zone of each property in the RADAR is shown in the pink area. Compounds with acceptable oral-bioavailability properties should have a recommended size ≤ 500 g/mol, polarity (POLAR) determined using the total surface area should be between 20 and 130 Å2, and flexibility (FLEX) determined using the rotatable bond should be less than ten. The lipophilicity (LIPO) and insolubility (INSOLU) are determined using xlogP3 and ESOL (log S)and their recommended values are between −0.7 to +5.0, and from 0 to 6, respectively. The unsaturation (INSATU) examined using fraction Csp3 should fall between the value of 0.5 and 1. As clearly shown in

Figure 8.4: Bio-availability RADAR of the selected compounds (a) caffeoquinic acid, (b) roseoside, (c) chlorogenic acid, (d) feruloylglycoside, (e) citroside A, (f) ceroximine, (g) tamoxifen.

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8 Anti-breast cancer activities of phytochemicals Anonna muricata

Figure 8.4, the selected compounds have most of these properties within the recommended pink area, and thus possess good oral-bioavailability properties.

8.3.6 Prediction of biological activities of the selected compounds The biological activities of the selected compounds were examined using the “prediction of activity spectra for substances (PASS)” web server [34]. This exercise will reveal the probable bioactivities of the identified lead. For a compound to be biologically active against an infection, its bioactivity potential (Paa) should exceed its probability of being inactive (Pbi). As expected, all the selected compounds and standards (Table 8.5) show outstanding anticarcinogenic, breast cancer-resistance Table .: Biological activities prediction using prediction of activity spectra for substance (PASS). S/N

Ligands

.

Caffeoylquinic acid

.

Roseoside

.

Chlorogenic acid

.

Feruloyglycoside

.

Citroside

.

Coreximine

.

Tamoxifen

Pa

Pi

. . . . . . . . .  .  .  . . . . . . . . . . . . . . .

. . . . . . . . .  .  .  . . . . . . . . . . . . . . .

Activity Anticarcinogenic Chemopreventive Antineoplastic antibiotic Antineoplastic Chemopreventive Anticarcinogenic Antineoplastic, alkylator Antineoplastic(breast cancer) Anticarcinogenic Chemopreventive Antineoplastic Antimetastatic Chemoprotective Antineoplastic (carcinoma) Chemopreventive Anticarcinogenic Antimetastatic Anticarcinogenic Antineoplastic Chemopreventive Antimetastatic Antineoplastic antibiotic Breast cancer-resistant protein inhibitor Anticarcinogenic Chemoprotective Antineoplastic (breast cancer) Antineoplastic Anticarcinogenic Antimetastatic

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protein inhibition, antineoplastic (breast cancer), antimetastatic, and chemopreventive activities, with (Paa) values greater than (Pbi).

8.4 Conclusions This study evaluates the inhibitory potential of one hundred and thirty-one (131) phytochemicals against human placental aromatase (3S7S) using a computer-aided drug-design approach. The virtual screening was done using Pyrex docking tool, while toxicity and other pharmacokinetic properties of the studied ligands were evaluated using ADMET SAR-2. The oral-bioavailability, bioactivities and drug-likeness properties of the ligands were analyzed using swissADME, PASS software, and Molinspiration web server, respectively. The results obtained show that caffeoylquinic acid (−8.4 kcal mol−1), roseoside (−8.3 kcal mol−1), chlorogenic acid (−8.2 kcal mol−1), feruloylglycoside (−8.1 kcal mol−1), citroside A (−8.1 kcal mol−1), and coreximine (−7.8 kcal mol−1), are potent inhibitors of human placental aromatase owing to their outstanding binding affinities, toxicity profile, pharmacokinetics properties, drug and lead likeness properties, bioactivities and their ability to be used as oral drugs. The identified lead show similar properties with tamoxifen, a commercially approved antibreast cancer drug. However, lead optimization, molecular dynamics, and in vivo investigations are thereby recommended on the identified potent compounds in the quest of developing new therapeutic agents against breast cancer. Acknowledgments: The contributions of the members of the Computationalbiophysical and Drug-Design Laboratory, Department of Pure and Applied Chemistry, Ladoke Akintola University of Technology, LAUTECH, Ogbomoso, Oyo State, Nigeria to the success of this research is duly acknowledged.

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Imee Su Martinez*, Daniel Ashok Maria Innasi and Rohan P. Perera

9 Development of an online assessment system to evaluate knowledge on chemical safety and security Abstract: Education and information dissemination are fundamental to safety and security risk management and mitigation. A web-based examination system called OPCW eQChemSS was developed to assess individuals on their knowledge in chemical safety and security. This can be used as an evaluation tool for chemical safety and security courses, workshops, and seminars. A database of questions was included in this web-based software, which was divided into three categories. Category A is general chemistry level safety, Category B is chemical safety related to organic and inorganic compounds, and Category C is on chemical safety and security and the Chemical Weapons Convention (CWC). The system was designed to automatically check the examination and instantly provide the result of the test in terms of percentage correct answers. A feedback mechanism from the examinees was also included to assess the effectiveness of this e-learning educational material. The importance of e-learning materials in this time of COVID-19 pandemic cannot be more emphasized. The need for readily accessible e-sources that will aid in virtual learning for various topics will be significant even in the coming new normal of hybrid or flexible learning where both online and face-to-face learning may be implemented. Keywords: chemical education; chemical safety; chemical security; CWC; e-questionnaire; PhP.

9.1 Introduction 9.1.1 Chemical safety and security In a world where chemicals can be used for humanity’s benefit, and at the same time misused for its destruction, chemical safety and security knowledge becomes very important for undergraduate and graduate chemistry students, professional chemists and other practicing professionals related to chemistry. This defines the need to

*Corresponding author: Imee Su Martinez, Institute of Chemistry, National Science Complex, University of the Philippines-Diliman, Quezon City, 2100, Philippines, E-mail: [email protected] Daniel Ashok Maria Innasi, National Authority for the Implementation of Chemical Weapons Convention in Sri Lanka, Ministry of Industry and Commerce, No. 73/1, Galle Road, Colombo, Sri Lanka Rohan P. Perera, Organization for the Prohibition of Chemical Weapons, Johan de Witlaan 32, 2517 JR, The Hague, The Netherlands As per De Gruyter’s policy this article has previously been published in the journal Physical Sciences Reviews. Please cite as: I. S. Martinez, D. A. M. Innasi and R. P. Perera “Development of an online assessment system to evaluate knowledge on chemical safety and security” Physical Sciences Reviews [Online] 2022. DOI: 10.1515/psr-2021-0177 | https://doi.org/10.1515/ 9783110783643-009

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delineate and classify what chemical is beneficial and what is harmful. The implications of chemical production, use, and disposal to health and the environment are so vast that a lot of work is required, and global cooperation is needed to attain a society where chemical safety and security is a way of life. In fact, the chemical life cycle is very much embedded in the various UN Sustainability Development Goals directly or indirectly affecting our ability to achieve these goals [1]. According to the World Health Organization, chemical safety is defined as a condition where activities such as syntheses, production, use, disposal, and transport pertaining to all forms of chemicals, whether natural or manufactured are ensured to be safe to human health and the environment [2]. It involves chemical risk assessment in terms of biological effects of exposure encompassing toxicology, as well as ecotoxicology. Chemical security on the other hand, involves the protection of chemicals and sensitive information relating to chemicals from threats, which are considered intentional nefarious human activities such as theft, terrorism, and illicit drug trafficking [3, 4].

9.1.2 The role of the OPCW in chemical safety and security The Organization for the Prohibition of Chemical Weapons (OPCW) is a key player in chemical safety and security since it is in charge of implementing the Chemical Weapons Convention, which is focused on the prohibition and control of chemicals that can be used as weapons of mass destruction and related compounds that may inflict the highest level of harm to humanity [5]. These are also the same types of chemicals, which have the highest risk in terms of security because they are most attractive to individuals or factions with malicious intentions such as terrorism. Looking at the specific mandates of the OPCW, particularly the protection of state parties from chemical weapons and security provision for state parties from the use and threat of chemical weapons and related compounds, then the OPCW should be at the very core of chemical safety and security. Even the destruction of chemical weapons implies several risks such as health risks from generated products during destruction and security risks particularly during transport. OPCW with its direct relationship to state parties committed to uphold the CWC will help in the easy and straightforward dissemination, and creation of a global chemical safety and security culture. This means that OPCW is in the most strategic position to promulgate chemical safety and security to different parts of the world. In addition, the expertise of OPCW in handling risky chemicals makes it a very suitable focal point for such a cause. The success of OPCW in the demilitarization and control of chemical weapons, which garnered them the Nobel Prize in 2013 shows that they will be able to achieve the lofty task of establishing a global and cooperative chemical safety and security culture. The cooperation between OPCW and the different national authorities will make this enormous task achievable.

9.1 Introduction

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Examples of OPCW’s work on chemical safety and security are seminars and workshops organized in various parts of globe such as in Africa, the GRULAC region, and Asia, which are listed in the Chemical Safety and Security Program calendar of the OPCW [6]. A very recent example is a seminar entitled ‘Seminar on the Chemical Weapons Convention and Chemical Safety and Security Management for Members States of the OPCW in the Eastern European Region” held in Croatia in December, 2021 [7]. The OPCW also launched its chemical safety and security management guidelines in June, 2021 [8].

9.1.3 The significance of an e-based questionnaire in chemical safety and security monitoring Web-based examinations are very helpful in chemical safety and security monitoring because these types of materials allow the immediate assessment of individuals in terms of their knowledge and perception towards chemical safety and security. The evaluation results can be beneficial to an organization, company, or academic institution to determine the capabilities of the personnel on this topic. The examination can also be a form of self-assessment to enable an individual to determine, which part of chemical safety and security to review or rehash. The examination results will also provide the examinee awareness of his or her skills and capabilities to handle, produce, synthesize, process, store, and dispose chemicals. In this set of web-based questionnaires, knowledge of the individual on the CWC will also be tested in addition to chemical safety and security. The advantage of these types of e-learning materials is that they can be taken at a time and place most convenient to the individual. The duration for an individual to answer each question can also be recorded. E-questionnaires can be created in such a way that it is not only testing the knowledge of the examinee, but also providing the individual with new information to learn. The ease of grading, which comes with webbased questionnaires will also provide an institution, company, or organization with an immediate overview of their prevailing chemical safety and security culture and climate. In addition, the current COVID-19 pandemic has evidently given a whole new dimension for online learning resources such as this developed online examination platform. The importance of these types of materials cannot be more useful than at present when individuals are forced to learn and work remotely.

9.1.4 Brief background of web-based chemical safety and security exams Web-based testing started with the need to automate scoring systems, and to interpret these scores quickly for occupational assessment [9]. The historical background of

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web-based testing is clearly intertwined with that of the history of the Internet. It was only in 1995 that the widespread use of the Internet became apparent because of hardware affordability, reliability, and most importantly Internet connection availability [10]. The TCP/IP or packet switching protocol made a lot of difference in networking, allowing expansion of its capabilities. At this point in time, the Internet has become the focal point for communication, information, and business. There were already 16M “Net users” all over the world in 1995. When the number reached a staggering 150 M in only a span of three years, an additional form of internet activity was developed called web-based testing. In 1998, ACCUPLACER, which is the first large-scale secure web-based testing system, was introduced by Vantage Learning in the United States [11, 12]. The examination was specifically developed for The College Board for reading, writing and mathematics assessment of incoming college students. Computer-based testing should not be confused with web-based testing, which is made specifically for world wide web delivery, and may be linked to other web-based platforms such as databases [11, 13]. In the US in 1986, the National Institute of Environmental Health Sciences (NIEHS) was funded under the Superfund Amendments and Reauthorization Act (SARA) to promote research and training that will ultimately reduce the occurrence of human illness from exposure to hazardous environmental substances, as well as worker protection from said substances [14]. Under NIEHS, the Worker Education and Training Program (WETP) funded the development, deployment, and utilization of state-of-theart safety and health training for workers handling hazardous wastes and chemical emergency responders. In 2001, a project entitled “Development of Innovative E-learning Products for Worker Safety and Health Training In Hazardous Waste and Chemical Emergency Response” was promoted by NIEHS WETP. These programs involved creating databases for case studies in hazardous materials response and electronic distribution of self-study and classroom-based curricular studies in health and safety pertaining to hazardous substances. There were also pre-practical or prehands-on learning materials, electronic forum comprising instructors and learners where they can provide feedback on e-courses and testing, as well as e-certification in safety and health training. In 2002, the European Union Community emphasized the need to strengthen the prevention culture through education and training [15]. It was decided that Occupational Health and Safety (OSH) should be integrated into education, training, research, and innovation. This program aimed to instill health and safety culture in the very young. This of course led to the e-learning chemical safety and security training in universities, particularly in research universities. It was around mid-2000, that web-based tests were initiated and administered in various employment assessments, including topics on chemical safety and security [11, 15]. Of course, the COVID-19 pandemic drastically changed the web-based testing or examinations landscape [11, 16]. At present, online examinations and learning management platforms have in fact become the new normal given the continuous

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lockdowns in various part of the globe, and people are forced to learn and work from home [12, 17]. 9.1.4.1 Available web-based chemical safety and security examinations Table 9.1 shows examples of web-based chemical safety and security examinations and trainings. Listed here are 10 examples from various institutions. The choice of examples was based on site accessibility when chemical safety and security was searched in the worldwide web.

9.2 Methodology 9.2.1 Developing the software The web-based examination called the OPCW EQChemSS developed in this project is intended for chemistry students, chemists, and chemical engineers, who at least have a basic background on general chemistry, organic chemistry, chemical engineering, the Chemical Weapons Convention, and chemical safety and security. The exam may be used as assessment for seminars, workshops, courses, or classes on chemical safety and security. It can also be used as an aptitude test for applications to similar activities. The two types of users in this program are the administrator and the applicant. 1. Administrator The administrator is required to provide a username and password to manage the program, and may choose to change the password after the initial log-in. The Administrator is also given the capability to manage the software content including operations such as adding, editing, and deleting offered courses Table .: Examples of web-based trainings and examinations in chemical safety and security. Title of Examination/Training

Institution

Country

Introduction to Weapons of Mass Destruction [] WWMD/Terrorism Awareness for Emergency Responders []

Johns Hopkins Center for Public Health Preparedness National Response and Rescue Training Center Texas Engineering Extension Service OSHAcademy Abhisam Software Brookhaven National Laboratory VelocityEHS AZ Safety and Training Ltd. Illinois Fire Service Institute The University of British Columbia Palomar College

USA

Personal Protective Equipment [] Safe Chemical Warehousing [] BNL Web Training Center [] SDS and Chemical Management Software[] Accident Investigation Online Course [, ] Hazardous Materials Awareness Online [] Laboratory Chemical Safety Course [] Palomar College Chemical Safety Course []

USA

USA USA USA USA Canada USA Canada USA

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and questions including the scheduling of the courses and course description. The administrator can also change the questions and set the time limit to answer each question, as well as choose the questions and the number of questions that will be included in the examination. Lastly, the administrator can view the list of applicants who have taken the tests, and the results they have obtained from the examination. 2. Applicant The applicant is required to register with the system before taking the courses or exams by providing his/her/their name, country, passport number, date of birth, educational background, and the name of the course he/she/they wants to take. The system will check the details of the applicant including the last date the applicant took the examination. Applicants may retake the examination depending on what is allowed by an organization. The questions will appear on the screen individually, and the questions will be answered one at a time. After answering the final question, the applicant will be able to get the results of the examination. The applicant may also answer the feedback questionnaire to provide insight on how to improve the examination and the examination process. B. Encoding The encoding regime diagram for the OPCW eQChemSS online is shown in Figure 9.1. It shows the relationships between the two types of users describe above, and how each interacts with the developed software. It also shows how the software addresses the needs of both users. Data flow in the system is shown in Figure 9.2. It shows how information travels from the users to the online examination system and back.

9.2.2 Scripting language The scripting language used in the programming of this web-based testing system is called hypertext preprocessor (PhP) embedded into HyperText Markup Language (HTML) [28]. The software is platform or operating system independent. It requires PHP 5.1 or higher for editing purposes. MySQL database should at least be version 5.6.

Figure 9.1: Encoding regime diagram.

9.2 Methodology

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Figure 9.2: System dataflow.

9.2.3 Creating the question pool or database The questions in the examination are categorized according to difficulty and specificity. There are objective type questions, as well as reasoning types. In terms of scripting, there are two types of questions, which are the text type and the image type questions. Three categories of questions were created, such that Category A is composed of general chemistry questions, Category B, which is made up of basic organic and inorganic chemistry questions and Category C are questions on the CWC, and chemical safety and security. Examples per category are shown in this section, however the entire question pool created for this program is placed in the Supporting Information. All in all, 80 questions were created with at least 25 questions per category. The program developed, however can accommodate more questions, if necessary. Five examples of Category A questions were written as follows [29,30]: 1. In case of an acid spill, what is the first thing that should be done? A. Wipe the affected area B. Pour a neutralizer on the affected area C. Pour water into the affected area D. Wipe the affected area with soapy water 2. Which of the following elements will react violently with water? A. Hg B. Na, C. Ca, D. Kr 3. Since KClO3 is a strong oxidizer, it has been used in explosives, fireworks, and matches. What is the IUPAC name of this compound? A. potassium chlorite B. potassium and chlorine salts C. potassium (I) chloride D. potassium (I) chlorate 4. Which of the following compounds is used as a coagulant in sewage and industrial wastes? A. Fe(Cl∙6H2O)3 B. FeCl3∙6H2O C. Fe(6H2O)Cl D. Fe3Cl(H2O)6

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5. Which acid is formed when the gas H2S is dissolved in water? A. Sulfidic acid B. Sulfuric acid C. Hydrosulfuric acid D. Sulfonylic Acid The basic organic chemistry questions on the other hand are grouped under Category B, which examples are shown below [31,32]. 1. Carbon tetrachloride is produced through the chlorination of methane gas. This is the fluid commonly used in fire extinguishers. Are there any other products produced from this reaction? A. No, only CCl4 is produced. B. Yes, chloroform, methyl chloride, and dichloromethane are also produced. C. Yes, methyl chloride and dichloromethane are also produced. D. No, some methane gas may remain unreacted. 2. Which of the following is not a constituent of petroleum? A. Asphalt B. Ligroin C. Gasoline D. Lanolin 3. The reduction alkyl halides to alkane will require the following reagents except A. Mg B. Zn, H+ C. NaCl D. Li, CuX 4. What is the general formula of a Grignard reagent which is a reactive organometallic compound? A. RMgX B. Mg(OH)X C. MgX2 D. RMgR 5. The following refer to the same chemical compound except A. cis-9,12-octadecadienoic acid B. linoleic acid C. C18H32O2 D. C16:0 Lastly, Category C question examples are written below [5]. 1. Company X transports hazardous chemicals from Factory A to Factory B. There are two different routes that may be used to transport these chemicals. Route 1 is shorter, in very poor condition, and goes through a heavily populated part of the city. Route 2 is longer, in better condition, and does not go through any populated areas. A review of the transport logs of Company X shows that trucks traveling along Route 1 experience a breakdown or minor accident one time in every 20 trips. However, no major chemical spill has resulted yet. The company also performed risk analysis, and found that for every 50 accidents, there is only one chance that a truck will overturn, where its hazardous cargo could spill. Which route should be taken for transport? A. Either Route 1 or 2 B. Route 1 C. Route 2 D. None of the above choices

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2. Which of the following reactions involves the Schedule 3 chemical phosgene? A. Methane generation B. Polycarbonate reaction with bis-phenol A C. Claisen condensation of esters D. PR3 catalyzed dimerization of acrylonitrile 3. Which of the following best describes 3-quinuclidinyl benzilate or BZ? [34] A. It affects the central nervous system B. It affects the peripheral nervous system C. It is an anticholinergic D. It is a hallucinogen 4. Which of the following describes proper storage of Schedule 1 chemicals? A. Schedule 1 chemicals may be stored with acids and inorganics only B. Schedule 1 chemicals should be stored in locked cabinets only C. Schedule 1 chemicals should be stored in ventilated cabinets only D. Schedule 1 chemicals may be stored with peroxides, perchlorates, and nitrates only 5. Which of the following labels listed below provides information on chemical hazards during chemical transport and emergencies? A. DOT placard only B. OSHA HazCom 2012 and DOT placard C. NFPA 704 Diamond and DOT placard D. None of the above choices

9.3 Results and analyses 9.3.1 Description of the developed eQchemSS program and installation in the OPCW website The home screen for OPCW eQchemSS Program is shown in Figure 9.3. A sign-up and log-in part is shown on the right hand side of the screen. Registration is necessary before an individual can participate in the examination. The current web address where the examination can be accessed is at http://e-test.byethost13.com. A screen shot of a particular question in the exam is shown in Figure 9.4. Take notice of the time icon represented as an alarm clock indicating that each question is designated with a particular duration. On the right hand side, the number of questions answered by the applicant is also shown, as well as the number of questions, which were answered correctly. Lastly, Figure 9.5.1 and 9.5.2 show the screens where the questions and courses, respectively can be edited or added by the administrator. a. Capabilities and strength of the program The OPCW eQchemSS Program is advantageous to both administrator and applicant. The administrator is provided with the freedom to add and change courses and questions to the system, as well customize the settings on the course schedule deadline and time limit for the exams. The system also allows for the instant grading of exams,

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Figure 9.3: Home screen of OPCW eQchemSS.

Figure 9.4: Question screen shot.

Figure 9.5.1: Questions list on the screen.

9.3 Results and analyses

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Figure 9.5.2: Courses list on the screen.

and ranking of applicants, enabling facile assessment and decisions. For the applicant, the software allows the applicant to take the exam at the most convenient time and place. Since the exams are also informative, the test process is educational to the applicant, and may even be used for self-study or knowledge review purposes. Previous studies have also shown the favorability of using internet-based tests and surveys compared to pen-paper mode [33–35]. A higher response rate was observed in internet-based testing due to convenience of use and the anonymity it provided. This online learning assessment system is also very advantageous at this time of the pandemic, where e-learning materials are needed for the continuation of learning and evaluation processes during school and workplace lockdowns. b. Limitations of the program The main weakness of the program is security [36]. Just like any other distance learning tools, the identity of the applicant may be falsified. It is possible that the person taking the test is not really the one who applied for the position. This may be with or without the consent of the applicant. It is also possible that a person providing false information into the computer will still get access to the system. The materials can also be illegally accessed, hence cheating may happen [37]. An applicant can also look for answers on the web, while taking the examination leading to inaccurate assessment of the individual’s skills and knowledge. Hardware usability is also another problem that may affect the scores of the examinees. This can be monitor size or mouse usability, which can greatly affect timeconstrained examinations [38,39].

9.3.2 User feedback The user feedback form is placed in the Supporting Information. The feedback is not only concerned about the how the interface appears to the user, but also tries to rate how the user finds the questions in terms of difficulty. The feedback rating is created in such a way that numerical assignments can be assigned to each question in the feedback form, and an overall numerical rating can be obtained for the overall testing

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experience. Five means that the user found the experience quite excellent, while 1 would mean that the experience was quite poor. Preliminary feedback from OPCW Associate Program participants, and 10 graduate students who took this as a final examination for a chemical safety and security course, ranged from very good to excellent, showing promise.

9.4 Conclusions A chemical safety and security online testing system called OPCW eQchemSS was successfully developed. The e-questionnaire is intended for testing applicants or attendees to seminars, workshops, and courses pertaining to chemical safety and security. It can also serve as an evaluation system for applicants vying for technical positions in the industry, and various organizations. The developed web-based testing allows the administrator to edit the questions, add questions, schedule the administration of the test, and link the e-questionnaire system to other online courses. This e-questionnaire will not only improve an individual’s knowledge on chemical safety and security through evaluation, but the platform may also support learning modules on this topic. This e-based examination is very helpful particularly in the current situation where there is an ongoing COVID-19 pandemic, where individuals including chemical professionals and students are working and learning remotely. Clearly, it is also useful for blended learning activities, as institutions ease into limited face-to-face engagements. Given the promising preliminary feedback on the use of the program, the next phase is to perform further assessment through proper statistical analyses on the feedback of users. In addition, another limitation of this program, which requires improvement is software security particularly on examination proctoring and access. Acknowledgments: Our sincerest gratitude goes to the Technical Secretariat (TS) of the International Cooperation Branch of the Organization for the Prohibition of Chemical Weapons (OPCW) in their dedication to make every OPCW Associate Program a success, and the Philippine Department of Science and Technology (DOST)-Philippine Council for Industry, Energy, and Emerging Technology Research and Development (PCIEERD) for personnel support. Special thank you to the following TS members Dawsar Drissi, Julia Gonzales, Anisoara Novacescu, and Victor Barros-Correia.

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Supplementary Material: The online version of this article offers supplementary material (https://doi. org/10.1515/PSR-2021-0177).

Linda Ouma*, Agnes Pholosi and Martin Onani

10 Optimizing Cr(VI) adsorption parameters on magnetite (Fe3O4) and manganese doped magnetite (MnxFe(3-x)O4) nanoparticles Abstract: Magnetite as an adsorbent is efficient since iron oxides have high affinities for heavy metal pollutants and are environmentally friendly. Manganese oxides provide catalytic properties which are desirable during the remediation of multi valent pollutants. Magnetite (Fe3O4) and manganese doped magnetite (MnxFe(3-x)O4) nanoparticles were synthesized and characterized to determine the manganese doping effects on magnetite’s crystal and surface properties. Fe3O4 and MnxFe(3-x)O4 showed similarities in crystal morphology indicating that manganese doping did not alter the nature of Fe3O4 nanoparticles. Manganese doping improved magnetite’s thermal properties as well as its surface area providing improved adsorption characteristics. The as-synthesized particles were applied in the optimization of hexavalent chromium adsorption. Adsorption proceeded under similar conditions for both adsorbents indicating their structural similarities. Higher efficiencies were observed on the doped adsorbent due to increased surface area and the presence of additional functional groups. Solution pH significantly affected the adsorption process aiding in the reduction of Cr(VI) ions to the less toxic Cr(III) species. The adsorption distribution coefficient KD indicated that manganese doping significantly improved magnetite’s affinity for hexavalent chromium. Adsorption and reduction were determined to responsible for pollutant reduction in solution at optimal conditions of pH 2, 5 g/L and 100 mg/L for adsorbent mass and solution concentration. Keywords: adsorption; Fe3O4 nanoparticles; hexavalent chromium; manganese doping; multi-valent pollutants.

10.1 Introduction Hexavalent chromium contamination of water is concerning due to its toxicity and mobility. Adsorption is a reliable, efficient and cost effective method for pollutant

*Corresponding author: Linda Ouma, Department of Chemistry, University of the Western Cape, Private Bag X17, Bellville 7535, South Africa; and Department of Science, Technology and Engineering, Kibabii University, P. O. Box 1699, Bungoma 50200, Kenya, E-mail: [email protected]. https:// orcid.org/0000-0002-4975-2495 Agnes Pholosi, Department of Chemistry, Vaal University of Technology, Private Bag X021, Vanderbijlpark 1900, South Africa Martin Onani, Department of Chemistry, University of the Western Cape, Private Bag X17, Bellville 7535, South Africa As per De Gruyter’s policy this article has previously been published in the journal Physical Sciences Reviews. Please cite as: L. Ouma, A. Pholosi and M. Onani “Optimizing Cr(VI) adsorption parameters on magnetite (Fe3O4) and manganese doped magnetite (MnxFe(3-x)O4) nanoparticles” Physical Sciences Reviews [Online] 2022. DOI: 10.1515/psr-2021-0149 | https:// doi.org/10.1515/9783110783643-010

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sequestration and recovery [1]. The adsorption process however requires optimization to suit the individual adsorbent-adsorbate system as it is influenced by several variables [2]. A suitable adsorbent is a key consideration in designing suitable and efficient adsorption systems [3]. The adsorbent’s properties include its pore size or porosity, particle size which has a bearing on crystallinity, colloidal stability, surface area and functionalization [4]. Magnetite nanoparticles are unique adsorbent materials owing to their magnetization ability which provides for easy retrieval [5]. They are composed of naturally occurring oxides which are ecofriendly and stable in nature. Additionally their surfaces are easily modified to provide for required surface groups [6, 7]. Controlling the size, homogeneity, crystallinity, and surface characteristics of these nanomaterials is possible owing to the preparation method used. For the synthesis of magnetite nanoparticles, there are numerous synthetic approaches. One of the most widely used methods is co-precipitation, which has advantages over other techniques, such as minimal cost, low energy requirements, high product integrity, and particle homogeneity [6]. Magnetite synthesis through ferrous (Fe2+) and ferric (Fe3+) co-precipitation in the presence of excess hydroxyl ions proceeds according to equation (10.1) [7]. Fe2+ + 2Fe3+ + 8OH− → Fe3 O4 + 4H2 O

(10.1)

Magnetite doping is considered a promising option to improve its affinity towards pollutants since doping has been reported to improve metal oxides’ properties [8]. Manganese doping increases magnetite’s affinity for pollutants as well as improving its magnetic properties. Divalent manganese cations consist of five unpaired electrons which are responsible for its paramagnetism [9]. This property increases the magnetic susceptibility of magnetite which is a superparamagnetic material [10]. As a result, a material composed of iron and manganese oxides would be superior in terms of both pollutant affinities and magnetic retrieval properties. However, to attain the required properties and composition, doping needs to be monitored carefully [3, 9]. The effects of manganese doping on magnetite adsorption were studied and reported in a previous study, the results from that study informed the dopant concentration applied herein [3]. To optimize the adsorption of hexavalent chromium (Cr(VI)) on Magnetite (Fe3O4) and Manganese doped magnetite (MnxFe(3-x)O4), their affinities were evaluated under varying conditions. The evaluation provided for the determination of optimal conditions for adsorbate concentration reduction while efficiently utilizing the adsorbent hence maximizing adsorption capacity. This chapter reports on the synthesis, characterization and optimization of hexavalent chromium adsorption onto the aforementioned adsorbents.

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10.2 Experimental 10.2.1 Materials Reactants used in this study were ammonium hydroxide (25%), potassium dichromate (>99%) and sodium hydroxide (>98%) from ACE Chemicals, South Africa. Hydrochloric acid (32%), ferrous sulphate (>98%) and ferric chloride (>99%) from Merck, Germany. All chemicals were used without any further purification.

10.2.2 Methods 10.2.2.1 Magnetite synthesis: Magnetite nanoparticles were prepared following a one-pot coprecipitation procedure as described in an earlier study [10]. Deionised water was heated under vacuum with continuous stirring at 80 °C for 15 min and nitrogen gas for a further 15 min to maintain an inert atmosphere. Iron salts (FeCl3.6H2O and FeSO4.7H2O) were separately dissolved in deionized water and introduced into the solution, ammonium hydroxide was added as a precipitating agent with continuous stirring till a black precipitate was formed. The precipitated particles were washed with deionized water, rinsed with ethanol and dried under vacuum to prevent further oxidation. Doping was achieved by adding manganese sulphate solution then precipitated in situ with ferric and ferrous ions to achieve the desired dopant concentration of Mn0.06Fe2.94O4 [3]. The optimization of manganese dopant concentration was described in detail in a previous study [3].

10.2.2.2 Characterization: Various analytical techniques were used to characterize the samples in order to determine their composition, crystal properties and dopant effects. X-ray diffraction (XRD) analysis was used to determine the particles’ crystal properties and dopant effects on the crystal structure. An advanced diffractometer (Bruker AXS D8; Cu Kα (λ = 1.5418 Å)) was used for the analysis. A PerkinElmer Fourier transform infrared spectrometer (400 FT-IR/NIR) was used to characterize the adsorbent surface and dopant effects on the magnetite surface functionalization while a thermogravimetric analyser (PerkinElmer TGA 4000) was used in thermal degradation investigation. Perkin Elmer (Lambda 25) UV–Vis spectrophotometer and Shimadzu AA 7000 (air/acetylene flame, λ = 357.9 nm) were used for UV–Vis spectroscopy and atomic absorption analysis, respectively.

10.2.2.3 Adsorption: Potassium dichromate was dissolved in deionized water to prepare a Cr(VI) stock solution. Hydrochloric acid and sodium hydroxide (0.1 M) were used to achieve desired solution pH. Hexavalent chromium concentration was determined by complexation with 1,5-diphenylcarbazide resulting in a pink complex which was analysed on a UV–Vis at 540 nm [11]. Atomic absorption spectrophotometry was applied in total chromium concentration determination while trivalent chromium concentration was indirectly determined as the difference between the total and hexavalent chromium concentrations. Adsorbent dose effects were studied by using adsorbent masses ranging between 0.1 and 1 g. The effect of solution concentration was studied between 25 and 150 mg/L.

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10 Optimizing Cr(VI) adsorption on Fe3O4 and MnxFe(3-x)O4

10.3 Results and discussions 10.3.1 Characterization Figure 10.1 depicts the XRD pattern of as-synthesised Fe3O4 and Mn0.06Fe2.94O4 nanoparticles. The pattern reveals crystalline plane peaks with the indicated miller indices for each peak: 30.75° (220), 36.09° (311), 43.81° (400), 54.03° (422), 57.67° (511) and 63.26° (440). The pattern compares well with the Joint committee for powder diffraction (JCPDS) file No. 19-0629 (Figure 10.1) confirming the as-synthesized magnetite nanoparticles’ inverse spinel structure [12]. Manganese doping was performed to improve the adsorbent’s redox properties and was deemed a suitable dopant due to its charge and size. The doped nanoparticles retained the characteristic magnetite peaks without the formation of any secondary phases implying that manganese was incorporated into the magnetite lattice [3]. Manganese resulted in slight shifts to lower diffraction angles as observed from the major plane (311). The shift indicates that manganese slightly altered the crystalline plane resulting in slight increases in the crystals’ lattice parameter and cell volume [3, 13]. The infrared spectrum of magnetite nanoparticles is depicted in Figure 10.2, revealing the appearance of hydroxyl surface functional groups attributed to adsorbed water. The surface hydroxyl groups allow amphoteric reactions on the oxide surfaces depending on the solution pH [4]. As indicated the oxide surfaces are protonated and

Figure 10.1: Powder X-ray diffraction patterns of magnetite and manganese doped magnetite with reference peaks (JCPDS Card 19-0629).

10.3 Results and discussions

141

Figure 10.2: FT-IR spectra of synthesized samples (Fe3O4 and Mn0.06Fe2.94O4).

deprotonated as solution pH is raised. The Fe-O surface group assigned to 435 cm−1 confirmed iron oxidation forming iron oxide particles [6]. Manganese substitution in the iron oxide structure caused the Fe-O peak to shift to 466 cm−1 from 435 cm−1, while the appearance of Mn-substitution at 552 cm−1 on the iron oxide surface affirmed manganese doping. Manganese substitution affirms diffraction results indicating manganese substitution in the Fe3O4 lattice [14, 15]. Thermal analysis curves for the as-synthesized materials are shown in Figure 10.3. From the data, the samples underwent thermal decomposition in stages, initially

Figure 10.3: DTA curves for Fe3O4 and Mn0.06Fe2.94O4 nanoparticles.

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10 Optimizing Cr(VI) adsorption on Fe3O4 and MnxFe(3-x)O4

losing surface bound water followed by the loss of pore bound water and dihydroxylation [16]. Finally the Fe3O4 sample was reduced to FeO and Fe after deoxidation [6]. At higher temperatures, the decomposition profile of doped magnetite nanoparticles (Mn0.06Fe2.94O4) revealed slight differences from Fe3O4. Manganese substitution in the Fe3O4 lattice resulted in higher temperatures for magnetite reduction and deoxidation (approx. 700 °C). It also presented an additional decomposition stage at approximately 830 °C attributed to manganese oxide deoxidation [15]. The isoelectric points for the Fe3O4 and Mn0.06Fe2.94O4 nanoparticles used in this study were reported previously as pH 7.1 and pH 6.8 respectively while the BET surface areas were 113 and 127 m2/g, respectively [3]. In the same study, the pore sizes of Fe3O4 were observed to decrease upon manganese substitution due to the partial replacement of the smaller Fe ions with the larger Mn ions [17]. The incorporation of manganese into the magnetite lattice resulted in increased adsorption sites as evident from the increased surface area. Manganese oxide has been applied in the adsorption of chromium from water and its incorporation in activated alumina was reported to improve the adsorption properties of both adsorbents [18]. The presence of Mn-O sites in the Mn0.06Fe2.94O4 adsorbent would therefore provide additional adsorption sites therefore improving its efficiency.

10.3.2 Adsorption optimization Adsorption process is dependent on binding on the adsorbent surface and its properties, however chromium adsorption is also influenced by surface redox reactions since it exists in multiple stable oxidation states [19]. Redox reactions at the adsorbent surface are greatly influenced by surface properties as well as solution properties including pH and concentration. One factor at a time (OFAT) experimental design was applied to determine the effects of adsorbent quantities, solution pH and concentration on the efficiency of hexavalent chromium (Cr(VI)) adsorption onto magnetite and manganese doped magnetite nanoparticles. The effect of redox reactions was also determined by determining trivalent chromium (Cr(III)) concentrations in solution. The % efficiency of adsorption and adsorbent capacity were calculated following equations (10.2) and (10.3) [20]. (C 0 − C t ) E=[ ] × 100% C0 q = (C 0 − C t ) x 

V m

(10.2) (10.3)

where E is the adsorption efficiency (%), q is the adsorption capacity, C0 and Ct are the solution concentrations pre and post adsorption, V is the solution volume and m is the adsorbent dose.

10.3 Results and discussions

143

Chromium speciation in solution is pH dependent thus influences adsorption properties at varied pH ranges [20]. Solution pH also has a significant influence on the adsorption process since it affects electrostatic attraction and surface binding [3]. Both iron and manganese oxides have amphoteric surface groups that protonate and deprotonate as solution pH is raised. At acidic pH ranges electrostatic attraction is majorly responsible for binding anionic HCrO4− to protonated surface groups [14, 21]. Figure 10.4a shows Cr(VI) adsorption efficiency as a factor of solution pH. As pH increases, the binding capacity of the adsorbents decrease as a result of the increasing negative charge on the adsorbent surface causing repulsion with anionic chromium species. Figure 10.4b shows the concentration of trivalent chromium species when adsorption is performed at varying pHs. The highest reduction in Cr(VI) concentration was observed at pH 1 while simultaneously observing the highest concentration of Cr(III) in solution. The reduction in Cr(VI) concentration was therefore largely attributed to surface reduction hence pH 2 was selected as the optimal pH. Ferrous ions in magnetite assist in surface reduction of hexavalent chromium, the resulting trivalent ions are then either released into the solution (Figure 10.4b) or bound on the surface through complexation [21, 22]. As the solution becomes more basic, lower concentrations of trivalent ions are recorded (Figure 10.4b). This results from increased binding sites and electrostatic attraction between the increasingly anionic surface and the cationic trivalent species in solution [23]. The binding of Cr(III) ions was more efficient on Mn0.06Fe2.94O4 surfaces as compared to Fe3O4 surfaces with the highest Cr(III) sequestration occurring at pH 6 (Fe3O4) and pH 8 (Mn0.06Fe2.94O4) where the surface complexation was highest [24]. Improved binding of Cr(III) on the doped adsorbent surface was due to the presence of lone pairs of electrons on MnO2 species [4, 24]. Malek et al. and Xiong et al. observed improved adsorbent properties resulting from manganese incorporation similar to the observations in the current study [15, 25].

(a)

(b)

Figure 10.4: Effect of solution pH on (a) Cr(VI) adsorption capacity on Mn0.06Fe2.94O4 and Fe3O4 and (b) Cr(III) formation in solution.

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10 Optimizing Cr(VI) adsorption on Fe3O4 and MnxFe(3-x)O4

The quantity of adsorbent that comes into contact with the adsorbate impacts uptake and is thus significant in determining the best possible adsorption conditions. As the adsorbent dosage increases, so do the adsorption sites, increasing adsorption efficiency [19]. However, at low adsorbent doses, high sorption capacity is observed due to adsorbate molecules competing for limited binding sites (Figure 10.5). With a fixed adsorbate concentration, increasing the adsorbent dose provides more adsorption sites resulting in decreased saturation. This in turn leads to lower adsorbent capacities [2, 19]. To maintain optimal efficiencies with desirable capacities, 5 g/L was selected from the tested values to be used for further experiments since it provided moderate efficiencies while not adversely affecting the adsorption capacities. The adsorbent efficiency can further be described by a solid-phase distribution coefficient KD. KD calculates the partition coefficient of the adsorbate between the solid (adsorbent surface) and solution phases according to equation (10.4) [26]. KD =

C0 − Ce V x Ce m

(10.4)

where KD, C0 and Ce refer to the distribution coefficient, initial and equilibrium Cr(VI) concentrations, V and m refer to the solution volume in milliliters adsorbent mass in grams. Its value indicates the adsorbent’s affinity to the target species under the specified conditions, higher values therefore point to a more efficient adsorbent material. Figure 10.5 shows the adsorption efficiencies of the adsorbents with increasing adsorbent mass. It indicates that manganese significantly increases magnetite’s affinity towards Cr(VI) ions. The slight decrease in KD values at lower doses was attributed to increased competition for active sites resulting in decreased complexation ability as some of the sites were involved in chromium reduction [26].

Figure 10.5: Cr(VI) sorption efficiency on Fe3O4 and Mn0.06Fe2.94O4 and adsorption distribution coefficient with increasing adsorbent doses.

10.4 Conclusions

145

Figure 10.6: Effect of solution concentration on Cr(VI) adsorption.

Adsorbate concentration is a critical factor in determining optimal adsorption parameters since the solution concentration influences the efficiency of the adsorption process. Figure 10.6 shows that higher solution concentrations resulted in increased adsorption per unit mass of adsorbent. The observation was attributed to greater adsorbate mass transfer from solution to the solid phase as adsorbate ions were increased while surfaces remained constant. Increased mass transfer improved the interaction and complexation between adsorbate ions and adsorbent surfaces [10]. Increasing the solution concentration resulted in lower % efficiencies owing to the increased chromium molecules in solution [27, 28]. The decrease was caused by adsorbent saturation therefore, further increasing the chromium concentration would not result in a significant increase in surface complexation [12]. 100 mg/L was considered as the optimal solution concentration since both adsorption efficiency and capacity were optimized.

10.4 Conclusions Magnetite (Fe3O4) and manganese doped magnetite (Mn0.06Fe2.94O4) nanoparticles were synthesized via a one-pot co-precipitation technique and applied in the determination of optimum parameters for Cr(VI) adsorption. The particles diffraction data was consistent with the inverse cubic spinel structure of synthetic magnetite. Manganese was incorporated into iron lattice through the formation of a solid solution with magnetite resulting in a uniform phase with no secondary phases being observed. Cr(VI) adsorption on both adsorbents was optimized and all the tested factors had consistent results for Fe3O4 and Mn0.06Fe2.94O4 with the latter providing higher affinities, efficiencies and capacities. The improved performance was due to increased adsorption sites provided by manganese oxides on the adsorbent surface and lone

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10 Optimizing Cr(VI) adsorption on Fe3O4 and MnxFe(3-x)O4

pairs of electrons responsible for chromium reduction and binding on the surface. Magnetite modification provided a cost effective adsorbent for pollutant remediation through surface sequestration and redox reactions producing the less toxic Cr(III) species. Acknowledgements: The authors acknowledge financial support from South Africa’s National Research Foundation (NRF).

References 1. Luo J, Meng X, Crittenden J, Qu J, Hu C, Liu H, et al. Arsenic adsorption on α-MnO2 nanofibers and the significance of (1 0 0) facet as compared with (1 1 0). Chem Eng J 2018;331:492–500. 2. Santra D, Sarkar M. Optimization of process variables and mechanism of arsenic (V) adsorption onto cellulose nanocomposite. J Mol Liq 2016;224:290–302. 3. Ouma L, Ofomaja A. Probing the interaction effects of metal ions in MnxFe(3−x)O4 on arsenite oxidation and adsorption. RSC Adv 2020;10:2812–22. 4. Ouma L, Onani M. Sequestration of heavy metal pollutants by Fe3O4-based composites. In: Lichtfouse E, Muthu SS, Khadir A, editors. Inorganic-organic compos. water wastewater treat. Singapore: Springer Singapore; 2022:101–16 pp. 5. Pholosi A, Naidoo EB, Ofomaja AE. Batch and continuous flow studies of Cr(VI) adsorption from synthetic and real wastewater by magnetic pine cone composite. Chem Eng Res Des 2020;153: 806–18. 6. Masuku M, Ouma L, Pholosi A. Microwave assisted synthesis of oleic acid modified magnetite nanoparticles for benzene adsorption. Environ Nanotechnol Monit Manag 2021;15:100429. 7. Roth H-C, Schwaminger SP, Schindler M, Wagner FE, Berensmeier S. Influencing factors in the coprecipitation process of superparamagnetic iron oxide nano particles: a model based study. J Magn Magn Mater 2015;377:81–9. 8. Yuan Z, Liu B, Zhou P, Zhang Z, Chi Q. Aerobic oxidation of biomass-derived 5-hydroxymethylfurfural to 2,5-diformylfuran with cesium-doped manganese dioxide. Catal Sci Technol 2018;8:4430–9. 9. Yang L, Ma L, Xin J, Li A, Sun C, Wei R, et al. Composition tunable manganese ferrite nanoparticles for optimized T2 contrast ability. Chem Mater 2017;29:3038–47. 10. Ouma ILA, Naidoo EB, Ofomaja AE. Iron oxide nanoparticles stabilized by lignocellulosic waste as green adsorbent for Cr(VI) removal from wastewater. Eur Phys J Appl Phys 2017;79:30401. 11. Bishop ME, Glasser P, Dong H, Arey B, Kovarik L. Reduction and immobilization of hexavalent chromium by microbially reduced Fe-bearing clay minerals. Geochem Cosmochim Acta 2014;133: 186–203. 12. Rajput S, Pittman CU, Mohan D. Magnetic magnetite (Fe3O4) nanoparticle synthesis and applications for lead (Pb2+) and chromium (Cr6+) removal from water. J Colloid Interface Sci 2016; 468:334–46. 13. Akl AAS, Elhadi M. Estimation of crystallite size, lattice parameter, internal strain and crystal impurification of nanocrystalline Al3Ni20Bx alloy by williamson-hall method. J Ovonic Res 2020;16: 323–35. 14. Liu Y, Luo C, Cui G, Yan S. Synthesis of manganese dioxide/iron oxide/graphene oxide magnetic nanocomposites for hexavalent chromium removal. RSC Adv 2015;5:54156–64.

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15. Malek TJ, Chaki SH, Chaudhary MD, Tailor JP, Deshpande MP. Effect of Mn doping on Fe3O4 nanoparticles synthesized by wet chemical reduction technique. Iran J Energy Environ 2018;9: 121–9. 16. Otieno BO, Apollo SO, Naidoo BE, Ochieng A. Photodecolorisation of melanoidins in vinasse with illuminated TiO2-ZnO/activated carbon composite. J Environ Sci Heal 2017;52:616–23. 17. Güner S, Amir M, Geleri M, Sertkol M, Baykal A. Magneto-optical properties of Mn3+ substituted Fe3O4 nanoparticles. Ceram Int 2015;41:10915–22. 18. Punia S, Wu L, Khodadoust AP. Adsorption of hexavalent chromium from water using manganesealuminum coated sand: kinetics, equilibrium, effect of pH and ionic strength. J Environ Sci Heal Part A 2021;56:334–45. 19. Roy P, Dey U, Chattoraj S, Mukhopadhyay D. Modeling of the adsorptive removal of arsenic (III) using plant biomass: a bioremedial approach. Appl Water Sci 2017;7:1307–21. 20. Padmavathy KS, Madhu G, Haseena PV. A study on effects of pH, adsorbent dosage, time, initial concentration and adsorption isotherm study for the removal of hexavalent chromium (Cr (VI)) from wastewater by magnetite nanoparticles. Procedia Technol 2016;24:585–94. 21. Fathy NA, El-Wakeel ST, Abd El-Latif RR. Biosorption and desorption studies on chromium (VI) by novel biosorbents of raw rutin and rutin resin. J Environ Chem Eng 2015;3:1137–45. 22. Ouma ILA, Naidoo EB, Ofomaja AE. An insight into the adsorption mechanism of hexavalent chromium onto magnetic pine cone powder. Chem. a Clean Heal. Planet. Cham: Springer International Publishing; 2019:185–95 pp. 23. Pan J, Jiang J, Xu R. Adsorption of Cr(III) from acidic solutions by crop straw derived biochars. J Environ Sci 2013;25:1957–65. 24. Wassie AB, Srivastava VC. Teff straw characterization and utilization for chromium removal from wastewater: kinetics, isotherm and thermodynamic modelling. J Environ Chem Eng 2016;4: 1117–25. 25. Xiong T, Yuan X, Cao X, Wang H, Jiang L, Wu Z, et al. Mechanistic insights into heavy metals affinity in magnetic MnO2@Fe3O4/poly(m-phenylenediamine) core−shell adsorbent. Ecotoxicol Environ Saf 2020;192:110326. 26. Zolfaghari G. β-Cyclodextrin incorporated nanoporous carbon: host–guest inclusion for removal of p-Nitrophenol and pesticides from aqueous solutions. Chem Eng J 2016;283:1424–34. 27. Ouma ILA, Mushonga P, Onani MO. Effects of reaction parameters on the growth and optical properties of PbSe nanocrystals. J Nano Res 2015;34:79–89. 28. Zhang J, Lin S, Han M, Su Q, Xia L, Hui Z. Adsorption properties of magnetic magnetite nanoparticle for coexistent Cr(VI) and Cu(II) in mixed solution. Water 2020;12:446.

Liliana Mammino*

11 The spontaneity of chemical reactions: challenges with handling the concept and its implications Abstract: The spontaneity concept plays crucial roles in the description of chemical reactions and entails a variety of implications, including the determination of the difference between galvanic and electrolytic cells. Students experience challenges with handling the concept and its implications within chemistry contexts. Everyday-life examples do not provide immediate evidence of chemistry-related spontaneity, and some features may be misinterpreted. The ΔG < 0 spontaneity criterion does not have an everyday-life correspondence and mostly remains abstract. Tendencies to equate exothermic or fast with spontaneous appear frequently. Using the spontaneity or nonspontaneity concepts in the interpretation of observed simple electrochemical phenomena may pose difficulties. The challenges are greatly enhanced by two diffuse contextual features: tendency to rote learning and inadequate language-mastery, with the latter being a major cause of the former and generally hindering conceptual understanding. The paper highlights the main difficulties diagnosed within an action research approach, documenting them with a sufficiently ample selection of illustrative examples. The ways in which diagnoses are utilised as guidelines for in-class interventions aimed at addressing identified challenges are delineated and discussed. The integration of chemistry-concepts analysis and language-analysis is viewed as the most powerful instrument to address identified difficulties in real time. Keywords: action research; error analysis; interpretation of experimental observations; language mastery importance; spontaneity of chemical reactions.

11.1 Introduction 11.1.1 The perception of physical chemistry as a difficult area Physical chemistry courses are often considered particularly difficult. This has attracted the attention of several educators, including with straightforward questions like “What Makes Physical Chemistry Difficult?” [1]. Researchers have investigated the factors that may cause difficulties for physical chemistry in general [2] or specifically for

*Corresponding author: Liliana Mammino, University of Venda, Thohoyandou, South Africa, E-mail: [email protected] As per De Gruyter’s policy this article has previously been published in the journal Physical Sciences Reviews. Please cite as: L. Mammino “The spontaneity of chemical reactions: challenges with handling the concept and its implications” Physical Sciences Reviews [Online] 2022. DOI: 10.1515/psr-2021-0144 | https://doi.org/10.1515/9783110783643-011

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its various areas (thermodynamics, kinetics, electrochemistry, quantum chemistry, etc.). The recognition of the perception of difficulties has also prompted specific surveys extended to a whole country [3] (which also included a survey of the “instructor beliefs about the challenging nature of physical chemistry education versus proposed strategies to overcome those challenges”), or to an institutions’ selection [4]. It has also prompted suggestions to address the problems for the entire physical chemistry (e.g., [5–7]) or for specific topics (e.g., [8]). A detailed review of these works would go beyond the scope of the current work. It is however interesting to note that the perceived “abstract nature of concepts in physical chemistry” [1] appears to be one of the major problems recognised by students. The present work focuses on the difficulties that students encounter with a specific concept – the spontaneity concept. This concept is linked to the question whether a chemical reaction may occur on its own (or in which direction it tends to occur on its own), thus playing crucial roles in its description and in the evaluation of its possible utilizations. It also defines the difference between galvanic cells and electrolytic cells in electrochemistry. Most works considering spontaneity from a chemistry education perspective refer to the study of chemical equilibria. A series of articles (e.g., [9, 10]) focuses on the analysis of the approaches with which the spontaneity concept is presented in first year general chemistry books, considers it to be misrepresented and proposes a more rigorous treatment. This is in a way emblematic of some of the challenges facing the presentation of physical chemistry concepts. On one hand, rigour is essential both at the conceptual and at the wording levels. On the other hand, the presentation of concepts needs to maintain a sophistication level that remains accessible to the students to which the concepts are introduced. For instance, in a context (like the one to which the current work refers) where the primary objective is to acquaint students with relevant basic concepts and to ensure understanding despite a disadvantaged reality, the teacher has the task of designing approaches that pursue this objective and prevent misinterpretations, i.e., faces the challenge of balancing the essential need for rigour with the need for accessibility. A basic outline of the thread through which the concept has been presented in the considered context is offered in the next subsection. The outline recalls the key features, which will also serve as reference for the analysis and discussion in the rest of the paper. The other Sections (11.2–11.4) present the context, the approaches utilised in the investigation and the diagnosed challenges. It is concluded that addressing languagemastery inadequacies is the major pre-requisite for any attempt to enhance students’ conceptual understanding and their abilities at the application and interpretation levels.

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11.1 Introduction

11.1.2 Key steps in the presentation of the spontaneity concept The first step in the presentation of the spontaneity concept needs to provide an easily understandable and applicable definition. The concept refers to processes, i.e., a process can be qualified as “spontaneous” or “non-spontaneous”. Therefore, its definition [11] is better given with direct reference to processes: A process is spontaneous if it tends to occur on its own, without the intervention of human technology. Examples of spontaneous physical phenomena are familiar from everyday life, and some of them are recalled in the textbook recommended for the course [11]. The most familiar are probably the phenomena determined by the action of gravity, such as the fact that an object falls downwards, or water flows from a higher area to a lower area. Students have no hesitation in saying that an object, left to its own, falls downward. These examples also offer easy illustrations of the fact that, if a process is spontaneous, its opposite is non-spontaneous and may occur only as a result of our (humans’) intervention. If we want an object to shift from a lower position to a higher position, we have to move it. If we want a certain mass of water to reach a higher level, we have to employ some technology. There are also familiar physical phenomena that are easily recognisable as spontaneous, but for which the analysis of the opposite, non-spontaneous phenomenon may be less immediate, although still fairly understandable. A gas escapes from its container if an outlet appears: for instance, air escapes from a punctured tire. Our technology allows us to pump air into a tire, but does not allow us to gather the same air (same molecules) that have initially escaped from the punctured tire, what makes the escaping of the initial molecules an irreversible process. Where chemical reactions are concerned, the spontaneity concept is not related to the laws of mechanics (whose effects are often familiar from everyday life), but to the laws of chemical thermodynamics – something that is much more elusive in terms of direct experience. The concept pertains to the essence of the second law of thermodynamics, which can be stated in a way making direct reference to it [11] The entropy of an isolated system increases in the course of a spontaneous change. This requires the understanding of the nature of the entropy (S) state function and how to consider and calculate its changes. When the system is not isolated, one has to consider the total entropy change, i.e., the sum of the entropy changes in the system where the process occurs and in its surrounding. Therefore, the criterion for a process to be spontaneous is expressed as ΔSsystem + ΔSsurroundings > 0

(11.1)

When considering chemical reactions, in place of evaluating ΔSsystem and ΔSsurroundings, it is more convenient to utilise the Gibbs free energy function G = H−TS. Then, the spontaneity criterion (Eq. (11.1)) is written as

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11 The spontaneity of chemical reactions

ΔG  <  0

(11.2)

that is, a process is spontaneous if is accompanied by a free energy decrease.

11.2 The context and the approaches The results and analyses presented in this work pertain to a systematic investigation of the difficulties encountered by underprivileged students in their approach to chemistry concepts and applications, carried out at the University of Venda (UNIVEN, South Africa) during the 1997–2018 years and considering the courses taught by the author (all the physical chemistry courses, first year general chemistry and third year process technology). UNIVEN is a Historically Black University (HBU), thus also qualified as “historically disadvantaged”. The meaning of “historically disadvantaged” has been explained in previous works (e.g., [12–14] and will not be analysed in detail here. Suffices to recall that the majority of students are underprivileged both in terms of economic background and in terms of the background preparation acquired in preuniversity instruction. It can be noted since now (and will be recalled more in detail in the conclusions) that the information identified from research in disadvantaged contexts is relevant not only for those contests, but also for the design of student-friendly approaches in any context, because it diagnoses more details of the difficulties that students may encounter. The approach utilised in the investigation pertains to an action research option, selected by the author because it is the most functional for the purpose of continuous optimisation of teaching approaches in order to respond to diagnosed and changing students’ needs in real time. It entails subsequent loops that can be summarised as follows: – diagnosing difficulties experienced by students in a given course or with reference to a given topic; – designing and implementing approaches to address them; – analysing students’ responses to these approaches; – using the results of the analysis to design improved approaches and implementing them. A more detailed description is given in [15, 16, and references therein] and a diagram illustrating its recursion (subsequent loops) nature is included in [16]. Only inherent components of students’ learning activities have been considered reliable sources of information [17]. These comprise their written works (which correspond to their maximum efforts to produce their best [17, 18] in view of assessment) and their questions, comments and answers during in-class interactions (which highlight perceptions and often clarify the details of the difficulties they encounter). The analysis of the errors appearing in these works or answers constitutes the major diagnostic tool.

11.2 The context and the approaches

153

The collection of extensive documentation is necessary to enable the identification of as many details as possible for each diagnosed problem, as details-richness is tantamount to richness of guidelines in the design of addressing approaches. The latter pertain to two major categories: – interventions during in-class interactions, as on-the-spot responses to the features indicated as challenging by the feedback from students, or within sessions specifically devoted to facilitate students’ understanding, such as tutorials or post-test sessions; – enhanced approaches to the first explanation of a given theme, incorporating the indications from the diagnoses made during a given course, and utilised in the subsequent edition of that course. Since the documentation was needed as source of guidelines for subsequent optimisations of the teaching approaches for each individual topic, only qualitative information was collected, as the only one apt to fulfil this role. The investigation of students’ difficulties [16] has singled out two major categories of challenges-entailing issues: – specific chemistry concepts or topics; – specific language-related or method-related inadequacies, where the adjective “specific” signifies that those individual concepts or individual inadequacies deserve to become individual objects of study within a systematic investigation. The two categories are intertwined in several ways. The level of understanding of a certain concept may condition the level of understanding of a new concept. Inadequacies in the mastery of tools essential for understanding a text or an explanation condition the level and depth at which several or most concepts are understood. Language-mastery inadequacies (further aggravated by the use of a second language as a medium of instruction) proved the most serious understanding-hampering factor. Figure 11.1 outlines a basic scheme of their impacts. Furthermore, they become a “confounding variable” in assessment operations [19] because, on considering an error in a student’s work, it is usually impossible to untangle the conceptual component and the language-mastery-inadequacy component [20]; this thwarts assessment-reliability by preventing a “pure” evaluation of the chemistry knowledge attained by the student. Errors easily identifiable as purely grammar-type can be ignored on assessment; but, too often, the replacement of correct wordings by unsuitable ones engenders doubts regarding the actual understanding extent. The present work focuses on the spontaneity concept, considering the main diagnosed challenges regarding the various aspects of students’ handling of the concept and its implications, as well as the approaches designed to try and address them. The information was collected in the chemical thermodynamics and electrochemistry courses (which respectively constituted the content of the second year

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Figure 11.1: Schematic summary of the impacts of inadequate language mastery on science learning.

physical chemistry course and one of the components of the third year physical chemistry course). Selected preliminary diagnostic results were reported in [21] for the thermodynamics component and in [22] for the electrochemistry component. The present work aims at offering a comprehensive picture of the diagnoses, analyses and ensuing inferences regarding this concept. Since it focuses on a specific theme within a broader systematic investigation (diagnosed difficulties in UNIVEN students’ approach to chemistry), it is natural to include references to results and analyses reported for other themes of the same investigation and which may be relevant for the current discussion, to avoid repetitions of already communicated information. Both the explanations and the assessment tools devoted great attention to conceptual understanding and to clear recognition of the links between concepts and problem-solution algorithms or experimental observations. Classes largely utilised interactive teaching approaches, including extensive resort to in-class written

11.2 The context and the approaches

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Figure 11.2: The analysis of errors: from a diagnostic tool to an explanation tool and a design guideline for enhanced pedagogical approaches.

questions and answers when students’ verbal participation remained low [23 and references therein]. In-class written questions automatically extend active participation to all students and provide real-time feedback to the teacher, who can thus add needed clarifications in real time. Explanations and clarifications made also extensive use of visualization through representations designed on the spot, for them to be better tuned to students’ questions or difficulties. The analysis of errors [20, 24, 25] proved a

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powerful explanation tool both for crucial conceptual aspects and for relevant expression aspects. It institutes a route to engage students actively, demanding reflections and guiding them through subsequent reflection stages until a satisfactory answer (statement or description) is reached. The efforts inherent in searching for the reasons why a certain statement is not correct engage students’ attention much more actively than listening to an explanation, thus making error analysis an efficient tool to clarify concepts and facilitate understanding. Figure 11.2 outlines the routes for the roles of error analysis: a diagnostic tool for the teacher (initial role), an explanation tool in the class, and a guideline for the design of enhanced teaching approaches. Some features of Figure 11.2 may entail additional clarifications. The adopted procedure excludes any possibility of identification of the “authors” of the errorsamples proposed to students’ consideration; besides being functional to the overall practice, this ensures students’ full comfort with the activity. The discussion of errorsamples based on frequently occurring errors responds to diffuse needs (including from students who have correctly reproduced memorised statements, but may not be fully aware of their conceptual meaning). Rarely occurring errors are proposed for discussion only if their analysis can add insights apt to enhance the understanding of the concerned concept. Sentences with no identifiable literal meaning are clear indicators of serious language-mastery inadequacies (thus constituting relevant information for the teacher to build a realistic picture of the overall situation of the group); on the other hand, the absence of an identifiable meaning does not allow any form of pedagogically significant analysis and, therefore, these sentences cannot contribute to the construction of error-samples. Sections 11.3 and 11.4 provide an overview of diagnosed challenges, formally separating those related to thermodynamics and those related to electrochemistry for the sake of discussion convenience. Categories of errors are utilised to identify the challenges, and error-samples provide illustrations. The key terms of their analysis and in-class discussion are outlined briefly for each sample. These include languagerelated terms, because the recognition that language-related challenges are a major cause (likely, the major cause) behind the errors entails the need to integrate the analysis of language aspects and conceptual aspects. Considerations about assessment-related challenges (for the teacher) are included in some cases, to illustrate the nature of this type of challenges. For the sake of referencing-convenience across the text, the reported error-samples are numbered progressively, and these numbers are utilised to refer to them in the text, placing them within parentheses (thus being clearly distinguishable from the numbers denoting literature references, which are enclosed in brackets). Several samples are reported in the electronic supplementary material (ESI, Table S1) for obvious space reasons; their numbering is independent of the numbering within the text and their numbers are preceded by an uppercase S to indicate that they pertain to the ESI; they are discussed in the text with the same level and extent as the samples included in the text.

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11.3 The spontaneity concept in chemical thermodynamics 11.3.1 Defining the concept and providing illustrative examples When students are asked to provide a definition or an explanation of the spontaneity concept, they tend to reproduce the memorised statement from the textbook. While memorisation embedding understanding is acceptable for definitions, passive memorisation does not add to a student’s knowledge and leads to a variety of errors on attempting to reproduce memorised sentences. Some errors are clearly related to language-mastery inadequacies, like those in (1, 2, S1); on the other hand, it is difficult to evaluate whether the concept is truly understood when words having key roles in the expression of its meaning appear not to be known. 1. A process is spontaneous if it tend to do without the intervention of our technology. 2. A process on the spontaneous if it trend without the intervention of the technology. The purely grammar errors in these three cases (the absence of the “s” at the end of the verbal forms tend and trend) can be ignored at assessment level (where one tries to evaluate the understanding of the concept), although they get highlighted during interactions with students. The use of on the instead of is (2) responds to an observed tendency to use wordings that appear to ascribe sorts of spatial assignations to categories: the spontaneous adjective defines a category, and the subject (a process) is viewed as spatially pertaining to that category (in a way, sitting on it). The use of to do in place of to occur (1) pertains to a frequent absence of distinction between verbs appropriate for actions entailing an object on the “receiving side” of the action (like to do) and verbs indicating a process (like to occur); this, in turn, relates to frequent difficulties in the distinction between systems (objects) and processes – an epistemological (method-related) aspect for which the selection of the appropriate terms requires both adequate conceptual knowledge and adequate language mastery [26]. The use of trend in place of tends to occur and of conversion in place of intervention can be ascribed to passive memorisation: a not-so-focused memory of the sounds of words present in the original sentence substitutes the memory of the concept, and words whose sounds are similar (or perceived as roughly similar in the second language [27]) replace corresponding terms of the definition. Interactive discussions involve the analysis of the meanings of each pair of words (those present in the definition and those replacing them in the sample answers); students are firstly asked to explain their meanings; the fact that the two words are presented (written on the board) at the same time facilitates reflections on their meaning-difference and their suitability for the expression of the desired concept. Language mastery is the key to the acquisition of logic mastery [28]. When language mastery is not adequate, sentences may violate basic logic assumptions, like the

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need to avoid internal contradictions. For instance, statement (3) includes two opposite conditions: 3. Spontaneous process is a process that takes place without our intervention through technology or with our technology. Discussions highlight the conceptual contradiction resulting from the simultaneous presence of without and with with reference to the same item (our technology) and that this would lead to the inference that all processes are spontaneous. The challenges determined by poor language mastery may be so serious that some sentences do not contain more than few words, often not enough to express a meaning. Sample (4) suggests that the basic idea of the concept (the non-interference from human technology) has likely been grasped; but the sentence does not contain information about the reference term (what it is about) and is not sufficiently complete to convey a meaning: 4. Technology must not affect Asking students to provide examples of their own choice for concepts that are focus of attention at a certain moment is a straightforward and informative way to test their understanding of those concepts. The in-class introduction of the spontaneity concept includes detailed analysis and discussion of the examples mentioned in the textbook [11], followed by invitations to students to search for other examples from their own experience. Then, questions at assessment level may ask students to provide examples. Attempts to reproduce memorised examples often result in confuse or incorrect statements, above all because the more complex a sentence, the more difficult is a correct recall from passive memorisation. For instance, an example in the textbook mentions that ethanol can burn to give carbon dioxide and water, but the reverse process does not happen. A three-clause sentence of this type turns out to be too complex for correct recalling from memorisation that has not been accompanied by understanding. An illustration is offered by sample (5), answering a question asking for an example of a spontaneous process: 5. When ethanol is heated to give carbon dioxide and water, but the reverse can happen The discussion compares the sample to the original statement in the textbook. The first focus is the identification of the part that constitutes an example of spontaneous process (the information that ethanol burns to give carbon dioxide and water). The second focus considers the last clause of the original statement, inviting students to formulate a suitable question to which it can provide an answer (the request for an example of non-spontaneous process) and to reflect on why the concept is expressed correctly by cannot happen and not by can happen. Two purely language-related issues are also discussed, because of their frequent occurrence: the use of when at the beginning of an answer, in place of a subject related to the question, and the frequent confusion between heating and burning (which is at least partly related to the fact that

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the two concepts are often expressed by the same term in the students’ mother tongues). Another example of spontaneous process mentioned in the textbook considers the fact that a hot body cools to the temperature of the surrounding. Sample (6) responds to attempts to reproduce it on the basis of passive memorization: 6. A hot body cools the temperature of its surrounding The discussion utilises the analysis of the meanings of the statement when the preposition to before the temperature is present and when it is removed. Assessment challenges appear when a likely language-related error suggests the possibility that terms expressing concepts that are part of the course content may not yet be mastered. Sample (S2) uses is expressed instead of expands, likely because sounds belonging to a foreign language may easily be perceived as similar even when the terms express completely different concepts [27], as already seen for (2, 3). Since the terms expansion and to expand are technical terms in the course, it is important that students acquire and master them. Samples like (S3, S4) are also proposed for discussion, to highlight extreme impacts of passive memorisation (recall of too few words to convey a meaning), and to reflect on the distinction between systems and processes. (S3) is meant to refer to the same example as (5), and (S4) to the example of a gas expanding to fill the volume available. The samples considered in the previous paragraphs already highlight major challenges. They are summarised here to facilitate their recognition when they are encountered again in the next sections. Passive memorisation and language-mastery inadequacies combine as causes of errors, and are often interlocked (Figure 11.1). Many errors highlight the importance of training students to analyse the meaning actually conveyed by the sentences they write and compare it with the scientific meaning they want to convey. On the other hand, this analysis can be carried out effectively only if language mastery (including grammar knowledge) is adequate. Although students appear to understand the problems when they are explained during interactive discussions, the acquisition of sufficient independence to perform the analysis on their own is rare. This appears to be confirm the limitations to the possibility of building real language mastery (as ability to efficiently use language as a communication tool) after 18–20 years of age, if foundations have not been set earlier. The outcome sadly responds to Qorro’s diagnosis [29] that many young persons in second language instruction contexts in which the theory of the mother tongue is not taught adequately end up with “two underdeveloped languages” (the mother tongue and the second language), without efficient mastery of either. The impacts on science learning are severe.

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11.3.2 Entropy – an elusive physical quantity? Entropy is introduced together with the second law of thermodynamics, in the chemical thermodynamics course. At introductory level, its physical meaning is presented as an indication of the degree of disorder at the molecular level. Some cases are easier to illustrate through visualization, like the greater order of a solid with respect to a liquid, because a common image of a crystal structure conveys the message of “order” and an image of a disordered arrangement of molecules in contact with each other can convey the perception of greater disorder in the liquid. Some analogies are cautiously used for processes where immediate perceptions may not be easy, like the heating process, where the disorder increase is related to the higher velocity of molecules. Students are invited to imagine being in a room and walking at normal pace in random directions, and then gradually increasing their speed. This analogy is actually utilised earlier in the course to stimulate a perception of why the rate of collisions between gas molecules increases as temperature increases; it is then recalled to stimulate a perception of how a temperature increase corresponds to greater disorder at the molecular level. Analogies are always treated with great caution, to make it clear that they are meant to clarify a concept, not to provide examples of entropy or entropy changes [30]. The fact that entropy does not refer to the disorder of macroscopic objects in the macroscopic world, but only to the molecular level, is stressed repeatedly. The purpose of any utilised analogy is also stressed repeatedly. For instance, it is not immediate to perceive the fact that mixing two substances implies an entropy increase. Simple visualizations show two separate containers with spheres (molecules) coloured differently for each container (initial situation), and then a container in which they are mixed (final situation). This provides an illustration of the meaning of mixing, but does not automatically engender the perception that the final situation is more disordered than the initial one. Then, an analogy is added to facilitate the perception of greater disorder when different things are mixed. For instance, the teacher may ask whether there would be more order among our things if all the shirts are in one drawer and all the socks in another, or if they are mixed in the same drawer. The obvious answer from students is then analysed as providing an indication of greater disorder when different items are mixed. The next step invites student to transfer this concept (greater disorder when different items are mixed) to the molecular level, when the mixing of two substances is considered. Subsequently, on suitable occasions, the distinction is recalled by asking students whether we can say that their drawers (or the author’s desk in her office) have high entropy, and most of them recall that we cannot say it, because the objects in the drawers or on the desk are not individual molecules. The issue of analogies is very delicate, because on one hand they can be useful to clarify concepts that may be perceived as too abstract by students, and on the other hand they need attentive cautions to prevent misunderstandings. The teacher needs to make all the terms of an analogy very explicit. This is particularly critical for themes

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like entropy, where the concept refers to the microscopic world of atoms and molecules, but is used to predict phenomena occurring and observable in the macroscopic world. Students’ understanding of the concept is initially tested by asking them to explain whether the entropy of the system increases or decreases during specified processes (selected among those that have been discusses in the class), or to provide examples of processes where it increases or decreases. A typical sample question asks which ones among five listed processes (e.g., melting, solidification, heating, mixing of two substances, separation of the components of a mixture) are accompanied by an entropy increase of the system, and to explain the reasons for their answers. Not all students answer questions of this type, and several of those who answer list the processes accompanied by an entropy increase or decrease without providing explanations. The answers where explanations are attempted indicate difficulties in handling the concepts of order and disorder at the molecular level. Some answers replace one or more of the processes specified in the question with their opposite (mostly replacing heating by cooling). Sample (7) reverses the association between entropy and order, considering that greater degree of order corresponds to greater entropy: 7. (a) Entropy increases on solidification, since the solid has a higher degree of order (b) Entropy increases on cooling, since a cool substance has a higher degree of order due to the stability of the molecules of a cool substance (c) Entropy increases on mixing of two substances: when two substances are mixed, the entropy increases since they develop a higher degree of order. The discussion first of all focuses on the implications of the correspondence between entropy and degree of disorder, and its implications for whether it increases or decreases during a given process. Statement (a) is also used to recall the importance of specifying the comparison term (which in this case should be the liquid). Statement (b) is also used to underline that the entropy change on heating or cooling is related to the change in the average velocity of the molecules, not to the internal stability of individual molecules. Statement (c) is used to recall the greater disorder (less order) in the mixture with respect to the separate substances. Although the comparison of the degree of disorder in the initial and final states of a process is the most straightforward criterion for qualitative questions of this type, several answers discuss the processes in terms of spontaneity, like in (8), referred to entropy increases: 8. (a) Mixing of two substances, because mixing is spontaneous and happens in nature (b) Cooling, because the substance cools to the surrounding temperature The discussion stresses that the question refers to the entropy change of the system, whereas the spontaneity criterion refers to the total entropy change. It also stresses the

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distinction between general occurrences (something that always occur) and specific cases. Not all pairs of substances can mix, so, mixing is not always spontaneous, but, when it occurs, it is always accompanied by entropy increase. Statement (b) (which attempts to recall the example of spontaneous process to which (6) refers) loses its validity because of the absence of the specification (adjective) hot for the ‘item’ expected to cool, as not all bodies cool to the temperature of the surrounding, but only those whose initial temperature is greater than that of the surroundings. It also has to be noted that this type of analysis (what occurs always; what occurs only under specific circumstances and the nature of these circumstances) requires epistemological bases and rather complex thinking, which cannot be concomitant with passive memorization and require a certain level of language-mastery sophistication. In other cases, explanations in terms of spontaneity search for familiar examples from everyday life phenomena instead of focusing on the relationship between entropy and the disorder at molecular level, and this easily engenders errors. Selections (b) and (c) in (S4) are not correct; the analysis of the wording in (c) also stresses the importance of proofreading.

11.3.3 The spontaneity criteria Handling the spontaneity concept and its implications within chemistry contexts – above all, with reference to chemical reactions – appears to entail more challenges than handling it for physical phenomena. Everyday life examples do not provide immediate evidence of chemistry-related spontaneity, and some features may be misinterpreted. The ΔG < 0 spontaneity criterion Eq. (11.2) does not have everyday life correspondence and remains largely abstract. Viewing it as equivalent to Eq. (11.1) remains particularly difficult (despite a demonstration being outlined in the class), and several answers indicate ΔS > 0 (entropy increase for the sole system) as the criterion indicating whether a chemical reaction may occur spontaneously. The diffuse perception that a reaction is spontaneous if it is exothermic results in frequent provision of ΔH < 0 as spontaneity criterion, either alone or together with ΔS > 0. These errors unavoidably entail negative evaluation on assessment. On the other hand, the presence of errors on the internet (for instance, a source giving ΔG < 0 and ΔH < 0 as criteria to be satisfied simultaneously) stresses the importance of training students to distinguish between reliable and non-reliable information from the internet [15]. This, in turn, would require students to consider recommended textbooks as the primary source of information for a course, and to compare the information from an internet source with that of the textbook. Not all students are available to accept this, as their trust in the internet is overwhelming. They rarely follow the repeated recommendation to inform the lecturer when they find something on the internet that is not in agreement with the textbook or with the explanations given in the class – a recommendation meant to provide opportunities for needed clarifications.

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Misinterpretations of experiences from everyday life may also affect spontaneity identification. For instance, the fact that combustion requires ignition is often perceived as indication of it being non-spontaneous, despite the evidence that fires proceed on their own and interventions are required to extinguished them or stop their propagation (a striking example being wild fires). This indicates the importance of emphasising the distinction between activation and spontaneity. Another frequent misinterpretation equates spontaneous to fast, with the inference that a spontaneous reaction occurs fast and a non-spontaneous reaction needs more time to start and then proceeds slowly. The next section contains samples expressing such misinterpretations.

11.4 The spontaneity concept in electrochemistry 11.4.1 Definitions and their implications The spontaneity concept is fundamental for the identification of galvanic and electrolytic cells: the former utilise a spontaneous redox reaction to produce electric current, whereas the latter utilise electric current to force a non-spontaneous redox reaction to occur. The concept also plays significant roles in the interpretation of experimental information [22]. The compartmentalisation with which individual courses are often perceived and treated frequently influences the answers expressing a definition of spontaneity. Although the general character of the definition provided within the thermodynamics course (Section 11.1.2) is emphasised at the very beginning of the electrochemistry course-component, answers often “adapt” it to the fact that the question is asked within an electrochemistry course. The “without the interference of human technology” condition is thus replaced by “without the use of electric current” or “without any supply of energy or electric current”. The wording of some answers suggests a perception of electric current as a substance (S6). Statements of this type would also deserve investigations along the line of a return to historically-past images in some of the perceptions expressed by students [31]. Conceptions equating spontaneous to fast appear frequently. The more elaborated answers (e.g., (9)) may also recall tendencies diagnosed in various topic-contexts. 9. A reaction that tends to happen very fast. It happens on its own. It does not need electric current to occur fast. This would imply that the reverse, non-spontaneous reaction would occur slowly if electric current is not supplied. This perception might relate to an apparent refusal of something being impossible (as noted, e.g., within the quantum chemistry course, where answers often state that it is difficult to solve the Schrödinger equation exactly for systems containing more than one electron, instead of stating that it is impossible).

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The way in which students handle examples is greatly informative about their understanding of a definition. A two-part question meant to check the understanding of the definition of galvanic cell asked students to select reactions suitable for use in galvanic cells from a given list (first part) and to explain the reasons for each of their answers (second part, requiring the explanation of why each of the proposed reactions is or is not suitable). A typical question of this type contained two suitable reactions (simultaneously spontaneous and redox), two reactions that are spontaneous but not redox (more often, an acid-base reaction or the formation of an acid from the hydration of the corresponding oxide) and one or two reactions easily recognisable as redox but non-spontaneous (e.g., the decomposition of compounds with which students are expected to be familiar, like CO2 or H2O), listed in a random sequence. The greatest challenge of these exercises is the need to verify two conditions for each case. The answers often verify only one of them, mostly the spontaneity condition. Samples (10) and (S7) miss the fact that the given reaction is not redox; sample (S8) does not recognise the concerned reactions as spontaneous and misses the fact that they are not redox. 10. KOH + HCl / KCl + H2O. YES. The reaction is spontaneous. Only a comparatively small proportion of answers consider the oxidation numbers of the elements involved in the proposed reactions, indicate them somewhere (e.g., above the symbols of the elements), verify whether some numbers change from reactants to products, and then clearly state whether a reaction is redox or not. Most answers do not verify the oxidation numbers and do not specify whether a reaction is redox or not. Some answers (S9) refer to a not-clearly-specified oxidised state, but do not inspect the oxidation numbers. It also happens that the fact that a reaction is not redox is considered an indication of non-spontaneity (S10). When the proposed reactions entail the decompositions of familiar compounds into their elements, explanations are often skipped. Although students know that, e.g., water does not decompose into hydrogen and oxygen on its own, several answers fail to connect this practical knowledge to the question and to recognise the reaction as redox but non-spontaneous (S11, S12). Sample (12) entails a confusion between possibility and spontaneity: the fact that the decomposition of water is possible should not be taken as implying spontaneity (it is possible – i.e., it can be realised – if appropriate technologies are used). The analysis of the proposed reactions in interactive discussions considers the oxidation numbers of each element, verifies whether any of them changes, and invites students to utilise experience and acquired chemical knowledge to evaluate which reactions are spontaneous. Another frequent question meant to check conceptual understanding asks students to explain the reasons why galvanic cells can only utilise reactions that are both spontaneous and redox (the reaction must be redox because there must be an electron transfer to generate electric current, and must be spontaneous because we want to

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obtain the current from the reaction without having to force it). The answer must be built by students, because of the absence of readily available sentences that could be passively memorised from some sources. After it became clear that the challenge of expressing two cause-effect relationships in the same answer was too great for the language-mastery level of most students, the question was separated into two subquestions, each requiring the explanation of the reasons for one of the two conditions. All the same, many answers remain limited to repeating the definition, or stating the definition itself as a cause (11). Some answers simply reiterate the view that all redox reactions are spontaneous (S13). 11. Because galvanic cell is a device utilising a spontaneous redox reaction to produce electric current. Interactive discussions of samples of this type are particularly challenging, because they entail method-related (or epistemological-related) aspects like the fact that a definition cannot constitute an explanation of itself, or the identification of causeeffect relationships (identifying the reasons for what the definition states). Greater language and epistemological mastery would be needed for these concepts to attain adequate internalisation extent. The spontaneity concept plays an important role also in the analysis of the relationship between the free energy change accompanying a reaction and the electromotive force (e.m.f., E) of a galvanic cell utilising that reaction. A question asking for the derivation of the relationship between ΔG and E usually gets a large number of adequate answers, word-by-word reproducing the steps provided in the lecture notes. This question was often followed by a question asking to show that the relationship (ΔGo = −n F Eo if standard conditions are considered) is consistent with the fact that the reaction utilised in a galvanic cell is spontaneous. The explanation is based on the analysis of the −n F Eo term and entails the following considerations: (i) n, being a number of electrons, is positive; (ii) F (Faraday’s constant) is positive; (iii) Eo is positive because of the way in which it is defined (difference between the standard potentials of the species that is reduced and the species that is oxidised); (iv) therefore, the −n F Eo term is negative; (v) this implies that ΔGo (being equal to −n F Eo) is also negative, as expected for spontaneous reactions. This explanation was routinely discussed in class; however, there was no source that students could memorise passively and, therefore, answering the question entails the ability to build a comparatively complex discourse. Only a limited number of answers managed to provide sufficiently complete explanations. Few others indicated basic understanding, although omitting some logical steps (e.g., S14, S15). Several answers simply state that ΔG must be negative because of the definition of galvanic cell, without considering the relationship to which the question refers (S16), or state the conclusion (ΔG having to be negative) without any steps leading to it (S17):

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11.4.2 The interpretation of experimental information Good understanding of the spontaneity concept is crucial in the interpretation of a classical simple experiment meant to qualitatively compare the relative tendencies of selected metals to be in an oxidised state through a series of tests in which each metal is dipped into solutions containing ions of another metal. The guidelines to the experiment and lab-report specify the natures of the metals and the solutions and invite students to define the main concepts (theoretical background section), to describe what they observe for each test, identify the cases in which a spontaneous reaction occurs, write chemical equations for the reactions and – as conclusion – list the given metals in order of increasing relative tendencies to be in an oxidised state [32]. The identification of spontaneous reactions is expectedly straightforward: a spontaneous reaction occurs in the cases where some changes are observed, whereas no reaction occurs in the cases where no change is observed, which implies that the reaction between the solid metal and the ions is non-spontaneous for those cases. The lab reports often highlight students’ perceptions about spontaneity. A certain space is here devoted to these perceptions, because of the interest of diagnoses that are related to a practical use of the concept (use of the concept at a sort of implementation level). Only a minority of students clearly state that spontaneous reactions have occurred in the cases where some changes have been observed. The modes of expression often reflect diffuse language challenges, which may also be associated with epistemological challenges. For instance, the confusion between the roles of the noun spontaneity and the adjective spontaneous (S18, S19) suggests the perception of spontaneity as something that may occur or not occur, instead of an abstract term designating a quality; however, it might also be solely related to inadequate grammar knowledge preventing operational distinction between the roles of nouns and adjectives. The fact that the experiment considers redox reactions leads to frequent incorporation of the redox concept into the spontaneity concept, and the ensuing view that redox reactions are the only reactions that can be spontaneous (S20–S23). Inherent characteristics of redox reactions such as the presence of electron transfer (S23) or the simultaneity of oxidation and reduction (S24) may then be expressed as spontaneity criteria. Inherent characteristics of galvanic cells, such as the fact that the electron transfer occurs through a distance, may also be expressed as spontaneity criteria (S24, S25), likely as an extension of the fact that these cells utilise spontaneous reactions. Some perceptions that had appeared in the thermodynamics course resurface in the interpretation of experimental observations, such as the idea that only exothermic reactions are spontaneous and that non-spontaneous reactions can occur if one supplies heat (S26–29). Some answers also consider that the addition of a catalyst can force a non-spontaneous reaction to occur (S30). Interactive discussions focus on the difference between H and G and, for (S30), on the state function nature of G, for

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which ΔG depends on the reactants and products, not on the route taken between them (and, therefore, is not changed by the addition of a catalyst). The idea that a nonspontaneous reaction needs activation (already encountered in answers considering combustion as non-spontaneous) reappears in the interpretation of experimental observations (S31–S33), and so does the idea that spontaneous reactions are fast and non-spontaneous reactions are slow (S34–S38). The interactive discussion of these samples also largely focuses on the nature of G. After the first diagnoses of perceptions considering that, in the cases where no changes were observed, reactions would occur if allowed enough time, the experiment guidelines invited students to leave the beakers with the various solutions and dipped pieces of metal on the benches until the following morning, in order to verify whether there would be additional changes in a longer time. The non-spontaneous reactions obviously did not start, but statements referring to the need for longer time continued to appear. Their appearance may relate to a rather diffuse tendency to write what students think should have happened (even if they did not observe it) instead of what they observed [33]. In turn, this may relate to inadequate confidence in their own observations (diagnosed also for other experiments), and also to the previously mentioned tendency to avoid stating that certain things are not possible. Only few students mention that electrolysis would be the option to force a nonspontaneous redox reaction to occur although, by the time the lab reports for this experiment were due, electrolysis had already been discussed in the course. The fact that most students do not mention it likely relates to a diffuse perception of dichotomy between what is done in the class and what is done in the lab, for which the information acquired in the class is seldom transferred to the interpretation of the experiments. Some of the more elaborated reports highlight perceptions confirming the necessity to emphasise each individual detail of a theme at all levels (during explanations and classroom-interactions, and through specific questions in assessment tools). For instance, emphasising the reasons why the reactions suitable for use in a galvanic cell must be redox and spontaneous may help prevent the idea that it could be possible to produce electric current by forcing a non-spontaneous redox reaction to occur (S39). Some of the errors relate to problems diagnosed across various themes. For instance, the previously mentioned confusion between objects and processes, and the related terms, appears also in lab reports, e.g., in statements ascribing the possibility of being spontaneous or non-spontaneous to elements (i.e., to objects, (S40)). The responses to the guidelines’ recommendation to write chemical equations for the reactions for each case largely differ for the spontaneous and non-spontaneous cases. While most students write reasonable chemical equations for the cases where a reaction is observed, only very few write chemical equations for the non-spontaneous reactions (the cases where no change is observed). This may be due to difficulties in identifying what would be the reverse reactions with respect to the spontaneous cases. For instance, most students write a correct equation for the case in which a zinc metal piece is dipped in a copper sulphate solution (Zn(s) + Cu2+(aq) / Zn2+(aq) + Cu(s)) and say

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that it is spontaneous, but very few students write that the reverse reaction (Cu(s) + Zn2+(aq) / Cu2+(aq) + Zn(s)) is non-spontaneous, although the experiment also comprises a test in which a copper rod is dipped in a solution containing Zn2+ ions and, therefore, the fact that the reaction is non-spontaneous is part of the observations and the chemical equation would be part of its interpretation. For the final part of the reports, the guidelines invite students to express their overall comments, or suggestions for improvements in future editions of the experiment. Significant challenges with the spontaneity concept appear also in this part. For instance, stating that the observed phenomena were non-spontaneous (S41) contains an internal contradiction because – given the setup of the experiment – if a phenomenon (change) is observed, it has occurred spontaneously. Qualifying the entire experiment as spontaneous (S42) states an impossibility, because we are the ones who set it up. It appears appropriate to conclude the reported samples with a statement highlighting the impact of language mastery inadequacies in cases in which students actually show creative thinking: 12. When the experiment is performed, I think they have to be performed spontaneously and even non-spontaneously, so that we will be able to know whether those who do not react, will be able to react non-spontaneously or not, and also what happen to those who react spontaneously when there is outside intervention. The statement attempts a suggestion for improvement that is meaningful, although the way in which it is expressed requires some interpretation efforts from the reader. The students’ idea about the importance of doing something for the non-spontaneous reactions is appropriate and, actually, the second experiment in the course involved electrolysis for the reactions that proved non-spontaneous in the first experiment.

11.5 Discussion and conclusions The previous sections offered a comprehensive overview of identified challenges regarding the spontaneity concept. Students’ works and comments are taken as the most reliable and complete source of information about the challenges they experience. The review of a considerable number of examples illustrating different aspects of these challenges has the role of documentation for the inferred diagnoses. In addition, they document how the consideration of all the details of each answer and the analysis of imprecisions and errors provide guidelines for the design of fact-based addressing interventions and simultaneously constitute a major resource for explanations and clarifications. The character of some interventions has been outlined in comments to individual samples. A presentation of all the details of the interventions related to each of the included samples would require much more space than that of an article. The main

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categories of interventions are briefly summarised in the next paragraphs (reporting relevant sample-numbers in parentheses) to better highlight the wealth of indications deriving from careful and detailed scrutiny of students’ answers and comments. Purely language-related interventions include: the explanation of the meanings of different terms that are confused because of perceived sound similarity or for other reasons (2, S1, S2); the role of prepositions (2, 6); the difference between adjectives and nouns, with particular focus on the difference between spontaneous and spontaneity, sometimes also adding their difference with the adverb spontaneously (S18, S19); the difference between heating and burning (5); the explanation that a sentence defining something should not start with when, but with the name of the item to be defined or described (5); the importance to state the comparison reference whenever a comparison is involved (7); the analysis of incorrect wordings for clauses and sentences (many of the samples). Logic-related, method-related and epistemological aspects-related interventions include: the need to avoid internal contradictions within a sentence (3, S41); the nonviability of using a certain definition to justify or explain the same definition (11); the difference between objects (systems) and processes, and between the qualifications and verbs suitable for each of them (1, S3, S4, S40); the difference between statements with general validity and statements referring only to specific cases or categories of cases (8, S6). Other writing-related interventions include the importance of: writing complete sentences, i.e., sentences that convey an identifiable meaning (4, S3, S4); verifying whether a sentence actually expresses the wanted meaning, by proofreading it (5, 6, 8); recognising when a given question is answered, and not adding things that – although parts of memorises items – are not needed and may introduce errors (5); checking whether an answer responds to the given question or is just the reproduction of some memorised parts which actually would constitute the answer to a different question (5). Content-related interventions include clarifications and discussions of the following concepts: the meaning of spontaneous processes (1–4, S1); the meaning of entropy (7); how entropy changes during specified processes (7); the difference between what pertains to individual molecules, what pertains to ensembles of molecules and what pertains to macroscopic descriptions (7); the meaning of disorder at the molecular level and how it compares in the initial and final states of selected processes (7, 8, S5); the spontaneity criteria (Section 11.3.3); the fact that spontaneous and redox are not necessarily coupled (8, S10, S11, S13, S20–S23); practical training for the verification of whether a reaction is redox (S8–S12); use of acquired chemical knowledge to state whether familiar reactions are spontaneous or not (S8–S12); the difference between requiring activation and being spontaneous (S29, S31, S33); the difference between non-spontaneous and endothermic (S26–S29); the difference between spontaneous and fast (9, S34–S38); recalling that a catalyst does not influence the ΔG of a reaction (S30); sticking to observations in the description and discussion of experiments (S38).

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11 The spontaneity of chemical reactions

Recommendations generally related to learning approaches include: avoiding subjects’ compartmentalisations (what is learnt in a course remains valid and can be used in other courses); asking questions when something is not clear or when discrepancies between different sources are detected (e.g., between the textbook and some internet source). The interventions prompted by the analysis of students’ errors concern both posttest and tutorial discussions and the teaching in the next edition of the same course (Figure 11.2). However, their benefits are often limited by language-mastery challenges. Furthermore, it appears that the interactive situation in the class facilitates the recognition of aspects that are not always recalled when the student is “alone” in front of an assignment or a test paper. The first three groups of interventions listed in the previous paragraphs (interventions concerning language, logic, method and epistemological aspects, and other writing aspects) are practically tantamount to introducing language education into the teaching of chemistry courses, and trying to foster the awareness of the relationship between concepts and their expression. Doing it is necessary to promote understanding to the greatest extent allowed by circumstances: the explanation of language aspects (always analysed with reference to concepts) becomes a necessary route to foster the understanding of chemistry concepts. On the other hand, doing it should not become necessary within advanced courses like the physical chemistry ones. Ideally, language mastery should be built within pre-university instruction. When this is not realised, the best “place” for the integration of language teaching into chemistry teaching would be the course bridging secondary and tertiary instruction [34]. Some of the reasons why it is not yet adequately pursued are mentioned in [35]. Two “take home” messages appear evident from the overall discussion: the recognition of the dominant role of language-mastery inadequacies as causes of inadequate understanding and of errors in answers and reports, and the benefits of turning errors from students’ works into educational resources. The dominant role of language-mastery inadequacies as source of difficulties has been recognised also through the investigation of the problems encountered by students with regards to several other general and physical chemistry themes (e.g., [35]). Furthermore, language-related challenges constitute an all-permeating “confounding variable” in the investigation of students’ difficulties regarding specific concepts, similarly to how they do it for assessment [19]: it is not easy to diagnose the extent and impact of conceptual challenges, when both conceptual understanding and the expression of acquired knowledge are heavily hampered by language mastery inadequacies. Many of the samples reported in the previous sections entail substantial, and often radical, conceptual differences with respect to corresponding correctly-worded statements. These differences suffice to void the frequently encountered opinion that “language is not important for science” of any foundation or justification, turning it into a misconception. Any mode perpetuating this misconception needs to be addressed, including positions that transmit it automatically or indirectly, such as the

References

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presence of lower language-mastery demands in the admission criteria for science degrees. The benefits of turning errors in students’ works into educational resources for in-class activities deserve greater recognition and diffusion. Within an action research framework, educational research is thoroughly embedded in the teaching approaches and primarily aimed at improving these approaches. Turning errors or categories of errors into explanation resources for a given topic requires considerable time from the teacher. In contexts where language-mastery inadequacies are spread, it also requires sufficient language awareness from the teacher. On the other hand, it turns the marking of students’ works into an active and investigative process. The teacher selects the errors whose analysis is suitable to provide clarifications and stimulate reflections and turns them into routine work material – a resource ready for utilization whenever needed. This resource provides a route to attract students’ active attention to a high number of details, thus also contributing to counteract the tendency to rote learning. It appears realistic to expect that sharing experiences of this type would enhance the possibilities of physical chemistry teachers to improve the building, management and benefits of these resources. Finally, it is worthy to note that educational studies in disadvantaged contexts provide information that is precious for chemistry education research in general, both because the diagnoses refer to most difficult scenarios and because many problems are increasingly encountered also in other contexts, above all in relation to the continuous impoverishment of language mastery among the young generations. Interactions with colleagues from several other countries highlighted concerns analogous to those expressed here for the way in which the clearly noticeable decrease in language abilities hampers conceptual understanding, above all in areas (like physical chemistry) that require abstract thinking and complex thinking, which, in turn, require adequate language sophistication. Designing efficient ways to integrate language education into chemistry teaching becomes a shared need and challenge.

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6. Miller SR. Rethinking undergraduate physical chemistry curricula. J Chem Educ 2016;93:1536–42. 7. Donnelly J, Hernández FE. Fusing a reversed and informal learning scheme and space: student perceptions of active learning in physical chemistry. Chem Educ Res Pract 2018;19:520–32. 8. Atare´ s L, Canet MJ, Trujillo M, Benlloch-Dualde JV, Paricio Royo J, Fernandez-March A. Helping pregraduate students reach deep understanding of the second law of thermodynamics. Educ Sci 2021;11:539. 9. Quílez J. First-year university chemistry textbooks’ misrepresentation of Gibbs energy. J Chem Educ 2012;89:87–93. 10. Quílez-Díaz A, Quílez-Pardo J. Avoiding first-year university chemistry textbooks’ misrepresentations in the teaching of spontaneous reactions. Quim Nova 2015;38:151–5. 11. Atkins PW. Physical chemistry, 5th ed. Oxford: Oxford University Press; 1994. (or subsequent editions) for the fifth edition. 12. Mammino L. Teaching chemistry with and without external representations in professional environments with limited resources. In: Gilbert JK, Reiner M, Nakhlekh M, editors Visualization: theory and practice in science education. Dordrecht, The Netherlands: Springer; 2008. pp. 155–85. 13. Mammino L. Teaching physical chemistry in disadvantaged contexts: challenges, strategies and responses. In: Gupta-Bhowon M, Jhaumeer-Laulloo S, Li Kam Wah H, Ramasami P, editors. Chemistry education in the ICT age. Netherlands: Springer; 2009. pp. 197–223. 14. Mammino L. Teaching chemistry in a historically disadvantaged context: experiences, challenges, and inferences. J Chem Educ 2011;88:1451–3. 15. Mammino L. Educational components in the supervision of chemistry postgraduate students: experiences and reflection. Phys Sci Rev 2021:5. https://doi.org/10.1515/psr-2020-0116. 16. Mammino L. Chemistry teaching and chemical education research: 30-year experience in integration pathways. In: Mammino L, Apotheker J, editors. Research in chemistry education. Cham, Switzerland: Springer; 2021. pp. 143–60. 17. Mammino L. Roles of system thinking within green chemistry education. Reflections from identified challenges in a disadvantaged context. J Chem Educ 2019;96:2881–7. 18. Mammino L. Language-related difficulties in science learning. I. Motivations and approaches for a systematic study. J Educ Studies 2005;4:36–41. 19. Clerk D, Rutherford M. Proceedings of the sixth annual meeting of the SAARMSE. Pretoria, South Africa: UNISA; 1998. pp. 126–31. 20. Love A, Mammino L. Using the analysis of errors to improve students’ expression in the sciences. Zimbabwe J Educ Res 1997;9:1–17. 21. Mammino L The spontaneity concept: an investigation of the dichotomy between learning the definition and handling the concept. ALDEQ; 2012. XXVII:p. 120–5. 22. Mammino L. An investigation of students’ difficulties in handling the spontaneity concept in electrochemistry. ALDEQ 2012;XXVII:155–60. 23. Mammino L. Teacher-students interactions: the roles of in-class written questions. In: Chiu M-H, editor. Chemistry education and sustainability in the global age. Dordrecht: Springer; 2013. pp. 35–48. 24. Mammino L. Utilizando el análisis de errores para aclarar conceptos de química general. Enseñanza de las Ciencias 2002;20:167–73. 25. Mammino L. Clarifying chemistry concepts through language analysis. In: Lundell J, Aksela M, Tolppanen S, editors. LUMAT (International journal of math, science and technology education), special issue of ECRICE, vol 3; 2015. pp. 482–500. 26. Mammino L. La distinción entre sistemas y procesos por parte de alumnos de química (= the perception of the distinction between systems and processes by chemistry students). ALDEQ 2002;XV:125–9.

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Supplementary Material: The online version of this article offers supplementary material (https://doi. org/10.1515/PSR-2021-0144).

Liliana Mammino*

12 Conformational preferences and intramolecular hydrogen bonding patterns of tetraflavaspidic acid BBBB – a tetrameric acylphloroglucinol Abstract: Tetraflavaspidic acid BBBB is a tetrameric acylphloroglucinol of natural origin isolated from Dryopteris aitoniana. Its molecule consists of four acylphloroglucinol units linked by methylene bridges and having the same R = propyl in their R−C=O groups. In one of the terminal monomers, one of the OHs ortho to R−C=O is replaced by a keto O. The paper reports the results of a conformational study performed at the HF/6-31G(d,p) and DFT/B3LYP/6-31+G(d,p) levels; two options are utilised for the latter, without and with the inclusion of the Grimme’s dispersion correction. Given the importance of intramolecular hydrogen bonds (IHBs) for the stabilisation of acylphloroglucinol conformers, only conformers containing the maximum IHBs’ number were calculated. The IHBs comprise an IHB between the sp2 O of R−C=O and a neighbouring OH in each monomeric unit and two inter-monomer IHBs between each pair of units. The single C−C bonds of the methylene bridges enable a variety of mutual orientations of the monomeric units, giving rise to a variety of conformations and IHB patterns. The results indicate greater stability for conformers in which individual monomers take lower energy conformations, and significant influence of the dispersion correction on the estimation of the energetics and of other molecular properties. The inclusion of the dispersion correction also strongly limits the number of low energy conformers. The influence of dispersion effects is consistent with the presence of four aromatic rings. Keywords: acylphloroglucinols; dispersion effects; Grimme’s dispersion correction; intramolecular hydrogen bonding; tetrameric acylphloroglucinols.

12.1 Introduction Acylphloroglucinols (ACPLs, [1], Figure 12.1) are a large class of compound structurally derived from phloroglucinol (1,3,5-trihydroxybenzene) and characterised by the presence of an R−C=O group (acyl group), where R is more often an alkyl chain. Many of them are of natural origin and exhibit a variety of biological activities [1]. The interest in their activities and in their potentialities for drug development spans a broad range of

*Corresponding author: Liliana Mammino, School of Mathematical and Natural Sciences, University of Venda, Thohoyandou, South Africa, E-mail: [email protected] As per De Gruyter’s policy this article has previously been published in the journal Physical Sciences Reviews. Please cite as: L. Mammino “Conformational preferences and intramolecular hydrogen bonding patterns of tetraflavaspidic acid BBBB – a tetrameric acylphloroglucinol” Physical Sciences Reviews [Online] 2022. DOI: 10.1515/psr-2021-0239 | https://doi.org/ 10.1515/9783110783643-012

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12 Tetraflavaspidic acid BBBB

Figure 12.1: General structure of monomeric acylphloroglucinols and atom-numbering utilized for each monomer. In the tetraflavaspidic acid BBBB molecule (Figure 12.2), R^ of the first monomer, R* of the last monomer, and both R^ and R* of the two inner monomers correspond to methylene bridges.

possibilities, from the prevention of neurodegenerative diseases [2] to possible uses against SARS-CoV-2 [3]. The present work pertains to a systematic computational study of ACPLs, which has investigated the properties of monomeric ACPLs (M-ACPLs) in vacuo [4–7] and in solution [8, 9], and also dimeric ACPLs (D-ACPLs, [10]) and trimeric ACPLs (T-ACPLs, [11]), i.e., ACPLs in which two or three acylphloroglucinol units are linked by methylene bridges (MBs). The interest in the properties of ACPLs with more than one acylphloroglucinol unit is motivated by the fact that their biological activity is often better than that of M-ACPLs [1]. Tetraflavaspidic acid BBBB (TFVAB, Figure 12.2) is a tetrameric ACPL (TT-ACPL) of natural origin isolated from Dryopteris aitoniana [1, 12]. Its molecule consists of four acylphloroglucinol units linked by MBs and having the same R = propyl in their R−C=O groups. In one of the terminal monomers, the OH ortho to R−C=O and positioned towards the inner side of the molecule (O12″′−H17″′) is replaced by a keto O (O12″′). The interest of the study relates both to the interest in investigating an acylphloroglucinol molecule with four monomeric units computationally for the first time and to the confirmed possibility of conformers whose shapes are close to those of cavity-containing bowl-shaped molecular structures built from ACPLs [13]. The study of M-ACPLs highlighted the dominant stabilising role of the intramolecular hydrogen bond (IHB) between the sp2 O of R−C=O (O14) and an ortho OH (O8−H15 or O12−H17), termed ‘first IHB’ [4–6]. Lesser, but not negligible, stabilising roles pertain to C−H⋅⋅⋅O IHBs [7] and to certain orientations of the OH groups not engaged in the first IHB [4, 6, 14]. The study of D-ACPLs [10] highlighted the stabilising

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177

Figure 12.2: Molecular structure of tetraflavaspidic acid BBBB and atom numbering utilized in this work. The same atom numbering is utilized for corresponding atoms in each monomer and reflects the numbering shown in Figure 12.1. The numbers of the atoms of the second monomer are primed, those of the third monomer are double-primed and those of the fourth monomer are triple primed. Hydrogen atoms attached to carbon atoms are given the same number as the C atom to which they are attached; if more than one H atom are attached to the same C atom, they are distinguished by ‘a’, ‘b’, etc. subscripts.

roles of intermonomer hydrogen bonds (IMHBs) on either side of the MB. The study of T-ACPLs [11] confirmed the stabilising roles of IMHBs for each pair of monomers and highlighted two additional factors influencing conformational preferences: the mutual orientations of the monomers and the mutual orientations of the MBs. Both studies also showed that, in the low-energy conformer of D-ACPLs and T-ACPLs, all the monomers have low-energy conformations. The results for TFVAB show analogous trends. The calculations including Grimme’s dispersion correction also show the considerable influence of electron correlation on the estimation of the energetics and of some other molecular properties. Besides the figures and tables reported in the text, tables reporting all the computed data and figures reporting the geometries of all the calculated conformers, or diagrams visualizing trends and trend-comparisons for relevant quantities, are included in the electronic supporting information (ESI). Figures and tables pertaining to the ESI may be cited in the text; their numbers are preceded by an uppercase S to clearly identify them as pertaining to the ESI.

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12 Tetraflavaspidic acid BBBB

12.2 Computational details Calculations were performed in vacuo, with fully relaxed geometry, at the HF/6-31G(d,p) and DFT/B3LYP/6-31+G(d,p) levels; these are the same levels utilised in the previous studies on ACPLs within this series, and their selection in this work is meant to ensure the possibility of meaningful comparisons. HF/6-31G(d,p) had proved the cheapest option enabling reasonable descriptions and yielding reasonable trends for ACPLs [4–7]. The B3LYP functional [15–17] was the most commonly used at the time of the earlier sets of studies and is still one of the most widely utilised. The study of T-ACPLs [11] highlighted the importance of correlation effects on the estimation of their molecular properties. Given the presence of four aromatic rings in the TFVAB molecule and the consequent expectation of significant correlation effects, a second set of DFT/ B3LYP/6-31+G(d,p) calculations was performed with the inclusion of the Grimme’s dispersion correction [18–21]. The comparison between the DFT/B3LYP/6-31+G(d,p) results without and with the Grimme’s correction enables the evaluation of dispersion effects on the estimation of molecular properties. The combination of the B3LYP functional and Grimme’s dispersion correction has been proved among the best to provide fairly accurate molecular geometries [22]. IR vibrational frequencies (harmonic approximation) were calculated at all these three levels. No imaginary frequencies were encountered, confirming that the identified stationary points are true minima. All the calculations were performed with Gaussian-16 [23]. Visualizations used GaussView [24] and Chem3D [25]. For the sake of conciseness, the calculation methods are denoted with acronyms in the rest of the text: HF for HF/6-31G(d,p), DFT for DFT/B3LYP/6-31+G(d,p) and DFT-D3 for DFT/B3LYP/6-31+G(d,p) with the Grimme’s dispersion correction. All the reported energy values are in kcal/mol and all the distances are in Å.

12.3 Results and analyses 12.3.1 Atom numbering and naming of conformers A comparison of Figure 12.2 with Figure 12.1 shows that the atom numbering used here for the TFVAB molecule reflects the atom numbering used in the previous works on ACPLs (e.g., [4–9]), in order to maintain analogous terms in the description of individual monomers. For instance, H15⋅⋅⋅O14, H15′⋅⋅⋅O14′, H15″⋅⋅⋅O14″ and H15″′⋅⋅⋅O14″′ identify the same type of first IHB in the different monomers of TFVAB (denoted as ‘d’ for M-ACPLs; Figure 12.3). Figure 12.2 also highlights the convention (selected for the present work) of considering the monomer with the keto O at C6″′ as the last monomer.

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12.3 Results and analyses

Figure 12.3: Symbols utilized to denote the four conformations of the monomeric units considered in the calculated conformers of tetraflavaspidic acid BBBB. The symbols follow those utilized in the other studies of acylphloroglucinols [4–11]. With reference to the atom numbering in Figure 12.1, the letter d informs that the first IHB is of the H15⋅⋅⋅O14 type and the letter s that it is of the H17⋅⋅⋅O14 type. A d-type conformer is r if H16 is oriented towards the side of the first IHB and w if it is oriented towards the other side; an s-type conformer is w if H16 is oriented towards the side of the first IHB and r if it is oriented towards the other side.

The conformers of TFVAB may differ by one or more of the following features: the conformations of the individual monomers, the mutual orientations of the individual monomers, and the mutual orientations of the methylene bridges. Following a practice consolidated through the previous works on ACPLs, conformers are denoted by acronyms concisely specifying their characteristics. The orientations of the monomers are conventionally described as ‘up’ or ‘down’ with reference to the mutual positions of their acyl groups: the orientation of the first monomer is conventionally taken as ‘up’, and is represented as such in all the images (as also shown in Figure 12.2); the orientations of the other monomers are taken as ‘up’ if their acyl group is on the same side as that of the first monomer, and ‘down’ if it is on the other side. Given the absence of symmetrical options determined by the difference in the fourth monomer (having the OH at C6″′ replaced by a keto O), eight different mutual orientations are possible; they are denoted by numbers, as listed in Table 12.1 and illustrated in Figure 12.4.

Table .: Meaning of the numbers utilised in the acronyms denoting the conformers of tetraflavaspidic acid BBBB. Number

   

Orientations of the monomers First

Second

Third

Fourth

Up Up Up Up

Up Down Down Up

Up Up Down Up

Down Down Down Up

Number

   

Orientations of the monomers First

Second

Third

Fourth

Up Up Up Up

Up Down Up Down

Down Down Down Up

Up Up Down Up

The numbers indicate different combinations of the orientations of the monomers.

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The mutual orientations of the MBs determine whether the geometry is totally outstretched, totally bowl-shaped, or a mixture of the two options. They are identified by comparing the orientations of the first and second MBs (MBs centred on C9 and C9′, respectively) and the orientations of the second and third MBs (MBs centred on C9′ and C9″). The orientations are compared in terms of whether the H atoms of the two MBs point to the same side or to different sides (e.g., ‘towards us’ or ‘away from us’) and can be ‘same’ or ‘opposite’. The four possible combinations of ‘same’ and ‘opposite’ are denoted by lowercase letters, as listed in Table 12.2 and illustrated in Figure 12.5. Two consecutive MBs with the same orientations (both ‘toward us’ or both ‘away from us’) give rise to a half-bowl shape in the part where they are present. When all the three MBs have the same orientation, the entire geometry is bowl-shaped; when the individual monomers have the same orientation, it recalls the shape of tetrameric bowls from ACPLs [13]. Since the study of D-ACPLs [10] and T-ACPLs [11] had shown that conformers in which one of the monomers does not have the first IHB have high energy and are unpopulated, only conformers in which all the monomers have their first IHBs were considered. This amounts to the four monomers’ conformers shown in Figure 12.3 (for the fourth monomer, s-conformers are not possible because O12″′ is a keto O). The combinations of the conformations of individual monomers are denoted by Greek letters, as listed in Table 12.3. Figure 12.4 also offers illustrations of conformers of the α, β, γ, ε, ρ, λ, η Table .: Meaning of the lowercase letters utilised in the acronyms denoting the conformers of tetraflavaspidic acid BBBB. Letter

Orientations of the MBs

a b c e

Resulting geometry

First and second

Second and third

Same Same Opp Opp

Same Opp Same Opp

Bowl-shaped First part bowl-shaped Second part bowl-shaped Totally outstretched

The letters indicate the mutual orientations of the methylene bridges (MBs), where the first MB in centred on C, the second on C′ and the third on C″.

Table .: Meaning of the Greek letters utilised to denote the conformations of the individual monomers in the acronyms denoting the conformers of tetraflavaspidic acid BBBB. Letter

α β γ ε

Conformations of the monomers First

Second

Third

Fourth

d-r d-r d-r d-r

d-r d-r s-w s-w

d-r s-w d-r s-w

d-w d-w d-w d-w

Letter

η ρ λ ξ τ

Conformations of the monomers First

Second

Third

Fourth

s-w s-w s-r s-r s-r

s-w d-r s-r s-r d-r

d-r d-r s-r s-r s-r

d-w d-w d-w d-r d-w

The symbols utilised to denote the conformations of individual monomers are explained in Figure ..

12.3 Results and analyses

181

Figure 12.4: Geometry differences in the conformers of tetraflavaspidic acid BBBB resulting from different orientations of the monomers. Although they are generally high-energy, the conformers with the most outstretched geometry (‘e’ conformers) are selected for illustration, because they offer clearer visualization of the different

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12 Tetraflavaspidic acid BBBB

Figure 12.5: Geometry differences in the conformers of tetraflavaspidic acid BBBB resulting from the different orientations of the methylene bridges. The lower energy conformers of the series denoted as ‘1’ are selected for illustration. Both ball-andstick models and space-filling models are reported for the reasons explained in the caption to Figure 12.4. The acronym denoting the conformer is reported under each image; the orientations of the second and third methylene bridges with respect to the first one are indicated after the acronym, and the resulting geometry-type is indicated in the following row.

and τ types. The combinations denoted by these letters are univocally related to specific combinations of monomers’ orientations, in associations that can be denoted as 1α, 2-γ, 3-ε, 4-λ, 5-η, 6-ρ, 7-β and 8-τ. On the other hand, the combinations denoted as ξ may associate with each combination of monomers’ orientations, maintaining the same meaning in terms of conformations of individual monomers (s-r, s-r, s-r, d-r, Table 12.3), but with different IMHB patterns. It is opted to distinguish them by repeating the same number denoting the combination of monomers’ orientations after the ξ letter, leading to conformer types distinguished as 1-ξ1, 2-ξ2, 3-ξ3, etc. While this specification would not be needed as long as one considers a complete acronym (such as 1-a-ξ1, 2-a-ξ2, etc.), it is important to have an independent specification for the IHBs orientations. They also offer visualization of the different combinations of monomers’ conformations (indicated by the Greek letter in the acronyms). Both stick models and space-filling models are shown as the former better highlight the bond details and the latter better highlight the geometrical shapes, the intramolecular hydrogen bonds, and the mutual orientations of the planes of the four benzene rings.

12.3 Results and analyses

183

patterns associated with a given ξ-type for other discussions. Table 12.4 specifies the IHBs (both first IHBs and IMHBs) for all the 16 cases. The acronyms provide complete specifications of the conformers’ characteristics. For instance, in the 1-a-α acronym, the number ‘1’ informs that the first three monomers Table .: Combinations of intramolecular hydrogen bonds in relation to the orientations and conformations of the individual monomers. Conformer type

First IHBs

-α

H⋅⋅⋅⋅O H′⋅⋅⋅⋅O′ H″⋅⋅⋅⋅O″ H″′⋅⋅⋅⋅O″′ H⋅⋅⋅⋅O H′⋅⋅⋅⋅O′ H″⋅⋅⋅⋅O″ H″′⋅⋅⋅⋅O″′ H⋅⋅⋅⋅O H′⋅⋅⋅⋅O′ H″⋅⋅⋅⋅O″ H″′⋅⋅⋅⋅O″′ H⋅⋅⋅⋅O H′⋅⋅⋅⋅O′ H″⋅⋅⋅⋅O″ H″′⋅⋅⋅⋅O″′ H⋅⋅⋅⋅O H′⋅⋅⋅⋅O′ H″⋅⋅⋅⋅O″ H″′⋅⋅⋅⋅O″′ H⋅⋅⋅⋅O H′⋅⋅⋅⋅O′ H″⋅⋅⋅⋅O″ H″′⋅⋅⋅⋅O″′ H⋅⋅⋅⋅O H′⋅⋅⋅⋅O′ H″⋅⋅⋅⋅O″ H″′⋅⋅⋅⋅O″′ H⋅⋅⋅⋅O H′⋅⋅⋅⋅O′ H″⋅⋅⋅⋅O″ H″′⋅⋅⋅⋅O″′

-γ

-ε

-λ

-η

-ρ

-β

-τ

Intermonomer IHBs Between st and nd Between nd and rd Between rd and th monomers monomers monomers H′⋅⋅⋅⋅O H⋅⋅⋅⋅O′

H″⋅⋅⋅⋅O′ H′⋅⋅⋅⋅O″

H″′⋅⋅⋅⋅O″ H″⋅⋅⋅⋅O″′

H′⋅⋅⋅⋅O H⋅⋅⋅⋅O′

H″⋅⋅⋅⋅O′ H′⋅⋅⋅⋅O″

H″′⋅⋅⋅⋅O″ H″⋅⋅⋅⋅O″′

H′⋅⋅⋅⋅O H⋅⋅⋅⋅O′

H″⋅⋅⋅⋅ O′ H′⋅⋅⋅⋅O″

H″′⋅⋅⋅⋅O″ H″⋅⋅⋅⋅O″′

H⋅⋅⋅⋅O′ H′⋅⋅⋅⋅O

H′⋅⋅⋅⋅O″ H″⋅⋅⋅⋅O′

H″⋅⋅⋅⋅O″′ H″′⋅⋅⋅⋅O″

H⋅⋅⋅⋅O′ H′⋅⋅⋅⋅O

H′⋅⋅⋅⋅O″ H″⋅⋅⋅⋅O′

H″⋅⋅⋅⋅O″′ H″′⋅⋅⋅⋅O″

H⋅⋅⋅⋅O′ H′⋅⋅⋅⋅O

H′⋅⋅⋅⋅O″ H″⋅⋅⋅⋅O′

H″⋅⋅⋅⋅O″′ H″′⋅⋅⋅⋅O″

H′⋅⋅⋅⋅O H⋅⋅⋅⋅O′

H″⋅⋅⋅⋅O′ H′⋅⋅⋅⋅O″

H″′⋅⋅⋅⋅O″ H″⋅⋅⋅⋅O″′

H⋅⋅⋅⋅O′ H′⋅⋅⋅⋅O

H′⋅⋅⋅⋅O″ H″⋅⋅⋅⋅O′

H″⋅⋅⋅⋅O″′ H″′⋅⋅⋅⋅O″

184

12 Tetraflavaspidic acid BBBB

Table .: (continued) Conformer type

First IHBs

-ξ

H⋅⋅⋅⋅O H′⋅⋅⋅⋅O′ H″⋅⋅⋅⋅O″ H″′⋅⋅⋅⋅O″′ H⋅⋅⋅⋅O H′⋅⋅⋅⋅O′ H″⋅⋅⋅⋅O″ H″′⋅⋅⋅⋅O″′ H⋅⋅⋅⋅O H′⋅⋅⋅⋅O′ H″⋅⋅⋅⋅O″ H″′⋅⋅⋅⋅O″′ H⋅⋅⋅⋅O H′⋅⋅⋅⋅O′ H″⋅⋅⋅⋅O″ H″′⋅⋅⋅⋅O″′ H⋅⋅⋅⋅O H′⋅⋅⋅⋅O′ H″⋅⋅⋅⋅O″ H″′⋅⋅⋅⋅O″′ H⋅⋅⋅⋅O H′⋅⋅⋅⋅O′ H″⋅⋅⋅⋅O″ H″′⋅⋅⋅⋅O″′ H⋅⋅⋅⋅O H′⋅⋅⋅⋅O′ H″⋅⋅⋅⋅O″ H″′⋅⋅⋅⋅O″′ H⋅⋅⋅⋅O H′⋅⋅⋅⋅O′ H″⋅⋅⋅⋅O″ H″′⋅⋅⋅⋅O″′

-ξ

-ξ

-ξ

-ξ

-ξ

-ξ

-ξ

Intermonomer IHBs Between st and nd Between nd and rd Between rd and th monomers monomers monomers H⋅⋅⋅⋅O′ H⋅⋅⋅⋅O′

H′⋅⋅⋅⋅O″ H′⋅⋅⋅⋅O″

H″⋅⋅⋅⋅O″′ H″⋅⋅⋅⋅O″′

H⋅⋅⋅⋅O′ H⋅⋅⋅⋅O′

H′⋅⋅⋅⋅O″ H′⋅⋅⋅⋅O″

H″⋅⋅⋅⋅O″′ H″⋅⋅⋅⋅O″′

H⋅⋅⋅⋅O′ H⋅⋅⋅⋅O′

H′⋅⋅⋅⋅O″ H′⋅⋅⋅⋅O″

H″⋅⋅⋅⋅O″′ H″⋅⋅⋅⋅O″′

H⋅⋅⋅⋅O′ H⋅⋅⋅⋅O′

H′⋅⋅⋅⋅O″ H′⋅⋅⋅⋅O″

H″⋅⋅⋅⋅O″′ H″⋅⋅⋅⋅O″′

H⋅⋅⋅⋅O′ H⋅⋅⋅⋅O′

H′⋅⋅⋅⋅O″ H′⋅⋅⋅⋅O″

H″⋅⋅⋅⋅O″′ H″⋅⋅⋅⋅O″′

H⋅⋅⋅⋅O′ H⋅⋅⋅⋅O′

H′⋅⋅⋅⋅O″ H′⋅⋅⋅⋅O″

H″⋅⋅⋅⋅O″′ H″⋅⋅⋅⋅O″′

H⋅⋅⋅⋅O′ H⋅⋅⋅⋅O′

H′⋅⋅⋅⋅O″ H′⋅⋅⋅⋅O″

H″⋅⋅⋅⋅O″′ H″⋅⋅⋅⋅O″′

H⋅⋅⋅⋅O′ H⋅⋅⋅⋅O′

H′⋅⋅⋅⋅O″ H′⋅⋅⋅⋅O″

H″⋅⋅⋅⋅O″′ H″⋅⋅⋅⋅O″′

Only the symbols indicating the combinations of the orientations of individual monomers (initial number) and the symbols indicating the conformations of the individual monomers (Greek letters) are reported in the first column, because the combinations of intramolecular hydrogen bonds do not depend on the mutual orientations of the methylene bridges. For each conformer-type, the first IHBs are listed in the second column, with the monomers clearly identified by the atom numbers, and the intermonomer IHBs are listed in a way that specifies the two monomers and enables fast comparisons.

12.3 Results and analyses

185

are oriented ‘up’ and the fourth one is oriented ‘down’, the letter ‘a’ informs that the three MBs have the same orientation, thus resulting in a bowl shape, and the letter α informs that the conformation of the first three monomers is d-r and that of the last monomer is d-w.

12.3.2 Conformational preferences and energetics All the conformers corresponding to different patterns of monomers’ orientations, IHB types, and MB orientations, and maintaining ten IHBs (four first IHBs and six IMHBs) have been calculated with the three selected methods. It was opted to calculate also high energy conformers because they enable the evaluation of the effects of energyinfluencing factors by comparison with the lower energy ones. Figure S1 reports the optimized geometries of all the conformers in the DFT-D3 results. Table 12.5 reports the relative energies (ΔE) of the ten lowest-energy conformers in the DFT-D3 results and the lowering effect of the Grimme’s correction on the estimation of their energies, and Table S1 reports the ΔE of all the calculated conformers. Table S2 analyses the ΔE trends in terms of the mutual orientations of the monomers and of the mutual orientations of the MBs, and Table 12.6 reports the ΔE ranges according to the mutual orientations of the monomers. The diagrams in Figure S2 visualise the analyses concerning all energeticsrelated values and those in Figure S3 visualise the values-ranges identified by these Table .: Relative energies of the ten lowest-energy conformers of tetraflavaspidic acid BBBB and lowering effect of Grimme’s correction on the estimation of their energy. Conformer

-a-α -a-γ -a-ε -c-γ -c-α -B-γ -a-λ -a-η -B-ε -B-α

Relative energy (kcal/mol)

Dispersion effect (kcal/mol)

HF

DFT

DFT-D

. . . . . . . . . .

. . . . . . . . . .

. . . . . . . . . .

. . . . . . . . . .

Relative energies from HF/-G(d,p), DFT/BLYP/-+G(d,p) and DFT/BLYP/-+G(d,p)-D results, respectively denoted as HF, DFT and DFT-D in the columns’ headings. The energy lowering due to dispersion effects is evaluated as the difference between the energy in the DFT results and the energy in the DFT-D results. The conformers are listed in order of increasing relative energy in the DFT-D results.

186

12 Tetraflavaspidic acid BBBB

analyses. Because of the relationships between the mutual orientations of the conformers and their individual conformations, the analyses with respect to these two characteristics merge. HF and DFT results show rather close trends, whereas DFT-D3 results are substantially different. In the DFT-D3 results, only six conformers have ΔE < 4.0 kcal/mol, versus 22 in the DFT results and 31 in the HF ones. This substantially influences the interpretation of results. The DFT-D3 results indicate a limited number of conformers which might be responsible for the properties of TFVAB (the low energy ones) whereas the HF and DFT results suggest a high variety of low energy conformers, whose ΔE are often so close that they may be able to interconvert rapidly. A closer look at the conformers’ types helps relate this difference to correlation and dispersion effects. Stacking interactions among the aromatic rings pertain to these effects; they are expected to be comparatively weaker in conformers with totally outstretched geometry, to which DFT-D3 results ascribe higher energies. For instance, 1-e-α is the lowest energy conformer with outstretched geometry in the DFT-D3 results, with ΔE = 5.974 kcal/mol, while its ΔE is close to 0 kcal/mol in the HF and DFT results. Additional observations

Table .: Ranges of the relative energies of the calculated conformers of tetraflavaspidic acid BBBB in terms of the mutual orientations of the monomers and the conformations of the individual monomers. Conformer type

-α -γ -ε -λ -η -ρ -β -τ -ξ -ξ -ξ -ξ -ξ -ξ -ξ -ξ

Relative energy range (kcal/mol) HF

DFT

DFT-D

.–. .–. .–. .–. .–. .–. .–. .–. .–. .–. .–. .–. .–. .–. .–. .–.

.–. .–. .–. .–. .–. .–. .–. .–. .–. .–. .–. .–. .–. .–. .–. .–.

.–. .–. .–. .–. .–. .–. .–. .–. .–. .–. .–. .–. .–. .–. .–. .–.

HF/-G(d,p), DFT/BLYP/-+G(d,p) and DFT/BLYP/-+G(d,p)-D results, respectively denoted as HF, DFT and DFT-D in the columns’ headings. The ranges are listed according to increasing relative energy of the lower end of the range in the DFT-D results.

12.3 Results and analyses

187

further confirm the relevant roles of dispersion interactions and their greater effect in bowl-shaped conformers: the fact that some bowl-shaped conformers with higherenergy conformations of the individual monomers (like 5-a-ξ5, 7-a-ξ7, 8-a-ξ8 and 2-a-ξ2) have ΔE = 8.15–9.00 kcal/mol in the DFT-D3 results, >14.03 kcal/mol in the HF results and >17.11 kcal/mol in the DFT results; and the fact that the ΔE of the higher energy conformers reach considerably greater values in the DFT-D3 results. The study of M-ACPLs had shown that the d-r, d-w and s-w geometries of individual monomers (Figure 12.3) have considerably lower energy than the s-r ones [4–7]. Correspondingly, the TFVAB conformers in which the first three monomers have s-r conformations (ξ-type conformers) have high ΔE (the ΔE of the lowest-energy one among them, 4-a-ξ4, is 8.64/HF, 11.87/DF and 7.67/DFT-D3 kcal/mol). Because of this behaviour-dichotomy between the ξ-type and the other (non-ξ-type) conformers, tables focusing on analyses (e.g., Table S2-a) consider them as separate subgroups and diagrams of values-ranges consider their ranges separately (as explained in detail in the first page of Figure S3). Diagram (a-i) in Figure S3 shows that, in the DFT-D3 results, the ΔE ranges of non-ξ-type conformers remain in the lower region, with the highest value being 10.56 kcal/mol for 4-e-λ (the highest value for conformers in which no monomer is s-r is 9.18 kcal/mol for 6-e-ρ), whereas the ranges of ξ-type conformers are much broader and reach values beyond 24 kcal/mol. Diagrams (a-ii) and (a-iii) in Figure S3, respectively considering the ΔE ranges in the HF and DFT results, show different behaviours, with narrow ranges and a marked separation between the ranges of nonξ-type and ξ-type conformers. The fact that Grimme’s correction makes the distinction between non-ξ-type conformers and ξ-type conformers less sharp suggests that correlation effects to some extent prevail over the effects of monomers’ orientations or individual monomers’ conformations. The inclusion of Grimme’s correction has a lowering effect on the estimation of the conformers’ energy. The lowering (Table 12.5 for the ten lower energy conformers and Table S3 for all the calculated conformers) remains mostly between 91 and 100 kcal/ mol, with values >100 for the 4-a-ξ4, 5-a-ξ5, 7-a-ξ7, 8-a-ξ8 and 2-a-ξ2 conformers. Diagram (b) in Figure S2 visualizes this trend. Table S4 analyses the lowerings in terms of mutual orientations of the monomers and in terms of the mutual orientations of the MBs, and diagrams (c) and (d) in Figure S3 visualize the ranges identified by these analyses. The calculation of IR vibrational frequencies enables the determination of the zeropoint energy (ZPE). Table S5 compares the ΔE not corrected for ZPE, those corrected for ZPE, and the relative free energies (sum of electronic and thermal free energies) of the calculated conformers of TFVAB in the results of the three calculation methods utilized, and the (c-i), (c-ii) and (c-iii) diagrams in Figure S2 visualize the comparisons. The trends of these three quantities are fairly close (often very close) in the results of the three calculation methods.

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Table S6 reports the values of the ZPE correction in the HF, DFT and DFT-D3 results, as well as the effect of the inclusion of the Grimme’s correction on the estimation of the ZPE correction, and diagram (d) in Figure S2 visualizes the comparison across calculation methods. The values of the ZPE correction for different conformers are very close in the results of the same calculation method. The correction is considerably greater in the HF results. This is likely related to the frequent underestimation of H-bond strength by HF and its overestimation by DFT/B3LYP. The underestimation of the strength of an IHB implies greater estimation of the IR vibrational frequency of the donor (smaller frequency decrease caused by the IHB), which, in turn, leads to greater estimation of the vibrational energy of that bond [11]. Diagrams (e) and (f) in Figure S2 analyse the ZPE correction respectively in terms of the mutual orientations of the monomers and of the mutual orientations of the MBs in the DFT-D3 results. The inclusion of the Grimme’s dispersion correction has significant influence also on the calculated geometry of the conformers. It causes a ‘coming closer’ of the flexible components of the conformers, as a result of the largely attractive nature of dispersion [22]. The phenomenon had already been observed for T-ACPLs [11] and for cavitycontaining molecular structures built from ACPLs [26]. Figure 12.6 and the incorporated table illustrates it for the DFT and DFT-D3 geometries of conformers of the 1-α series.

12.3.3 Characteristics of the intramolecular hydrogen bonds IHBs are the strongest non-covalent intramolecular interactions and, therefore, often play dominant roles in conformers’ stabilisation. They may also play important roles in relevant components of biological activities, including molecular recognition [27–30]. For this reason, they are given specific attention in the study of molecules in which they are present. Although the distinction between an ‘upper rim’ and a ‘lower rim’ would strictly be definable only for bowl-shaped geometries with all the monomers oriented in the same way, it is here opted to extend it to the other conformers, because it is expedient in the discussion of certain IHBs. Then, the ‘upper rim’, and the ‘lower rim’ are respectively the rims in the upper part and lower part of the images based on the convention introduced in Section 12.3.1. As already mentioned, the calculated conformers of TFVAB contain ten IHBs. Table S8 reports their bond lengths, separating the first IHBs (table a) and the IMHBs (table b) to facilitate comparisons of IHBs of the same category. Table S9 analyses the lengths according to suitable criteria, and Table 12.7 summarises these analyses through the ranges of the IHB lengths in terms of IHB types and of the molecular context in which they are present; the way in which the ‘molecular context’ is identified is

12.3 Results and analyses

189

Figure 12.6: Examples of geometry differences resulting from the inclusion of Grimme’s dispersion correction. The lower energy conformers of the series denoted as ‘1’ are selected for illustration. The difference in the bottom region of the bowl-shape is visually detectable from the images of the 1-a-α conformer and, therefore, both images (from DFT and DFT-D3 results) are shown. Only the DFT-D3 images are shown for the other conformers. The table compares representative distances in the two sets of results.

explained in the table caption. H-bond lengths generally provide a criterion for the identification of H-bonds [31]. In addition, the way in which they compare provides rough indications of how the H-bond strengths compare; this is particularly important for IHBs, because of the impossibility of estimating their energy simply by removing them, as their removal causes substantial changes in the molecular context, which contribute to the energy difference between the form containing a certain IHB and the form not containing it.

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12 Tetraflavaspidic acid BBBB

Table .: Ranges of the bond length of the different intramolecular hydrogen bonds (IHBs) in the calculated conformers of tetraflavaspidic acid BBBB. IHB-type

H⋅⋅⋅⋅O

H⋅⋅⋅⋅O

H′⋅⋅⋅⋅O H′⋅⋅⋅⋅O H′⋅⋅⋅⋅O H⋅⋅⋅⋅O′

H⋅⋅⋅⋅O′ H⋅⋅⋅⋅O′ H′⋅⋅⋅⋅O H″⋅⋅⋅⋅O′ H′⋅⋅⋅⋅O″ H″⋅⋅⋅⋅O′ H′⋅⋅⋅⋅O″ H″⋅⋅⋅⋅O′ H′⋅⋅⋅⋅O″ H″⋅⋅⋅⋅O′ H′⋅⋅⋅⋅O″

H″′⋅⋅⋅⋅O″ H″⋅⋅⋅⋅O″′ H″′⋅⋅⋅⋅O″ H″⋅⋅⋅⋅O″′ H″⋅⋅⋅⋅O″′

Molecular context

Length range (Å) HF

DFT

DFT-D

First monomer Inner monomers Fourth monomer First monomer Inner monomers

.–. .–. .–. .–. .–.

.–. .–. .–. .–. .–.

.–. .–. .–. .–. .–.

& H⋅⋅⋅⋅O′ & H′⋅⋅⋅⋅O & H⋅⋅⋅⋅O′ & H⋅⋅⋅⋅O′ & H′⋅⋅⋅⋅O & H⋅⋅⋅⋅O′ & H⋅⋅⋅⋅O′ & H′⋅⋅⋅⋅O & H⋅⋅⋅⋅O′ & H′⋅⋅⋅⋅O & H⋅⋅⋅⋅O′ & H⋅⋅⋅⋅O′

.–. .–. .–. .–. .–. .–. .–. .–. .–. .–. .–. .–.

.–. .–. .–. .–. .–. .–. .–. .–. .–. .–. .–. .–.

.–. .–. .–. .–. .–. .–. .–. .–. .–. .–. .–. .–.

& H′⋅⋅⋅⋅O″ & H″⋅⋅⋅⋅O′ & H′⋅⋅⋅⋅O″ & H′⋅⋅⋅⋅O″ & H″⋅⋅⋅⋅O′ & H′⋅⋅⋅⋅O″ & H′⋅⋅⋅⋅O″ & H″⋅⋅⋅⋅ O′ & H′⋅⋅⋅⋅O″ & H′⋅⋅⋅⋅O″ & H″⋅⋅⋅⋅O′ & H′⋅⋅⋅⋅O″

.–. .–. .–. .–. .–. .–. .–. .–. .–. .–. .–. .–.

.–. .–. .–. .–. .–. .–. .–. .–. .–. .–. .–. .–.

.–. .–. .–. .–. .–. .–. .–. .–. .–. .–. .–. .–.

& H″⋅⋅⋅⋅O″′ & H″′⋅⋅⋅⋅O″ & H″⋅⋅⋅⋅O″′ & H″⋅⋅⋅⋅O″′ & H″′⋅⋅⋅⋅O″ & H″⋅⋅⋅⋅O″′

.–. .–. .–. .–. .–. .–.

.–. .–. .–. .–. .–. .–.

.–. .–. .–. .–. .–. .–.

The H-bonds are grouped by types and by molecular contexts in which they are present. For the first IHBs, the molecular context is indicated as the types of monomers in which they are present. For the intermonomer IHBs (IMHBs), the name of the IMHB indicates the monomers between which it forms, and the “molecular context” column reports the other IMHB present between the same two monomers. For the sake of clarity, the IMHBs are grouped into three blocks, corresponding to the three pairs of monomers between which they form. The bond lengths are from HF/ -G(d,p), DFT/BLYP/-+G(d,p) and DFT/BLYP/-+G(d,p)-D results, respectively denoted as HF, DFT and DFT-D in the columns’ headings.

12.3 Results and analyses

191

Like in all the other ACPLs considered [4–7, 10, 11], the first IHB is the strongest IHB (moderate bordering to strong) for a variety of reasons: its acceptor is an sp2 O; it closes a six-member ring, which is inherently more stable; and it is stabilised by the presence (in the ring) of two double bonds separated by a single bond (resonance assisted H-bond, [32–35]). The length values highlight the same trends identified for M-ACPLs [4–7] and maintained in D-ACPLs [10] and T-ACPLs [11], namely: IHBs of the H15⋅⋅⋅⋅O14 type are shorter than IHBs of the H17⋅⋅⋅⋅O14 type; an IHB of the same type is shorter in the inner conformers than in the first conformer; and H15″′⋅⋅⋅⋅O14″′, although being in an outer conformer, is the shortest, because the other OH ortho to the acyl group is replaced by a keto O [5]. All the IMHBs close eight-member rings (which are inherently less favourable than six-member rings) and are not resonance assisted. The IMHBs with sp2 O12″′ as acceptor are stronger; for all the others, the acceptor is an sp3 O, and they are comparatively weaker (they all remain moderate IHBs, but nor bordering with strong). Their lengths are significantly influenced by the type of the other IMHB between the same monomers, as highlighted in Table 12.7 and Table S9. For instance, the H16″⋅⋅⋅⋅O12″′ IMHB is shorter when the other IMHB is H16″′⋅⋅⋅⋅O8″ and longer when it is H15″⋅⋅⋅⋅O10″′. Both in the upper and in the lower rim, an IMHBs is often consecutive to a first IHB and/or to another IMHB, resulting in two or three consecutive IHBs (as highlighted by the space-filling models in Figure 12.4); the upper rim of the 3-ε conformer contains four consecutive IHBs. Consecutive IHBs are likely cooperative, which usually entails mutual strengthening as well as the possible conferment of specific properties to the molecule [36–41]. Table S10 lists the sets of cooperative IHBs present in the various conformer-types of TFVAB. The estimation of the IHB lengths varies according to the calculation methods. As already noted for M-ACPLs, HF underestimates IHB strength, thus yielding longer lengths, whereas DFT tends to overestimate it, yielding shorter lengths, and the experimental values often lie within the interval defined by these two values [4, 5]. The inclusion of the Grimme’s dispersion correction influences the estimation of the IHB lengths (Tables S8 and S9). It nearly always causes a slight increase in the estimation of the first IHB lengths; the increase is smaller for the first monomer and greater for the others. On the other hand, it causes a slight decrease in the estimation of the IMHB lengths; the decrease mostly concerns the second decimal digit. The greater influence on the IMHBs is likely related to the greater influence of dispersion effects on weaker H-bonds [42] and to the fact that these IHBs are closer to the aromatic rings ‘belt’. C−H⋅⋅⋅⋅O IHBs have been recognised as true IHBs for some decades [43, 44]. Although being weak, they have non-negligible influence. In M-ACPLs, they influence the orientation of the R chain and of substituents and have non-negligible stabilising effects [7]. In ACPLs with more than one monomer, their number increases as the

192

12 Tetraflavaspidic acid BBBB

number of monomeric units increases [11]. Their types and positions depend on the conformer. Given the high number of such IHBs present in each conformer and the high number of conformers of TFVAB, analysing all of them would become burdensome. The 1-a-α conformer is then selected for illustration. Table S11 reports the lengths of its 27 identifiable C−H⋅⋅⋅⋅O IHBs and Figure 12.7 highlights those that are identifiable from a one-perspective image of its geometry. It can be noted that their bond lengths remain shorter than the sum of the van der Waals radii of H and O (2.7 Å) in the results of the three calculation methods (with only one length being slightly greater than this sum): this qualifies them as true H-bonds. The shortest of them is H11⋅⋅⋅O10, where H11 belongs to a free methyl with the C atom co-planar to the benzene ring of the first monomer. After it, the shortest types are the IHBs in which the donor pertains to an MB and the acceptor to the 1st, 2nd or 3rd monomer (2.317–2.377/HF, 2.339–2.390/DFT and 2.317–2.399/DFT-D3 Å); they close 5-member rings. The H13⋅⋅⋅O12-type IHBs close 6-member rings and have lengths (Å) 2.398–2.484/HF, 2.411–2.518/DFT and 2.402– 2.507/DFT-D3. The H18⋅⋅⋅O14-type IHBs are the longest because H18 is attached to a C atom of the R chain further away from the ring; their lengths are 2.619–2.672/HF, 2.649– 2.706/DFT and 2.647–2.672/DFT-D3. The patterns for the fourth monomer are somewhat different because of the fact that O12″′ is an sp2 O and of the presence of two

Figure 12.7: C−H⋅⋅⋅⋅O IHBs identifiable from a one-perspective image of the 1-a-α conformer of tetraflavaspidic acid BBBB. DFT-D3 results. The C−H⋅⋅⋅⋅O IHBs are indicated by blue segments joining the H and O atoms concerned.

12.4 Discussion and conclusions

193

methyl groups at C3″′. The inclusion of the Grimme’s dispersion correction may cause non-negligible or negligible length changes, depending on the IHB; in most cases, it causes a slight decrease of the length, as common for weak H-bonds [42].

12.3.4 Other molecular properties The energy difference between the frontier molecular orbitals (HOMO-LUMO energy gap) is related to properties like reactivity and the ability to conduct electric current, and is often a descriptor for quantitative structure properties relationships (QSPR). Table S12 reports the values of the HOMO-LUMO energy gap for the calculated conformers of TFVAB, and Figure S4 highlights their trends. The HF and DFT values differ substantially; this is a known phenomenon, with the HF values corresponding to better estimations. The trends are however similar, with the values decreasing for higher energy conformers, and with considerably smaller values for the ξ-type conformers. The DFT and DFT-D3 results are very close for non-ξ-type conformers, whereas the DFT-D3 values are greater than the DFT ones for the ξ-type conformers (diagram (b) in Figure S4). This suggests that the dispersion correction leads to somewhat better estimations of the energy gap between the frontier orbitals for ξ-type conformers. The dipole moment is an important property of molecules and can be a descriptor for QSPR analyses. Table S13 reports the values of the dipole moments of the calculated conformers of TFVAB, Table S14 analyses them in terms of the monomers’ orientations and in terms of the orientations of the MBs, and Figure S5 visualizes these analyses. As a general trend, the dipole moment is greater for higher energy conformers; within this general trend, values for ‘energetically consecutive’ conformers (‘consecutive’ in the DFT-D3 increasing ΔE sequence) may vary significantly depending on the conformers’ types. The dipole moment of the non-ξ-type conformers remains below 5.5 debye. The values for ξ-type conformers range from 7.67 to 29.15 debye (with the exception of 3.54 debye for 4-a-ξ4). The DFT-D3 values are mostly close to the DFT values for the lower energy conformers, and often smaller for the ξ-type conformers, highlighting considerable influence of dispersion effects for the latter.

12.4 Discussion and conclusions The current study is likely the first extensive conformational study of a naturallyoccurring ACPL molecule with four monomeric units. Comparisons of the DFT and DFT-D3 results highlight important effects of the dispersion correction on the energetics estimations. The DFT-D3 results indicate only four conformers with ΔE ≤ 3.5 kcal/mol (a cautious threshold value above which conformers can be considered unlikely to contribute to a substance’s properties). The HF and DFT results indicate a high number of conformers with low ΔE. Given the

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12 Tetraflavaspidic acid BBBB

expected relevance of dispersion interactions for a molecule containing four aromatic rings, it is reasonable to conclude that the DFT-D3 results are the ones to be taken as reference for studies like QSPR. Additional confirmations would come from a study of the molecule in solution, which will be part of a separate work. The presence of ten IHBs in the calculated conformers of TFVAB enables comparisons of their characteristics and stabilising effects according to their types, molecular contexts and cooperativity patterns, thus also contributing information on H-bonding in general. Similarly, the presence of a high number of C−H⋅⋅⋅⋅O IHBs in each conformer contributes information on their stabilising effects for the TFVAB conformers, and on H-bonding in general. The fact that the three lowest energy conformers in the DFT-D3 results (and also low energy conformers in the other results) have bowl-shaped geometry indicates this geometry as the most stable and preferred. This, in turn, suggests that quadrimeric ACPLs with all the monomers in fully enolic form may be viewed as potential naturallyoccurring precursors of four-unit cavity-containing molecular structures built from ACPLs [13, 26], because the geometry of their low energy conformers is favourable. Acknowledgments: The author expresses her gratitude to the Centre for High Performance Computing (CHPC) in Cape Town (South Africa) for providing the facilities to perform the calculations needed for this work.

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Supplementary Material: The online version of this article offers supplementary material (https://doi. org/10.1515/psr-2021-0239).

Ededet A. Eno, Hitler Louis*, Tomsmith O. Unimuke, ThankGod C. Egemonye, Stephen A. Adalikwu, John A. Agwupuye, Diana O. Odey, Abu Solomon Abu, Ishegbe J. Eko, Chukwudubem E. Ifeatu and Tabe N. Ntui

13 Synthesis, characterization, and theoretical investigation of 4-chloro-6(phenylamino)1,3,5-triazin-2-yl)asmino-4(2,4-dichlorophenyl)thiazol-5-yl-diazenyl) phenyl as potential SARS-CoV-2 agent Abstract: The synthesis of 4-chloro-6(phenylamino)-1,3,5-triazin-2-yl)amino-4-(2,4 dichlorophenyl)thiazol-5-yl-diazenyl)phenyl is reported in this work with a detailed structural and molecular docking study on two SARS-COV-2 proteins: 3TNT and 6LU7. The studied compound has been synthesized by the condensation of cyanuric chloride with aniline and characterized with various spectroscopic techniques. The experimentally obtained spectroscopic data has been compared with theoretical calculated results achieved using high-level density functional theory (DFT) method. Stability, nature of bonding, and reactivity of the studied compound was evaluated at DFT/ B3LYP/6-31 + (d) level of theory. Hyper-conjugative interaction persisting within the molecules which accounts for the bio-activity of the compound was evaluated from

*Corresponding author: Hitler Louis, Computational and Bio-Simulation Research Group, University of Calabar, Calabar, Nigeria; and Department of Pure and Applied Chemistry, Faculty of Physical Sciences, University of Calabar, Calabar, Nigeria, E-mail: [email protected] Ededet A. Eno, Tomsmith O. Unimuke, ThankGod C. Egemonye and John A. Agwupuye, Computational and Bio-Simulation Research Group, University of Calabar, Calabar, Nigeria; and Department of Pure and Applied Chemistry, Faculty of Physical Sciences, University of Calabar, Calabar, Nigeria Stephen A. Adalikwu and Chukwudubem E. Ifeatu, Computational and Bio-Simulation Research Group, University of Calabar, Calabar, Nigeria Diana O. Odey, Computational and Bio-Simulation Research Group, University of Calabar, Calabar, Nigeria; and Department of Biochemistry, Faculty of Physical Sciences, Cross River University of Technology, Calabar, Nigeria Abu Solomon Abu, Computational and Bio-Simulation Research Group, University of Calabar, Calabar, Nigeria; and Department of Marine Biology, Faculty of Biology Sciences, University of Calabar, Calabar, Nigeria Ishegbe J. Eko, Department of Polymer and Textile Engineering, Ahmadu Bello University Zaria, Kaduna, Nigeria Tabe N. Ntui, Computational and Bio-Simulation Research Group, University of Calabar, Calabar, Nigeria; and Department of Chemistry, Faculty of Physical Sciences, Cross River University of Technology, Calabar, Nigeria As per De Gruyter’s policy this article has previously been published in the journal Physical Sciences Reviews. Please cite as: E. A. Eno, H. Louis, T. O. Unimuke, T. C. Egemonye, S. A. Adalikwu, J. A. Agwupuye, D. O. Odey, A. S. Abu, I. J. Eko, C. E. Ifeatu and T. N. Ntui “Synthesis, characterization, and theoretical investigation of 4-chloro-6(phenylamino)-1,3,5-triazin-2-yl)asmino-4(2,4-dichlorophenyl)thiazol-5-yl-diazenyl)phenyl as potential SARS-CoV-2 agent” Physical Sciences Reviews [Online] 2022. DOI: 10.1515/psr-2021-0161 | https://doi.org/10.1515/9783110783643-013

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natural bond orbital (NBO) analysis. Adsorption, Distribution, Metabolism, Excretion and Toxicity (ADMET) properties of the experimentally synthesized compound was studied to evaluate the pharmacological as well as in silico molecular docking against SARS-CoV-2 receptors. The molecular docking result revealed that the investigated compound exhibited binding affinity of −9.3 and −8.8 for protein 3TNT and 6LU7 respectively. In conclusion, protein 3TNT with the best binding affinity for the ligand is the most suitable for treatment of SARS-CoV-2. Keywords: ADMET; DFT; docking; synthesis; thiazole.

13.1 Introduction Thiazoles are heterocyclic compounds containing sulfur and nitrogen atom. In pharmaceutical, several bioactive materials have been made with Thiazole as starting material [1]. However, several studies have reported possible germicidal [2, 3], analgetic [4], anti-inflammatory [5], anticonvulsant [6], cardiotonic [7], anticancer [8–10], antitubercular [11] and anthelmintic [12] effect of thiazoles. The significance of the thiazole moiety is largely attributed to its utilization as an active ingredient in various pharmaceutical products [13]. Recently, Thiazole derivative with phenylamino-1,3,5-triazine backbone has gained full attention as new nonsteroidal progesterone receptor (PR) antagonists which is of great importance in several physiological systems, most especially in the case of the female reproductive system [14]. In a study, the synthesis of 14 derivatives of 2-phenylamino-thiazole as an antimicrobial agent was conducted using Hantzsch reaction. The resulting 14 structures were later screened in order to verify their antimicrobial effect against two Gram-positive, one Gram-negative bacterial strains, and two fungal strains [15]. Also, DFT study was conducted on a novel 2-amino-4(4-chlorophenyl) thiazole derivatives. The result reflects that the investigated compound demonstrated moderate anti-bacterial effect against Staphylococcus aureus and Bacillus subtilis and also high anti-fungal action against candida Glabrata and Candida albicans [16]. In another work, four novel derivatives of 3-(2-(3-Phenyl-5-substituted phenyl-4,5-dihydropyrazol-1-yl)thiazol-4-yl)-2H-chromen-2-one named 1–4 were synthesized and characterized via spectroscopic methods. All the compounds involved were screened to ascertain their use as a bio-active material (acetyl cholinesterase inhibition potential). Among the studied compounds, compound 3 was the most effective as conducted in the acetyl cholinesterase (AChE) inhibition assay having IC50 of 27.29 μM. Molecular docking was also carried out on the compounds. The results obtained from the docking showed a strong relationship with their binding energies and the in vitro AChE inhibition assay [17]. Molecular docking is a crucial computational technique that has gained popularity over time, especially in drug discovery research, where it is used to model the

13.2 Experimental and computational details

199

interaction between a ligand and a protein at the atomic level. It is also utilized to predict and ascertain the behavior of a ligand in the binding site of a particular protein of interest [18]. The binding affinity and orientation of some drug at a binding site have been predicted quite often with the use of this technique. In this research, we experimentally and computationally evaluated the structural properties of 4-chloro6-phenylamino-1,3,5-Trianzin-2,4-dichlorophenylthiazol, and also the interaction of this compound with two corona virus proteins; 3TNT and 6LU7 using molecular docking.

13.2 Experimental and computational details 13.2.1 Experimental 13.2.1.1 Synthesis of 4-chloro-6(phenylamino)-1,3,5-triazin-2-yl)amino-4-(2,4 dichlorophenyl)thiazol-5-yl-diazenyl)phenyl The synthesis of 4-chloro-6(phenylamino)-1,3,5-triazin-2-yl)amino-4-(2,4 dichlorophenyl)thiazol-5-yl-diazenyl)phenyl was achieved by condensing cyanuric chloride with aniline, using equimolar mixtures of initial starting materials in aqueous solution of toluene at temperature range of −10 to 50 °C. Cyanuric chloride (9.22 g, 0.05 mol) was reacted with aniline (27 g, 0.3 mol) at molar ratio of 1:6. The compound formed (2-aniline-4, 6-dichloro-1, 3, 5-triazine (0.05 mol) was later reacted with 2-amino, 4(p-chlorophenyl) thiazole in the ratio of 1:1 at 0–5 °C for 3 h resulting in the formation of six hetero-bifunctional compounds. The schematic reaction route is reported in Figure 13.1.

Figure 13.1: Optimized structure of CPTDT with atomic labelling.

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13 Synthesis, characterization, and theoretical investigation

13.2.1.2 NMR 1

H-NMR and 13C-NMR of the investigated compound was conducted using Bruker AVANCE DPX NMR spectrometer operating at 400 and 101.6 MHz. Chloroform (CDCl3) and tetramethyl silane (TMS) (CH3)4Si were utilized as solvent and internal reference standard respectively for the NMR analysis. The 1H-NMR and 13C-NMR spectra of CPTDT is reported in Figure S1 of the ESI.

13.2.2 Computational details In this research, all the objectives were analyzed computationally with little experimental analysis for some objectives such as NMR and UV. The studied compound was optimized using Gaussian09 and GaussView 6.0.16 [19] software packages. Natural bond orbital (NBO) analysis was evaluated with the aid of NBO 3.1 software [20] embedded in GaussView 6.0.16 in order to verify the type of interaction, inter molecular charge transfer (ICT) existing within the studied compound and its stability. Geometry optimization was executed at DFT/B3LYP/6-31 + G(d) level of theory. The optimized structure of the studied compound with atomic labelling is presented in Figure 13.2. Multiwfn programme [21] was used for plotting of the spectroscopy results. The ADMET properties of the investigated compound were evaluated using PKCSM online tool kit [22]. Molecular docking of covid-19 proteins with the ligand (CPTDT) was conducted using the crystal structure of 3TNT and 6LU7 co-crystallized with CPTDT. The optimized structure of CPTDT was utilized for molecular docking analysis. Proteins used were obtained from protein data bank (PDB) database. AutoDock [23] and discovery studio visualizer [24] were employed to view the docking result while pyMOL [25] was used to build the protein-ligand complex and to visualize the binding sites.

13.3 Result and discussion 13.3.1 Quantum chemical descriptors Relevant information on the electronic property of a compound is obtained from the molecular orbital analysis. Quantum chemical descriptors such as highest occupied molecular orbital energy (EHOMO), lowest unoccupied molecular orbital energy (ELUMO) and ΔE (ELUMO − EHOMO) energy gap present relevant information on the nature of reactivity and kinetic stability of a compound [26]. It is predicted that the higher the energy gap value of a particular compound, the lesser the reactivity and more stable will the compound be [27]. Table 13.1 shows the evaluated quantum chemical parameters of the analyzed compound. The chemical phenomenal of donating and accepting electrons by a molecule is tied to the HOMO/LUMO energy values [28]. The HOMO is

13.3 Result and discussion

Figure 13.2: Reaction pathways for the studied compound.

Table .: Calculated quantum chemical parameters of the studied compound. Descriptors

Values

Vertical IP Vertical EA Mulliken electronegativity Chemical potential Hardness (=fundamental gap) Softness Electrophilicity index Nucleophilicity index HOMO LUMO Energy gap

. eV . eV . eV −. eV . eV . eV− . eV . eV −. eV −. eV . eV

201

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13 Synthesis, characterization, and theoretical investigation

responsible for electron donation while the LUMO accounts for electron acceptance. This implies that the higher the HOMO value, the greater the tendency of a compound to donate an electron and lower values indicate that the molecule presents a strong electron accepting property. HOMO-LUMO of the studied compound was estimated at DFT/B3LYP/6-31 + G(d) level of theory. The calculated values are seen as thus: HOMO (−0.211891 a.u), LUMO (−0.095156 a.u), and energy gap of (0.116735 a.u). To further explain the property of HOMO-LUMO energy levels, 3D plots of HOMO and LUMO energy levels of CPTDT is depicted in Figure 13.3. The figure represents the positive and negative charges distributed within the compound. The red portion is positive and the green is the negative. It also suggests that the HOMO and LUMO is almost localized throughout the compound. The energy gap value confirmed that either C-N or N-N orbital are involved in HOMO and LUMO energy levels, this implies that electron donation by the HOMO energy level, or acceptance of an electron by the LUMO energy level can deteriorate the molecular skeleton framework. The HOMO-LUMO gap is where the most excitations occur, which make it the most important parameter that is considered in terms of reactivity and stability of a compound. HOMO-LUMO gap (0.116735) of the compound indicates large aromatic system, which leads to mobile π-electrons within the compound since it is easy for electrons to jump to a higher energy level. Moreover, the high mobility of the π-electron in the CPTDT system indicates that there is a greater energy distribution throughout the entire compound and as such stabilizing it.

13.3.2 Aromaticity index 13.3.2.1 PDI and PLR Para-delocalization index (PDI) is an important parameter used to verify aromatic nature of a six-membered ring (6-MR) [29, 30]. Para linear response index (PLR) is

Figure 13.3: HOMO-LUMO plot of CPTDT.

13.3 Result and discussion

203

similar to PDI, but with a slight difference. The linear relationship between PLR and PDI has been reported to be R2 = 0.96 [31]. In this analysis only the 6-MR of CPTDT were considered for the PDI value. Ring three in reference to atomic ring 14–19 is considered to have a stronger aromaticity due to its high PDI value of 0.4962. The observed PDI values in the four 6-MR of the studied compound is a confirmation of the aromatic nature of the compound. Similarly, atomic ring 14–19 manifested para linear response (PLR) of 0.5979 which is the highest PLR of all the atomic rings. This also explained the more aromatic nature of atomic ring 14–19 as shown in Table 13.2. 13.3.2.2 FLU and FLU-π Aromatic fluctuation index (FLU) is mostly implemented for investigating rings possessing any number of atoms unlike PDI which is focused on only six-membered rings [32]. FLU is defined using Eq. (13.1) [32–34] FLU =

1 ring V(B) δ(A, B) − δ(A, B) ∑ [( ) ( )] n A−B V(A) δ(A, B)

(13.1)

where the summation runs over all adjacent pairs of atoms around the ring, n represents the number of atoms present in the ring, σref depicts the reference DI value. Σ is utilized in order to ensure that the ratio of atomic valences is greater than one. While a

FLUπ =

1 ring Vπ(B) δπ(A, B) − δavg ∑ [( ) ( )] n A−B Vπ(A) δavg

2

(13.2)

where σπ represent the average value of the DI-π for bonded atom pairs in the ring while the remaining symbols signifies that the above-mentioned parameters were estimated with the aid of π-orbitals. However, flu-π has advantage over flu this is due to the fact that flu-π is independent on the predefined reference DI value while the main limitation of FLU is its dependence on the reference value. For both parameters, the ring with the lowest value is said to possess the strongest aromaticity which also implies more stability and less Table .: Aromaticity index values for the various rings in CPTDT molecule. Atomic ring – – – – –

PDI value

FLUE value

FLUE pi-

PLR

HOMA value

Bird aromaticity

. – . . .

. . . . .

. . . . .

. . . . .

. . −. . .

. . −. . .

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13 Synthesis, characterization, and theoretical investigation

reactivity. Atomic ring 14–19 as shown in Table 13.2 has the lowest FLU and FLU-π value of 0.0029 and 0.0004 respectively which is the same ring with the highest PDI value, this correlation confirms the stronger aromaticity of atomic ring 14–19. 13.3.2.3 HOMA and BIRD Table 13.2, highlights the HOMA and BIRD indices for each ring evident in the studied compound. HOMA is a commonly used parameter utilized for measuring aromaticity [30]. BIRD index is also a parameter that measures aromaticity of a compound with respect to geometry [33]. From the HOMA index of the investigated compound, it is obvious that none of the rings is completely non-aromantic. For instance, ring 1–6, 9– 13, 24–29 and 31–36 with indices of 0.9713, 0.9372, 0.9645 and 0.9618 respectively still exhibit aromaticity since its HOMA index value does not completely correspond to a zero value [35]. However, anti-aromaticity was observed within atomic ring 14–19 due to the negative index of HOMA. For BIRD index, the closer the value is to 100, the stronger the aromaticity [36]. Thus, from the estimation, the atomic ring with BIRD value closer to 100 is atomic ring 1–6 (96.0287) followed by ring 9–13 (96.0087) with a slight difference of 0.02.

13.3.3 Conceptual density functional theory (CDFT) The CDFT of the investigated compound was estimated using DFT method at B3LYP/ 6-31 + G(d) theory level with the aid of multiwfn software [21]. The result obtained from the CDFT of the compound under study is highlighted in Table S1 of ESI. From the computational analysis of the compound, the result revealed that the change in f− of the studied compound is greater than zero in all the component atoms. This is a clear indication that the compound is favorable for nucleophilic attack with the exception of C15 and N20 with values of −0.0019 and −0.0002 respectively. This result indicates that these two positions are possible sites for electrophilic attack to take place since the two atoms have values less than zero. For relative electrophilicity (S+/S−) and relative nucleophilicity (S−/S+), atomic sites N8, N12, C6, C9, Cl20 are the most probable atomic sites susceptible to nucleophilic attack within the investigated compound. This result can be attributed to the atomic sites possessing the highest relative electrophilicity with respective values of 0.2890, 0.2734, 0.2361, 0.2246, 0.2241 as represented in the table of supporting information. More so, atomic sites H48 = 124.6627, C36 = 7.3039, C11 = 4.6823, N22 = 3.2754, C32 = 2.6725, C3 = 2.6636, H44 = 2.5473, N30 = 2.3909, C34 = 2.3516, C35 = 2.2128, H47 = 2.0223, are the sites with the highest S−/S+ ratio which makes them the most probable sites for electrophilic attack to take place.

13.3 Result and discussion

205

13.3.4 Natural bond orbital (NBO) Analysis Natural bond orbital (NBO) analysis is an efficient computational method utilized for comprehending intramolecular and intermolecular bonding interaction existing within a compound [37]. It also gives insight on charge transfer or hyperconjugative interaction taking place in the molecular system [38]. DFT/B3LYP/6-31G + (d) level of theory was utilized to evaluate the stabilization energy (E(2)) of the selected compound. Stabilization energy E(2) of the studied compound evaluated with the aid of second order perturbation theory of the fock matix is represented in Table S2 of ESI. The higher the stabilization energy, the stronger the interaction persisting within the investigated compound. The stabilization energy E(2) associated with electron delocalization from the donor (i) to acceptor (j) orbital of CPTDT was estimated using Eq. (13.3) [37, 38]. E(2) = ΔE i, j = qi

F(i, j)2 εj − εi

(13.3)

Here, qi represent the donor orbital occupancy, εi and εj depicts the diagonal elements and F (i,j) indicates off-diagonal elements of the Fock matrix. The intramolecular hyperconjugative interactions resulting from overlapping of bonding (σ/π) and anti-bonding (σ*/π*) orbitals leading to intramolecular charge transfer and stabilization in the compound is observed as π (C15–C16) to π* (C14–C19), π (C17–C18) to π* (C14–C19), π (C25–N29) to π* (C24–N28) with stabilization energy of 199.35 kcal/mol, 148.33 kcal/mol, 48.40 kcal/mol respectively. Such strong interactions arise from the electronic delocalization between intermolecular ring fragments in form of resonance delocalization which is responsible for stabilizing the compound. Also, the strongest non-bonding interaction resulting from LP (3) N30 →π* (C25 − N29) and LP (1) N22→π*(C26 − N27) gives the highest stabilization of 67.96 kcal/ mol and 58.59 kcal/mol respectively. Such strong interaction aids in stabilizing the compound under study. This result offers an intuitive insight into the mechanism of stabilization, interaction, delocalization of electron within the various ring fragment and heteroatom’s present in the molecule.

13.3.5 Atomic dipole moment corrected Hirshfeld (ADCH) charge ADCH value of CPTDT was estimated using multiwfn software [21]. The electrostatic potential reproducibility of the CPTDT population was conducted with the aid of ADCH population charge [39]. ADCH corrected the poor electrostatic potential reproducibility challenge encountered by the Hirshfeld population which has an attribute of neglected atomic dipole moment. From the result presented in S3 of ESI, all the hydrogen atoms displayed no negative charge and also none of the correction charges of the hydrogen atoms is zero. This indicates that all the hydrogen atoms have contributed to the atomic dipole moment of the compound [40]. It was also observed from the results, that major

206

13 Synthesis, characterization, and theoretical investigation

contributions to the atomic dipole moment originated from neighboring atoms which are mostly hydrogen. The trend of atoms possessing negative charge is displayed as thus, C14 > N22 > N27 > N13 > N28 > N29 > C32 > C17 > C2 > C34 > C33 > C36 > C1> C6 > C5 > C35 > N30 > N38 > C4 > Cl21 > Cl20 > N8 > Cl39 with corresponding values of −0.3736 > −0.3551 > −0.3399 > −0.2897 > −0.2593 > −0.1476 > −0.1342 > −0.1327 > −0.1236 > −0.1230 > −0.1213 > −0.1180 > −0.1076 > −0.1056 > −0.1036 > −0.1032 > −0.0986 > −0.0593 > 0.0530 > 0.0381 e. This result implies that these sites would be the most preferred sites for electrophilic substitution in this order of preference since some charges are more negative than others. The negative and positive charges are due to the unequal distribution of electrons in the compound by atoms due to the fact that one of the atoms is more electronegative than the other, especially the nitrogen atoms. This uneven distribution of electrons within the compound results in polarization of the atoms.

13.3.6 NMR analysis NMR elucidates the structure based on paramagnetic properties of molecules and this is useful for other physicochemical properties’ prediction. The paramagnetic shield is dependent on the bonded atoms. The NMR chemical shift of CPTDT was estimated with the aid of B3LYP/6-31 + G(d) GIAO method using TMS as the reference and the results are tabulated in Table 13.3. The corresponding theoretical NMR spectra of CPTDT is shown in Figures S1 and S2 of ESI. Experimentally, chemical shift involving the aromatic carbon atoms was recorded between 120–190 ppm in range [41], while the aliphatic carbon atoms are mostly observed behind the aromatic compounds. The results obtained from the studied compound (CPTDT), indicates that the evaluated chemical shift of the aromatic ring present in CPTDT ranges from 116.3 ppm to 165.4 ppm for C35 and C24. This correlates with the experimentally observed values at 115.9 ppm to 162.4 ppm as presented in Figure S3 of ESI. The aromatic carbon atoms bonded directly with halogen such as chlorine possesses higher chemical shifts compared to others. In the compound, the aromatic carbon atoms with the Cl atom bonded to it demonstrated a chemical shift of 165.4 ppm in C24 and it is shifted downfield which is attributed to the electron withdrawing effect of N and Cl attached to C24, but mostly affected by the Cl atom. Also, carbon atoms with N atoms experienced high chemical shift downfield as observed in C10 and C25 with the values of 147.8 ppm and 147.9 ppm respectively. However, C42 experienced a chemical shift higher than others, which could be due to the high electronegativity of the Cl atom attached to it. It can be deduced from the spectra that almost all the chemical shift falls within the aromatic region which confirms the aromatic nature of the compound (CPTDT). The once that deviated slightly might be due to the chemical effect of their neighboring atoms as observe in C6, C17, C2 with values of 116.9 ppm, 117.2 ppm and 119.8 ppm individually. In the 1HNMR spectrum, the three-proton signal at 6.8 ppm is assigned to the protons on the terminal

13.3 Result and discussion

207

Table .: Experimental and Calculated HNMR chemical shift (ppm) obtained from the investigated compound at DFT/BLYP/- + G(d,p) GIAO method using TMS as the reference. Atom with position -C -C -C -C -C -C -C -C -C -C -C -C -C -C -C -C

Chemical shift (ppm) TMS BLYP/ - + G(d,p) GIAO

Experimental chemical shift

. . . . . . . . . . . . . . . .

. . . . . .

Hydrogen atoms with positions -H -H -H -H -H -H -H -H -H -H -H -H -H -H -H

Chemical shift (ppm) TMS BLYP/ - + G(d,p) . . . . . . . . . . . . . . .

aromatic rings while the three-proton signal at 7.1 ppm is assigned to H41, H42, H50 which are magnetically equivalent due to long range coupling. The aromatic protons are within the range of 6.5–8.5 ppm [42]. The calculated chemical shift of the studied compound is observed between 6.03–8.2 ppm which correlates with experimental. The proton signal at 5.46 ppm is assigned to the NH proton on the ring, the standard chemical shift of the NH is expected in 3.5–4.5 ppm [43]. The shift in chemical shift at downfield is due to the strong electronegativity of the nitrogen atom.

13.3.7 Theoretical ADMET prediction (PKSCM) Lipinski’s rule of five (RO5) is a physicochemical parameter widely used to ascertain if a particular compound can be used as a drug [44]. According to the rule, “drug-like” molecules should exhibit; logP ≤ 5, molecular weight