Sustainable Chemistry Research. Volume 3: Analytical Aspects [3] 9783111328171

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Ponnadurai Ramasami (Ed.) Sustainable Chemistry Research

Also of interest Sustainable Chemistry Research Volume : Chemical and Biochemical Aspects Ponnadurai Ramasami (Ed.),  ISBN ----, e-ISBN ----

Sustainable Chemistry Research Volume : Computational and Industrial Aspects Ponnadurai Ramasami (Ed.),  ISBN ----, e-ISBN ----

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

Sustainable Chemistry Research Volume 3: Analytical 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 address: [email protected]

ISBN 978-3-11-132817-1 e-ISBN (PDF) 978-3-11-132841-6 e-ISBN (EPUB) 978-3-11-132846-1 Library of Congress Control Number: 2023939448 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. © 2023 Walter de Gruyter GmbH, Berlin/Boston Cover image: Petmal/iStock/Getty Images Plus 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-2022) was organized online from 8th to 12th August 2022. The theme of the virtual conference was “Resilience and Sustainable Research through Basic Sciences”. There were 210 presentations for the virtual conference with 500 participants from 55 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 twelve accepted manuscripts covering analytical aspects. I hope that these chapters of this volume 3 will add to literature and they will be useful references for researchers. To conclude, VCCA-2022 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, UNESCO Chair in Computational Chemistry Computational Chemistry Group, Department of Chemistry, Faculty of Science, University of Mauritius, Réduit 80837, Mauritius E-mail address: [email protected]

https://doi.org/10.1515/9783111328416-201

Contents Preface V List of contributing authors

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Samuel Tetteh 1 The Cambridge structural database (CSD): important resources for teaching 1 concepts in structural chemistry and intermolecular interactions 1 1.1 Introduction 3 1.2 Methodology 3 1.2.1 ConQuest Program 4 1.2.2 Mercury Program 4 1.3 Results and discussion 12 1.4 Conclusions 13 References Reagan Lehlogonolo Mohlala and Elena Mabel Coyanis 2 The vital use of isocyanide-based multicomponent reactions (MCR) in 15 chemical synthesis 15 2.1 Introduction to multicomponent reactions 17 2.2 Strecker reaction (S-3CR) 18 2.3 Hantzsch reaction (H-3CR) 19 2.4 Biginelli reaction (B-3CR) 21 2.5 Mannich three component reaction (M-3CR) 21 2.6 Passerini reaction (P-3CR) 22 2.6.1 Substrate scope in the Passerini reactions 26 2.6.2 Chirality in Passerini reactions 27 2.7 Ugi reaction: U-4CR and U-3CR 27 2.7.1 Ugi-four component reaction (U-4CR) 29 2.7.2 Ugi-three component reaction (U-3CR) 32 2.8 van leusen reaction (V-3CR) 2.9 The application of dimethyl acetylenedicarboxylate in organic synthesis 34 2.9.1 DMAD and isocyanides in multicomponent reactions 41 2.9.2 DMAD in Michael reactions 44 2.9.3 DMAD in cycloaddition reactions 45 2.10 Conclusions 46 References

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Abdul Nashirudeen Mumuni, John McLean and Gordon Waiter 3 Spectral peak areas do not vary according to spectral averaging scheme used in functional MRS experiments at 3 T with interleaved visual 53 stimulation 54 3.1 Introduction 55 3.2 Methodology 55 3.2.1 Study volunteers 55 3.2.2 Presentation of visual stimulus 56 3.2.3 Functional MRI 57 3.2.4 Functional MRS 59 3.2.5 Spectral analysis 59 3.2.6 Quantification of the BOLD effects on the spectra 60 3.2.7 Statistical analysis 60 3.3 Results 60 3.3.1 Single stimulation experiments 62 3.3.2 Interleaved stimulation experiments 64 3.4 Discussion 66 3.5 Conclusions 67 References Adeleke Adeniyi, Mayowa Ibidokun and Ojo Oluwole 4 A comparative assessment of potentially harmful metals in the Lagos 69 Lagoon and Ogun river catchment 70 4.1 Introduction 70 4.2 Materials and methods 70 4.2.1 Materials and reagents 71 4.2.2 Study area 71 4.2.3 Sampling and sample preparation 73 4.3 Results and discussion 76 4.4 Conclusions 77 References Enrico Daniel R. Legaspi, Ma. Stefany Daennielle G. Sitchon, Sonia D. Jacinto, Blessie A. Basilia, and Imee Su Martinez 5 XRD and cytotoxicity assay of submitted nanomaterial industrial samples in 79 the Philippines 80 5.1 Introduction 81 5.2 Methods 81 5.2.1 X-ray diffraction 81 5.2.2 MTT cytotoxicity assay 81 5.3 Discussion

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5.3.1 5.3.2 5.4

Analysis of X-ray diffraction patterns for select nanoparticle samples 86 MTT cytotoxicity assay 89 Conclusions 90 References

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Agnes Pholosi, Saheed O. Sanni, Samson O. Akpotu and Vusumzi E. Pakade 6 Pine bark crosslinked to cyclodextrin for the adsorption of 2-nitrophenol 93 from an aqueous solution 93 6.1 Introduction 95 6.2 Experimental 95 6.2.1 Materials 95 6.2.2 Procedures 96 6.3 Results and discussion 96 6.3.1 Adsorbent characterization 98 6.3.2 Adsorption studies 105 6.4 Conclusions 106 References Daniel O. Omokpariola, Patrick L. Omokpariola, Patrice A. C. Okoye, Victor U. Okechukwu, Joseph S. Akolawole and Ogochukwu Ifeagwu 7 Concentration evaluation and risk assessment of pesticide residues in selected vegetables sold in major markets of Port Harcourt South-South 109 Nigeria 110 7.1 Introduction 111 7.2 Methodology 111 7.2.1 Sample collection and preparation 111 7.2.2 Chemicals 112 7.2.3 Extraction of pesticide residues from samples 112 7.2.4 Clean-up 112 7.2.5 Analysis of organochlorine and organophosphate pesticides 113 7.2.6 Quality control 113 7.2.7 Statistical analysis 113 7.2.8 Risk assessment 114 7.2.8.2 Carcinogenic assessment 115 7.3 Results and discussion 7.3.1 Concentration of organochlorine and organophosphate pesticides in 115 vegetables 119 7.3.2 Noncarcinogenic assessment 119 7.3.3 Carcinogenic risk assessment 124 7.4 Conclusions 124 References

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Rafia Azmat, Rohi Bano, Sumeira Moin, Tahseen Ahmed, Ailyan Saleem and Waseem Ahmed 8 Detection of iodine in aqueous extract of plants through modified Mohr’s 127 method 127 8.1 Introduction 129 8.2 Materials and methods 129 8.2.1 Collection of Ipomoea pes-caprae from three coastal sites 129 8.2.2 Preparation of plant samples for analysis of bioactive iodine 129 8.2.3 Standard curve of KI 130 8.2.4 Chemical analysis of iodine 130 8.2.5 Separation of iodine 130 8.2.6 Determining LOD and LOQ 130 8.3 Results and discussion 135 8.4 Conclusions 135 References Uche E. Ekpunobi, Fabian M. Onyekwere, Rosemary U. Arinze, Daniel N. Enenche, Daniel O. Omokpariola and Victor U. Okechukwu 9 Appraisal and health risk assessment of potential toxic element in fruits 137 and vegetables from three markets in Anambra state, Nigeria 137 9.1 Introduction 138 9.2 Materials and methods 138 9.2.1 Study area 139 9.2.2 Sample collection 139 9.2.3 Digestion of soil samples 139 9.2.4 Digestion of fruits and vegetables samples 139 9.2.5 Health risk assessment 140 9.2.6 Statistical analysis 140 9.3 Results and discussion 140 9.3.1 Concentrations of potential toxic elements in soil 9.3.2 Concentrations of potential toxic elements (mg/kg) in fruits and vegetables in 142 Atani market 9.3.3 Concentrations of potential toxic elements (mg/kg) in fruits and vegetables in 143 Omor market 9.3.4 Concentrations of potential toxic element (mg/kg) in fruits and vegetables in 143 Eke Awka market 145 9.3.5 Implication of potential toxic element concentration in plants 147 9.3.6 Health risk assessment 150 9.4 Conclusions 150 Supplementary Material 150 References

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Liliana Mammino 10 Complexes of a model trimeric acylphloroglucinol with a Cu2+ ion: a DFT 153 study 153 10.1 Introduction 156 10.2 Computational details 157 10.3 Results 157 10.3.1 Selection and geometry of the calculated complexes 160 10.3.2 Energetics of the calculated complexes 163 10.3.3 Properties of the ion in the complexes 164 10.3.4 How closely the ion approaches the molecule 165 10.3.5 Effects of complexation on the intramolecular hydrogen bonds 166 10.3.6 Other molecular properties of the complexes 168 10.3.7 Discussion and conclusions 169 References Davor Margetić 11 Mechanochemistry as a green method in organic chemistry and its 171 applications 171 11.1 Introduction 172 11.2 Mechanochemistry 173 11.3 Synthetic applications 180 11.4 Future prospects 180 11.5 Conclusions 181 References Liliana Mammino 12 Maximizing advantages and minimizing misinterpretation risks when using analogies in the presentation of chemistry concepts: a design 183 challenge 184 12.1 Introduction 184 12.1.1 Analogies as an expression, emphasising and clarification tool 186 12.1.2 Analogies in education and in chemistry education 189 12.2 Analogies in chemistry teaching: examples and reflections 189 12.2.1 Models, visualization and analogies 191 12.2.2 When an analogy fails its purpose 192 12.2.3 Designing analogies in the classroom 202 12.3 Discussion 205 12.4 Conclusions 206 References Index

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List of contributing authors Adeleke Adeniyi Department of Chemistry Lagos State University P.M.B.0001 LASU Post Office Ojo Lagos Nigeria E-mail: [email protected] Tahseen Ahmed Department of Chemistry University of Karachi 75270 Karachi Pakistan Waseem Ahmed Department of Horticulture The University of Haripur Hatter Road 22620 Haripur Pakistan Joseph S. Akolawole Department of Pure and Industrial Chemistry Nnamdi Azikiwe University Awka Anambra Nigeria Samson O. Akpotu Adsorption and Water Remediation Research Laboratory Department of Biotechnology and Chemistry Faculty of Applied and Computer Sciences Vaal University of Technology P. Bag X021 Vanderbijlpark 1900 South Africa Rosemary U. Arinze Department of Pure and Industrial Chemistry Nnamdi Azikiwe University Awka Anambra State Nigeria

https://doi.org/10.1515/9783111328416-202

Rafia Azmat Department of Chemistry University of Karachi 75270 Karachi Pakistan E-mail: rafi[email protected] Rohi Bano Department of Botany University of Karachi 75270 Karachi Pakistan Blessie A. Basilia Department of Science and Technology Materials Science Division Industrial Technology Development Institute Taguig City Metro Manila 1631 Philippines Elena Mabel Coyanis Advanced Material Division and Chemistry Mintek Inc 200 Malibongwe Street Randburg 2194 Gauteng 2125 South Africa Uche E. Ekpunobi Department of Pure and Industrial Chemistry Nnamdi Azikiwe University Awka Anambra State Nigeria E-mail: [email protected] Daniel N. Enenche Department of Pure and Industrial Chemistry Nnamdi Azikiwe University Awka Anambra State Nigeria

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Mayowa Ibidokun Centre for Environmental Studies and Sustainable Development Lagos State University P.M.B. 0001 LASU Post Office Ojo Lagos Nigeria Ogochukwu Ifeagwu Department of Pure and Industrial Chemistry Nnamdi Azikiwe University Awka Anambra Nigeria Sonia D. Jacinto Institute of Biology College of Science University of the Philippines Diliman Quezon City Metro Manila 1101 Philippines Enrico Daniel R. Legaspi Institute of Chemistry College of Science and Natural Sciences Research Institute University of the Philippines Diliman Quezon City Metro Manila 1101 Philippines Liliana Mammino Faculty of Science Engineering and Agriculture University of Venda Thohoyandou South Africa E-mail: [email protected] Davor Margetić Laboratory for Physical Organic Chemistry Division of Organic Chemistry and Biochemistry Ruđer Bošković Institute Bijenička c. 54 10000 Zagreb Croatia E-mail: [email protected]

Imee Su Martinez Institute of Chemistry College of Science and Natural Sciences Research Institute University of the Philippines Diliman Quezon City Metro Manila 1101 Philippines E-mail: [email protected] John McLean Department of Clinical Physics and Bioengineering Queen Elizabeth University Hospital Glasgow UK Reagan Lehlogonolo Mohlala Advanced Material Division and Chemistry Mintek Inc 200 Malibongwe Street 2194 Randburg Gauteng 2125 South Africa E-mail: [email protected] Sumeira Moin Department of Botany University of Karachi 75270 Karachi Pakistan Abdul Nashirudeen Mumuni Department of Medical Imaging University for Development Studies Tamale Ghana E-mail: [email protected] Victor U. Okechukwu Department of Pure and Industrial Chemistry Nnamdi Azikiwe University Awka Anambra State Nigeria Victor U. Okechukwu Department of Pure and Industrial Chemistry Nnamdi Azikiwe University Awka Anambra Nigeria

List of contributing authors

Patrice A. C. Okoye Department of Pure and Industrial Chemistry Nnamdi Azikiwe University Awka Anambra Nigeria Ojo Oluwole Centre for Environmental Studies and Sustainable Development Lagos State University P.M.B. 0001 LASU Post Office Ojo Lagos Nigeria Daniel O. Omokpariola Pure and Industrial Chemistry Faculty of Physical Science Nnamdi Azikiwe University Eligbolo Anambra 420261 Nigeria and Department of Pure and Industrial Chemistry Nnamdi Azikiwe University Awka Anambra Nigeria E-mail: [email protected] Patrick L. Omokpariola Chemical Evaluation and Regulation Isolo Industrial Estate Oshodi Expressway National Agency for Food and Drug Administration and Control Isolo Lagos 101263 Nigeria Fabian M. Onyekwere Department of Pure and Industrial Chemistry Nnamdi Azikiwe University Awka Anambra State Nigeria

Vusumzi E. Pakade Adsorption and Water Remediation Research Laboratory Department of Biotechnology and Chemistry Faculty of Applied and Computer Sciences Vaal University of Technology P. Bag X021 Vanderbijlpark 1900 South Africa Agnes Pholosi Adsorption and Water Remediation Research Laboratory Department of Biotechnology and Chemistry Faculty of Applied and Computer Sciences Vaal University of Technology P. Bag X021 Vanderbijlpark 1900 South Africa E-mail: [email protected] Ailyan Saleem Department of Chemistry University of Karachi 75270 Karachi Pakistan Saheed O. Sanni Adsorption and Water Remediation Research Laboratory Department of Biotechnology and Chemistry Faculty of Applied and Computer Sciences Vaal University of Technology P. Bag X021 Vanderbijlpark 1900 South Africa Ma. Stefany Daennielle G. Sitchon Institute of Chemistry and Institute of Biology College of Science University of the Philippines Diliman Quezon City Metro Manila 1101 Philippines

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

Gordon Waiter Aberdeen Biomedical Imaging Centre University of Aberdeen Aberdeen UK

Samuel Tetteh*

1 The Cambridge structural database (CSD): important resources for teaching concepts in structural chemistry and intermolecular interactions Abstract: The Cambridge Structural Database (CSD) is a repository of all published organic and metal-organic crystal structures of small molecules. These compounds have been crystallized under different conditions and have variable bond parameters and molecular landscapes. Entries in the database therefore serve as real models that can be used to illustrate structural properties such as bond angles, bond distances, torsion angles and other intra- and intermolecular interactions. This paper illustrates how the CSD programs ConQuest and Mercury can be used to search the database for 3D molecular structures to teach concepts such as molecular geometry, symmetry and group theory, organometallic chemistry and intermolecular interactions involving hydrogen bonding and Full Interaction Maps (FIMs) to explore molecular landscapes for halogen bond interactions. Results obtained from these studies are beneficial to understand and predict crystal properties as well as the structural properties of molecules and ions in crystal environments. Keywords: intermolecular interactions, molecular gemetry, substructure search

1.1 Introduction Structural chemistry is fundamental to the understanding of physical and chemical properties of both small molecules and macromolecules. It deals with the spatial arrangement of atoms, bond lengths, bond angles and torsion angles. These principles are important to understand the geometric shapes, stereochemistry, conformation and the type of bonds present in molecular species. These concepts are important in disciplines such as chemistry, biochemistry, molecular biology, pharmacy and agrochemicals. A complete understanding of these principles will require the use of molecular visualization tools such as molecular modelling kits to aid student learning

*Corresponding author: Samuel Tetteh, Department of Chemistry, School of Physical Sciences, College of Agriculture and Natural Sciences, University of Cape Coast, Cape Coast, Ghana, E-mail: [email protected]. https://orcid.org/0000-0002-8989-6346 As per De Gruyter’s policy this article has previously been published in the journal Physical Sciences Reviews. Please cite as: S. Tetteh “The Cambridge structural database (CSD): important resources for teaching concepts in structural chemistry and intermolecular interactions” Physical Sciences Reviews [Online] 2023. DOI: 10.1515/psr-2022-0325 | https://doi.org/10.1515/9783111328416-001

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[1, 2]. Although there are advanced molecular modelling software to model specific molecules under different conditions, there are always challenges in getting the right basis sets to predict the correct structure in order to explain experimental observations [3]. Therefore idealized structures are always used to predict molecular properties which usually come with some uncertainties [4]. Thus when the student is finally confronted with experimental data, it becomes difficult for him to explain real experimental structural data. The CSD is a repository of small molecule organic and metal-organic crystal structures with more than 1.1 million entries as at the time of writing this article. These three-dimensional structures were crystallized under different conditions and the crystallographic data verified and certified by experts before deposition. Each entry in the database is identified by a specific reference code (refcode) made up of six letters and two digits to distinguish polymorphs or similar forms of the same compound crystallized under different conditions. For each published structure, the CSD provides information on the crystallographic unit cell and space group, the atomic coordinates in each cell, chemical structure information specifying atomic ‘nodes’ and bonded edges as well as bibliographic information comprising the author name(s) and journal reference [1]. It also gives information on the name of the compound, molecular formular, precision indicators, density, melting point, colour, etc. which are important indicators for the characterization of crystal systems. The molecules in these crystal systems are perfect models for teaching structural chemistry and molecular interactions. Since they were crystallised under different conditions with different substituents and crystal systems, they provide a statistical distribution of bond parameters as compared to the information obtained from molecular modelling software. Some of the concepts which can be taught with resources in the database include valence shell electron pair repulsion (VSEPR) theory of bonding, stereochemistry, coordination chemistry, organometallic chemistry and molecular symmetry and group theory. This is important because these concepts require three-dimensional representation of the molecules and the CSD has excellent programs to illustrate these principles. This article will explore the use of two packages in the CSD: ConQuest [5] and Mercury [6] to illustrate some basic concepts in VSEPR theory, molecular symmetry and group theory and Organometallic Chemistry. according to the VSEPR theory, the distribution of electron pairs (both bonding and nonbonding) determines the overall shape of the molecule. Based on this model, the shape of most small inorganic molecules can be unambiguously predicted from a knowledge of the number of valence electrons contributed by the central and terminal atoms. Figure 1.1 shows a two-dimensional representation of molecular structures based on the VSEPR theory. However, this model does not take into account, the effect of the spatial distribution, bulkiness and intramolecular interactions of the ligands or substituents which can cause distortions in most instances. These details can only be observed by studies involving real crystal data.

1.2 Methodology

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Figure 1.1: Basic molecular geometries and their respective notations, A = central atom, X = terminal atom E = lone pair of electrons.

1.2 Methodology 1.2.1 ConQuest Program ConQuest is an integrated package in the CSD suite of software. It is the primary program for searching and retrieving information from the database [7]. It provides the opportunity to search the database using queries such as drawing a substructure of the molecule, using author/journal name, name of the compound, the type of elements present or even the type of space group. Other search queries include the use of the ‘refcode’ as well as the use of ‘text search’ where for example the word ‘brown’ will return all brown coloured crystals in the database. This program also gives the opportunity to do combined queries where Boolean algorithms are used to search the database for crystals with similar substructures. For each search option, the database has filters to specifically define and narrow down the search results. These include 3D coordinates determined, R factor (≤0.05, ≤0.075, ≤0.1) which shows the accuracy of the structure determination. Other search filters include Only (non-disordered, disordered), No errors, Non polymeric, No ions, Only (single crystal structures, powdered structures) and Only (organics, organometallic). These are crystallographic terms. The program also gives specific information about each retrieved entry in the database. These include information on the crystal system such as the space group, type of unit cell, number of molecules in the asymmetric unit, the temperature at which the data were collected and colour of the crystal. It also has bibliographic information about the author and journal, the chemical formular and name of the compound as well as a diagram tab to view the structure in two dimensions. There is a 3D visualizer to view the molecule in three dimensions where it can be rotated on screen to get details about the bond angles and bond distances.

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1.2.2 Mercury Program Mercury is a visualization tool for studying crystal and molecular structures as well as inter- and intramolecular interactions. Some of the functionalities include visualization of molecular geometries in wireframe, capped sticks, ball-and-stick, space fill, ellipsoid and polyhedral styles. Atoms colouring can also be customized to the preference style of the user. It also gives the opportunity to present the molecular or crystal structures in different styles such as work, presentation, ORTEP, 3D print and the style preference of the user. Other display options include packing, asymmetric unit, short contacts and inter-/intramolecular hydrogen bond display. Molecules can also be edited by adding missing hydrogen atoms. Some of the packages within the mercury program include CSD-Community, CSD-Core, CSD-Materials, CSD-Theory, CSD-Particle, CSD-Discovery and CSD Python API.

1.3 Results and discussion This article will illustrate how a combination of ConQuest and Mercury can be used to search the database for resources to illustrate concepts in molecular geometry, molecular symmetry and group theory, organometallic chemistry and intermolecular interactions. For molecular geometry searches, the draw window in ConQuest is a perfect platform for substructure searches. Four-coordinate first-row transition metal complexes will be used as suitable examples for this illustration. To start the search, first open the draw window in ConQuest and select more from the bottom panel and click on other elements. This will pop up the periodic table, as shown in Figure 1.2a. Click on 1R to choose the first-row transition series. You can use the ADD 3D tab on the left panel to define the X-M-X bond angle as shown in Figure 1.2b. You can also restrict

Figure 1.2: Screen shot of the ConQuest window for substructure search of tetra-coordinated first-row transition metal complexes.

1.3 Results and discussion

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the number of atoms bonded to the terminal atoms to be equal to one so that simple molecular structures can be retrieved. Finally click on the search bottom to search the database for all the four-coordinate first-row transition metal complexes in the database. The 2022.2.0 version of the CSD returned 3861 hits (entries) of tetra-coordinated first-row transition metal complexes. A sample of these complexes with their respective refcodes, X-M-X bond angles and the type of transition metal (or ion) is shown in Table 1.1. It can be observed that the bond angles range from 90.00° (refcodes: QOTHUE and QOTJIU) depicting square planarity to 153.90° (ROVYOS) which approaches linearity. Some of the structures, however, have bond angles close to tetrahedral geometry (CAGCEW, FAVKIA03, MAVXAM and NUMZAW) The distribution of the X-M-X bond angles is shown in Figure 1.3. It is evident that most of the retrieved complexes have bond angle around 110° (nearly tetrahedral) with a minimum of 74.37° (PEBKOY) and a maximum of 180° (XURHUQ01). One thing about the data is that you can click on any of the bars to get access to the number and type of compounds with the given bond angles. For example, the selected (red) bar corresponds to the refcodes of crystal structures containing compounds with X-M-X bond angles around 110°. Table .: Selected refcodes and bond angles of tetra-coordinated first-row transition metal complexes retrieved from the CSD. Refcode AYUDUW CAGCEW FAVKIA HANSUO MANKEV MAVXAM NUMZAW NUTDOV OBOJIV OBUGOH OCUQUC ODAPOB QOTHUE QOTJIU QOTSOK RAVKAB RENCAP RITPOB ROHWAN ROVYOS

X-M-X angle (°) . . . . . . . . . . . . . . . . . . . .

Central metal Fe Fe Fe Cu Co Co Zn Cu Ni Zn Cu Ti Cu Cu Co Cu Cu Zn Zu Cu

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Figure 1.3: Histogram of the distribution of X-M-X bond angles in four-coordinate first-row transition metal complexes.

Some of these compounds are given in Figure 1.4. These compounds have approximately tetrahedral bond angle which can be used to illustrate sp3 hybridization in molecular species. Structures with compounds of square-planar geometry can also be accessed by clicking on bars corresponding to a bond angle of approximately 90°. Some of these structures are illustrated in Figure 1.5. These structures can be rotated, translated and the

Figure 1.4: Structures of tetrahedral molecules retrieved from the database.

1.3 Results and discussion

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Figure 1.5: Structures showing square-planar molecules retrieved from the CSD.

bond lengths also measured so that the students will appreciate the effect of certain substituents on the bond lengths and bond angles of chemical structures. These structures are ideal for the illustration of sp2d hybridized molecular structures. The database could also be searched for structures which exhibit specific molecular structures. A ConQuest substructure search by restricting the number of groups bonded to specific central atoms as well as specifying the bond angles gives the representative structures and symmetry point groups shown in Figure 1.6. These molecules can be used to illustrate point groups such as C2v (6a), D6h (6b) and Oh (6c), respectively. Organometallic chemistry deals with compounds and reactions involving metallic elements and carbon-based fragments [8]. The mode of bonding has generally been described by ionic, covalent and dative covalent modes. These compounds are usually described as complexes since no one particular model can be used to describe the

Figure 1.6: Retrieved molecular structures to illustrate C2v (a), D4h (b) and Oh (c) point groups.

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bonding mode. Molecular orbital theory has however been successfully employed to describe most interactions involving organometallic compounds. According to this model, organic ligands donate a pair of electrons in a sigma fashion dative covalent bond. This is augmented by backbonding of π-electron density from the metal-d π-orbitals into empty π* ligand orbitals. This type of bonding increases the electron density on the metal atom or ion and decreases electron density in ligand bonds, a typical example is the C-O bond in metal-carbonyl complexes. A substructure search in ConQuest together with statistical analysis in Mercury can be used to successfully study this synergistic interaction involving transition metal-carbonyl complexes. To illustrate this phenomenon, we will use tetra-coordinated first-row transition metal carbonyl complexes. A substructure search with added 3D and both M-C (DIST1) and C-O (DIST2) bond lengths defined is shown in Figure 1.7. A database search revealed 694 entries. Analysis of the retrieved data in mercury shows that the M-C bond length ranges from 1.55 to 1.99 Å with an average of 1.77 Å (Figure 1.8). Likewise, the C–O bond length also ranges from 0.86 to 1.32 Å with an average of 1.14 Å (Figure 1.9). The differences and ranges of these bond parameters can be attributed to factors such as the type of metal atom or ion, the type of ligand and the environment of the ligand as well as the crystal structure involved. The student will appreciate the fact that several factors control bond parameters in real crystal systems as compared to theoretical predictions which use only gas phase models. A scatter plot (Figure 1.10) shows a negative correlation between the M–C and C–O bond lengths in tetra-coordinated first row transition metal carbonyl complexes retrieved from the CSD. This supports the idea that the σ-donor interaction involving the carbonyl and the metal centre leads to the strengthening of the M–C bond whereas the

Figure 1.7: Results of the substructure search of tetra-coordinated first-row transition metal complexes retrieved from the CSD.

1.3 Results and discussion

Figure 1.8: Histogram of the distribution of the retrieved M–C bond lengths.

Figure 1.9: Histogram of the distribution of the retrieved C–O bond lengths.

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Figure 1.10: A scatter plot of the correlation between the M–C and C–O bond lengths.

back donation of metal π-electrons into the CO π* orbitals weaken the C–O bond length [9]. The CSD therefore provides experimental data to support this theoretical model. Furthermore, each data point on the scatter plot, representing a particular crystal structure, can be clicked upon to further explore the bond parameters in order to understand the factors that contribute to the strengthening or weakening of a particular M–C or C–O bond. Figure 1.11 shows a tetrahedral tetracarbonyl cobalt complex (HEFGEE) with Co–C distance of 1.96 Å and the shortest C–O distance of 0.90 Å. Intermolecular interactions are basically classified as non-covalent interactions that hold molecular and ionic species together. Typical examples include hydrogen bonding, short contacts and halogen bonding [10]. These interactions are the principal forces in macromolecules such as supramolecules, proteins, biomolecules and pharmaceuticals [11]. They also determine the physical state of substances such as water and organic solvents. As an example, we shall explore inter and intramolecular hydrogen bonding in the aspirin molecule. Crystal structures containing aspirin and its derivatives can be retrieved from the database by typing the word ‘aspirin’ in the compound name of the Name/Class tab of ConQuest. The search returns a total of 91 entries in the CSD. These structures can be visualized in mercury to study these interactions. The entry with refcode ACSALA will be used for this illustration. By clicking on the H-bond tab displays the possible contacts of all hydrogen bond donor and acceptor sites in the aspirin

1.3 Results and discussion

11

Figure 1.11: A tetracarbonyl cobalt complex exhibiting a relatively short C–O bond length.

molecule as shown in Figure 1.12a. It also shows the presence of intramolecular hydrogen bond interaction between the carboxylic acid OH and the acetyl-O group. Clicking on the ‘hanging’ (red) contacts in Figure 1.12a shows that aspirin exists as a dimer Figure 1.13b with inter and intramolecular hydrogen bond interactions. This can be used to explain the boiling and melting points of aspirin as well as its solubility in different solvents. Another type of noncovalent interaction which has received increasing attention is halogen bonding [12]. It has great significance in biological systems, drug design, electronics and nonlinear optical activities. This interaction usually occurs between a halogen atom in one molecule and a negative site in another. The Full Interaction Maps functionality in the CSD-Materials package of the Mercury program is an easy-to-use

Figure 1.12: Screen shot of intra (a) and inter (b) molecular hydrogen bonding in the aspirin molecule.

12

1 Structural chemistry and intermolecular interactions

Figure 1.13: Full interaction maps of 4-fluorophenol (a), 4-chlorophenol (b), 4-bromophenol (c) and 4-iodophenol (d).

resource which can be used to explore the propensity for halogen bonding in molecular compounds. This can be done by searching for refcode of the crystal structure in the Structure Navigator pane. When the structure appears, select Full Interaction Maps from the drop-down menu of the CSD-Materials tab. Specific probes are then selected to identify the possible landscape for interaction around the molecule. Figure 1.13 shows the interaction landscape for four halogenated phenols. Regions where there is a likelihood of finding an acceptor are shown in red with those of donors shown in blue. Those for hydrophobic regions are shown in brown. It can be observed that the interaction maps around the hydroxy group are constant across the series. The region around the halogen, however varies with the highest acceptor peaks around bromine and iodine. The possibility for halogen bonding increases in the order F < Cl < Br < I which is related to the electron density around these halogens [13].

1.4 Conclusions This work has illustrated how a combination of the CSD programs: ConQuest and Mercury can be used for substructure searches and molecular visualization of molecular models which can be used to teach concepts in molecular geometry, symmetry and group theory, organometallic chemistry and intermolecular interactions. These 3D models are motifs in chemical structures which have been crystallized under different conditions and are excellent models for understanding experimental data. The database has more than 1.1 million entries which can be explored to study various phenomena involving molecular compounds. This work also demonstrated how to use the database to differentiate between tetrahedral and square-planar models as well as the use of ConQuest to search the database for specific geometries. Intermolecular interactions involving hydrogen bonding and halogen bond propensities were also explored using the Mercury program as well as statistical analysis of bond lengths in transition metal carbonyl complexes to explain the concept of synergistic bonding in these compounds.

References

13

Acknowledgements: The author thanks the organizers of the virtual conference on chemistry and its applications (VCCA-2022) for the opportunity to present this work. The author is also grateful to the FAIRE program of the Cambridge Crystallographic Data Centre (CCDC) for the opportunity to use the Cambridge Structural Database (CSD) for substructure searches, molecular visualization and statistical analysis.

References 1. Battle GM, Allen FH, Ferrence GM. Teaching three-dimensional structural chemistry using crystal structure databases. 1. An interactive web-accessible teaching subset of the Cambridge Structural Database. J Chem Educ 2010;87:809–12. 2. Battle GM, Ferrence GM, Allen FH. Applications of the Cambridge structural database in chemical education. J Appl Crystallogr 2010;43:1208–23. 3. Huzinaga S, Andzelm J, Klobukowski M, Radzio-Andzelm E, Sakai Y, Tatewaki H. Gaussian basis sets for molecular calculations. Amsterdam: Elsevier; 2012. 4. VandeVondele J, Hutter J. Gaussian basis sets for accurate calculations on molecular systems in gas and condensed phases. J Chem Phys 2007;127:114105. 5. Moghadam PZ, Li A, Wiggin SB, Tao A, Maloney AGP, Wood PA, et al. Development of a Cambridge structural database subset: a collection of metal–organic frameworks for past, present, and future. Chem Mater 2017;29:2618–25. 6. Macrae CF, Sovago I, Cottrell SJ, Galek PTA, McCabe P, Pidcock E, et al. Mercury 4.0: from visualization to analysis, design and prediction. J Appl Crystallogr 2020;53:226–35. 7. Allen F, Johnson O. The Cambridge structural database. New York: NATO ASI Series C Mathematical and Physical Sciences-Advanced Study Institute; 1996, 480:55–66 pp. 8. Crabtree RH. The organometallic chemistry of the transition metals. New Hersey: John Wiley & Sons; 2009. 9. Tetteh S. Imidazol-2-ylidene stabilized tetrahedral cobalt carbonyl complexes: a computational and structural database study. Heliyon 2019;5:e02125. 10. Vologzhanina AV. Intermolecular interactions in functional crystalline materials: from data to knowledge. Crystals 2019;9:478. 11. Politzer P, Lane P, Concha MC, Ma Y, Murray JS. An overview of halogen bonding. J Mol Model 2007;13: 305–11. 12. Groom CR, Allen FH. The Cambridge structural database in retrospect and prospect. Angew Chem Int Ed 2014;53:662–71. 13. Fourmigué M. Halogen bonding: recent advances. Curr Opin Solid State Mater Sci 2009;13:36–45.

Reagan Lehlogonolo Mohlala* and Elena Mabel Coyanis

2 The vital use of isocyanide-based multicomponent reactions (MCR) in chemical synthesis Abstract: Multicomponent (MCRs) reactions are classified as one-pot reaction where more than two starting materials are employed to form a single product that contains the building blocks of the starting components. MCRs are considered a convenient approach in synthetic chemistry and have many advantages over the traditional one or two-component reaction, by reducing the number of sequential multiple steps required and often producing better yields. This chapter dissects the use of isocyanide-based MCRs and the elegant chemistry that they offer to build useful scaffolds in the chemical synthetic field. In addition MCRs are considered as one of the recognisable options for increasing “greenness” during the synthesis of pharmaceutical and industrial products. keyword: isocyanide; multicomponent reactions; zwitterion.

2.1 Introduction to multicomponent reactions Multicomponent reactions (MCRs) are also known as one-pot reactions that comprise of at least three reactants chosen to form a single product containing building blocks of atoms from all the reactants [1–3]. The general practical concept of MCRs is depicted in Figure 2.1.1 adopted from Rocha and co-workers [4], which shows at least four different reactants with different functional groups converging to form one product. There are a number of well-known MRCs such as (a) the Ugi four-component reaction [5] (U-4CR) (b) the Passerini three-component reaction [6, 7] (P-3CR) (c) the Biginelli threecomponent reaction [8] (B-3CR) and (d) the Hantzsch three-component reaction [9] (H-3CR) represented in Figure 2.1.2. MCRs have been exploited for over 100 years; the initial discovery and report of MCR remains difficult to identify, however, the Strecker [10] multicomponent reaction (S-3CR) was reported in 1850 followed by the Hantzsch dihydropyridine (DHP) synthesis in 1882. The Biginelli 3-CR was reported in 1891 while the Mannich [11] reaction (M-CR) was

*Corresponding author: Reagan Lehlogonolo Mohlala, Advanced Material Division, Mintek Inc, 200 Malibongwe Street, Randburg, 2194, Randburg, Gauteng, 2125, South Africa; and Chemistry, Mintek Inc, Randburg, South Africa, E-mail: [email protected]. https://orcid.org/0000-0003-2683-8656 Elena Mabel Coyanis, Advanced Material Division, Mintek Inc, 200 Malibongwe Street, Randburg, 2194, Randburg, Gauteng, 2125, South Africa; and Chemistry, Mintek Inc, Randburg, South Africa As per De Gruyter’s policy this article has previously been published in the journal Physical Sciences Reviews. Please cite as: R. L. Mohlala and E. M. Coyanis “The vital use of isocyanide-based multicomponent reactions (MCR) in chemical synthesis” Physical Sciences Reviews [Online] 2023. DOI: 10.1515/psr-2022-0349 | https://doi.org/10.1515/9783111328416-002

16

2 The use of multicomponent reactions in chemical synthesis

Figure 2.1.1: Schematic representation of multicomponent reactions (Rocha and co-workers [4]).

O O

O

(a)

HO

R3

R4

R1

Ugi 4-CR

H2N

N C

R2

R2

N

R4 O

R3

NH R1 O

O

(b)

R2

R3

OH

Passerini R4

R1

N C

3-CR

O R 3

R2

R4 R1 N

O

H

O O O

O

(c)

R1

H

R2

R3

X

O O

R3

H2N

NH2

Biginelli

R1 NH

O N H

R2

3-CR

X

X: O,S,NH R4 O

O

(d)

R4

H

R2

R3O2C

O O

R3

R1

NH2

CO2R3

Hantzsch 3-CR

R2

N R1

Figure 2.1.2: Examples of MRCs: (a) Ugi, (b) Passerini, (c) Biginelli and (d) Hantzsch.

R2

2.2 Strecker reaction (S-3CR)

17

reported in 1912. The first isocyanide-based MCRs (IMCR) was reported in 1921 and 1959 by Passerini [6] (3-CR) and Ugi [12], respectively. MCRs are considered a convenient approach in synthetic chemistry and have many advantages over the traditional one or two-component reactions by reducing the number of sequential multiple reactions required and often producing better yields [13, 14]. The most famous MCRs are the Passerini and Ugi reactions which regained attention in the early 1990s [15, 16]. These reactions rely on the dual electrophilic/nucleophilic reactivity of the isocyanides-carbon to provide distinct complex products. The reactions of isocyanides in MCRs are well represented by the Passerini 3-CR, Ugi 3-CR and Ugi 4-CR [17, 18]. MCRs continue to attract attention in medicinal chemistry and drug discovery applications, due to the possibility of readily obtaining different heterocyclic frameworks of interest [19, 20]. Interestingly, MCRs additionally possess considerable potential for contributing to green production processes and protocols of pharmaceuticals, by generating complex products in a single step [21, 22]. Compared to sequential multistep reactions, MCRs contribute significantly to minimising the large amount of waste generated daily in pharmaceutical and industrial production [21, 23–25]. Mohlala and co-workers [26] reported catalyst-free and room temperature/mild condition of 4CR and 3CR of acetone, benzimidazoles and isocyanide giving rise to benzodiazepines and dihydro-quinoxalines. A comprehensive summary of active pharmaceutical ingredients (API) successfully prepared by means of MCRs has recently been published by Menendez and co-workers [27]. It is very interesting that the ever-historical discovery of the MRCs was recorded by Strecker in 1850 which paved the way to the discovery of other reactions such as Hantzsch, Biginelli and Mannich. The above-mentioned reactions are playing a vital role to date in medicinal chemistry synthesis, natural products preparation, the chemical industry and green chemical production. The IMCR remains one of the important interesting elegant chemistry applied in the construction of important end products. Hence the review presents a unique dual electrophilic/nucleophilic reactivity of the isocyanides-carbon which provides distinct complex products demonstrated by Passerini, Ugi and zwitterion intermediates reactions.

2.2 Strecker reaction (S-3CR) The Strecker reaction [10] was first reported in 1850 after condensation of an aldehyde 1.1 with a cyanide source 1.2 in the presence of ammonia 1.3 to give intermediate 1.4 followed by hydrolysis of the nitrile group to form an amino acid 1.5 as shown in Scheme 2.1.1.

O

Aqueous HCN

R1 1.1

1.2

NH3 1.3

Scheme 2.1.1: The Strecker 3-CR (S-3CR).

media

H

N

R1

H H

1.4

Hydrolysis H

CN

N

R1

H CO2H H

1.5

18

2 The use of multicomponent reactions in chemical synthesis

The fact that the Strecker reaction occurs in aqueous medium and under catalyst-free conditions made it one of the favourable methods for application in industries and laboratories because it is a convenient approach to produce amino acids, natural products and bioactive compounds. However another factor to consider is the commercial availability and affordability of the substrates required [28–30]. To date the Strecker reaction has produced a myriad of publications and is still under ongoing investigation. There are several biologically active compounds produced using the S-3CR [27], such as boroncontaining retinoids [31], hepatitis C virus NS3 serine protease inhibitors [32] and (±)-phthalascidin 622 [33]. Several methods used for the synthesis of amino acids and bioactive compounds involve the use of catalysts, such as Al-MCM-41 [34] and other nanostructured silicates [35], nanocrystalline magnesium oxide [36], BINOL-phosphoric acid [37], N-heterocyclic carbene (NHC)-amidate palladium(II) complex [38], gallium (III) triflate [39] and bisformamides [40]. The use of catalysts and non-green solvents results in additional work-up steps and the requirement for additional waste treatments. On the other hand the use of catalyst can provide a more efficient pathway to the desired products, avoiding by-products. This is of particular importance when the stereoselectivity of the MCR needs to be controlled. Neto and co-workers discuss the role of different types of catalyst in improved and “greener” MCRs [41]. Based on these disadvantages there is a continuous search for safer and more practical Strecker methods to synthesise bioactive compounds. An example of this is the direct coupling of a Streker synthesis with an enzyme, for the synthesis of chiral non-proteinogenic amino acids (intermediates of semi-synthetic β–lactam antibiotics such as ampicillin, amoxicillin and cephalexin). The α-aminonitriles intermediates of the Strecker reaction become substrates of nitrile converting enzymes (nitrilases) for the formation of an enantiomeric product [42].

2.3 Hantzsch reaction (H-3CR) The first Hantzsch 3-CR was reported in 1882 after an aldehyde 1.6 and two equivalents of a β-ketoester 1.7 were condensed in the presence of a nitrogen donor 1.8 to give dihydropyridine (DHP) derivatives 1.9 [9, 43, 44] as shown in Scheme 2.1.2. R4 O

O R4

H 1.6

R2

O O

R3

1.7

R1

NH2

1.8

H2O Reflux

R3O2C R2

CO2R3 N R1 1.9

Scheme 2.1.2: The Hantzsch 3-CR (H-3CR).

R2

2.4 Biginelli reaction (B-3CR)

19

Dihydropyridines are considered a significant group of heterocyclic compounds in drug discovery, APIs, and they can also be found in nature [26, 27, 45–47]. There are several catalysts which have also been comprehensively applied to promote the formation of the DPH ring such as ferric chloride (Fe3Cl) or potassium permanganate, nano-γ-Fe2O3-SO3H [48] Al-MCM-41 and Fe/Al-MCM-41 [49], CuBr2 [50], SiO2/P2O5-SeO2 [51], H2O2/Co(OAc)2 [52], Na2S2O4/TBHP [53] and NH4VO3 [54]. In consideration of green chemistry principles [55] and the use of safe practices in laboratories, functionalised Brønsted acidic ionic liquids are used to replace traditional catalysts used for synthesis of DPH derivatives: Sharma and co-workers [56] used a SO3H-functionalised imidazolium ionic liquid, Liu and co-workers [57] used 3-(N,N-dimethyldodecylammonium) propanesulfonic acid hydrogen sulphate ([DDPA][HSO4]), Hu and co-workers [58] used H2O2 catalysed by V2O5 in ionic liquid [C12mim][HSO4], and Lui and Lui [59] used 3-(N,N-dimethyldodecylammonium) propanesulfonic acid hydrogen sulphate ([DDPA][HSO4]) with NaNO3. The formation of the DPH ring as represented in Scheme 2.1.3 (see Katritzky and co-workers) [60] occurs by reaction of an aldehyde with a β-ketoester to give one intermediate 1.10, and the nitrogen source reacts with another molecule of β-ketoester to form another intermediate 1.11. These two intermediates subsequently react to generate intermediate 1.12, leading to the formation of DPH 1.9.

2.4 Biginelli reaction (B-3CR) The 3-CR Biginelli reaction [8] was discovered in 1891 and involves condensation of an aldehyde 1.14 with a β-ketoester 1.7 in the presence of urea 1.15 or thiourea or guanidine hydrochloride under acidic catalysis to obtain 3,4-dihydropyrimidin-2(1H)-one (DHMP) 1.16 as shown in Scheme 2.1.4

R4

R3 O

R3 O O

O O 1.10

R2 HN R1

R4

R4 R3O2C R2

R2 1.11

CO2R3 R O N H2 R1

R3O2C

CO2R3

R2 HO

N

R2

R1 1.13

1.12

R4 R3O2C R2

CO2R3 N R1 1.9

Scheme 2.1.3: Formation of DPH via H-3CR.

R2

20

2 The use of multicomponent reactions in chemical synthesis

The B-3CR initially had some challenges with poor yields and a very small substrate scope range. The discovery of the first B-3CR was followed by a thorough investigation into understanding the course of the reaction and structural variations. The reactions towards the formation of DHMP were explored by investigating solvent compatibility and effects, acid catalyst and increasing substrate scope [61]. The exploration was extended from solution reactions to solid phase reactions using microwave assistance. The DHMPs have interesting biological activities [27] such as calcium channel modulation [62], antimicrobial and antitubercular activity [63]. The proposed mechanism of the B-3CR (see Kappe) [64] is shown in Scheme 2.1.5. A Lewis or a Brønsted acid activates the aldehyde at event 1 in order to react with urea/thiourea to form a hemiaminal species which leads to the elimination of water forming an N-acyliminium cation at event 2. The carbocation at event 2 readily reacts with the nucleophilic α-carbon atom centre of a β-ketoester to form an open chain ureide at intermediate 3, followed by ring closing to form a hexahydropyrimidine intermediate and elimination of water to form DHMP. There are other alternative mechanisms reported and discussed in the literature [65, 66]. O O

O R1

X

O

R2

H 1.14

R3

O

R3

H2N

Biginelli

NH2 1.15

1.7

R1

O

NH R2

3-CR

X N H X: O,S,NH 1.16

Scheme 2.1.4: The Biginelli 3-CR (B-3CR). H O R3 H

X H2N

O R1

O

O

R4

R3

X NH

R1

R2

H

O

1

N H

O

H

NHR2 N H X 3 open chain ureide

R1

R2

2 carbocation

O H=acid X= O, S, NH R3, R4= alkyl, aryl

NH

R3

R4

O

O N

R1

N H

R2 X

O

R3 H2O

1.16

Scheme 2.1.5: Possible mechanism for the Biginelli 3-CR (B-3CR).

R4 OH

O

N R1

N H

R2 X

hexahydropyrimidine

21

2.6 Passerini reaction (P-3CR)

2.5 Mannich three component reaction (M-3CR) The Mannich reaction [11] was discovered in 1912 after the condensation of formaldehyde 1.18 with an amine source 1.17 in the presence of ketone 1.19 to give rise to product 1.20 as shown on Scheme 2.1.6. Mannich reaction is also considered as an example of nucleophilic addition of an amine to a carbonyl group. The Mannich reaction is one of the most important and useful method for the construction of carbon–carbon bonds in synthetic chemistry forming α-amino ketones β-aminocarbonyl compounds [67–69]. The M-3CR of aldehydes, amines and unmodified ketones remains facile and efficient method to the synthesis of β-aminocarbonyl with considerable atom-economic factor [70, 71]. These kinds compounds serves as useful intermediate for the synthesis of β-lactams [65, 72], α-and γ-aminoalcohols [73, 74], α and β-amino acid derivatives [75], bioactive-pharmaceutical and natural products [76, 77]. There are many catalysts applied to accelerate M-3CR such as chiral primary amines and chiral asymmetric [78–80], transitional-metal [81, 82], organo-chemicals, Brønsted and Lewis acids [83, 84], ionic liquids [85, 86] and phase transfer catalyst [87, 88]. Mannich reactions can also be applied as chelating ligands, as intermediates bases and as anion receptors [89]. In addition Hozien and co-workers reported the use of Mannich bases as removers of heavy metals from aqueous solution and as antimicrobial agents [90].

2.6 Passerini reaction (P-3CR) Isocyanide-based multicomponent reactions (IMCRs) have gained much prestige, interest and growth over the last few decades. This area has become one of the exciting and robust tools for peptidomimetic synthesis. The first IMCR was reported by Passerini [6] as a 3-CR in 1921 after the reaction of a carboxylic acid 1.21 with an aldehyde or ketone 1.22 in the presence of an isocyanide 1.23 to give rise to an α-acyloxy carboxamide product 1.24 containing both amide and ester functionalities as shown in Scheme 2.1.7.

R1

H N

O

O R2

R3

1.17

R4

R5

Mannich R6

R2

3-CR

R4 O

N

R6

R1

1.19

1.18

R3

R5

1.20

Scheme 2.1.6: The Mannich 3-CR (M-3CR).

O

O R2

OH 1.21

R3

O R 3

Passerini H

R1

1.22

N C 1.23

3-CR

R2

O 1.24

Scheme 2.1.7: The Passerini 3-CR (P-3CR).

R4 R1 N O

H

22

2 The use of multicomponent reactions in chemical synthesis

The first proposal by Passerini was that the reaction mechanism might involve a zwitterion intermediate. The extensive research described in the literature to investigate the Passerini reaction mechanism has suggested various intermediates including carbocations, hemiacetals and hydrogen-bonded adducts [91]. However, the original Passerini reaction mechanism is generally considered to be a concerted reaction, especially in apolar solvents as shown in Scheme 2.1.8 [92]. The reaction proceeds by the activation of an aldehyde by the carboxylic acid and subsequent addition of an isocyanide to form nitrilium as shown at events 1 and 2. The nitrilium is trapped by the carboxylate followed by a Mumm rearrangement leading to the α-acyloxy amide 1.24. Maeda and co-workers [92] used the artificial force induced reaction (AFIR) method in gas phase for studying the Passerini reaction mechanism. In their study it shows that the mechanism may involve an extra acidic component prior to the desired product, which shows the Passerini reaction as a pseudo four-component reaction shown in Scheme 2.1.9. The mechanism proposed by Maeda and co-workers was supported by the studies done by Ramozzi and Morokuma [93] and also Alvim and co-workers [65] after perfoming high-level DFT calculations which also show a pseudo four-component reaction (Scheme 2.1.10). These studies also reveal that the nitrilium intermediate (B) is stable in solution and its generation is a rate-determing step, catalysed by an extra carboxylic acid molecule leading to Mumm rearrangement and affording 1.24 as the final product.

2.6.1 Substrate scope in the Passerini reactions The scope of the Passerini reaction has been widely studied and some of the reaction components can be replaced by suitable isosteres. The first acid isostere used by Ugi [94] was hydrazoic acid (HN3) 1.25 and aluminium azide Al(N3)3 1.27 in the Passerini reaction, which was regarded as a template for the synthesis of α-hydroxy tetrazoles 1.29 (Scheme 2.1.11). Alternatively NaN3 1.26 can also be used in place of HN3 1.25. Zahoor and co-workers [95] prepared peptidomimetic compounds 1.33 by reacting protected γ-oxo-amino acids 1.30, carboxylic acid 1.31 and isocyanide 1.32 as a neat reaction (Scheme 2.1.12).

O

H

H

O H

R2

O

1

R3 C N R1

O R2

O O 2

H

O

R3 N

R2

R3

R1 N

O

H

O

R1 1.24

Scheme 2.1.8: Nucleophilic and electrophilic reactivity of the isocyanide carbon is illustrated for the Passerini reaction.

23

2.6 Passerini reaction (P-3CR)

O

O R1

OH

R2

O H R3

O

O H R4

O

R1

N

R1

O OH

O

O H

R4 N

R1

OH R3 R2

N

R4

H O

O

O O

R3

R2 R4

R3

R2

R1

O

R1

N C

O

R1

HO

R1COOH

O R4

O

N

R3

R1 O

R2

R3

R2 O O R1

R1 OH

O

H O O

O H

N R4

R2

R1 O R3

R1COOH

R1

O R3

R2 H N

R4

O

Scheme 2.1.9: Passerini reaction mechanism using AFIR method in gas phase (pseudo-four component reaction).

Recyclable magnetic core–shell nanoparticle supported TEMPO (2,2,6,6-tetramethylpiperidin-1-oxyl) catalyst was applied for the oxidative synthesis of Passerini products 1.37, by reacting caboxylic acid 1.34, isocyanide 1.23 and primary or secondary alcohol 1.35 using toluene as a solvent at room temperature (Scheme 2.1.13) [96]. Presumably, in situ oxidation of the alcohol is followed by the 3-CR Passerini reaction. Basso and co-workers [97] reacted carboxylic acid 1.34, isocyanide 1.23 and diazoketones 1.38 under photocatalytic conditions to produce 3-CR product 1.39. Under these conditions, the ketenes generated in situ from 1.38, are able to participate in the three-component reaction (Scheme 2.1.14). Neo and co-workers [98] reacted isocyanide 1.23, carboxylic acid 1.40 and α-ketoaldehyde 1.41 to give product 1.43. The desired β-keto-amides 1.43 were achieved via metal-mediated removal of the acetoxy group (Scheme 2.1.15).

24

2 The use of multicomponent reactions in chemical synthesis

R1

O O

R1

H

O

O R2

OH

R2

O

H

H

H

C N R3

O

O R1

R1

H O

H

O

R1 R2

O O

O

H O

O

H

N

R1

O

Mumm reaarangement

H

H O

R2 N O R1

R1

O R1

B nitrilium

R1COOH

O

O

C N R3

H H

C imidate

R3

R2

O

R3

R1

O H

O O

H

O

R2

N H

R3

1.24 Scheme 2.1.10: Passerini reaction mechanism using high-level DFT in solution (pseudo-four component reaction).

R3

N C 1.23

HN3 1.25

or

NaN3 1.26

O Al(N3)3 1.27

R1

R2 1.28

Scheme 2.1.11: Passerini reaction for synthesis of hydroxy tetrazoles.

R2

OH R R1 3 N N N N 1.29

25

2.6 Passerini reaction (P-3CR)

O O

R

F3C

N H

MeOOC N C

OH

COOt-Bu

R

AcO

O

1.31

neat

COOMe

N H

t-BuOOC

1.32

1.30

O

HN

O CF3 1.33

Scheme 2.1.12: Passerini reaction for preparation of peptidomimetic compounds. O

O R2

OH

R3

1.34

N C 1.23

R1

TEMPO

R3

Toluene

R1

O O N

R2

Mumm R2 rearangement

OH

OH 1.35

O

O

R1 HN

1.36

R3

1.37

Scheme 2.1.13: Passerini reaction using TEMPO catalyst.

R2

O

O

O R3

OH 1.34

Piperylene

R1

N C

N2 Heptane

1.23

R1 O

1.38

N H

O R2

1.39

O

O

R3

Scheme 2.1.14: Passerini reaction for synthesis of ketenes.

R1

N C 1.23 O

O

O Ar OH

O

1.40 O

N H

R

Zn, NH4Cl MeOH

1.42

O H

Ar O 1.41

Scheme 2.1.15: Passerini reaction for synthesis of β-keto-amides.

Ar 1.43

N H

R1

26

2 The use of multicomponent reactions in chemical synthesis

2.6.2 Chirality in Passerini reactions The strategies and methods for controlling the stereochemical outcome of carbon–carbon bond forming reactions from the achiral substrates used in Passerini reaction are finite. The generation of a highly enatioselective Passerini 3-CR is one of the most significant goals. Kusebauch and co-workers [99] used stoichiometric amounts of a Ti-taddol complex 1.46 as a catalyst to obtain moderate enantioselectivity of α-acyloxyamides with 32–42% ee as shown in Scheme 2.1.16. Zhu and co-workers [100] obtained moderate to excellent yield of α-acyloxyamides enantioselectively using stable aluminium salen complex 1.48 as a chiral Lewis acid catalyst from isocyanides 1.23, non-chelating aldehydes 1.44 and carboxylic acids 1.45 (Scheme 2.1.17). Andreana and co-workers [101] managed to achieve good yields of product 1.51 with reasonable enantioselectivity using isocyanide 1.23, chelating aldehydes 1.44 and

R2

N C

O

1.23

Ph

O R1

O

Ph

H 1.44

O Ph H Ph 1.46

OH

R3

O

H N

R1

Ti(i-OPr)4

R2

O 1.47

O

32- 42% ee

OH

R3 1.45

Scheme 2.1.16: Enantioselective Passerini reaction using Ti-taddol complex.

R2

N C 1.23 N O

R1

H 1.44

O Cl

N O O

t-Bu O

R2 t-Bu

O R3

t-Bu

Al

1.48

t-Bu

R1

Toluene OH

1.45

Scheme 2.1.17: Passerini enantioselective reaction using aluminium salen complex.

H N

Ar

O 1.49 52-68% 68->99% ee

27

2.7 Ugi reaction: U-4CR and U-3CR

carboxylic acid 1.45 after employing chiral tridentate Lewis acidic Cu-pybox complex 1.50 as a catalyst (Scheme 2.1.18). Zhang and co-workers [102] activated the reaction of isocyanide 1.23, aldehyde 1.44, and carboxylic acid 1.45 using chiral phosphoric acid 1.52 to obtain high yields of enantioselective products 1.53 (Scheme 2.1.19).

2.7 Ugi reaction: U-4CR and U-3CR 2.7.1 Ugi-four component reaction (U-4CR) The definitive Ugi reaction is a four-component reaction (U-4CR) between an isocyanide 1.23, amine 1.54, carbonyl compound 1.55 (aldehyde or ketone) and carboxylic acid 1.56 to give product 1.57 (Scheme 2.1.20) [5]. R2

N C

2 OTf

1.23

O

O

N Cu

N

R1

O

2

O

R3

N

R1

H 1.44

75- 75% 62- 98% ee

DCM OH

R3 1.45

Scheme 2.1.18: Enantioselective Passerini reaction using Cu-pybox complex.

R2

N C 1.23

HO O P O O

O R1

H

R2

O 1.53

1.52 OH 1.45

H N

R1

O R3

O R3

1.44

DCM

R2

O 1.51

1.50

O

H N

up to 99% up to 99% ee

Scheme 2.1.19: Passerini enantioselective reaction using chiral phosphoric acid.

28

2 The use of multicomponent reactions in chemical synthesis

O O R1

H2N

N C 1.23

R2

O

R3 1.55

1.54

HO

Ugi 4-CR R4

R2

N

R4 O

R3

NH

1.56

R1 1.57

Scheme 2.1.20: The Ugi 4-CR (U-4CR).

During the Ugi-4CR a carbonyl compound 1.55 reacts with an amine 1.54 to form an imine intermediate at event 1. The imine is activated by the carboxylic acid proton and it is then attacked by the isocyanide leading to the nitrilium intermediate which is then attacked by the conjugate base of the carboxylic acid at event 2. This is followed by the Mumm rearangement at event 3 to give the final product 1.57 (Scheme 2.1.21) [14, 103]. The use of the Ugi reaction for the synthesis of a tripeptide from the reaction of amino acid 1.58, dimethoxybenzylamine 1.59, chiral isocyanide 1.60 and aldehyde 1.61 was reported by Mroczkiewicz and Ostaszewski [104]. The peptide aldehyde 1.62 was achieved after deprotection and oxidation of 1.63 (Scheme 2.1.22).

O

H2N

R3 1.55

R2

1.54 R2

R2 R3

R3

NH

O

O C N R1 1

N

R2

HN

NH

O

R4

R3 N

R4

R1

O

3

H

2

R1

Mumm rearangement

O R2

N

R4 O

R3

NH R1 1.57

Scheme 2.1.21: Nucleophilic and electrophilic reactivity of the isocyanide carbon is illustrated for the Ugi reaction.

2.7 Ugi reaction: U-4CR and U-3CR

29

NH2 O Cbz N H

OH O

O

1.59

Dmb O

1.58 Cbz

O

C

OAc

N

N

N H

OAc

N H

O

1.62

1.61

1.60

Cbz

N H

H N O

O N H

O

1.63

Scheme 2.1.22: Synthesis of a tripeptide using U-4CR.

Che and co-workers [105] describe the use of coumarin-3-carboxylic acid 1.64, isocyanide 1.65, aniline 1.66 and pyridine-2-aldehyde 1.67 in U-4CR to achieve annulated products 1.68. The intramolecular annulation depends on the nature of the aldehyde in application: only pyridine-2-and pyridine-4-aldehydes gave rise to the annulated product, whereas the conventional U-4CR product 1.69 was obtained when benzaldehyde or pyridine-3-aldehydes were employed (Scheme 2.1.23). The reaction of a secondary amine 1.70, glycolaldehyde dimer 1.71, isocyanide 1.23 and carboxylic acid 1.72 gave rise to a novel MCR scaffold 1.74 [106]. This reaction proceeded differently from classical U-4CR intermediate 1.73, because the secondary amine prevents the usual Mumm rearrangement from occurring and the hydroxyl group from the glycolaldehyde permits O-acyl rearangement to occur, leading to a novel scaffold (Scheme 2.1.24).

2.7.2 Ugi-three component reaction (U-3CR) The U-3CR was initially described in 1960 while investigating water as a nucleophile [107]. The reaction involved isocyanide 1.23, primary amine 1.75 and an aldehyde 1.76 in the presence of water and a catalyst (Scheme 2.1.25). However, there are many Ugi-3CRs reported under different conditions to the one mentioned here (Scheme 2.1.25).

30

2 The use of multicomponent reactions in chemical synthesis

O

O

O

OH

N

O

C

1.65

1.64

O

O

O

N HN

H

OH

NH2 N

1.68 1.66

1.67

O

O

H

O

N N

H O HN 1.69

Scheme 2.1.23: Synthesis of Ugi adducts.

O R

OH

H N

R

R 1.70

R1

HO

O 1.71

C

R2

1.23

R N R1

OH

O N

R

N

O

O

R2

R2

OH 1.72

O

1.73

Scheme 2.1.24: Ugi reaction using glycolaldehyde dimer.

R1

N C 1.23

H2N

Ugi 3-CR Catalyst

O R2

1.75

R3 1.76

H

H2O

R2

O

HN

N R3 1.77

Scheme 2.1.25: The Ugi 3-CR.

O

H

R1

N

R H N R1 O 1.74

31

2.7 Ugi reaction: U-4CR and U-3CR

The mechanism is quite similar to U-4CR, however carboxylate in this case is replaced by a molecule of water that attacks the electrophilic carbon that might be activated by the catalyst, which consequently in many cases eliminates the Mumm rearrangement (Scheme 2.1.26) [103]. Ramezanpour and co-workers [108] used the U-3CR for synthesis of hydrazino amides 1.80 from the reaction of isocyanides 1.23, cyclohexanone 1.78 and hydrazides 1.79 and using ethanol as a solvent (Scheme 2.1.27). There were several catalysts that were employed and tested for this reaction such as p-toluenesulfonic acid (p-TsOH), ZnCl2 and ZrOCl2, S-1,1-binaphthyl-2,2-diylhydrogen phosphate, phosphorous acid (H3PO4) and camphor sulfonic acid. Shaabani and co-workers [109] describe the ability of zinc chloride to act as a catalyst for the Ugi-3CR of cyclohexyl isocyanide 1.81, 2-aminophenols 1.82 and aldehydes 1.83

O

H2N

R3 1.76

R2

1.75 R2

R2 R3

R3

NH

R2

HN

NH

H O

H

R3 1

N

N

C N R1

R1

O H

R1

H

3 Catalyst

R2

O

HN

N R3

R1

H 1.77

Scheme 2.1.26: Ugi 3-CR mechanism.

O R3

R2

N C 1.23

O

R1 1.78

O N H

NH2

Catalyst EtOH

R2

N H

H N

1.79

Scheme 2.1.27: Ugi-3CR for synthesis of hydrazino amides.

1.80

O

H N

R1

R3

32

2 The use of multicomponent reactions in chemical synthesis

using methanol as a solvent at room temperature to achieve synthesis of N-cyclohexyl-2(2-hydroxyphenylamino)acetamides 1.84 (Scheme 2.1.28). Faggi and co-workers [110] reported the reaction of isocyanide 1.23, aniline 1.85 and 2-formylbenzoic acid 1.86 to afford isocoumarins 1.88 (Scheme 2.1.29). However in this case one of the reagents, formylbenzoic acid 1.86, contains two components of carboxylic acid and an aldehyde that participate in the product formation. These types of reactions are regarded as 4 centre Ugi 3CR. interestingly, they were able to obtain the product prior to Mumm rearrangement after optimising conditions and applying low nucleophilicity amines. The reactions were carried out at higher temperatures or in the presence of trace acid to afford Ugi adducts. These types of reactions are dominated by formation of a new stereogenic centre and the products are reported as racemic. The challenges related to enantioselective U-3CR, U-4CR and Passerini reactions include: spontaneous background reaction in appropriate solvent at room temperatures, product inhibition causes low catalyst turnover, complexity of the possible mechanism pathways and the possibility of the isocyanide coordinating to the metal catalyst.

2.8 van leusen reaction (V-3CR) The van Leusen 3-CR involves reaction of α-substituted tosylmethyl isocyanide (TosMIC) 1.89, a primary amine 1.90 and an aldehyde 1.91 to give rise to imidazole 1.92 [111, 112]. The reaction occurs in the presence of solvents such as methanol, dimethoxyethane or water with base including potassium carbonate or sodium hydride as shown in Scheme 2.1.30. NH2

C N

R1

R3

1.81

H 1.83

R2

OH

O

OH

ZnCl2 MeOH

R2

O

H N R1

N H

R3 1.84

1.82

Scheme 2.1.28: Ugi-3CR for synthesis of N-cyclohexyl-2-(2-hydroxyphenylamino)acetamides.

R

N C

Ar

O

1.23

H

NH

MeOH

OH Ar NH2 1.85

H N R

CONHR H

N Ar

O

O

O

1.86

1.87

Scheme 2.1.29: 4 Centre-Ugi-3CR for synthesis of isocoumarins.

1.88

O

33

2.9 The application of dimethyl acetylenedicarboxylate in organic synthesis

The 3-CR van Leusen reaction generally proceeds by the initial reaction of the primary amine 1.90 with an aldehyde to form a Schiff base which reacts with deprotonated TosMIC at event 1 in order to react with activated TosMIC to give the intermediate shown at event 2. Here an internal nucleophililic attack occurs leading to cyclisation to form intermediate 3 followed by elimination of p-toluenesulfinic acid to afford imidazole 1.92 (Scheme 2.1.31) [113].

2.9 The application of dimethyl acetylenedicarboxylate in organic synthesis Dimethyl acetylenedicarboxylate (DMAD) is an electron deficient alkyne diester which exists as a liquid at room temperature and is highly electrophilic. DMAD versatility makes it useful in cycloaddition reactions as a dienophile and a dipolarophile. In addition, DMAD is also used to form carbon–carbon bonds and in multicomponent reactions for developing heterocyclic scaffolds [114]. In line with the essential discovery of DMAD

Ts

O

N C

R1

NH2

R2

1.90

R3 1.89

Solvent H

Base

1.91

R3

N

R2

N R1 1.92

Scheme 2.1.30: van Leusen 3-CR.

R1

Ts

N R3

C

base

Ts

N

NH2 1.90

R2

C

Ts

R2

R3

R1

R2

R2

N N 1.92

R1

Ts

R1

R3

-TsH

N N

R2

Scheme 2.1.31: van Leusen 3-CR mechanism.

C

N

1

R3

N

N

R3

1.89

O 1.91

4

R1

base

Ts

R2

R3

N N

H 3

R1

2

34

2 The use of multicomponent reactions in chemical synthesis

reactions with heterocyclic compounds by Diels and co-workers [115] and Acheson and Elmore [116], the utility of DMAD in organic synthesis has gained tremendous attention [117, 118]. The common fundamental properties and applications of DMAD in organic synthesis includes Michael reactions, Diels–Alder, 1,3-dipolar and [2 + 2] cycloadditions and in MCRs. DMAD is one of the economically affordable and accessible reagents. The preparation of DMAD was originally reported by Bandrowski [119] in 1877, after brominating maleic acid 1.93 to form intermediate 1.94 which is dehydrohalogenated by potassium hydroxide resulting in 1.95 which was treated with excess sulfuric acid to yield compound 1.96. Compound 1.96 was then esterified with methanol and sulfuric acid to afford DMAD 1.97 (Scheme 2.1.32).

2.9.1 DMAD and isocyanides in multicomponent reactions Isocyanide-based multicomponent reactions (IMCRs) are considered as one of the special subclasses in chemical synthesis, which are extremely versatile and interesting. IMCRs possess considerable potential that lies in the diversity of bond-formations and high substrate scope. This subclass comprises of interesting chemistry, which includes a nucleophile and an electrophile reacting at the same atom, leading to formation of adducts. The reaction of isocyanides with DMAD was first described by Winterfeldt and co-workers [120] as an exceptional reaction that required more attention. The addition of neutral nucleophiles to electrophilic acceptors generates a transient zwitterion intermediate. One of the known examples of DMAD in MCRs, arises from the reaction of an isocyanide with DMAD (event 1, Scheme 2.1.33) leading to a zwitterion adduct (event 2) which might be trapped in different ways to give multiple reaction possibilities [15]. One possibility is that the zwitterion reacts with a nucleophile possessing an acidic proton [121, 122]. Under these conditions, the two possible pathways for the zwitterion (event 2) to react are shown in Scheme 2.1.33. The first step that occurs is deprotonation of the nucleophile containing an acidic proton such as C-H, N-H, O-H or SH. Subsequently,

HO

O

Br2,H2O

O

HO

O

Br

OH

KOH

O

KO2C

OH Br 1.94

1.93

MeO2C

CO2Me 1.97

Scheme 2.1.32: Preparation of DMAD.

CO2K 1.95 H2SO4 (excess)

MeOH H2SO4

HO2C

CO2H 1.96

35

2.9 The application of dimethyl acetylenedicarboxylate in organic synthesis

the anionic nucleophile may possibly attack the nitrilium ion via a 1,2 route (path A) or a 1,4 route (path B) to yield an imine 1.97 or ketenimine 1.98, respectively, as shown in Scheme 2.1.33. The second possible reaction of a zwitterion generated from an isocyanide and DMAD involves an electrophile. During this development event the electrophile 1.99 acts as a dipolarophile, where the zwitterion at event 1 initially attacks the electrophilic centre giving rise to a nucleophile which subsequently attacks the nitrilium ion. One of the examples reported by Shabaani and co-workers [123] is represented in Scheme 2.1.34. MeO2C C N

MeO2C

CO2Me

C

H

N

3 R

Nu

R

CO2Me H Nu 1.97

path A Nu-H

MeO2C MeO2C

CO2Me

CO2Me C

R

N

1

N C

2

R Nu-H

MeO2C MeO2C

CO2Me

C

Nu

N

H

C N R

4

CO2Me Nu H 1.98

path B

R

Scheme 2.1.33: Possible zwitterion reaction pathways with nucleophile.

O

O

R N

O 1.99

C

R

O N

O O C

MeO2C

O

R

MeO2C

CO2Me 1

MeO2C

CO2Me 1.100

Scheme 2.1.34: Zwitterion reaction with an electrophile.

CO2Me

N

O 1.101

36

2 The use of multicomponent reactions in chemical synthesis

2.9.1.1 Synthesis of heterocyclic scaffolds using zwitterions and nucleophiles containing acidic protons Shaabani and co-workers [124] reported the synthesis of chromene derivatives using a zwitterion adduct formed by isocyanides and DMAD to react with 2,5-dihydroxycyclohexa2,5-diene-1,4-dione 1.102 containing an acid proton (C-H) as shown in Scheme 2.1.35. The proposed mechanism proceeds by the removal of a proton from compound 1.102 at event 1 followed by 1,4-nucleophilic attack by a carbon nucleophile at event 2 to give the ketenimine. The intramolecular attack by oxygen on the ketenimine at event 3 leads to the formation of chromene 1.103, in instances where there is excess isocyanide and DMAD the same reaction proceeds on the second hydroxyl group to afford compound 1.104. Shaabani and co-workers [125] reported the one-pot three-component reaction of alkyl isocyanide 1.23 with DMAD 1.97 in the presence of triphenylphosphonium bromide O

R OH

R

O

N

N

OH

C C

MeO2C

H O MeO2C

O

CO2Me

O

1.102

1

MeO2C

O

CO2Me 2

O

MeO2C

OH

MeO2C

H

MeO2C HN

O

R

C

O

R N

1.103

MeO2C

O

HN R

R O

O

NH CO2Me

O

CO2Me

1.104

Scheme 2.1.35: Synthesis of chromene derivatives.

OH

O O 3

MeO2C

O

2.9 The application of dimethyl acetylenedicarboxylate in organic synthesis

37

1.105 which contains an acidic C-H using dichloromethane as a solvent at room temperature. The resulting six-membered heterocyclic compounds 1.106 were obtained in a yield range of 51%–63% as shown in Scheme 2.1.36. The reaction of isocyanide 1.23 with DMAD 1.97 in the presence of compound 1.107 consisting of an acidic proton (C-H) using acetonitrile as a solvent at room temperature gave rise to pyrano[2,3-c]pyrazole derivatives [126] (Scheme 2.1.37). Zangouei and co-workers [127] reported a facile one-pot synthesis method using isocyanide 1.23, DMAD 1.97 and 2,3-dihydro-5H-[1,3]thiazolo[3,2-a]pyrimidine-5,7(6H)dione 1.109 which contains an acidic C-H to afford pyranothiazolopyrimidines 1.110 using N,N-dimethylformamide as solvent at 100 °C (Scheme 2.1.38). Esmaeili and co-workers [128] reported the reaction of isocyanide 1.23 and DMAD 1.97 with 2-imino-1,3-thiazolidin-4-one 1.111 containing an acidic N-H proton and the internal nucleophile to react with the ketenimine as shown at event 3, to afford 3-oxo2,3-dihydro-5H-[1,3]thiazolo[3,2-a]pyrimidines 1.12 (Scheme 2.1.39).

CO2Me O R

N C 1.23

DMAD

DCM

Ph3P Br

1.97

Ph3P

O

O

1.105

CO2Me N

O

R 1.106

Scheme 2.1.36: Synthesis of 6-membered heterocyclic compounds from triphenylphosphonium bromide.

CO2Me

N R N C 1.23

DMAD 1.97

N Ph

MeCN

H

N

Ph

N

CO2Me

O

O 1.107

HN R 1.108

Scheme 2.1.37: Synthesis of pyrano[2,3-c]pyrazole derivatives.

N

S R

N C 1.51

DMAD 1.95

O

DMF

S

O

O

CO2Me

N

N O 1.109

Scheme 2.1.38: Synthesis pyranothiazolopyrimidine derivatives.

H R N

N

CO2Me 1.110

38

2 The use of multicomponent reactions in chemical synthesis

R

NH

R N

H C

MeO2C

N O

CO2Me

NH

N C

S

N

MeO2C

1.111

CO2Me

CO2Me

N 1.112

O

MeO2C MeO2C

CO2Me

N S

O

2

1

O

S

N S

C

NH

R N

R

HN 3

Scheme 2.1.39: 3-oxo-2,3-dihydro-5H-[1,3]thiazolo[3,2-a]pyrimidine derivatives.

Hassanabadi and co-workers [129] reported the reaction of isocyanide 1.23 and DMAD 1.97 in the presence of benzimidazole carbamate 1.113 which contains an acidic proton (N-H) and internal nucleophile to afford 2H-pyrimido[1,2-a]benzimidazoles 1.114 (Scheme 2.1.40) Ghandi and co-workers [130] reported the reaction of 3-acetyl-2Hchromen-2-ones 1.115 with a zwitterion adduct to obtain cyclopentadiene-fused chromanones 1.116 in moderate to good yield under reflux using toluene as solvent (Scheme 2.1.41). 2.9.1.2 Synthesis of heterocyclic scaffods using zwitterions and electrophiles The reaction of isocyanide 1.23 and DMAD 1.97 with phthalic anhydride 1.117 at room temperature using dichloromethane as a solvent gave rise to benzo-fused spirolactones 1.118 as shown in Scheme 2.1.42 [123].

O R

N C 1.23

DMAD 1.97

O O

N NH

O

N N

DCM

N H 1.113

N R NH 1.114

Scheme 2.1.40: Synthesis of 2H-pyrimido[1,2-a]benzimidazoles derivative.

CO2Me CO2Me

2.9 The application of dimethyl acetylenedicarboxylate in organic synthesis

MeO2C

39

CO2Me CO2Me O

MeO2C

N

N CMe3

CMe3 O O 1.115

1

O

O

CO2Me

MeO2C

O

2

CO2Me

CO2Me

MeO2C

N CMe3

NH CMe3 O

O

O O

1.116

O

3

Scheme 2.1.41: Synthesis of cyclopentadiene-fused chromanones derivative.

R N CO2Me

O R

N C 1.23

DCM

DMAD 1.97

O

O O 1.117

CO2Me

O O 1.118

Scheme 2.1.42: Synthesis of benzo-fused spirolactones derivatives.

Zhao and co-workers [131] reported the reaction of two molecules of isocyanide 1.23 and DMAD 1.97 in the presence of carbon dioxide to afford dioxospiro compounds 1.120 using toluene as a solvent at 80 °C (Scheme 2.1.43). Yavari and co-workers [132] reported the reaction of isocyanide 1.23 and DMAD 1.97 with alkyl cyanoformates 1.121 to obtain dialkyl 2-cyano-5-alkyl(aryl)imino-2-alkoxy2,5-dihydrofuran-3,4-dicarboxylates 1.122 under solvent-free conditions at room temperature as shown in Scheme 2.1.44. The use of benzoyl chloride 1.123 as an electrophile for synthesis of heterocyclic compounds by employing a zwitterion adduct arising from the reaction of DMAD and alkyl isocyanide gave rise to five-membered heterocyclic rings 1.124 (Scheme 2.1.45) as reported by Yavari and co-workers [133]. The heterocyclic products 1.124 were achieved at room temperature using dichloromethane as a solvent in a percentage yield range of 75%–82%.

40

2 The use of multicomponent reactions in chemical synthesis

R N C 1.23

DMAD 1.97

CO2Me

R N

CO2 Toluene

CO2Me

O

O 1.119

R N

CO2Me

O

CO2Me

MeO2C

O

MeO2C

N R 1.120

Scheme 2.1.43: Synthesis of dioxospiro derivatives. R

O R N C 1.23

DMAD 1.97

NC

O

1.121

CO2Me

N

CO2Me

O

R1

NC

OR1 1.122

Scheme 2.1.44: Synthesis of dialkyl 2-cyano-5-alkyl(aryl)imino-2-alkoxy-2,5-dihydrofuran-3,4-dicarboxylates derivatives.

MeO2C

O R N C 1.23

DMAD

DCM

Cl

1.97 1.123

O

CO2Me OH N R 1.124

Scheme 2.1.45: Synthesis of 5-membered heterocyclic compounds.

Hazeri and co-workers [134] used pyridine-containing carbonyl compounds 1.123a and 1.123b as electrophiles in order to react with the zwitterion adduct generated from DMAD 1.95 and alkyl isocyanides 1.51 to obtain stable 5-membered heterocyclic compounds 1.124 (90%–95%) and 1.125 (90%–95%) in excellent yields (Scheme 2.1.46).

41

2.9 The application of dimethyl acetylenedicarboxylate in organic synthesis

O

O N

N

N R 1.125a

N C 1.23

DMAD 1.97

1.125b benzene

DCM

MeO2C

H

CO2Me CO2Me

MeO2C R

N

O N

N

R

O

N H

N

1.127

1.126

Scheme 2.1.46: Synthesis of 5-membered heterocyclic compounds from pyridines.

Esmaeili and Vesalipoor [135] reported the reaction of alkyl isocyanides 1.23 and DMAD 1.97 with benzofuran-2,3-dione derivatives 1.128 to obtain γ-spiroiminolactones 1.129 in good yield under reflux using dichloromethane as solvent (Scheme 2.1.47). Nair and co-workers [136] reported the reaction of alkyl isocyanides 1.23 and DMAD 1.97 with aldehydes 1.130 to obtain 2-aminofurans 1.131 in good yields at 80 °C using benzene as a solvent (Scheme 2.1.48).

2.9.2 DMAD in Michael reactions DMAD is a strong electrophile that is considered a powerful Michael acceptor which can react with various nucleophiles (Scheme 2.1.49). The commonly known nucleophiles that react with DMAD smoothly include sulfur, nitrogen, oxygen, carbon, sulphur and phosphorus nucleophiles. There are many reactions which apply DMAD as cyclising agent to

R1

O

R1

O

DCM R

N C 1.23

DMAD 1.97

O

O O

R2 R3

1.128

Scheme 2.1.47: Synthesis of γ-spiroiminolactones.

O

R2 R3

MeO2C 1.129

N

R

CO2Me

42

2 The use of multicomponent reactions in chemical synthesis

O R N C 1.23

H

MeO2C Benzene

DMAD 1.97

CO2Me

HN

NO2

R NO2 1.130

1.131

Scheme 2.1.48: Synthesis of 2-aminofurans.

achieve heterocyclic scaffolds such as pyrimidines [137], thiazines [138] and thiazoles [139]. In these reactions DMAD initially acts as a Michael acceptor. The reaction between DMAD 1.97 and thiourea 1.133 using methanol as a solvent gives rise to thiazole-based product 1.134 as represented in Scheme 2.1.50. Generally the reaction proceeds by initial attack of sulfur on one of the alkyne carbon atoms of DMAD at event 1 to form an intermediate at event 2 leading to formation of 1,3-thiazolidin-4-ones 1.134. Compound 1.134 was reported by Hendrickson and co-workers in 91% yield using methanol as solvent while Choudhary and Peddinti [140] prepared 1.134 in water in 100% yield (Scheme 2.1.49). When DMAD 1.97 was treated with thiosemicarbazide 1.135 under microwave irradiation in the presence of methanol as a solvent or in a solvent-free system, the reaction gave rise to thiazine derivative 1.136 (Scheme 2.1.51) as reported by Heravi and co-workers [141].

MeO2C

CO2Me

RNu

RNu

MeO2C

MeO2C

1.97

RNu

CO2Me

CO2Me

(E)-1.132b

(Z)-1.132a

Scheme 2.1.49: DMAD as a Michael acceptor. O O

O O

S

O

H2N NH S

O

H2N NH2 1.133 1

O

H N

O O

O 2

Scheme 2.1.50: DMAD as a Michael acceptor with thiourea.

NH S

O 1.134

2.9 The application of dimethyl acetylenedicarboxylate in organic synthesis

MeO2C

CO2Me

H2N

1.97

H N

S

S N H

NH2

MeOH

HN

O CO2Me

N H

1.135

43

1.136

Scheme 2.1.51: DMAD as a Michael acceptor with thiosemicarbazide.

Reimlinger and co-worker [137] reported the reaction of DMAD 1.97 with 1H1,2,4-triazol-5-amine 1.137 under ethanol reflux to achieve two isomeric products of pyrimidines 1.138a and 1.138b in a yield of 29% and 10%, respectively (Scheme 2.1.52). Shah and co-workers [138] reported the reaction of DMAD 1.97 with 1H-1,2,4-triazole5-thiol 1.139 using acetonitrile as solvent at room temperature to achieve 74% of thiazine 1.140 (Scheme 2.1.53).

N MeO2C

CO2Me

O

H N NH2

EtOH

N

1.97

N

N N

O

N H

1.137

O

1.138a

O N

N

N

O

N H

O 1.138b

Scheme 2.1.52: DMAD as a Michael acceptor with 1H-1,2,4-triazol-5-amine.

O N MeO2C

CO2Me 1.97

H N

MeCN SH

N 1.139

N

N

N

Scheme 2.1.53: DMAD as a Michael acceptor towards 1H-1,2,4-triazol-5-amine.

O

S 1.140

O

44

2 The use of multicomponent reactions in chemical synthesis

2.9.3 DMAD in cycloaddition reactions As mentioned earlier DMAD is extensively used as a dienophile in cycloaddition reactions. Importantly DMAD is used as a standard in Diels–Alder reactions to check the efficiency of various dienes and 1,3-dipolar cycloaddition reactions. DMAD is known to undergo [2 + 2] cycloaddition reactions [142] and 1,3-dipolar cycloaddition with diazoalkanes, nitrile oxide and carbonyl ylides [143]. The reaction of diene 1.141 with DMAD 1.97 via [2 + 2] cycloaddition gives rise to product 1.142 which subsequently undergoes ring opening to give product 1.143 (Scheme 2.1.54). This is one of the valid approaches for constructing π-conjugated systems. Uršič and co-workers reported the synthesis of butadienes 1.145 using microwaveassisted regiospecific [2 + 2] cycloadditions of DMAD and diene derivatives 1.144 (Scheme 2.1.55).

R

N

R

H

DMAD

R

N

CO2Me

N ring opening

[2 + 2] R

1.141

H

CO2Me

R R

CO2Me

CO2Me cis, trans-diene

1.142

1.143a or

N R

CO2Me

R

CO2Me

cis, cis-diene 1.143b

Scheme 2.1.54: DMAD in [2 + 2] cycloaddition reaction.

MeO2C CO2Me N

NHR 1.144

CO2Me

DMAD MeCN MW

CO2Me N

N 1.145

Scheme 2.1.55: DMAD in [2 + 2] cycloaddition under MW conditions.

2.10 Conclusions

45

One of the facile approaches for the construction of aromatic heterocyclic systems relies on the [4 + 2] cycloaddition reaction of DMAD and the 1,3-dienes, followed by an oxidation. As an examples Kotha and co-workers [144] reported the Diels–Alder reaction of the diene 1.146 with DMAD leading to the formation of product 1.147 which is subsequently oxidised to give rise to cycloadduct 1.148 (Scheme 2.1.56).

OAc

OAc CO2Me

DMAD Toluene MeO2C

NHAc

CO2Me AcHN

1.146

CO2Me 1.147

OAc CO2Me

DDQ Benzene AcHN

CO2Me CO2Me 1.148

Scheme 2.1.56: DMAD in [4 + 2] cycloaddition reaction.

Resvanian and co-workers [145] had recently described with mechanistic details the use of DMAD in the synthesis of a variety of five-, six-and seven-membered compounds, spiro and fused heterocycles.

2.10 Conclusions To date MCRs approach remains one of the alternative route for synthesis and preparation of desired products since were discovered back in 1850 by Strecker. MCRs are recommended to address challenges such as: atom economy, reducing the reaction time during chemical synthesis and environment awareness (green chemical production). The well-known IMCRs are Passerini and Ugi reactions, which rely on the dual electrophilic/nucleophilic reactivity of the isocyanide carbon to provide distinct complex products. The van Leusen 3-CR uses α-substituted tosylmethyl isocyanide (TosMIC) as a different to isocyanides used in Passerini and Ugi reactions to form products. Another interesting reaction occurs when the isocyanides react with DMAD to give rise to the zwitterion adducts. The background research about Passerini reactions and its mechanism has suggested various intermediates including carbocations, hemiacetals and hydrogen-bonded adducts. However, the original Passerini reaction mechanism is generally considered to be a concerted reaction, especially in apolar solvents. The definitive Ugi reaction is a four-component reaction (U-4CR) where the imine is activated by the carboxylic acid proton and react with isocyanide to form nitrilium intermediate which react with conjugate base of the carboxylic acid and this is followed by the Mumm rearangement to

46

2 The use of multicomponent reactions in chemical synthesis

give the final product. The van Leusen 3-CR gives rise to imidazole by using α-substituted tosylmethyl isocyanide (TosMIC) to react with primary amine and an aldehyde. In addition when isocyanides react with DMAD generates a transient zwitterion intermediate which is then trapped in different ways to give desired products.

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133. Yavari I, Mokhtarporyani-Sanandaj A, Moradi L, Mirzaei A. Reaction of benzoyl chlorides with Huisgen’s zwitterion: synthesis of functionalized 2,5-dihydro-1H-pyrroles and tetrasubstituted furans. Tetrahedron 2008;64:5221–5. 134. Hazeri N, Maghsoodlou MT, Habibi-Khorassani SM, Marandi G, Khandan-Barani K, Ziyaadini M, et al. Synthesis of novel 2-pyridyl-substituted 2,5-dihydro-2-imino- and 2-amino- furan derivatives via a three component condensation of alkyl isocyanides and acetylenic esters with di-(2-pyridyl) ketone or 2pyridinecarboxaldehyde. ARKIVOC 2007;i:173–9. 135. Esmaeili AA, Vesalipoor H. Reaction of isocyanides, dialkyl acetylenedicarboxylates, and α-keto lactones: Unexpected participation of an ester carbonyl group in the isocyanide-based three-component reaction. Synthesis 2009;10:1635–8. 136. Nair V, Vinod AU, Abhilash N, Menon RS, Santhi V, Varma RL, et al. Multicomponent reactions involving zwitterionic intermediates for the construction of heterocyclic systems: one pot synthesis of aminofurans and iminolactones. Tetrahedron 2003;59:10279–86. 137. Reimlinger H, Jacquier R, Daunis J. Synthesen mit heterocyclischen Aminen, VI Weitere Synthesen von 7-Oxo-7.8-dihydro-S-triazolo [4.3-a] pyrimidinen. Chem Ber 1971;104:2702–8. 138. Shah TA, Ahmad Z, Mir NA, Muneer M, Rath NP, Ahmad M. One step synthesis of highly functionalized thiazolo[3,2-b] [1,2,4]triazole, triazolo[1,5-a]pyrimidine and triazolo[3,4-b] [1,3,4]thiadiazine. RSC Adv 2015;5:107931–7. 139. El-Borai MA, Rizk HF, Ibrahim SA, El-Sayed HF. Microwave assisted synthesis of fused thiazoles in multicomponent system and their in vitro antitumor, antioxidant, and antimicrobial activities. J Heterocycl Chem 2017;54:1031–41. 140. Choudhary G, Peddinti RK. An efficient solvent-tuning approach for the rapid synthesis of thiazolidinone derivatives and the selective synthesis of 2-amino-4H-1,3-thiazin-4-one and dimethyl 3,3′-thiodiacrylates. Tetrahedron Lett 2014;55:5597–600. 141. Heravi MM, Nami N, Oskooie HA, Hekmatshoar R. Phosphorus, sulfur silicon relat. Elements 2006;181: 87–91. 142. Cook AG, Structure and physical properties of enamines. In: Cook AG, editor. Enamines: synthesis, structure, and reactions, 2nd ed. New York: Dekker; 1988:384 p. 143. Paquette LA, Shen CC. Isodicyclopentadienes and related molecules. 49. Synthesis, static structure, and kinetic stability of a syn-sesquinorbornatriene. J Am Chem Soc 1990;112:1159–64. 144. Kotha S, Halder S, Brahmachary E. Synthesis of highly functionalized phenylalanine derivatives via crossenyne metathesis reactions. Tetrahedron 2002;58:9203–8. 145. Rezvanian A, Khodadadi B, Tafresh S. Use of dialkyl acetylenedicarboxylates in the multicomponent synthesis of heterocyclic structures. ChemistrySelect 2022;7:e202202360.

Abdul Nashirudeen Mumuni*, John McLean and Gordon Waiter

3 Spectral peak areas do not vary according to spectral averaging scheme used in functional MRS experiments at 3 T with interleaved visual stimulation Abstract: Brain response to visual stimulation can be probed quantitatively using functional magnetic resonance spectroscopy (fMRS), which relies on the blood oxygenation level dependent (BOLD) contrast mechanism. BOLD effect in fMRS is associated with changes in the areas, widths, and heights of the MR spectra. This study investigated the effect of spectral averaging scheme (NEX value) on BOLD changes in the spectra. Using a visual stimulus at 8 Hz in single and interleaved stimulation paradigms, the BOLD effects in spectra acquired from the occipital brain region of three healthy volunteers (mean age ± SD = 32.7 ± 3.5 years) were compared for two fMRS data sets acquired with two NEX values (“2” and “8”) available on a 3 T MR scanner. BOLD signal changes were estimated as percentage changes in spectral areas, heights, and widths of six cerebral metabolites and water using the SAGE software package (version 7). There was a general trend of lower BOLD effects with NEX = 8 in both stimulation paradigms. In the single stimulation paradigm, NEX = 8 was associated with significantly lower N-acetyl aspartate (NAA) spectral height (p = 0.03), creatine (p = 0.04) and choline (p = 0.02) spectral widths, and NAA (p = 0.03), water (p < 0.01), and glutamate (p = 0.02) spectral areas. In the interleaved stimulation paradigm, NEX = 8 was associated with significantly lower glutamate spectral height (p = 0.02), water (p = 0.03), and glutamine (p = 0.03) spectral widths, but there was no significant difference in all spectral areas between the two NEX values. Even though the two NEX values offered some differences in observable BOLD effects, their spectral areas were not significantly different in the interleaved visual stimulation experiments. Keywords: BOLD; brain; functional MRS; metabolites; spectroscopy.

*Corresponding author: Abdul Nashirudeen Mumuni, Department of Medical Imaging, University for Development Studies, Tamale, Ghana, E-mail: [email protected]. https://orcid.org/0000-0003-4697-0654 John McLean, Department of Clinical Physics and Bioengineering, Queen Elizabeth University Hospital, Glasgow, UK Gordon Waiter, Aberdeen Biomedical Imaging Centre, University of Aberdeen, Aberdeen, UK As per De Gruyter’s policy this article has previously been published in the journal Physical Sciences Reviews. Please cite as: A. N. Mumuni, J. McLean and G. Waiter “Spectral peak areas do not vary according to spectral averaging scheme used in functional MRS experiments at 3 T with interleaved visual stimulation” Physical Sciences Reviews [Online] 2023. DOI: 10.1515/psr-2022-0301 | https://doi.org/10.1515/9783111328416-003

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3 Spectral peak areas do not vary according to spectral averaging scheme

3.1 Introduction Visual perception in humans is associated with elevated neuronal response, which is linked to increased oxygenated blood consumption and metabolic rate of occipital brain cells involved in the process [1]. In the presence of a magnetic field, the increased oxygenated blood circulation in the activated brain cells causes magnetic susceptibility effects that extend beyond the activated neuronal region [2]. This susceptibility signal offers blood oxygenation level dependent (BOLD) contrast in magnetic resonance imaging (MRI) [3] and spectroscopy (MRS) [4, 5], and the techniques used in acquiring this information from the activated neurons are called functional MRI (fMRI) and functional MRS (fMRS), respectively. In functional MRI, the activated neurons appear in images as high-intensity image pixels [3]. In functional MRS, however, this BOLD effect is associated with increased heights and areas, and decreased widths of the MR spectra in the frequency domain [6–8]. The theory relating to functional MRS is described in detail elsewhere [9]. Functional MRS (fMRS) studies using similar data acquisition protocols are expected to report comparable BOLD changes, assuming high data signal-to-noise ratio (SNR). However, the number of radiofrequency excitations (NEX) used for data acquisition has so far not been investigated by any previous study, to the best of our knowledge. The NEX value specifies the spectral averaging scheme for the recorded MRS data before it is processed. It is, however, unclear if varied NEX values in fMRS data acquisition could affect the BOLD characteristics observed in the spectra, particularly on the spectral peak area. The spectral peak area is particularly important for quantitative MRS studies where metabolite concentrations are estimated from their respective peak areas [10]. In this estimation, the area of the water peak is commonly used as a reference standard and is thus expected to remain fairly constant [11, 12]. The NEX value set for a functional MRS data acquisition regulates the period between consecutive spectral data lines recorded; this, therefore, defines the total number of data points that will be saved over the total data acquisition period: N Total = 16/NEX + NSA/NEX

(3.1)

where NTotal is the total number of spectral data points saved, NSA is the number of signal averages, (16/NEX) is the number of water spectral data points, and (NSA/NEX) is the number of neuro-metabolite spectral data points. The MRS pulse sequence installed on the scanner acquires 16 water data points (i.e., NSA = 16) before it acquires the neuro-metabolite data points . The values of NEX can alternate between 2 and 8 on the scanner. NEX = 8 is the default value and is commonly used in routine MRS studies. NEX = 2 on the other hand yields more lines (Equation (3.1)) in the data frame to allow for a better time-resolved data.

3.2 Methodology

55

In this functional MRS study, BOLD effects were recorded for the spectra of six metabolites and water using an external visual stimulation of the occipital brain region. The objective was to compare the BOLD effects on the spectra using NEX = 2 and NEX = 8 in separate acquisitions on the same volunteers scanned on a 3 T MRI/MRS magnet.

3.2 Methodology 3.2.1 Study volunteers The protocol for this study was approved by the West of Scotland Research Ethics Committee 4 (WoSREC4) as part of a larger study. Three healthy volunteers (2 males, 1 female) with an average age ± standard deviation (SD) of 32.7 ± 3.5 years were recruited into the study, following informed written consent to participate in the study. All volunteers were screened for contraindicated MRI implants and neurological/psychiatric conditions.

3.2.2 Presentation of visual stimulus An 8 Hz black/white color-reversal checkerboard visual display was created for visual stimulation. A completely dark screen (Figure 3.1a) was shown at baseline while 8 reversals per second between black and white squares (Figure 3.1b) was displayed to cause stimulation. In the baseline and activation videos, a small fixation cross was placed in the center of the display. Volunteers focused on this fixation cross while data were being collected, and this ensured that they remained attentive and focused on the same position on the screen over the whole duration of data acquisition. Lightening in the scanner room was turned off while the video of the visual stimulus was played on a laptop computer with Windows Media Player (Microsoft Corporation,

Figure 3.1: Black background (a) and 8 Hz flashing black/white checkerboard (b) displays for the functional MRI/MRS studies.

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3 Spectral peak areas do not vary according to spectral averaging scheme

USA, 2006). Volunteers watched the video display through a bifocal lens system (Nordic Neurolabs, NNL; https://www.nordicneurolab.com/product/fmri-acquisition) connected to the head coil in a vertical position close to the volunteer’s eyes. Adjustments were made to the lenses to ensure that a single, sharp image was projected to each volunteer. Connecting wires linked the lens system to the laptop.

3.2.3 Functional MRI Functional MRI (fMRI) and MRS (fMRS) data were collected on a 3 T General Electric Signa HD MRI/MRS scanner (software version 12.5; Milwaukee, WI, USA) equipped with an eight-channel receive-only head coil. Prior to the fMRS data acquisition, fMRI experiments were conducted to delineate the visual cortex accurately. This guided the accurate placement of the fMRS voxel in the exact activated brain area to capture the maximum BOLD signal. The single-shot gradient echo-planar imaging (GRE-EPI) sequence was used to collect the fMRI volumes. In a total of 3-minute data acquisition time, three 30 s rest scans and three 30 s stimulation scans were performed in an interleaved manner. The fMRI data acquired composed of four dummy scans followed by ten actual data sets collected in the six consecutive scan durations (thus, 60 image data sets were collected in total from each volunteer). The proprietary software of the scanner (Brainwave RT) automatically generated activation maps within the occipital brain region (Figure 3.2) in real time, along with on-screen time course plots (Figure 3.3) of the visual activation process. This information subsequently guided the fMRS voxel localization process during the fMRS studies.

Figure 3.2: fMRS voxel localized within the occipital brain region in the axial (a) and sagittal (b) views.

3.2 Methodology

57

Figure 3.3: Time course analysis plot automatically generated by Brainwave RT during the fMRI scans. The gray bars denote the activation periods; the white bars denote the rest periods.

3.2.4 Functional MRS The PRESS pulse sequence (Figure 3.4) that was used in the fMRS experiments collects 8 dummy scans at the start and then 16 scans (NSA = 16 in Equation (3.1)) of unsuppressedwater spectral lines, and then a set number of metabolite spectral averages (NSA/NEX part of Equation (3.1)). Sequence adjustments and shimming of voxel edges take place during the dummy scans but no MRS data is collected. The 8 dummies plus the 16 water spectral lines constitute a total of 24 averages prior to actual MRS data collection. Considering this nondata acquisition period for the 24 averages, the total MRS data acquisition time will be: T acq = TR × (24 + NSA)

(3.2)

where TR is the repetition time of the pulse sequence (which was 3 s in all experiments). Visual stimulation time (Tsti) was synchronized with the actual data acquisition time (i.e., the TR × NSA part of Equation (3.2)) in the following equation: T sti = T acq – 72 s

(3.3)

According to Equation (3.3), in theory, the PRESS sequence is expected to begin 72 s before visual stimulation. In implementation, however, a dark screen display with a white fixation cross was shown 9 s before the commencement of the sequence (so data was collected for 63 s during the rest period). The 9-s deadtime was implemented for two reasons [13]. Firstly, it minimized the effect of data contamination, which arises when the volunteer flinches at a sudden change in the lighting level. Secondly, turbulence in the flow of blood through the neurons reduces to stable flow in about 9 s as blood volume supply is varied due to change in lightening conditions. This momentary response occurs within 6.5–8 s for increased blood flow, which then stabilizes within 7–9 s [13, 14]. Two paradigms were created for visual stimulation: the first paradigm consisted of successive rest and stimulation displays of the same durations in separate scan sequences (Table 3.1a); the second paradigm consisted of three rest and three stimulation displays of the same duration in the same scan sequence (Table 3.1b). The fMRS protocol involved the use of the PRESS sequence (Figure 3.4) with the following data acquisition parameters: CHESS module to eliminate water signals;

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3 Spectral peak areas do not vary according to spectral averaging scheme

Figure 3.4: The standard Point RESolved Sprectroscopy (PRESS) sequence. Definitions: TE = echo time; CHESS = CHEmical Shift Selective excitation module for suppression of signals from water; Gx, Gy, and Gz = magnetic field gradients in the scanner’s x, y, and z axes, respectively; GC1 − GC4 = crusher gradients flanked to the 180° refocusing pulses to suppress signals from outside of the voxel of interest (red box); larger box = MR image of the brain; FT = Fourier transform of the raw FID into MR spectrum. Table .: Number of signal averages (NSA), visual stimulation (Tsti) and fMRS data acquisition (Tacq) periods used for the single (a) and interleaved (b) stimulation paradigms.

Tacq was calculated from Equation (.) using the respective NSA values shown in the single (a) and interleaved (b) stimulation paradigm tables, while Tsti was calculated from Equation (.) by subtracting  from the calculated Tacq values from Equation (.). Values in the total column of the interleaved stimulation Table (b) are divided by  to get the values for each block.

59

3.2 Methodology

prescribed voxel dimensions of 20 S/I × 20 A/P × 30 R/L mm3; TE = 23 ms; TR = 3000 ms; spectral width = 5000 Hz; NSA = 64 and 192 for the single and interleaved stimulation paradigms, respectively. After one complete set of experiments setting NEX = 2 in the above pulse sequence, the whole experiment was repeated setting NEX = 8 while the patient remained in the scanner in the same position. In each set of NEX experiments, water-suppressed data containing the metabolite spectra were acquired initially, followed by a second run of the same sequence to acquire the water spectra but in the absence of the CHESS module. Six metabolite spectra were recorded namely, N-acetyl aspartate (NAA), glutamate, glutamine, creatine, choline, and myo-inositol.

3.2.5 Spectral analysis The BOLD information, NBOLD, was recorded in the (NSA/NEX) part of Equation (3.1): N BOLD = NSA/NEX

(3.4)

In the single stimulation paradigm, using NEX = 2 and NSA = 64 yielded NBOLD = 32 spectral lines while using NEX = 8 and NSA = 64 yielded NBOLD = 8 spectral lines. In the interleaved stimulation paradigm, using NEX = 2 and NSA = 192 resulted in NBOLD = 96 spectra (each of the 6 blocks contained 16 averages) while using NEX = 8 and NSA = 192 yielded NBOLD = 24 spectra (each of the 6 blocks contained 4 averages). These acquired spectral averages were analyzed and quantified as follows. Spectra in the time domain from each channel of the head coil were corrected for eddy currents, combined, Fourier transformed into frequency-domain peaks, and corrected for phase and baseline distortions. Quantification of the heights, areas, and widths of the spectra involved manual selection of the frequency domain spectral peaks to be quantified. Lorentzian lineshapes were fitted to the peaks of interest using the Levenberg–Marquardt (LM) method of nonlinear least squares minimization [15] to yield the heights, linewidths, and areas of the selected peaks. The “local” LM method was used to produce values of the percentage changes in the spectral heights, widths, and areas that were used to plot time course graphs over the cycle duration (Figures 3.6 and 3.7). The “global” LM method was used to quantify the spectral peak heights, widths, and areas for the rest and stimulation periods that were used to quantify the BOLD effects on the spectra (Tables 3.2–3.3).

3.2.6 Quantification of the BOLD effects on the spectra The BOLD effect on a given spectral peak can be observed as a difference in height, area, and width between the rest and stimulus conditions as shown in Figure 3.5a–b. BOLD changes in spectral height, area, and width were quantified from: %ΔY = [(Y stimulation − Y rest )/Y rest ] × 100%

(3.5)

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3 Spectral peak areas do not vary according to spectral averaging scheme

(a)

(b)

Figure 3.5: Discernible height difference in the water spectral peak (a) and BOLD effect on the metabolite spectra (b) resulting from visual stimulation.

where Ystimulation and Yrest represent the spectral height, area, and width related to the visual stimulation and rest scans, respectively, determined from the “global” Levenberg– Marquardt scheme.

3.2.7 Statistical analysis The paired t-test was used to compare percentage changes in spectral heights, widths, and areas between NEX = 2 and NEX = 8 at a critical value of p < 0.05. Statistical analysis was conducted using the Minitab software package (version 16, Minitab Inc., State College, Pennsylvania, USA).

3.3 Results 3.3.1 Single stimulation experiments The time course plots showing the response behavior of cerebral water with NEX = 2 and NEX = 8 in the single stimulation paradigm are shown in Figure 3.6a–b. The 64 signal averages collected in the single stimulation experiments yielded 32 data points with NEX = 2 (Figure 3.6a) and 8 data points with NEX = 8 (Figure 3.6b) plotted for each of the rest and stimulation periods. The average percentage changes in the heights, widths, and areas of the spectral peaks recorded from the three volunteers are shown in Table 3.2.

(a)

(b)

Figure 3.6: Time course plot of BOLD effect on cerebral water peak height in the single stimulation paradigm when NEX = 2 (a) versus NEX = 8 (b).

Table .: Average (n = ) percentage changes in spectral peak heights, widths, and areas in the observed peaks during single visual stimulation. Peak

Water NAA Glutamate Glutamine Creatine Choline Myo-inositol

% Height increase ± SD

% Width decrease ± SD

% Area increase ± SD

NEX = 

NEX = 

p value

NEX = 

NEX = 

p value

NEX = 

NEX = 

p value

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

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

. .a . . . . .

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

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

. . . . .a .a .

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

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

. in all cases).

3.3 Results

63

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3 Spectral peak areas do not vary according to spectral averaging scheme

Percentage increase in all peak areas were higher with NEX = 2 than with NEX = 8, except for the NAA, glutamate, and myo-inositol peaks. No spectral peak area change differed significantly between the two NEX values.

3.4 Discussion This study aimed to compare the amounts of BOLD effects on MR spectra recorded in fMRS experiments using two different spectral averaging schemes, defined by the NEX value incorporated in the MRS pulse sequence. Using single and interleaved visual stimulation paradigms, comparisons were made between NEX values of 2 and 8 at a field strength of 3 T. The BOLD effect associated with neural stimulation causes T2* alterations in the MR spectra [6], resulting in narrowing of their spectral peak widths. This effect is particularly more discernible on the water, NAA, and creatine peaks [16] with the single stimulation paradigm using any of the NEX values (Tables 3.2–3.3). The spectral averaging scheme (NEX) used for fMRS experiments has no direct relationship with neural activation, but it affects the amount of BOLD effect captured in the fMRS data. In the single stimulation paradigm, acquisitions in the rest and stimulation sessions were carried out in a discontinuous fashion. After the no-stimulation scan, shimming was conducted again prior to the stimulation scan. The advantage of this process was that it offered the volunteer some period in-between the scans, and this enhanced their tolerance for the stimulus with associated reduced movement and habituation effects [14]. Another advantage was that consecutive fMRS data for the separate scans were not averaged together; therefore, the fMRS data in the single stimulation paradigm gave higher estimates of the BOLD effects than the estimates for the interleaved stimulation paradigm. In the interleaved paradigm, during a continuous MRS scan, consecutive rest and stimulation videos were displayed in an interleaved fashion. The prolonged scan period could have led to volunteer fatigue or reduced volunteer attentiveness to the visual stimulus [13]. The manner in which the data were collected could have caused BOLD signal saturation or infiltration of stimulation data into the interleaved rest data, and this could lead to underestimation of the BOLD effects. Consistent with previous studies [6, 7, 17], the number of signal averages (NSA) was varied between the single (NSA = 64) and interleaved (NSA = 192) paradigms to reduce the effect of volunteer fatigue. It was, however, observed that percentage changes in the spectral peak areas, widths, and areas were generally higher in the single than in the interleaved paradigms for both NEX values (Tables 3.2–3.3). NEX = 8 offers half the number of spectral lines compared to NEX = 2 that can be processed to yield the spectra. This, therefore, means that each line of data acquired with NEX = 8 is an average of a number of spectral lines as much as twice the number of spectral lines with NEX = 2. The likely event is that while the scanner averages the spectral lines with NEX = 8, it may either miss recording some BOLD information or combine

3.4 Discussion

65

rest data captured at the end of the rest scan and BOLD data at the beginning of the stimulation scan (particularly in the interleaved paradigm). This averaging gives a poorer temporal resolution over a 6-s period needed to acquire each spectral line and, therefore, leads to underestimation of the BOLD effect observed when NEX = 8. In a functional MRS study performed at 4 T using an interleaved stimulation paradigm with 8 Hz visual display [6], 20 spectral averages were collected in five successive scans (three rest scans interleaved with two stimulation scans), which yielded 100 spectral averages. From six volunteers, average (SE) percentage height increase of the water peak was 3.0 (0.4) % and width decrease was 2.3 (0.3) %; area change was not reported. Another fMRS study at 3 T used both single and interleaved stimulation paradigms at 6 Hz visual stimulation [7]. The authors [7] collected 128 spectral averages in the single stimulation paradigm of the rest and stimulation sessions; in the interleaved stimulation paradigms, 32 spectral averages were collected for eight successive scans (for interleaved periods of four rests and four stimulation scans), which resulted in 256 spectral averages. From five volunteers, average (SE) percentage height increase of the water peak was 3.0 (0.5) %, width decrease was 0.6 (0.1) %, and area increase was 0.8 (0.3) %. The calculated changes in height and width were found to be equal for the two paradigms, but the authors did not report on specific paradigm associated with estimated change in peak area. Only the decrease in spectral width using NEX = 2 in the interleaved paradigm (Table 3.3) in this study (2.10 (0.33) %) compared with the width decrease (2.3 (0.3) %) reported by Zhu and Chen [6]. All other average changes in the water peak reported in this study were higher in the single but lower in the interleaved stimulation paradigm than the literature estimates [6, 7]. Using an interleaved stimulation paradigm, Shih et al. [7] observed concentration changes of 2.7%, 2.3%, and 6.0% of NAA, creatine, and choline. Even though this study did not calculate concentration changes of the metabolites, the percentage changes due to the BOLD effect should still be comparable [8]. Compared to estimates reported by Shih et al. [7], calculated BOLD changes in the interleaved stimulation paradigm in this study were lower for the three metabolites; however, percentage change in the area (Table 3.3) of only the creatine peak using NEX = 2 (2.02 (0.63) %) compared with the concentration change (2.3%) reported by Shih et al. [7]. From their interleaved stimulation paradigm, Zhu and Chen [6] estimated percentage height increases of 2.5 (0.6) % and 3.1 (0.7) %, and decreases in spectral widths of 1.7 (0.5) % and 1.8 (0.5) % for NAA and creatine. Estimates from this study were relatively lower compared to those of Zhu and Chen [6]; only the width estimates for creatine for both NEX values were comparable to the literature estimates [6, 7]. The version of the PRESS sequence used by Shih et al. [7] recorded the water and metabolite data at the same time at TE = 30 ms. The clinical PRESS sequence was used in this study to collect the water and metabolite data separately at TE = 23 ms. Zhu and Chen [6] used the same TE as this study. The disagreements in results among the three studies cannot emanate from the different TE values or from the arrangement and number of rest and stimulus displays [8]. However, the discrepancies in the results could arise possibly from the influence of field strength [6], stimulus rate [18] habituation effects due

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3 Spectral peak areas do not vary according to spectral averaging scheme

to long visual stimulation periods (as was the case in this study) [13, 14], and inherent response differences by the volunteers to the visual stimulus [19]. Particularly at field strengths above 3 T, the susceptibility gradient related to changes in T2* increases, resulting in increased BOLD effects. The generally significant intervolunteer variability reported elsewhere [19] may be due to vascular architectural differences and blood volume changes in the brain associated with neuronal stimulation [20, 21], in addition to variations in how well the volunteers could tolerate the stimulus. Using a 7 T MRI/MRS scanner, Mangia et al. [17] studied the BOLD effect using two volunteers and reported the following percentage concentration changes due to the BOLD effect: NAA = 1.2%, glutamate = 1.1%, glutamine = 0.1%, creatine = 0.8%, choline = 1.4%, and myo-inositol = 1.0%. Both spectral height and area are directly proportional to metabolite concentrations. The percentage height increases of NAA, glutamine, choline, and myo-inositol with NEX = 8 in the interleaved stimulation paradigm in this study (Table 3.3) compared with the above estimates. All other estimates in this study were higher, particularly for NEX = 2. Another fMRS study at 7 T [17] found a 3.0 (1.0) % increase in glutamate concentration (from twelve volunteers) using the single stimulation paradigm. Their rest and stimulation periods had durations of 2.7 and 5.3 minutes; however, this study used equal durations of 3.2 minutes for both rest and stimulation periods and found 4.55 (1.78) % change in glutamate peak height with NEX = 2 (the NEX = 8 peak height was significantly lower). Glutamate peak area changes were relatively higher and lower with the NEX = 2 and NEX = 8 values, respectively (Table 3.2), than the average published by Mangia et al. [17]. It is, however, estimated elsewhere [22] that stimulation-induced changes in glutamate could range between 2 and 11% at 7 T, regardless of the stimulation paradigm used. This establishes a basis to conclude that the estimated BOLD changes for glutamate in this study (Tables 3.2–3.3) are within the expected range, though a relatively lower field strength (3 T) was used for the study. This study did not find significant differences in all spectral peak area changes between the two NEX values in the interleaved stimulation paradigm. NEX = 2 BOLD changes were, however, consistently higher. The NEX = 8 on the other hand is the default spectral averaging scheme for routine MRS studies and offers an advantage of quicker spectral averaging in the time domain. This reduces spectral distortion, which can potentially occur when the volunteer moves during data acquisition. Thus, the results show that even though the two NEX values offer some differences in observable BOLD effects, the spectral area changes with them are not significantly different in interleaved visual stimulation experiments.

3.5 Conclusions To the best of our knowledge, our study is the first to establish that both conventional (NEX = 8) and unconventional (NEX = 2) spectral averaging schemes are associated with

References

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the same change patterns in spectral peak areas of cerebral water and metabolites in interleaved functional MRS experiments at 3 T.

References 1. Blockley NP, Griffeth VE, Simon AB, Buxton RB. A review of calibrated blood oxygenation level-dependent (BOLD) methods for the measurement of task-induced changes in brain oxygen metabolism. NMR Biomed 2013;26:987–1003. 2. Malonek D, Grinvald A. Interactions between electrical activity and cortical microcirculation revealed by imaging spectroscopy: implications for functional brain mapping. Science 1996;272:551–4. 3. Bandettini PA. Twenty years of functional MRI: the science and the stories. Neuroimage 2012;62:575–88. 4. Hennig J, Speck O, Deuschl G, Feifel E. Detection of brain activation using oxygenation sensitive functional spectroscopy. Magn Reson Med 1994;31:85–90. 5. Malonek D, Grinvald A. Interactions between electrical activity and cortical microcirculation revealed by imaging spectroscopy: implications for functional brain mapping. Science 1996;272:551–4. 6. Zhu XH, Chen W. Observed BOLD effects on cerebral metabolite resonances in human visual cortex during visual stimulation: a functional 1H MRS study at 4 T. Magn Reson Med 2001;46:841–7. 7. Shih YY, Huang CJ, Büchert M, Chung HW, Liu YJ. INS-PRESS for functional MRS: simultaneous with-and without-water suppression spectral acquisition on visual cortex of human brains at 3T. Proc Int Soc Magn Reson Med 2009;17:1–7. 8. Mangia S, Tkac I. Dynamic relationship between neurostimulation and N-acetylaspartate metabolism in the human visual cortex: evidence that NAA functions as a molecular water pump during visual stimulation. J Mol Neurosci: MN 2008;35:245–8. 9. Mumuni AN, McLean J. Dynamic MR Spectroscopy of brain metabolism using a non-conventional spectral averaging scheme. J Neurosci Methods 2017;277:113–21. 10. Mumuni AN. Investigation of brain tissue water NMR response by optimised quantitative single-voxel proton magnetic resonance spectroscopy. Doctoral dissertation, University of Glasgow; 2013:350 p. 11. Mumuni AN, McLean J, Krishnadas R, Lopez-Gonzalez MR, Cavanagh J, Condon B. Assessment of brain water content in peripheral inflammation by an optimized single-voxel MR spectroscopy quantitation technique. In: Lhotska L, Sukupova L, Lacković I, Ibbott GS, editors World congress on medical physics and biomedical engineering 2018. IFMBE proceedings, vol 68. Singapore: Springer; 2019:91–6 pp. 12. Mumuni AN, Krishnadas R, Cavanagh J. Quantitative magnetic resonance spectroscopy with in situ acquisition of metabolite and reference unsuppressed-water signals from the human brain: implication for studies of psoriatic arthritis. Afr J Med Phys 2021;3:14–23. 13. Condon B, McFadzean R, Hadley DM, Bradnam MS, Shahani U. Habituation-like effects cause a significant decrease in response in MRI neuroactivation during visual stimulation. Vis Res 1997;37:1243–7. 14. DeYoe EA, Bandettini P, Neitz J, Miller D, Winans P. Functional magnetic resonance imaging (FMRI) of the human brain. J Neurosci Methods 1994;54:171–87. 15. Lourakis MI. A brief description of the Levenberg-Marquardt algorithm implemented by levmar. Found Res Technol 2005;11:1–6. 16. Mangia S, Tkáč I, Gruetter R, Van de Moortele PF, Maraviglia B, Uğurbil K. Sustained neuronal activation raises oxidative metabolism to a new steady-state level: evidence from 1H NMR spectroscopy in the human visual cortex. J Cerebr Blood Flow Metabol 2007;27:1055–63. 17. Mangia S, Tkáč I, Gruetter R, Van De Moortele PF, Giove F, Maraviglia B, et al. Sensitivity of single-voxel 1H-MRS in investigating the metabolism of the activated human visual cortex at 7 T. Magn Reson Imag 2006;24:343–8.

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18. Fox PT, Raichle ME. Stimulus rate determines regional brain blood flow in striate cortex. Ann Neurol 1985; 17:303–5. 19. Mumuni AN, Mclean J. Functional proton magnetic resonance spectroscopy of cerebral water and metabolites using eight radiofrequency excitations at 3.0 Tesla. EC Proteomics Bioinf 2017;1:7–18. 20. Ugurbil K, Xiaoping H, Wei C, Zhu XH, Kim SG, Georgopoulos A. Functional mapping in the human brain using high magnetic fields. Philos Trans R Soc Lond Ser B Biol Sci 1999;354:1195–213. 21. Weisskoff R, Zuo CS, Boxerman JL, Rosen BR. Microscopic susceptibility variation and transverse relaxation: theory and experiment. Magn Reson Med 1994;31:601–10. 22. Lin Y, Stephenson MC, Xin L, Napolitano A, Morris PG. Investigating the metabolic changes due to visual stimulation using functional proton magnetic resonance spectroscopy at 7 T. J Cerebr Blood Flow Metabol 2012;32:1484–95.

Adeleke Adeniyi*, Mayowa Ibidokun and Ojo Oluwole

4 A comparative assessment of potentially harmful metals in the Lagos Lagoon and Ogun river catchment Abstract: Metals are one of the most common pollutants of surface water around the world. The anthropogenic contribution to aquatic metal pollution is of global concern. This study investigates the levels of Ca, Cd, Cu, Fe, Mn, Pb, Ni and Zn in surface water and sediments in the Lagos lagoon (Ibafon-Apapa, University of Lagos waterfront, UWF) and Ogun river catchment at Agiliti-Ketu. The metal concentrations were determined using atomic absorption spectrophotometry. Ibafon-Apapa generally, accounted for the highest concentrations of metals in both water and sediment samples. The ranges of concentrations (µg/g) of metals in sediment samples were: 0.08 ± 0.05–2140.64 ± 1981.54, ND-7.19 ± 7.32, 4.59 ± 2.46–78.95 ± 49.15, 3276.22 ± 2059.57–25,307.60 ± 8759.66, 255.73 ± 98.54–4651.0 ± 1672.60, 19.84 ± 10.23–228.50 ± 84.17, 4.51 ± 4.02–24.45 ± 22.78 for Ca, Cd, Cu, Fe, Mn, Pb and Zn, respectively. Ni occurred in the water and sediment samples below the detection limit. The water and sediments samples from UWF have mean pH values of 7.71 ± 0.07 and 6.61 ± 0.40, respectively, which are higher than that of Agiliti-Ketu (7.65 ± 0.06 and 6.58 ± 0.95) and Ibafon-Apapa (7.60 ± 0.39 and 4.20 ± 0.50), respectively. The highest values for electrical conductivity (EC) was recorded in Ibafon-Apapa (8.54 ± 1.27 μS/cm) followed by UWF (6.50 ± 2.16 μS/cm) and Agiliti-Ketu (0.28 ± 0.40 μS/cm), respectively. The relatively high values of EC and total dissolved solids (TDS) in the Ibafon-Apapa and UWF axis of the Lagos lagoon is an indication of the brackish nature of the lagoon, while the low mean values of EC (0.28 ± 0.40 μS/cm) and TDS (78.0 ± 13.04 mg/L) recorded for Agiliti-Ketu is a pointer to the freshwater attribute of the water. Cd, Mn, Fe and Pb were found to exceed the WHO limit for drinking water. Results were compared with global background values. Cadmium, manganese, iron and lead levels in the water and sediments samples revealed metals pollution. Statistical analysis of variance and t-test were used to analyze the data obtained. Metals pollution source control is recommended. Keywords: lagoon; metals; pollution; salinity; sediment; water.

*Corresponding author: Adeleke Adeniyi, Department of Chemistry, Lagos State University, P.M.B.0001, LASU Post Office, Ojo, Lagos, Nigeria, E-mail: [email protected]. https://orcid.org/0000-0002-3859-6669 Mayowa Ibidokun and Ojo Oluwole, Centre for Environmental Studies and Sustainable Development, Lagos State University, P.M.B. 0001, LASU Post Office, Ojo, Lagos, Nigeria As per De Gruyter’s policy this article has previously been published in the journal Physical Sciences Reviews. Please cite as: A. Adeniyi, M. Ibidokun and O. Oluwole “A comparative assessment of potentially harmful metals in the Lagos Lagoon and Ogun river catchment” Physical Sciences Reviews [Online] 2023. DOI: 10.1515/psr-2022-0246 | https://doi.org/10.1515/9783111328416-004

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4.1 Introduction Studies of aquatic metal pollution have gained increased attention as a result of concerns that metal contaminants are detrimental to the health of aquatic ecosystems and by extension human health [1, 2]. Metals are natural components of both water and sediments and their source can also be anthropogenic [1–3]. Metals concentrations in sediments may be re-suspended and end up as aquatic secondary environmental contaminants [3–6]. Surface water – rivers, lagoons, lakes, streams and creeks are important components of the environment as source of drinking water, food resources and receptacle of minerals [3]. However, surface water is also known as repository of pollutants whose bioaccumulation are of global health concerns [7–10]. Metals are pollutants of high priority that are commonly encountered in aquatic ecosystems including marine, freshwater and brackish water ecosystems [11–16]. Organic and inorganic pollutants compromise the health and integrity of the aquatic ecosystem [1, 17]. In surface water systems, pollutants, like metals are partitioned within the various compartments – suspended and dissolved solids, water, sediments and biota [3]. The toxicity of cadmium and zinc in the aquatic system is known to diminish with increases in the concentrations of calcium and magnesium. This hardness effect is ascribed to the competition of cadmium and zinc with calcium and magnesium for binding sites [8]. Due to importation of refined petroleum products, activities around the Apapa, Lagos tank farms have intensified. These activities expose the lagoon to metal contaminants [18, 19]. Similarly, a large volume of waste is continually deposited into the Lagos lagoon due to a continuous increase in human activities [7, 19]. Most of the pollutants originate from anthropogenic sources through unregulated human activities like indiscriminate waste disposal [1, 8, 20–23]. This study investigated the levels of potentially harmful metals – cadmium, copper, iron, manganese, nickel, lead and zinc, pH, total dissolved solids (TDS) and electrical conductivity (EC) in Lagos lagoon at Ibafon in Apapa and University of Lagos waterfront. The Ogun river at Agiliti-Ketu Lagos served as control. The outcome of this study is expected to provide further information that would prod governments at various levels to sustainably control the pollution sources of the Lagos lagoon ecosystem.

4.2 Materials and methods 4.2.1 Materials and reagents Analytical grade nitric acid, standard metal solutions, buffer solutions, deionizeddistilled water, glass wares and sample bottles were used. Chemicals were sourced from Fluka Chemie GmbH, Switzerland.

4.2 Materials and methods

71

Table .: GPS of locations. Location

Latitude

Longitude

Agiliti-Ketu Ibafon-Apapa UWF

°′.″N °′″N °′.″N

°′.″E °′″E °′.″E

4.2.2 Study area The study areas are Ibafon-Apapa, University of Lagos waterfront (UWF) which falls within the Lagos lagoon and Agiliti-Ketu in the Ogun river catchments which act as a control. The coordinates of the global positioning system are indicated in Table 4.1. Lagos lagoon is at the mouth of the Atlantic ocean. The lagoon receives large amounts of untreated effluents and solid waste materials from adjoining residential and industrial areas. Municipal run-offs also empty into the lagoon from different areas within its catchments [8]. Replicate samples were taken from the three different study locations. The choice of Ibafon-Apapa and UWF within the lagoon system was based on their proximity to the potential contaminants sources. The sources are essentially point sources (industrial effluent discharge, shipping activities, boat cruise, fishing with outboard engine boats and domestic waste disposal) and Agiliti-Ketu as control due to its distance from the lagoon. The choice of metals is predicated on their association with petroleum products and municipal wastes considering their potential toxicity to humans and other aquatic organisms.

4.2.3 Sampling and sample preparation A total of 60 samples (surface water and sediments) were collected between October and December 2020 following standard sampling procedures [24–26]. 4.2.3.1 Water samples Water samples were collected randomly using 2 L plastic containers. After pH, total dissolved solids (TDS) and electrical conductivity (EC) determination, each sample was acidified by adding 4–5 mL HNO3. 50 mL of the acidified water samples were digested in a conical flask, using 5 mL HNO3 in a hot plate and filtered into a 25 mL volumetric flask for metal analysis [8, 24].

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4.2.3.2 Sediments samples Sediment samples were collected with a scooping bowl; the samples were air-dried and then pulverized using a mortar and pestle. The pulverized samples were sieved with a 2 mm mesh sieve and then stored before metal extraction [8]. An amount of 5 g of each of the air-dried samples was measured into a clean 250 mL conical flask and extracted with 100 mL 2 M HNO3 in a hot plate. The extracts were filtered and made up to 50 mL mark with deionized-distilled water in a volumetric flask [8, 26]. 4.2.3.3 Quality assurance To assure quality, blank samples using deionized-distilled water were prepared. Predigested water samples and extracted sediment samples were used for recovery studies after spiking with the eight metal standards. All analyses were done in triplicate. The flame atomic absorption spectrophotometer (FAAS) setting and operational conditions were carried out as specified by the manufacturer, and the instrument was calibrated with standard analytical metal solutions (1000 mg/L) with appropriate dilutions [8, 21]. 4.2.3.4 FAAS analysis Calcium, cadmium, copper, iron, manganese, nickel, lead and zinc metals in the water and sediment samples were quantified using FAAS, Buck 210 VGP model. The results are presented in Tables 4.2 and 4.3. The WHO guideline limits for drinking water and global lagoon background values were used to benchmark the results harvested [5–8, 11–16, 27]. Table .: Physico-chemical parameters of the water samples. Location Parameter

Agiliti-Ketu

Ibafon-Apapa

UWF

pH TDS (mg/L) Temperature (°C) EC (μS/cm) Ca (mg/L) Cd (mg/L) Cu (mg/L) Fe (mg/L) Mn (mg/L) Ni (mg/L) Pb (mg/L) Zn (mg/L)

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

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

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

ND, Not detected.

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

4.3 Results and discussion

73

Table .: Mean concentration (µg/g) of metals in sediments. Location Parameter

Agiliti-Ketu

Ibafon-Apapa

UWF

pH Ca Cd Cu Fe Mn Ni Pb Zn

. ± . . ± . . ± . . ± . ,. ± . . ± . ND . ± . . ± .

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

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

4.2.3.5 pH, TDS and EC pH, TDS and EC of the water samples were measured in-situ using a multi-meter water checker (Blue lab combo meter) after the requisite calibration protocols as prescribed by the manufacturer [8]. The pH of the air-dried sediment samples was equally determined using the Blue lab combo meter following methods reported earlier by Adeniyi and coworkers [21]. 4.2.3.6 Statistical analysis Statistically significant differences were ascertained using t-test and analysis of variance (ANOVA).

4.3 Results and discussion Tables 4.2 and 4.3 indicate the mean pH of water and sediments samples for the different locations. The results showed that the water samples from UWF recorded the highest pH value of 7.71 ± 0.07 among the three study locations. Ibafon-Apapa and Agiliti-Ketu recorded pH values of 7.65 ± 0.06 and 7.60 ± 0.39, respectively (Table 4.2). A noticeable phenomenon among the water samples from the three locations is their slightly alkaline nature. However, the pH values recorded for the sediment samples (Table 4.3) were generally acidic, Ibafon-Apapa obtained the lowest value (4.19 ± 0.50) followed by UWF (5.79 ± 0.40) and Agiliti-Ketu (6.16 ± 0.95) respectively. The discharge of industrial effluent from petroleum tank farms in Ibafon-Apapa may be a major source of pollution as corroborated by an earlier study [7]. According to the WHO [27], pH values not within the range of 6.50–8.50 are indicative of unwholesomeness of the water [28, 29]. Acidic

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4 Metals in the Lagos Lagoon and Ogun river catchment

conditions of sediments affect the mobility of metals [2, 3]. The pH values recorded in this study for Agiliti-Ketu water and sediment samples (7.65 ± 0.06 and 6.12 ± 0.95) contrast the range reported earlier [8] with values ranging from 6.08 ± 0.69 to 7.56 ± 1.10 (water) and 6.23 ± 0.45 to 8.73 ± 0.26 (sediments) for Ogun river catchments. Adeniyi and co-workers in 2011 also reported pH range of 7.38–7.79 and 4.02–5.20 for water and sediments respectively for Ebute Ogbo river catchment, Ojo [2]. Other studies on the Lagos lagoon reported a mean range of 6.73–8.39 water samples [28, 30]. Similar values of 7.73–8.58 were reported also for the lagoon water of Kpeshie, Accra, Ghana; Burullus, Egypt and Negombo, Sri Lanka [11–13]. Table 4.2 reveals the mean EC values for the locations. EC ranged from 0.28 ± 0.40 to 8.54 ± 1.27 μS/cm. The lowest value of 0.28 ± 0.40 μS/cm was recorded at Agiliti-Ketu (control), while the highest conductivity value, 8.54 ± 1.27 μS/cm was recorded at Ibafon-Apapa. High EC values are indicative of increased number of polluting ions in the water body [29]. The relatively high values of EC (8.54 ± 1.27 and 6.5 ± 2.16 μS/cm) and TDS (4273.6 ± 628.99 and 3504 ± 1029.62 mg/L) in locations along the Lagos lagoon axis (Ibafon-Apapa and UWF) are indicative of their brackish nature as previously reported [30], while the low electrical conductivity (0.28 ± 0.40 μS/cm) and total dissolved solids (78.0 ± 13.04 mg/L) is an indication of the freshwater nature of the Agiliti-Ketu water. Nonetheless, the water at the lagoons of Kpeshie (Ghana), Burullus (Egypt) and Ebrie (Ivory Coast) have very high EC values indicative of their marine nature [11, 12, 16]. The recorded TDS values for Ibafon-Apapa 4273.60 ± 628.99 mg/L and UWF 3504 ± 1029.62 mg/L were above the stipulated WHO guideline limit of 500 and Fe > Mn > Pb > Zn > Cd > Cu > Ni (Agiliti-Ketu); Ca > Fe > Cd > Pb > Mn > Cu > Zn > Ni (Ibafon-Apapa); Cu > Fe > Mn > Cd > Pb > Zn > Ca > Ni (UWF). Whereas the sediment samples are in the order, Fe > Mn > Ca > Pb > Zn > Cu > Cd > Ni (Agiliti-Ketu); Fe > Ca > Mn > Pb > Cu > Zn > Cd > Ni (Ibafon-Apapa); Fe > Mn > Pb > Cu > Zn > Ca > Cd > Ni (UWF). Cadmium and lead concentrations (mg/L) of 0.40 ± 0.55 to 0.99 ± 0.57 and 0.23 ± 0.16 to 0.68 ± 0.92, respectively, were found to be higher than the 0.003 mg/L and 0.01 mg/L the WHO’s permissible limits (Table 4.2) [27]. This is an indication of Cd and Pb pollution. Similarly, these values exceed those previously reported for Kpeshie lagoon, Accra, Ghana; Burullus, Ebie, Negombo and Segara Anakan in Egypt, Ivory Coast, Sri Lanka and Indonesia, respectively [4, 11–13, 16]. The relative cadmium level in the lagoon water has not changed markedly compared to the 0.18–0.93 mg/L previously reported by Obua and co-workers [30]. Copper concentration in the Lagos lagoon water (Table 4.2) particularly at UWF of 71.46 ± 62.29 mg/L is higher than the WHO guideline limit and the values of 1.97–5.60 mg/L reported earlier for the lagoon [30]. The values are also higher than the values of between ND and 0.05 mg/L reported for the lagoon systems of Burullus (Egypt),

4.3 Results and discussion

75

Negombo (Sri Lanka) and Segara Nakan (Indonesia) [12–14, 27, 30]. The iron concentrations shown in Table 4.2 are lower than the 31.60–86.17 mg/L for the Lagos lagoon [30]. This is an indication of the likely reduction of Fe from the pollution sources [8, 20]. Nevertheless, the 8.12 ± 6.01–16.36 ± 9.04 mg/L values (Table 4.2) iron values found in the Lagos lagoon water are higher than the WHO guideline limit of 0.3 mg/L and 0.01–0.03 mg/ L reported for the water of Burullus lagoon Egypt [12, 27]. Manganese concentration (mg/L) of Ebrie lagoon, Abidjan, Ivory Coast and Negombo lagoon in Sri Lanka reported as 0.01–0.13 were found to be lower than the Lagos lagoon values of 0.53 ± 0.31 to 0.99 ± 0.49, the values are also higher than the WHO guideline limit of 0.4 mg/L for Mn, this is an indication of pollution of the lagoon system [13, 16, 27]. Nickel was below detection limit in the Lagos lagoon water, whereas, Kpeshie lagoon, Ghana and Ebrie lagoon, Ivory Coast have water nickel values (mg/L) of 0.04–0.08 and 0.02–0.26 respectively. It is worthy of note that Obua and co-workers reported values ranging from 19.83 to 20.50 mg/L for the Lagos lagoon [30]. There is a drastic reduction in nickel concentration in the lagoon water and sediments. This may not be unconnected with the decline in local production by industries adjoining the lagoon system as a result of the declining economy [31]. The zinc concentrations for water samples presented in Table 4.2 were found to be higher than values reported earlier for Kpeshie (Ghana), Burullus (Egypt) and Negombo (Sri Lanka) lagoon water [11–13]. Nonetheless, the 1.20–39.32 mg/L values reported for the Ebrie lagoon in Abidjan, Ivory Coast is higher than the 0.07 ± 0.15–0.18 ± 0.25 mg/L values for the locations in the Lagos lagoon [16]. However, the values recorded in Table 4.2 are lower than the 1.58–6.99 mg/L reported earlier by Obua et al. [30] for the Lagos lagoon. This may be attributed to the reduced industrial activities in the vicinity of the Lagos lagoon, the major source of metal contaminants [5, 8, 31]. Cadmium sediment burden (Table 4.3) is higher than the values reported for Kpesie (Ghana) and Khnifiss (Tunisia) lagoons sediment samples [6, 11]. However, the values for the Segara Anakan lagoon (Indonesia) of ND – 21.88 μg/g are generally higher than the ND – 7.19 ± 7.32 μg/g for the Lagos lagoon [14]. Nevertheless, Bawa-Allah and co-workers reported earlier values of 0.13 ± 6.26 μg/g for Lagos lagoon sediments [7]. The cadmium levels in the sediments are still of concern because of the risk of bioaccumulation in the aquatic system [8]. Copper, nickel and zinc levels are lower than the values reported for Khnifiss, Tunisia, lagoon sediments [14]. Whereas, the lead levels in the Lagos lagoon sediments are higher than those of Kpesie (Ghana), Segara Anakan (Indonesia) and Khniffiss (Tunisia), respectively [6, 11, 14]. However, similar levels of manganese were recorded for Lagos and Khnifiss lagoons [6]. There is a general reduction in the present metal burden in the sediments of the Lagos lagoon compared with earlier levels [7]. This may be attributed to the reduced industrial activities within the adjoining areas of the lagoon system [31]. The t-test statistics of the water samples for Ibafon-Apapa for calcium, cadmium and copper reveal statistically significant levels at 95% confidence level in relation to the control (Agiliti-Ketu). However, all calculated t-values for UWF are non-significant

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4 Metals in the Lagos Lagoon and Ogun river catchment

Table .: t-Test (% confidence level) of control versus lagoon locations. Parameter

Ibafon-Apapa

UWF

. (. ) .a (.ns) .a (.ns) .a (.a) .ns (.a) .ns (.ns) – .ns (.ns) .ns (.ns)

. (.ns) .ns (.ns) .ns (.ns) .ns (.ns) .ns (.a) .ns (.ns) – .ns (.a) .ns (.ns)

ns

pH Ca Cd Cu Fe Mn Ni Pb Zn

a

ns

Values in brackets are for sediments; asignificant; ns, non-significant ttab = ..

Table .: ANOVA (% confidence level) of water and sediment samples. Parameter F < .

pH

Ca

Cd

Cu

Fe

.ns (.a)

.a (.a)

.a (.ns)

.a (.a)

.ns (.a)

Mn Ni .ns (.ns)

–

Pb

Zn

.ns (.a)

.ns (.ns)

Values in brackets are for sediments; asignificant; ns, non-significant Ftab = ..

compared to the control [20, 25]. Nevertheless, in the sediment samples, copper and iron t-test values are significant for Ibafon-Apapa. Iron and lead values for UWF are also statistically significant. The non-significant values (Table 4.4) are indications that the sources of pollution are similar [25]. ANOVA in Table 4.5 reveal significant variation in cadmium, calcium and copper (water); pH, calcium, copper, iron and lead (sediments). This may be as a result of differences in pollution sources. This inference is supported by the studies of surface water in the Ogun river and Ebute Ogbo catchments [2]. The nonsignificant differences in iron, manganese, lead and zinc (water); cadmium, manganese and zinc (sediment) in the sample locations suggests a common source of these pollutants [25]. A similar trend has been observed before [8, 20, 21].

4.4 Conclusions It is evident from this study that the water bodies from the three sampling locations are contaminated by metals. The levels of cadmium, iron, manganese and lead exceed the WHO permissible limits for drinking water, which gives cause for concern. This is not unconnected with the sundry anthropogenic activities in these locations. The activities of petroleum tank farms, shipping, and dredging operations in Ibafon-Apapa tend to contribute to the elevated level of Cd, Pb and Cu (water), cadmium, copper, iron and lead (sediment) compared to UWF. The findings were benchmarked with those of earlier

References

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studies locally and globally and it is obvious that the Lagos lagoon system is polluted by metal contaminants. It is recommended that identified sources of point source pollution and activities implicated in such be addressed by applying the polluter pays principle. Acknowledgement: We appreciate the Chemistry Department of Lagos State University, Ojo for partially supporting this study.

References 1. Shanbehzadeh S, Dastjerdi VM, Hassanzadeh A, Kiyanizadeh T. Heavy metals in water and sediment: a case study of Tembi river. J Environ Pub Health 2014;2014:5. 2. Adeniyi AA, Owoade OJ, Shotonwa IO, Okedeyi OO, Ajibade AA, Sallu AR, et al. Monitoring metals pollution using water and sediments collected from Ebute Ogbo river catchments, Ojo, Lagos, Nigeria. Afr J Pure Appl Chem 2011;5:219–23. 3. Ali MM, Rahman S, Islam MS, Rakib MRJ, Hossen S, Rahman MZ, et al. Distribution of heavy metals in water and sediment of an urban river in a developing country: a probabilistic risk assessment. Int J Sediment Res 2022;37:173–87. 4. Wardhani E, Roosmini D, Notodarmojo S. Status of heavy metal in sediment of Saguling lake, West Java. IOP Conf Ser Earth Environ Sci 2017;60:012035. https://doi/10.1088/1755-1315/60/1/012035. 5. Abidi M, Yahyaoui A, Amor RB, Chouba L, Gweddari M. Evaluation of heavy metal pollution risk in surface sediment of the south lagoon of Tunis by a sequential extraction procedure. Sci Mar 2022;86:e028. 6. Tnoumi A, Angelone M, Armiento G, Caprioli R, Crovato C, DeCassan M, et al. Heavy metal content and potential ecological risk assessment of sediments from Khnifiss lagoon national park (Morocco). Environ Monit Assess 2022;194:356. 7. Bawa-Allah KA, Saliu JK, Otitoloju AA. Integrated assessment of the heavy metal pollution status and potential ecological risk in the Lagos Lagoon, South West, Nigeria. Human and Ecol Risk Assess 2017;24: 377–97. 8. Adeniyi AA, Yusuf KA, Okedeyi OO. Assessment of the exposure of two fish species to metals pollution in the Ogun river catchments, Ketu, Lagos, Nigeria. Environ Monit Assess 2008;137:451–8. 9. Mantis I, Voutsa D, Samara C. Assessment of the environmental hazard from municipal and industrial wastewater treatment sludge by employing chemical and biological methods. Ecotoxicol Environ Saf 2005; 62:397–407. 10. Wepener V, Van Vuren JHJ, Du Preez HH. Uptake and distribution of a copper, iron and zinc mixture in gill, liver and plasma of a freshwater teleost, Tilapia sparrmanii. Water SA 2005;27:99–108. 11. Addo MA, Okley GM, Affum H, Acquah S, Gbadago JK, Senu JK, et al. Water quality and level of some heavy metals in water and sediments of Kpeshie Lagoon, La-Accra, Ghana. Res J Environ Earth Sci 2011;3:487–97. 12. El-Alfy MA, El-Azim HA, El-Amier YA. Assessment of heavy metal contamination in surface water of Burullus Lagoon, Egypt. J Sci Agric 2017;1:233–43. 13. Kanchana CM, Chandrasekara NK, Weerasinghe KDN, Pathirana S, Piyadasa RUK. Heavy metal contamination of water in Negombo Lagoon and interconnected water sources. Lakes Reserv Ponds 2014; 8:96–110. 14. Prayoga G, Hariyadi S, Sulistiono S, Effendi HD. Heavy metal (Pb, Hg, Cu) contamination level in sediment and water in Segara Anakan lagoon, Cilacap, Indonesia. IOP Conf Ser Earth Environ Sci 2021;744:012055. 15. El Quaty O, El M’rini A, Nachite D, Marrocchino E, Marin E, Rodella I. Assessment of heavy metal sources and concentrations in Nador Lagoon sediment, Northeast-Morocco. Ocean Coast Manag 2022;216:105900.

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16. Tuo AD, Soro MB, Trokourey A, Bokra Y. Assessments of water contamination by nutrients and heavy metals in Ebrie Lagoon (Abidjan, Ivory Coast). J Environ Toxicol 2012;6:198–209. 17. Martin S, Griswold W. Human health effects of heavy metals. Environ Sci Tech Briefs Citiz 2009;15:1–6. 18. Lawson EO. Physico-chemical parameters and heavy metal contents of water from mangrove swamps of Lagos Lagoon, Lagos, Nigeria. Adv Bio Res 2011;5:8–21. 19. Bawa-Allah KA, Saliu JK, Otitoloju AA. Heavy metal pollution monitoring in vulnerable ecosystems: a case study of the Lagos Lagoon, Nigeria. Bull Environ Contam Toxicol 2018;100:609–13. 20. Adeniyi A, Okedeyi O, Sowemimo M, Yusuf K, Oluwole O, Odili G, et al. Quantification of metal contaminants and risk assessment in some urban watersheds. J Water Resour Protect 2020;12:951–63. 21. Adeniyi A, Osifeko O, Owoade O, Omotayo Y, Ajede E, Ibrahim A, et al. Metal burden as template for assessing the quality of raw water sourced from two rivers by Lagos State Water Corporation, Nigeria. In: Bhowon MG, Jhaumeer-Laulloo S, Li Kam Wah H, Ramasami P, editors. Chemistry: the key to our sustainable future. Dordrecht: Springer; 2014:163–72 pp. 22. Al-Afify ADG, Abdel-Satar AM. Heavy metal contamination of the River Nile environment, Rosetta branch, Egypt. Water Air Soil Poll 2022;233:302. 23. Afzaal M, Hameed S, Liaqat I, Khan AMA, Manan RS, Altaf M. Heavy metals contamination in water, sediments and fish of freshwater ecosystems in Pakistan. Water Pract Technol 2022;17:1253–72. 24. American Public Health Association. Standard methods for the examination of water and wastewater, APHA, AWWA and WPCE, 20th ed. Springfield: Byrd Progress: American Public Health Association; 1998. 25. Adeniyi A, Giwa O. Accumulation and health effects of metals in selected urban groundwater. Phys Sci Rev 2021;7:20200089. 26. Fatoki OS, Bornman M, Ravandhalala L, Chimuka L, Genthe B, Adeniyi A. Phthalate ester plasticizers in freshwater systems of Venda, South Africa and potential health effects. Water SA 2010;36:117–25. 27. WHO. Guidelines for drinking water quality, 4th ed. Geneva: World Health Organization; 2011. 28. Ayoola SO, Kuton MP. Seasonal variation in fish abundance and physicochemical parameters of Lagos lagoon, Nigeria. Afr J Environ Sci Technol 2009;3:149–56. 29. Loock MM, Beukes JP, Van Zyl PG. Conductivity as an indicator of surface water quality in the proximity of ferrochrome smelters in South Africa. Water SA 2015;41:705–11. 30. Obua UH, Okeke OC, Onyekuru SO, Ibeneme SI, Obua PC. Geoenvironmental implications of heavy metals distribution in parts of Lagos Lagoon, Southern Nigeria. J Appl Geol Geophy 2019;7:26–34. 31. Ogbuabor JE, Egwuchukwu EI. The impact of climate change on the Nigerian economy. Int J Energy Econ Pol 2017;7:217–23.

Enrico Daniel R. Legaspi, Ma. Stefany Daennielle G. Sitchon, Sonia D. Jacinto, Blessie A. Basilia and Imee Su Martinez*

5 XRD and cytotoxicity assay of submitted nanomaterial industrial samples in the Philippines Abstract: Distinct properties that nanomaterials possess compared to their bulk counterparts are attributed to their characteristic high surface area to volume ratios, and the prevalence of structure and shape effects at the nanoscale. However, these interesting properties are also accompanied by health hazards that are not seen in bulk materials. In the context of Philippine research and industry, the issue of nanosafety and the creation of nanotechnology guidelines have long been overlooked. This is of particular importance considering that nanotechnology research in the Philippines leans heavily towards medicinal and agricultural applications. In this study, nanomaterial samples from the industry submitted through the Philippine Industrial Technology Development Institute (ITDI) were analyzed using XRD and MTT cytotoxicity assay. XRD results show significant band broadening in the diffraction patterns of halloysite nanoclay, bentonite nanoparticles, silver nanoparticles, and CaCO3 nanoparticles, indicating that samples were in the nanometer range. The diffraction pattern of TiO2, however, did not exhibit band broadening, which may be due to the tendency of TiO2 nanoparticles to aggregate. Submitted samples were also assessed for their effect on cell viability using MTT cytotoxicity assay. Among these samples, only silver nanoparticles exhibited cytotoxicity to the AA8 cell line. Keywords: cytotoxicity; nanomaterials; nanosafety policy; XRD.

*Corresponding author: Imee Su Martinez, Institute of Chemistry, College of Science, University of the Philippines Diliman, Quezon City, Metro Manila 1101, Philippines; and Natural Sciences Research Institute, University of the Philippines Diliman, Quezon City, Metro Manila 1101, Philippines, E-mail: [email protected] Enrico Daniel R. Legaspi, Institute of Chemistry, College of Science, University of the Philippines Diliman, Quezon City, Metro Manila 1101, Philippines; and Natural Sciences Research Institute, University of the Philippines Diliman, Quezon City, Metro Manila 1101, Philippines Ma. Stefany Daennielle G. Sitchon, Institute of Chemistry, College of Science, University of the Philippines Diliman, Quezon City, Metro Manila 1101, Philippines; and Institute of Biology, College of Science, University of the Philippines Diliman, Quezon City, Metro Manila 1101, Philippines Sonia D. Jacinto, Institute of Biology, College of Science, University of the Philippines Diliman, Quezon City, Metro Manila 1101, Philippines Blessie A. Basilia, Department of Science and Technology, Materials Science Division, Industrial Technology Development Institute, Taguig City, Metro Manila 1631, Philippines As per De Gruyter’s policy this article has previously been published in the journal Physical Sciences Reviews. Please cite as: E. D. R. Legaspi, Ma. S. D. G. Sitchon, S. D. Jacinto, B. A. Basilia and I. S. Martinez “XRD and cytotoxicity assay of submitted nanomaterial industrial samples in the Philippines” Physical Sciences Reviews [Online] 2023. DOI: 10.1515/psr-2022-0255 | https://doi. org/10.1515/9783111328416-005

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5 XRD and cytotoxicity assay of industrial nanomaterial

5.1 Introduction The advent of nanotechnology has brought about a vast number of developments in both consumer goods and research. Nanoparticles that comprise these materials possess properties that are distinct from the bulk. These differences can be attributed to factors such as high surface area to volume ratios and structure or shape effects. While these properties allow nanoparticles to be used in applications that are not suitable for their bulk counterparts, it also means that the hazards of nanoparticles cannot be derived simply from those of the bulk material. Possible health risks from these nanoparticles have always been a concern [1]. It was found that some nanoparticles exhibit sizedependent toxicity [2]. The higher the surface area, the higher the reactivity. In fact, nanoparticles were found to exhibit “altered cellular uptake, protein adsorption, accumulation in organelles, and distribution throughout the body”, which would require different safety precautions when handling [3]. In the Philippines, lack of policy regarding nanomaterials is evident. The absence of local guidelines that tackle the manufacture, handling, storage, and safety of nanomaterials negatively affects the progress of nanotechnology in both research and the industry. For example, products or commodities that are claimed in the local market to be nanomaterials may have already aggregated. Once these particles accumulate to sizes above the nanometer range, they revert into the bulk form and lose their unique properties [4]. Even worse, these materials may not have been comprised of nanostructures in the first place. At present, the only on-going effort to rectify these lapses in nanosafety and nanomaterial regulation is that of the Industrial Technology Development Institute (ITDI) of the Department of Science and Technology (DOST), in cooperation with the Bureau of Philippine Standards (BPS) under the Department of Trade and Industry (DTI). A report from the DOST published in August 2020 detailed their roadmap for nanotechnology starting with the creation of localized versions and the adoption of international nanosafety standards in 2020 and 2021 [5]. A committee called the BPS-Technical Committee 85 (BPS-TC 85) comprising various stakeholders, such as the academia, industry, and government were assigned tasks to perform certain experiments based on their capabilities, and to review ISO standards for possible adoption in the country. The goal is to “harmonize local and international standards for nanosafety” by 2024. Bearing the necessity of nanotechnology guidelines in mind, this study aims to tackle the issue of nanometrology and nanosafety as part of the initiative of DOST to aid the industry and relevant stakeholders in their nanomaterial research and production. In this particular study, assessment of the purity and qualitative size determination of samples received from the industry were performed using powder XRD. This was followed by MTT cytotoxicity assay studies to determine the effect of these samples on cell viability.

5.3 Discussion

81

5.2 Methods 5.2.1 X-ray diffraction The samples were analyzed as-received using a Shimadzu Maxima XRD-7000. A full scan was performed from 2θ = 3° to 90° using a CuKα radiation source (40 kV; 30 mA) with a wavelength of 1.54 Å. The resolution for this analysis was set at 0.020, with a scan speed maintained at 20 per minute. Five out of the 11 samples were analyzed using XRD based on technique viability. These samples were successfully analyzed in dry powder form. The results of the characterization were used to qualitatively determine whether the submitted sample particulate sizes were in the nanometer range. The Scherrer equation (Equation (5.1)) details the relationship between the particle size and the full width at half maximum seen in the diffraction pattern. D=

Kλ ; β cos θ

(5.1)

where D is the particle size, β is the full width at half maximum, K is the Scherrer constant, λ is the X-ray wavelength, and θ is the diffraction angle [6]. The inverse relationship between D and β predicts that band broadening will occur as particle size decreases. The Scherrer equation assumes spherical particles, providing an estimate of particle size from the β of the peaks [7].

5.2.2 MTT cytotoxicity assay The samples were subjected to an MTT cytotoxicity assay adapted from the work of Mosmann in 1983, using an AA8 Chinese hamster ovarian fibroblast cell line [8]. The cells were seeded at 4 or 6 × 104 cells/mL in sterile 96-well plates and incubated overnight at 37 °C and 5% CO2. The nanoparticle samples were added in various concentrations from 100 μg/mL down to 0.78 μg/mL. Doxorubicin was used as the positive control, while dimethyl sulfoxide was used as the negative control. The cells with the nanoparticle solutions were incubated for 72 h at 37 °C and 5% CO2. After incubation, 5 mg/mL MTT dye in phosphate buffered saline was added. The cells were then allowed to sit for another 4 h. DMSO was used to dissolve the formazan crystals formed by the cells, and absorbance was read at 570 nm.

5.3 Discussion 5.3.1 Analysis of X-ray diffraction patterns for select nanoparticle samples The XRD patterns of the samples were compared to those found in the literature and JCPDS reference patterns. The halloysite nanoclay, bentonite nanoparticles, silver

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5 XRD and cytotoxicity assay of industrial nanomaterial

nanoparticles, and CaCO3 nanoparticles were found to have particulate sizes in the nanometer range based on the band broadening seen in the diffraction patterns. TiO2 was the only sample that did not exhibit significant band broadening. The details of the characterization of each sample are discussed below. 5.3.1.1 TiO2 The submitted TiO2 sample as shown by its XRD pattern in Figure 5.1 is predominantly in the anatase form. Peaks observed at 2θ of around 25.52°, 37.21°, 38.12°, 38.93°, 48.21°, 54.06°, 55.37°, 62.94°, 69.98°, and 70.41°, which correspond to indices (101), (103), (004), (112), (200), (105), (211), (118), (116) and (220), respectively, refer to the anatase structure. The presence of small peaks at 2θ of 27.64°, 36.40°, 41.46°, and 56.88° corresponding to the indices (110), (101), (111) and (220), respectively, refer to that of rutile TiO2, suggesting that the sample is not purely in the anatase form [9]. Band broadening of XRD peaks, normally used to indicate nanometer particulate size shows that the average size may not be within the nanometer range. Comparison of this result to the work of Thamaphat and co-workers, which differentiated TiO2 micropowders from nanopowders supports the assessment

Figure 5.1: XRD pattern of TiO2 sample.

5.3 Discussion

83

that the sample is not made up of nanoparticles [10]. This may be due to the aggregation of TiO2 particles during storage or even as a result of the synthesis procedure itself. Size separation techniques may be used to isolate the particles with lower particle size. 5.3.1.2 Halloysite Kaolinite nanoclay samples submitted for analysis exhibited instead characteristic peaks of halloysite, as shown in the XRD pattern in Figure 5.2. These are peaks at 2θ of 11.60°, 20.00°, 24.98°, 45.46° and 55°, referring to indices (001), (110), (002), (123) and (114), respectively. There were peaks observed that points to the presence of kaolinite, such as peaks (130), (122), (200), (131), and (331), but the overall pattern indicates that the predominant component is halloysite [11–13]. The peak at 2θ = 26.65°, which is the characteristic peak of quartz was also observed. Other possible minerals such as cristobalite, which may be present in some halloysite samples were not observed in the submitted sample [14]. Instead, alunite was seen at around 2θ = 30.00°. The XRD pattern exhibited broad peaks, which indicate that the sample has particle dimensions in nanometer [15].

Figure 5.2: XRD pattern of halloysite sample.

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5 XRD and cytotoxicity assay of industrial nanomaterial

5.3.1.3 Bentonite The XRD pattern of the submitted bentonite sample exhibited in Figure 5.3 showed montmorillonite as the dominant phase, confirming that the sample is natural bentonite [16, 17]. Except for very small peaks that refer to cristobalite, each peak in the sample corresponds to montmorillonite, which means that there is negligible amounts of impurities in the sample. These are peaks at 2θ of 6.21°, 17.56°, 19.89°, 29.00°, 35.00°, 54.00°, 61.94°, and 73.42, referring to indices (001), (003), (110), (005), (200), (009), (060), and (400), respectively. The peak at 2θ = 20°, though it corresponds to montmorillonite (110) cannot discount the presence of goethite, which may have contributed to the intense peak at this angle. Band broadening is observed for the XRD spectrum of bentonite nanoclay. Comparison with literature data shows that except for the 2θ = 20°, the peaks in the spectrum have broadened pointing to particulate size at the nanometer range.

Figure 5.3: XRD pattern of bentonite.

5.3 Discussion

85

5.3.1.4 AgNP The XRD spectrum of the submitted silver nanoparticles samples confirmed both size and identity of the nanomaterial, as shown in Figure 5.4. Peaks observed at 2θ of 38.01°, 44.13°, 64.49°, 77.37° and 81.33° are indicative of Ag with indices (111), (200), (220), (311) and (222), respectively. Comparison of XRD results with that of Lanje and colleagues shows perfect alignment of generated peaks per diffraction angle [18]. Significant band broadening of XRD peaks was observed in the submitted sample showing nanometer range size of the material. 5.3.1.5 CaCO3 Although the submitted sample from the industry was labeled TiO2, the experimental XRD pattern points to CaCO3 nanoparticles. The work of Render and co-workers and JCPDS reference pattern clearly elucidate the identity of the compound at hand [19]. Figure 5.5 shows 2θ peaks pertaining to CaCO3, which are observed at 23.15°, 29.50°, 36.19°,

Figure 5.4: AgNP XRD spectrum.

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5 XRD and cytotoxicity assay of industrial nanomaterial

Figure 5.5: CaCO3 sample XRD pattern.

39.61°, 43.36°, 47.58°, 48.65°, 57.61°, and 60.87°, referring to indices (012), (104), (110), (113), (202), (016), (018), (122), and (214), respectively. Slight band broadening indicates that CaCO3 nanoparticles are present in the sample.

5.3.2 MTT cytotoxicity assay The MTT cytotoxicity assay measures the metabolic activity of cells based on the reduction of the tetrazolium salt, 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide to formazan crystals. Cytotoxic substances inhibit the cells’ ability to reduce the tetrazolium salt into formazan crystals. This is quantitatively determined through the half maximal inhibitory concentration (IC50), which is the amount of a substance required to inhibit 50% of a particular biological process. The study of Jokhadze and co-workers gave an IC50 value of 30 μg/mL as the cut-off value for cytotoxic substances based on the guidelines of the American National Cancer Institute [20]. Table 5.1 lists the nanoparticle samples and their corresponding IC50 determined using the MTT cytotoxicity assay. Succeeding discussions on the MTT cytotoxicity assay per tested sample refer to this table.

5.3 Discussion

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Table .: Experimental half maximal inhibitory concentration values of various submitted nanomaterials from the industry sector in the Philippines. Sample

IC (µg/mL)

TiO AgNP Bentonite Halloysite SiO ZnO CNT MWCNT CaCO

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

5.3.2.1 TiO2 TiO2 samples analyzed in this study fall above the cut-off value for cytotoxic materials. This shows that TiO2 based on the results of this study has no effect on the cell viability of the AA8 cell line. This is congruent with results from previous studies in literature on the cytotoxicity of TiO2. For example, is the work of Hamzeh and co-workers on the investigation of the cytotoxicity effects of various TiO2 nanostructures in vitro [21]. Their MTT assay results showed that while different structures led to different cell viability measurements, none of the TiO2 samples decreased cell viability below 50%. A paper by Lupu and Popescu noted that photocatalytic interactions caused by TiO2 can reduce the MTT cytotoxicity indicator into formazan crystals [22]. This resulted in a false increase in cell viability of up to 14%. This type of correction was also performed in this study, however, even when correcting for this phenomenon, the TiO2 samples analyzed were still above the cytotoxic level. It is also important to note that the submitted TiO2 sample was not within the nanoparticulate size range based on the XRD results. 5.3.2.2 AgNP The experimental results of the MTT assay for silver nanoparticles indicate that the substance is cytotoxic to the AA8 cell line. With an average IC50 of 26.61 μg/mL, the silver nanoparticulate sample was the only sample found to be cytotoxic [20]. Experiments performed by Mukherjee and colleagues showed similar results with an IC50 equal to 30.4 μg/mL in the HaCaT cell line, and an IC50 of as low as 0.04 μg/mL using the HeLa cell line [23]. 5.3.2.3 Bentonite The bentonite nanoclay sample, composed primarily of montmorillonite resulted in an IC50 of 78.33 μg/mL, and was observed to be non-cytotoxic. A study by Sabzevari and

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group, showed similar results in their investigation on the cytotoxicity of montmorillonite nanosheets in the HepG2, HT-29, and MRC-5 cell lines, which showed that montmorillonite is non-cytotoxic [24]. The cell viability did not go below 50% at concentrations of 100 μg/mL for any of the cell lines. 5.3.2.4 Halloysite Lai and colleagues investigated the cytotoxicity of halloysite nanotubes using an XTT assay rather than an MTT assay [25]. They stated that the insoluble formazan formed from the reduction of the dye in MTT assays can attach to the nanotubes, resulting in a lower calculated cell viability. Sawicka and co-workers conducted an MTT assay without correcting for the nanotube-formazan interaction [26]. The calculated IC50 values in the A549 and BEAS-2B cell lines after incubation for 72 h were 49 and 45 μg/mL, respectively. The determined IC50 for the halloysite sample at around 87.62 μg/mL, falls far from the cytotoxicity cut-off value despite correction for the false decrease expected from the nanotube-formazan interaction. This shows that the halloysite nanoclay is not cytotoxic to the AA8 cell line. 5.3.2.5 SiO2 Yang and colleagues (2016) compared the cytotoxicity of SiO2 nanoparticles with that of microscale SiO2 to highlight the difference between the cytotoxicity of nanomaterials from bulk materials [27]. Nano-sized SiO2 had an IC50 value of 5080 μg/mL in the RAW264.7 cell line, while microscale SiO2 had an IC50 of 16,690 μg/mL. Both forms of SiO2 are far from being considered cytotoxic. Another study by Yang and co-workers showed cell viability below 60% only at a SiO2 concentration of 200 μg/mL in the HL-7702 cell line [28]. The results of the MTT assay done in this experiment similarly suggest that SiO2 is noncytotoxic with an IC50 of 92.11 μg/mL. 5.3.2.6 ZnO Cierech and colleagues showed that cell viability of the HeLa cell line was 61.43% after incubation with 30 μg/mL ZnO nanoparticle solution, and 54.87% after incubation with 50 μg/mL ZnO solution [29]. Although IC50 was not calculated in the study, the data suggests that ZnO does not meet the cytotoxicity criterion. In this study, the addition of ZnO actually led to an increase in cell viability. A possible explanation for this is the photocatalytic property of ZnO, which leads to a phenomenon that is similar to what is seen in TiO2. The tetrazolium salt is reduced by the ZnO, leading to a false increase in cell viability. The determined IC50 of ZnO in this study is >100 μg/mL, showing noncytotoxicity.

5.4 Conclusions

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5.3.2.7 CNT and MWCNT Wörle-Knirsch and colleagues criticized the use of MTT assay for determining the cytotoxicity of carbon nanotubes [30]. When other assays such as WST-1, INT, and XTT were used, cytotoxicity was not observed. With MTT however, a strong cytotoxic effect was suggested from the results of the assay. Similar to what was observed in halloysite nanotubes, CNTs and MWCNTs attach to the insoluble formazan produced from the reduction of the MTT dye. This results in a lower absorbance and ultimately, a lower calculated cell viability. Experimental results from this work, however, show that CNT and MWCNT are non-cytotoxic with IC50 values of greater than 100 μg/mL. 5.3.2.8 CaCO3 In vitro studies done by D’amora and co-workers looked at the cell viability of NIH ST3 and MCF-7 cell lines exposed to calcium carbonate nanoparticles of up to 50 μg/mL for 72 h [31]. However, there was no decrease in cell viability observed in either cell line. Hammadi and group used an MTT assay to measure the cytotoxicity of CaCO3 and similarly found less than 20% decrease in cell viability up to concentrations of 1000 μg/mL [32]. This is in agreement with the MTT assay result in this study, which showed the IC50 to be higher than 100 μg/mL in the AA8 cell line.

5.4 Conclusions Band broadening observed in the XRD patterns of the as-received samples suggests the presence of nanoparticles, except for TiO2. This could be due to the tendency of TiO2 to form aggregates. However, other metrology experiments should be performed in addition to XRD, such as TEM, SEM, and dynamic light scattering studies. Through the MTT cytotoxicity assay, it was determined that submitted nanoparticle samples besides AgNP were non-cytotoxic. Numerous studies, however, have shown the shortcomings of the MTT assay in obtaining accurate IC50 values due to the reduction of the MTT dye by photocatalytic nanoparticles, and the attachment of insoluble formazan to nanotubes. Other forms of assays should be explored to ensure correctness of results. This study, which is part of the ongoing initiative of the ITDI–DOST, together with BPS–DTI in the adoption and review of ISO Nanomaterial Standards toward becoming Philippine National Standard (PNS) will aid various stakeholders in the Philippines, including the industry, as well as the academia in the production and use of these types of materials. Lack of policy and guidelines on nanosafety, including syntheses, metrology, and analyses of nanomaterials will be addressed through the ongoing work of the BPS Technical Committee 85, and research studies performed by members of this group, similar to the ones presented here.

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References 1. Furxhi I. Health and environmental safety of nanomaterials: O data, where art thou? NanoImpact 2022;25: 1–13. 2. Important issues on risk assessment of manufactured nanomaterials. Paris: Organisation for Economic Cooperation and Development Environment Directorate Chemicals and Biotechnology Committee; 2022. Available from: https://www.oecd.org/officialdocuments/publicdisplaydocumentpdf/?cote=ENV-CBCMONO(2022)3%20&doclanguage=en [Accessed 14 Jan 2023]. 3. Sahu D, Kannan GM, Tailang M, Vijayaraghavan R. In vitro cytotoxicity of nanoparticles: a comparison between particle size and cell type. J Nanosci 2016;2016:1–9. 4. Ashraf MA, Peng W, Zare Y, Rhee KY. Effects of size and aggregation/agglomeration of nanoparticles on the interfacial/interphase properties and tensile strength of polymer nanocomposites. Nanoscale Res Lett 2018;13:1–7. 5. Formulation of roadmap and sectoral plan for five emerging technologies: Advanced materials and nanotechnology. Taguig: DOST PCIEERD; 2020. Available from: https://pcieerd.dost.gov.ph/images/pdf/ 2021/roadmaps/Advanced%20Materials%20and%20Nanotechnology%20Strategy%20Paper%20v7.3.pdf [Accessed 14 Jan 2023]. 6. Holder CF, Schaak RE. Tutorial on powder X-ray diffraction for characterizing nanoscale materials. ACS Nano 2019;13:7359–65. 7. Mourdikoudis S, Pallares RM, Thanh NTK. Characterization techniques for nanoparticles: comparison and complementarity upon studying nanoparticle properties. Nanoscale 2018;10:12871–934. 8. Mosmann T. Rapid colorimetric assay for cellular growth and survival: application to proliferation and cytotoxicity assays. J. Immunol. Methods 1983;65:55–63. 9. Hosseini SN, Chen X, Baesjou PJ, Imhof A, Van Blaaderen A. Synthesis and characterization of anatase TiO2 nanorods: insights from nanorods’ formation and self-assembly. Appl Sci 2022;12:1–18. 10. Thamaphat K, Limsuwan P, Ngotawornchai B. Phase characterization of TiO2 powder by XRD and TEM\n. Nat Sci 2008;42:357–61. 11. Daou I, Lecomte-Nana G, Tessier-Doyen N, Peyratout C, Gonon M, Guinebretiere R. Probing the dehydroxylation of kaolinite and halloysite by in situ high temperature X-ray diffraction. Minerals 2020;10:1–19. 12. Liu L, Shen L, Li W, Min F, Lu F. Study on the aggregation behavior of kaolinite particles in the presence of cationic, anionic and non-ionic surfactants. PLos One 2018;13:1–15. 13. Falcon-Roque J, Sawczen T, Aoki I. Dodecylamine-loaded halloysite nanocontainers for active anticorrosion coatings. Front Mater 2015;2:1–14. 14. Pasbakhsh P, Churchman GJ, Keeling JL. Characterisation of properties of various halloysites relevant to their use as nanotubes and microfibre fillers. Appl Clay Sci 2013;74:47–57. 15. Londoño-Restrepo SM, Jeronimo-Cruz R, Millán-Malo BM, Rivera-Muñoz EM, Rodriguez-García ME. Effect of the nano crystal size on the X-ray diffraction patterns of biogenic hydroxyapatite from human, bovine, and porcine bones. Sci Rep 2019;9:1–12. 16. Orolinovaa Z, Mockovciakova A, Skvarla J. Sorption of cadmium (II) from aqueous solution by magnetic clay composite. Desal Water Treat 2010;24:284–92. 17. Damian G, Damian F, Szakacs Z, Lepure G, Astefanei G. Mineralogical and physico-chemical characterization of the Orasu-Nou (Romania) bentonite resources. Minerals 2021;11:1–19. 18. Lanje A, Sharma S, Pode R. Synthesis of silver nanoparticles: a safer alternative to conventional antimicrobial and antibacterial agents. J Chem Pharm Res 2010;2:478–83. 19. Render D, Samuel T, King H, Vig M, Jeelani S, Babu RJ, et al. Biomaterial derived calcium carbonate nanoparticles for enteric drug delivery. J Nanomat 2016;2016:1–8. 20. Jokhadze M, Eristavi L, Kutchukhidze J, Chariot A, Angenot L, Tits M, et al. In vitro cytotoxicity of some medicinal plants from Georgian Amaryllidaceae. Phytother Res 2007;21:622–4.

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Supplementary Material: This article contains supplementary material (https://doi.org/10.1515/PSR-20220255).

Agnes Pholosi*, Saheed O. Sanni, Samson O. Akpotu and Vusumzi E. Pakade

6 Pine bark crosslinked to cyclodextrin for the adsorption of 2-nitrophenol from an aqueous solution Abstract: Adsorbents that are less expensive and more effective at removing organic micropollutants from wastewater have been developed through several approaches. Pine bark was treated with sodium hydroxide and then cross-linked to cyclodextrin using hexamethylene diisocyanate, in this study as an efficient adsorbent in the removal of 2-nitrophenol. FTIR, TGA and pHpzc analysis were used to characterize the biosorbent. The effects of pH, adsorbent mass, contact time and initial concentration on 2-nitrophenol removal was examined through batch adsorption studies. Pine bark crosslinked to cyclodextrin (PB-CD) surface functionalities was confirmed by FTIR analysis. It was discovered that solution pH, adsorbent mass, concentration and contact time all played a crucial role in the 2-nitrophenol uptake on PB-CD biosorbent and pine bark (PB) treated with sodium hydroxide. 2-Nitrophenol equilibrium was achieved with 0.05 g of adsorbents, with an initial concentration of 100–200 mg/dm3 at pH 5 after 60 min. The pseudosecond-order kinetic model and the Langmuir isotherm model significantly fitted the adsorption process. The Langmuir maximum capacities for PB and PB-CD were 47.36 mg/g and 77.82 mg/g, respectively. Overall, in the removal of 2-nitrophenol from an aqueous solution, PB-CD biosorbent is more cost-effective and efficient, in comparison with previously reported biosorbents in literature. Keywords: 2-nitrophenol; cyclodextrin; hexamethylene diisocyanate; pine bark.

6.1 Introduction Industries release a variety of organic micropollutants into the environment, thus resulting in numerous serious environmental issues. Nitrophenols are organic micropollutants that are widely used as the intermediate for the manufacture of pesticides, herbicides, insecticides, dyes, pharmaceuticals and explosives [1, 2]. They find their way into the

*Corresponding author: Agnes Pholosi, Adsorption and Water Remediation Research Laboratory, Department of Biotechnology and Chemistry, Faculty of Applied and Computer Sciences, Vaal University of Technology, P. Bag X021, Vanderbijlpark, 1900, South Africa, E-mail: [email protected] Saheed O. Sanni, Samson O. Akpotu and Vusumzi E. Pakade, Adsorption and Water Remediation Research Laboratory, Department of Biotechnology and Chemistry, Faculty of Applied and Computer Sciences, Vaal University of Technology, P. Bag X021, Vanderbijlpark, 1900, South Africa As per De Gruyter’s policy this article has previously been published in the journal Physical Sciences Reviews. Please cite as: A. Pholosi, S. O. Sanni, S. O. Akpotu and V. E. Pakade “Pine bark crosslinked to cyclodextrin for the adsorption of 2-nitrophenol from an aqueous solution” Physical Sciences Reviews [Online] 2023. DOI: 10.1515/psr-2022-0332 | https://doi.org/10.1515/9783111328416006

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environment through improper discharge of industrial effluents. 2-Nitrophenol is reported to be more toxic than other isomeric forms of nitrophenols due to its high stability, making it a priority pollutant with maximum levels in water of 0.01–2.0 mg/L [3]. The treatment through chlorination has been shown to generate chlorinated by-products that are stable and poisonous due to the aromatic ring in the 2-nitrophenol structure [4]. Industries’ treatment systems are frequently viewed as a regulatory requirement, thus resulting in higher operating, and capital costs and lower economic returns [5]. Therefore, good, reliable cost-effective methods are necessary to treat effluents before discharge. Adsorption method for water remediation is recently preferred choice, over options such as ion-exchange, precipitation and membrane technology, due to low cost, readily availability, eco-friendly and good efficiency of the materials applied [6, 7]. These methods mentioned above have limitation, due to high operating and capital costs. Different materials comprising zeolite, clay, activated carbon and chitosan have been investigated as potential adsorbents for the removal of organic micropollutants in water [8–10]. However, these adsorbents have problems such as high cost, limited handling capacity, a slow adsorption rate and an energy-intensive regeneration process [11]. These led researcher to study or explore variety of low-cost adsorbents generated from natural materials, agricultural waste materials and industrial waste. Agricultural waste materials such as pine cone [12, 13], macadamia nutshells [14], malacantha alnifolia tree bark [15], fennel seeds [16] and rice straws and rice husk [17] are abundant in nature and have been widely explored and preferred as adsorbent for different pollutants removal. When compared to other biomaterials, tanin-rich plant barks were found to be one of the most effective adsorbents. Pine bark, which are readily available in nature in large quantities are one of the alternative biosorbent. Pine bark is made up of cellulose, hemicellulose, tannins, lignin and cutin, which contains hydroxyl, carbonyl, carboxylate, sulfhydryl and carboxylic groups in their surface. Due to the release of soluble organic compounds in the plant material, the use of untreated agricultural wastes as biosorbents comes with challenges, comprising a low adsorption capacity, high chemical oxygen demand (COD) and high biological oxygen demand (BOD). Chemical treatment has been shown to prevent the elution of bark components that would stain the treated water. On the other hand, β-cyclodextrin (β-CD) has received a lot of attention due to its exceptional structure and unique properties such that it can form guest-host interaction with wide range of organics, which is ascribed to the binding forces it exerts on the guest molecules [18, 19]. However, their application as adsorbents in wastewater treatment is constrained by their solubility in water. This can be overcome by grafting onto a matrix that is insoluble in water and reacting with coupling agents. Adsorbents with better properties could be made by incorporating agricultural waste into natural polymers, combining the benefits of both materials. In this study, a low cost and efficient material was prepared by treating pine bark with sodium hydroxide to remove unwanted plant extracts, then crosslinked to β-cyclodextrin using hexamethylene diisocyanate (HMDI). The biosorbent was then explored if it would thus offer an effective and economical alternative to other expensive treatments for the removal of

6.2 Experimental

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2-nitrophenol from an aqueous solution by adsorption. The kinetics and equilibrium isotherm of the adsorption process were evaluated and the regeneration efficiency of the crosslinked biosorbent was also determined.

6.2 Experimental 6.2.1 Materials Chemicals used in this study were beta-cyclodextrin (≥97%), hexamethylene diisocyanate, dibutyltin dilaurate, ethanol, 2-nitrophenol and anhydrous hexane from Sigma Aldrich. Hydrochloric acid (32%), sodium hydroxide (≥98%) and potassium nitrate (≥99%) were purchased from Labchem, South Africa. All chemicals were used without any further purification.

6.2.2 Procedures 6.2.2.1 Preparation and treatment of pine bark using NaOH Pine tree bark pieces were collected from pine tree on the Vaal University of Technology premises, Vanderbijlpark, South Africa. The bark pieces were then cut, crushed and sieved through a 90 µm and kept in a tightly closed container. Thereafter, 50 g of the crushed bark was mixed with 500 ml of 0.15 mol/dm3 sodium hydroxide using a multi stirrer for 18 h. After the two components was mixed, the mixture was then washed using a decantation method using distilled water until the water was clear, then the residue was left to dry overnight using the oven at 90 °C. 6.2.2.2 Crosslinking of cyclodextrin with pine bark using hexamethylene diisocyanate In a 250 ml three-necked round bottom flask with a magnetic stirrer and an inlet for N2 gas, approximately 0.5 g of pine bark and 0.5 g of cyclodextrin were dispersed in anhydrous 50 ml hexane with 5 ml dibutyltin dilaurate added as a catalyst. To finish the activation process, the mixture was heated for 15 min at 50 °C for in a reflux setup. A separate volume of 15 ml HMDI was dissolved in 50 ml anhydrous hexane and added dropwise into the activated pine bark. Under reflux and continuous stirring, the mixture was heated for 1 h in a nitrogen atmosphere. After being rinsed multiple times with fresh hexane, the HMDI-cross-linked pine bark was dried for 8 h in a vacuum at 60 °C. 6.2.2.3 Sample characterization To elucidate functional groups present on the pine bark and to confirm cyclodextrin cross-linking onto pine bark, the infrared spectra of the PB and PB-CD were gathered

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6 Pine bark crosslinked to cyclodextrin for the adsorption

using a Perkin-Elmer (USA) Fourier transform infrared (FTIR) Spectra 400 spectrometer in the range 650–4000 cm−1. In accordance with Ofomaja et al., [20], point of zero charge measurements (pHpzc) for PB and PB-CD were analyzed using the solid addition method. 6.2.2.4 Batch adsorption experiments for the removal of 2-nitrophenol Utilizing PB and PB-CD as biosorbents, batch adsorption experiments were carried out for the purpose of removing 2-nitrophenol. In a 250 cm3 flask containing 100 cm3 of 100 mg/ dm3 of 2-nitrophenol at a pH adjusted with 0.1 M HCl and 0.1 M NaOH, the required quantities of PB and PB-CD biosorbents were added. A Lambda 25 (UV/Vis) spectrometer made by Perkin-Elmer (USA) was used to analyze the filter solutions of 2-nitrophenol remaining at 430 nm. PB and PB-CD biosorbents were added to each flask containing a 100 cm3 solution of 2-nitrophenol at a pH range of 3–12, with an initial concentration of 100 mg/dm3, for effect of pH solution. Adsorbent dosage of 0.025–0.75 g for the biosorbents were added to each flask containing 50 cm3 of 2-nitrophenol solution at a pH 5 initial concentration of 100 mg/dm3 to investigate the effect of adsorbent dose. To investigate the kinetics for the removal of 2-nitrophenol, each flask containing 0.05 g of PB and PB-CD were filled with 50 cm3 of 2-nitrophenol solution of concentration ranging from 100, 125, 150, 175 and 200 mg/dm3. At regular time intervals (0.5; 1; 2; 3; 5; 20; 30; 60 and 120 min), an aliquot of the sample (0.1 cm−3) was taken out, filtered and the amounts of 2-nitrophenol that were still present in the solution were analyzed. For adsorption isotherms data, 100 cm3 of 2-nitrophenol solution of different initial concentration (100–200 mg/dm3) were treated with 0.05 g of PB and PB-CD biosorbents adjusted at pH 5. After that, the conical flasks were shaken in a shaker at varying temperatures of 299, 304, 309, 314 and 319 K at a constant speed of 145 rpm. For regeneration studies, centrifugation and distilled water were used to separate the biosorbent residue used in the system of 1.0 g of PB and PB-CD in contact with 100 cm3 of 100 mg/dm3 of 2-nitrophenol solution. The solid residue that had been washed was then dried overnight and stirred in 50 cm3 of ethanol as the desorbing agent. The PB and PB-CD solids were separated by filtration after the flask was shaken for 2 h at 200 rpm. The leached amount of 2-nitrophenol ions in the filtrate was further analyzed using UV–Vis spectrophotometry at 430 nm. The washing and reuse processes carried out 4 times.

6.3 Results and discussion 6.3.1 Adsorbent characterization FTIR spectra of sodium hydroxide treated PB and PB-CD evidenced the functional groups present on the PB and confirmed the crosslinking of β-CD onto the surface of PB as presented in Figure 6.1. It was observed that PB is made up of different functional groups characteristic of lignocellulose materials. PB spectra exhibited a broad O–H stretching

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Figure 6.1: FTIR spectra of PB and PB-CD.

vibration at 3307 cm−1 and a –CH2– of the cellulose component vibration at 2888 cm−1 [21]. The peaks at 1722 cm−1 and 1622 cm−1 are characteristics of C=O and C–O stretching of unconjugated ketones in lignin and vibration [22]. The antisymmetric glycosidic C–O–C vibration is observed at a peak of 1027 cm−1. After crosslinking -CD with hexamethylene diisocyanate, some groups’ intensities were observed to shift, and some new peaks appeared. After crosslinking, the broad OH group of PB at 3307 cm−1 was observed to sharpen and shift to 3318 cm−1, indicating the NH groups from the formation of the amide linkage. The presence of the long hydrocarbon chain from the cyclodextrin and hexamethylene diisocyanate cross linker was confirmed by the CH2 group, increased in intensity and split into two peaks at 2929 cm−1 and 2853 cm−1. Minute peaks appeared at 1619 cm−1 and 2261 cm−1 due to the C=O due to the carboxylic group from the amide linkage formed and the unreacted isocyanate group (–N=C=O) from the isocyanate group [23]. Additionally, the isocyanate-carboxylic acid reaction produced a sharp peak at 1562 cm−1, which corresponds to the amide bond. Thermal gravimetric thermogram along with derivative weight % profile for PB and PB-CD are shown in Figure 6.2a and b. From Figure 6.2a, the elimination of the adsorbed water and water molecules that may have been trapped in the adsorbent matrix accounts for the initial weight loss that can be observed at temperature below 100 °C. Due to the decomposition of cellulose and hemicellulose in the pine bark, the second step of weight loss is observed in the temperature range of 240–400 °C corresponding to DTA peak at 329 °C (Figure 6.2b) for PB [24]. On the other hand, the PB-CD biosorbent shows two overlapping steps from 300 to 500 °C. The first mass loss was ascribed to decomposition of pine bark cellulose and hemicellulose, and the β-cyclodextrin moiety was observed

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Figure 6.2: Thermal analysis curves of PB and PB-CD. (a) TGA and (b) DTA of PB and PB-CD.

between 300 and 370 °C, which corresponds to the DTA peak at 336 °C (Figure 6.2b) while the second step occurred from 370−500 °C due to the oxidation of organic matter from HMDI corresponding to the DTA peak at 464 °C [25, 26]. The thermal stability of the PB biosorbent was slightly improved by crosslinking CD onto it. The pHpzc which is the pH at which the surface’s net charge is zero, was used to analyze the ionic nature of the PB and PB-CD biosorbents as well as their adsorption behaviour. pHpzc of PB before and after β-CD crosslinking were conducted and observed to be 7.32 and 7.48, respectively. The results show that at low solution pHs below pH 7.32, pine bark surface carries a net positive charge which is due to acidic functional groups on the pine bark surface with hydrogen ions in solution at low pH. After cyclodextrin crosslinking with HMDI, the pHpzc was observed to slightly shift to a higher value of 7.48. This may be ascribed to the formation of urethane linkages which reduced acidic groups on the PB and β-CD polymer. Similar observations have been reported by [4] when crosslinking Fenton pre-treated pine cone using HMDI, with an increase in pHpzc from 5.40 to 6.12 for the Fenton pre-treated cross-linked pine material.

6.3.2 Adsorption studies 6.3.2.1 Effect of solution pH on the adsorption 2-nitrophenol The first step in determining an adsorbent’s capacity to adsorb pollutants is to determine the pH of the solution, the adsorbent’s surface charge, functional group chemistry and sorption of pollutants all have a significant impact on the adsorption process. Figure 6.3 depicts how pH of the solution affects the adsorption of 2-nitrophenol by NaOH treated pine bark (PB) and cyclodextrin cross-linked pine bark (PB-CD). For both adsorbent samples, it was found that the capacity to adsorb 2-nitrophenol increased as the pH of the solution rose until pH 5, at which point it steadily decreased until pH 9. The

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99

Figure 6.3: Effect of solution pH on the uptake of 2-nitrophenol from aqueous solution using PB and PB-CD.

results indicate that the removal of 2-nitrophenol from aqueous solution is highly pH-dependent. With an adsorption capacity of 17.82 mg/g for PB and 78.63 mg/g for PB-CD biosorbent, the optimal adsorption removal of 2 nitrophenol was observed at solution pH 5. By considering the pHpzc of PB and PB-CD as well as the pKa of the 2-nitrophenol, it is possible to explain how pH affects the adsorption of 2-nitrophenol. pHpzc values of PB and PB-CD are 7.32 and 7.48, respectively, while pKa value of 2-nitrophenol is 7.23. Optimum solution pH for both PB and PB-CD were below pKa of 2-nitrophenol and pHpzc of the biosorbents. 2-Nitrophenol exists as molecules or in a non-ionized form below pKa and pHpzc, and the PB and PB-CD functional groups are protonated, resulting in a positively charged adsorbent surface. 2-Nitrophenol is not positively charged at a low solution pH because the nitro-group on the benzene ring withdraws electrons [27]. The hydrophobic interaction and hydrogen bonding (π–π interaction) between oxygenated groups with lone-pair electron pairs in the PB and PB-CD and the OH of the aromatic rings of 2-nitrophenol will determine adsorption rather than the adsorbent’s surface charge [27]. As solution pH increases to pH 5, the charge on the surfaces of PB and PB-CD were approaching neutral and 2-nitrophenol exists as a mixture of molecular and anionic forms. Higher 2-nitrophenol adsorption capacity is observed for both PB and PB-CD at pH 5. Hydrophobic interaction between pine bark and cyclodextrin and 2-nitrophenol and hydrogen bonding between weakly acidic functional groups on pine bark and cyclodextrin may have caused higher 2-nitrophenol removal at pH 5. Due to the presence of higher hydroxyl groups, solution pH above 5 caused both the PB and PB-CD surfaces to become deprotonated, and the 2-nitrophenol molecule was ionized into an anionic form. Electrostatic repulsion between the surfaces of deprotonated biosorbents and the anionic 2-nitrophenol species resulted in a decrease in 2-nitrophenol’s adsorption capacity.

100

6 Pine bark crosslinked to cyclodextrin for the adsorption

6.3.2.2 Effect of adsorbent dose The effect of adsorbent dose on 2-nitrophenol adsorption was carried out at an initial concentration of 100 mg/dm3 and solution pH set at pH 5 at 26 °C and stirred at 200 rpm for 2 h, and presented in Figure 6.4a and b. An increase in adsorbent dose from 0.025 to 0.75 g resulted in a decrease in adsorption capacity from 39.12 to 3.60 mg/g and 72.05 to 6.93 mg/g for PB and PB-CD, respectively. However, as the dosage of the adsorbent increased, 2-nitrophenol percentage removal sharply increased from 9.78 to 26.70% and 18.01– 51.95% for PB and PB-CD, respectively. Adsorbents’ active sites are fully exposed and occupied by excess solution 2-nitrophenol ions at low doses, resulting in surface saturation and increased adsorption capacity. Adsorption capacity decreases as a result of an insufficient availability of 2-nitrophenol ions to occupy all of the active adsorption sites as the adsorbent dose increases. The optimal mass for further analysis was then determined to be 0.05 g for the adsorbent. 6.3.2.3 Effect of contact time on the adsorption of 2-nitrophenol onto PB and PB-CD The effect of contact time on the adsorption of 2-nitrophenol onto PB and PB-CD were examined at various contact times of 0, 0.5, 1, 2, 5, 10, 20, 30, 60 and 120 min with an initial concentration of 100–200 mg/dm3, and the results of this parameter are presented in Figure 6.5. Both PB and PB-CD biosorbents showed rapid uptake of 2-nitrophenol at the initial adsorption stage. This was followed by a gradual increase until reaching an almost constant stage at the end of adsorption process. This is because, at the beginning of the adsorption process, the surface sites on the adsorbents were unoccupied, while at the end of the process, the adsorbents’ surfaces were saturated with 2-nitrophenol molecules.

Figure 6.4: Effect of adsorbent dose on the uptake of 2-nitrophenol from aqueous solution using (a) PB and (b) PB-CD biosorbent.

6.3 Results and discussion

101

Figure 6.5: Effect of contact time on the adsorption of 4-nitrophenol onto (a) PB and (b) PB-CD at different initial concentrations.

Results indicate that both adsorbents attained 2-nitrophenol adsorption stable equilibrium at 60 min and PB-CD had higher 2-nitrophenol adsorption capacity than PB biosorbent. Due to the repulsion of the 2-nitrophenol molecules on the surface of PB and PB-CD with increasing contact time, it was difficult for the remaining vacant surface sites to become available for further nitrophenol removal after 60 min. 6.3.2.4 Kinetics for 2-nitrophenol adsorption onto PB and PB-CD Two kinetic models (pseudo first-order and the pseudo second-order kinetic models) were used to describe the adsorption of 2-nitrophenol onto PB and PB-CD. The adsorption kinetics, which involve diffusion across a boundary, can be monitored using the pseudofirst order kinetic model [28] and the nonlinear form of the pseudo-first order model is given as: qt = qe (1 − e−kt )

(6.1)

where qt and qe are the amount that were adsorbed at time t and at equilibrium, respectively, and k is the rate constant of the pseudo-first-order kinetic model (min−1). The non-linear form of the pseudo-second-order model, which accounts for adsorption processes that proceed for surface chemisorption [29] is given as: qt =

k 2 q2e t 1 + k 2 qet

(6.2)

where k2 is the rate constant of adsorption, (g/mg min), qe is the amount adsorbed at equilibrium, (mg/g), qt is the amount adsorbed at any given time (mg/g) on the adsorbent’s surface, t, (mg/g). For PB and PB-CD, the kinetics parameter and error analysis are shown in Tables 6.1 and 6.2, respectively. Based on the low correlation coefficient (R 2) ranging from (0.8889–0.9971) and from (0.9293–0.9742) and high % variable error ranging from

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6 Pine bark crosslinked to cyclodextrin for the adsorption

Table .: Kinetic data for the adsorption of -nitrophenols on PB. Kinetic model PB Pseudo-first order Exp. q (mg/g) Model q (mg/g) k (min−) r Variable error Pseudo-second order Exp. q (mg/g) Model q (mg/g) k (g/mg min) h (mg/g min) r Variable error

 mg/dm

 mg/dm

 mg/dm

 mg/dm

 mg/dm

. . . . .

. . . . .

. . . . .

. . . . .

. . . . .

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

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

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

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

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

Table .: Kinetic data for the adsorption of -nitrophenols on PB-CD. Kinetic model PB-CD Pseudo-first order Exp. q (mg/g) Model q (mg/g) k (min−) r Variable error Pseudo-second order Exp. q (mg/g) Model q (mg/g) k (g/mg min) h (mg/g min) r Variable error

 mg/dm

 mg/dm

 mg/dm

 mg/dm

 mg/dm

. . . . .

. . . . .

. . . . .

. . . . .

. . . . .

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

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

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

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

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

(0.5125–71.5988) and from (15.3144–52.1680) values obtained on PB and PB-CD, respectively, the pseudo first order model poorly described the adsorption process. On the other hand, high R2 values ranging from (0.9519–0.9944) and (0.9676–0.9993) and low % variable error ranging from (0.986–26.5693) and (4.3472–12.0839) were observed for pseudo second order model for PB and PB-CD, respectively. It is evident that the pseudo second order modelled qe values were closer to the experimental qe than for those of the pseudo first

6.3 Results and discussion

103

order model. Results therefore, reveal that pseudo second-order kinetic model performed better than the pseudo first order kinetic model in describing the adsorption process. The pseudo second order kinetic model strongly suggests that chemisorption, which involves the sharing or exchange of electrons between 2-nitrophenol and the biosorbent, such as a hydrogen bond, may be the determining factor in the adsorption of 2-nitrophenol onto PB and PB-CD [30]. Similar result were obtained by Afolabi et al. (2018) [31] when 2-nitrophenol is adsorbed from an aqueous solution using activated Vitis vinifera (grape) leaf litter. 6.3.2.5 Equilibrium modelling In order to gain deeper understanding of the interaction behaviour between the 2-nitrophenol pollutant, and the prepared biosorbents in this study, adsorption isotherms were applied. The Langmuir and Freundlich isotherms models, whose non-linear expressions are shown below, were used to model the equilibrium system: qe =

qm K a C e 1 + K a Ce

(6.3)

qe = K F C e1/n

(6.4)

The equilibrium concentration of 2-nitrophenol remaining in solution is Ce, whilst the quantities of adsorbate concentrated on the adsorbent surface at equilibrium is represented as qe (mol/g). The monolayer capacity and equilibrium constant are represented by the Langmuir isotherm constants qm and Ka, while the Freundlich constants KF and n are ascribed to adsorption capacity of the adsorbent, and adsorption affinity. Equilibrium isotherm models and their parameters for the adsorption of 2-nitrophenol onto PB and PB-CD at 299, 304, 309, 314 and 319 K are shown in Table 6.3 and 6.4, respectively. The isotherm data for 2-nitrophenol adsorption onto PB and PB-CD from Table 6.3 and 6.4, had high R2 Table .: Equilibrium data for the adsorption of -nitrophenol onto PB. Isotherm model PB Langmuir q (mg/g) Ka (dm/mg) r % Variable error Freundlich n KF (mg/g) (mg/dm)/n r % Variable error

 K

 K

 K

 K

 K

. . . .

. . . .

. . . .

. . . .

. . . .

. . . .

. . . .

. . . .

. . . .

. . . .

104

6 Pine bark crosslinked to cyclodextrin for the adsorption

Table .: Equilibrium data for the adsorption of -nitrophenol onto PB-CD. Isotherm model PB-CD Langmuir q (mg/g) Ka (dm/mg) r % Variable error Freundlich n KF (mg/g) (mg/dm)/n r % Variable error

 K

 K

 K

 K

 K

. . . .

. . . .

. . . .

. . . .

. . . .

. . . .

. . . .

. . . .

. . . .

. . . .

values ranging from 0.9912-0.9973 and from 0.9941-0.9987 for Langmuir model, as compared to the Freundlich isotherm model with R2 values ranging from 0.9754-0.9856 and 0.9723–0.9949, respectively. The % variable was observed to be lower for Langmuir isotherm with values ranging from 0.4486–3.6377 and from 0.8260–3.0492 for PB and PB-CD, respectively. The results shows that the data better fitted the Langmuir isotherm model than the Freundlich model suggesting homogenous adsorbent surface having similar adsorption energies and that adsorption is monolayer [32]. 6.3.2.6 Regeneration The efficiency of the PB and PB-CD biosorbents as reusable adsorbents was examined through regeneration studies. Regeneration of adsorbents is very vital, because it can significantly lower the material cost which is significant for industrial applications. In addition, the selection of a desorbing agent plays a major role and must be executed with care, to not harm the biosorbent but effectively desorb the metals. The 2-nitrophenol adsorption-desorption cycle on PB and PB-CD was repeated 4 times as presented in Figure 6.6, using ethanol as the desorbing solvent due to phenols’ high solubility in ethanol. The 2-nitrophenol adsorption capacity was observed to reduce gradually with adsorption–desorption cycles. The reduction in adsorption capacity may be ascribing to the structural damage of surface functional groups, and a decrease in active sites of the adsorbent [33]. The regeneration results show that both adsorbents can be reused for atleast three adsorption–desorption cycles with minimal strength loss. 6.3.2.7 Comparison of MNP-OA nanocomposite with other adsorbents Comparison of monolayer adsorption capacities of 2-nitrophenol onto PB and PB-CD with other adsorbents is shown in Table 6.5. PB-CD prepared in this study showed a good

105

6.4 Conclusions

Figure 6.6: Regeneration of 2-nitrophenol onto PB and PB-CD after 4 cycles of adsorption-desorption. Table .: Comparison of qmax values for the adsorption of -nitrophenol by different adsorbents. Adsorbents Fly ash Water hyacinth activated carbon Surfactant treated alumina Activated Vitis vinifera (grape) leaf litter Granulated cock Pine cone Fenton treated pine cone Fenton treated pine cone crosslinked HMDI PB PB-CD

Temperature (K)

qmax values/mg/g

  

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

     

References [] [] [] [] [] [] [] [] This study This study

potential for adsorption studies that is comparable to that of other biosorbents, used to remove 2-nitrophenol.

6.4 Conclusions This study employed sodium hydroxide treated pine bark crosslinked to cyclodextrin via hexamethylene diisocyanate, explored as a low cost effective biosorbent to effectively remove 2-nitrophenol from an aqueous solution. The hexamethylene diisocyanate

106

6 Pine bark crosslinked to cyclodextrin for the adsorption

crosslinking of β-CD onto pine bark was confirmed by FTIR and TGA/DTA characterization. The maximum adsorption capacity for 2-nitrophenol removal by PB and PB-CD was observed at solution pH of 5 and adsorbent mass of 0.05 g and equilibrium was reached in 60 min. The pseudo-second-order kinetics model and Langmuir isotherm model described well the adsorption of 2-nitrophenol onto PB and PB-CD, with Langmuir maximum capacities of 47.36 mg/g and 77.82 mg/g, respectively. The enhanced removal of 2-nitrophenol by PB-CD could be ascribed to the strong abilities of the multiple hydroxyl groups and the inner core of the hydrophobic cavity in β-CD to form complexes with 2-nitrophenol. The adsorbents were reusable for up to 3 cycles, according to the regeneration studies. Results suggest that PB-CD can be applied as a cost effective and efficient biosorbent for the removal of 2-nitrophenol from aqueous solution.

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Daniel O. Omokpariola*, Patrick L. Omokpariola, Patrice A. C. Okoye, Victor U. Okechukwu, Joseph S. Akolawole and Ogochukwu Ifeagwu

7 Concentration evaluation and risk assessment of pesticide residues in selected vegetables sold in major markets of Port Harcourt South-South Nigeria Abstract: Concentration levels and health risk assessment of residues of organochlorine and organophosphate pesticides in four commonly vegetables (Cucumber, carrot, cabbage, and eggplant) collected from major markets of Port Harcourt city, South-south Nigeria were assessed. The collected samples were analysed using QuEChERS (quick, easy, cheap, effective, rugged, and safe) extraction method by gas chromatography coupled with Electron Capture Detector (ECD). Pesticide concentrations were compared with UK/EU maximum residual limits (MRLs). Health risk estimates were analysed using estimated daily intake (EDI), hazard quotient (HQ), and hazard ratio (HR) for children (16.7 kg) and adults (60 kg) weight groups. The results of this study showed that 80% of the vegetable samples contained detectable pesticide residues, of which 70% had residues that exceeded MRLs while 20% had residues below detectable levels. The highest concentrations of HCH residues are present in cabbage with a concentration of 0.25 ± 0.15 mg/kg for α-HCH while the least are present in eggplant with concentration of 0.038 ± 0.025 mg/kg in lindane. Pirimophos-methyl was detected in cucumber at 0.017 mg/kg while parathion and isofenfos was detected only in eggplants at concentration of 0.042 mg/kg and 0.022 mg/kg respectively. Concentrations of parathion, chlorpyrifos, and pirimophos-methyl residues were lower than MRLs in all the detected vegetable samples analysed. Non-carcinogenic health risk estimates for the children consumer groups showed that mevinfos, p, pʹ DDD, aldrin, and heptachlor epoxide detected in eggplant, carrot, and cabbage had HQ > 1. While for adults, only p, pʹ DDD and heptachlor epoxide revealed non-carcinogenic effect in cabbage. Risk was highest

*Corresponding author: Daniel O. Omokpariola, Pure and Industrial Chemistry, Faculty of Physical Science, Nnamdi Azikiwe University, Eligbolo, Anambra, 420261, Nigeria; and Department of Pure and Industrial Chemistry, Nnamdi Azikiwe University, Awka, Anambra, Nigeria, E-mail: [email protected]. https:// orcid.org/0000-0003-1360-4340 Patrick L. Omokpariola, Chemical Evaluation and Regulation, Isolo Industrial Estate, Oshodi Expressway, National Agency for Food and Drug Administration and Control, Isolo, Lagos, 101263, Nigeria. https://orcid.org/ 0000-0002-4983-2719 Patrice A. C. Okoye, Victor U. Okechukwu, Joseph S. Akolawole and Ogochukwu Ifeagwu, Department of Pure and Industrial Chemistry, Nnamdi Azikiwe University, Awka, Anambra, Nigeria. https://orcid.org/00000002-0706-8898 (P.A.C. Okoye). https://orcid.org/0000-0003-2012-1646 (V.U. Okechukwu). https://orcid.org/ 0000-0002-2203-9251 (J.S. Akolawole) As per De Gruyter’s policy this article has previously been published in the journal Physical Sciences Reviews. Please cite as: D. O. Omokpariola, P. L. Omokpariola, P. A. C. Okoye, V. U. Okechukwu, J. S. Akolawole and O. Ifeagwu “Concentration evaluation and risk assessment of pesticide residues in selected vegetables sold in major markets of Port Harcourt South-South Nigeria” Physical Sciences Reviews [Online] 2023. DOI: 10.1515/psr-2022-0317 | https://doi.org/10.1515/9783111328416-007

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7 Pesticide residuesgl in selected vegetables in Port Harcourt, Nigeria

for child consumers. However, most of the pesticide residues were less than 1 for the HQs value which is indicative of insignificant health risk. Human risk estimations for the carcinogenic health effect for the studied vegetables showed that lindane and delta HCH could pose carcinogenic health risks to adult, while aldrin, dieldrin, heptachlor, α HCH, β HCH, delta HCH, and heptachlor epoxide could pose carcinogenic health risks to children. The HRI values in some of the detected residues indicate that the cancer benchmark concentrations exceeded the EDI for the respective organochlorine pesticide in the vegetable samples, thus raising serious concerns of possible carcinogenicity. Non carcinogenic and carcinogenic risk assessment of organochlorine pesticide residues in the studied vegetable indicates health threat. Hence, strict monitoring and control of pesticide residues in agricultural products is being suggested, to protect consumers, especially the children who are vulnerable to the adverse effects of pesticides. Keywords: hazard index; organochlorine; organophosphate; risk assessment; vegetables.

7.1 Introduction Pesticides are chemical compounds or mixture of substances aimed at preventing, combating, and mitigating the effect of pests and vectors on agricultural plants and domestic animals. They are design to kill animals, plants and insects in agricultural and domestic backgrounds such as herbicides, insecticides, fumigants, and fungicides with diverse methods [1]. Pesticides have become widespread pollutants and of global concern because of their persistence in the environment, bioaccumulation potential in the tissues of animals and humans through the food chain, and their toxic properties for humans and wildlife [2]. Most vegetable farmers use pesticides during agricultural production to control and eliminate parasites, insects, and fungal diseases with the aim to meet the demand for quantity and quality of their agricultural products and this result in entry of chemical contaminants into vegetable farms leading to pollution of environmental matrices (soils, rivers, and air) as well as food stuff. Pesticides can remain as residues in soils, crop surfaces or water from where consumers of the food products are constantly exposed [3]. Therefore, exposure of human to pesticides is mainly from residues in food, where the level of exposure depends on both the quantity of food consumed and the level of residues. Thus, presence of pesticides in food has health consequences such as dizziness, headaches, rashes, nausea, cancers, neurotoxicity, genotoxicity, birth defects, impaired fertility, and endocrine system disruption [4]. However, dependence on pesticide is difficult to sustain due to harmful effect on the environment. As a result, to their high degree of toxicity and persistence in the environment most pesticides are harmful to human, flora, and fauna. Although pesticides are manufactured under very strict regulations processes to function with logical certainty and minimal impact, excessive usage of these pesticide results into bioaccumulation of its residues [3, 5]. Therefore, pesticides used in agricultural products

7.2 Methodology

111

need to be regulated since they can have adverse effects on fatal growth, childhood, and adulthood, in addition to other effects caused to the crop yields, non-target organisms and the environmental matrices [6]. In south-south Nigeria, vegetable productions are on the increase to meet the essential diet requirement for humans, and for better health. It is well known that vegetables are essential source of vitamins, minerals, and fiber, as such the use of pesticides leads to immerse health effect from exposure over a long period. Across the world, several approaches have been developed by the United Kingdom/European Pesticide Directive 2009; the United State Environmental protection agency (USEPA), the World Health Organization, and other countries; especially Nigeria with achievable framework and mandate to ban the use of these chemicals that are harmful to humans and the environment but due to lack of surveillance and vigilance, these chemicals are used without due course on the risk involved. So therefore, contamination of agricultural produce poses a serious health risk to the public. However, pesticide contaminations in food products are not documented in the yearly environmental reports and information on pesticide contaminations is generally lacking. The study aims to assess the health risk and concentration levels of pesticide residues in four commonly vegetables (cucumber, carrot, cabbage, and eggplant) sold in major markets of Port Harcourt Nigeria.

7.2 Methodology 7.2.1 Sample collection and preparation Samples of four commonly vegetable (cucumber, carrot, cabbage, and eggplant) were purchased from three major markets (Rumuokoro, Rumuodumaya, and Elozu) in Port Harcourt, Rivers state, Nigeria in August 2019, which are grown by farmers at about 100–200 km radius at close proximity to market location. The samples were collected in labeled individual polythene bags, with the sample numbers and sources. These agro products were chosen because of their wide consumption by the populace and the impact of major plant pests and disease on them both at the pre-harvest and postharvest stages. Each sample was chopped into small pieces and appropriately mixed for homogeneity. Then, the sliced samples were stored at temperature not less than 20 °C using airtight zipper bags till further analysis.

7.2.2 Chemicals All organic solvents used in the study were pesticide grade or HPLC grade. Pesticide reference standards of organochlorine pesticides (alpha-HCH, beta-HCH, lindane, chlorothalonil, delta-HCH, heptachlor, aldrin, heptachlor epoxide, endosulphan I, dieldrin, endrin, endosulphan II, p, p’ – DDD, endosulphan sulfate, p, p’ – DDT), pyrethroids (lambda-cyhalothrin and permethrin), and organophospate pesticides (diclorvos,

112

7 Pesticide residuesgl in selected vegetables in Port Harcourt, Nigeria

mevinfos, diazinon, dimethoate, diclofenthion, phosphamidon, pirimophos-methyl, chlorpyrifos, parathion, fenthion, isofenfos, bromophos, and ethion) were obtained from pesticide reference standards (purity >95%) from Dr. Ehrensdorfer (Augsburg, Germany). Reference standard solutions (1000 μg/ml) of all the analysed pesticides were prepared in methanol and kept at −4 °C Acetonitrile (Lab-Scan; GC, assay >99%), methanol (Merck, 99.9% HPLC grade), ethyl acetate (Lab-Scan, Dublin, Ireland; pesticide grade), acetone, dichloromethane and petroleum ether (Lab-Scan, Pestiscan), formic acid, (Riedel–de Haen; 98–100%), ammonia solution, (Riedel–de Haen; 33%), sodium chloride (Riedel–de Haen; 99%), disodium hydrogen-citrate sesquihydrate (Fluka, 99%), trisodium citrate dihydrate (Fluka), sodium chloride and anhydrous magnesium sulphate (Merck), and anhydrous sodium sulphate (Riedel-deHaen) were used for experiments, and deionized water was obtained using a Milli-Q unit (Millipore Corporation, USA).

7.2.3 Extraction of pesticide residues from samples The QuEChERS (quick, easy, cheap, effective, rugged, and safe) method as describe by Prodhan et al. [7] was used in the extraction of the pesticide residue in which 10 g of homogenized composite vegetable samples were weighed using an electronic weighing balance, into a 250 mL beaker. The sample was Soxhlet extracted for 4 h using dichloromethane as the extraction solvent. The extract was then concentrated by distilling-off the solvent on a rotary evaporator at about 41 °C. The reduced extract was then conserved for clean-up.

7.2.4 Clean-up Clean-up of the extracts was done using a column of about 15 cm (length) × 1 cm (internal diameter) which was packed first with glass wool and then 5 g activated silica gel prepared in a slurry form in dichloromethane. About 5 g of anhydrous sodium sulfate was placed on top of the column to absorb any water in the sample or the solvent. The pre-elution was done with 15 mL of dichloromethane without exposing the sodium sulfate layer to air to prevent the cracking of packed silica gel adsorbent. The reduced extracts were run through the column and allowed to sink below the sodium sulfate layer. Elution was done with 3–10 mL portions of dichloromethane. The eluents were collected and accompanying solvent was then evaporated to dryness under a stream of pure nitrogen and taken for GC/MS analysis as described by [7, 8].

7.2.5 Analysis of organochlorine and organophosphate pesticides Detection and determination of the pesticide residues were done by reconstituting the concentrated extract eluents with 2 mL n-hexane before injecting 1 μL of the purified and

7.2 Methodology

113

cleaned up eluents into the injection port of an Agilent 6890A Gas Chromatography system equipped with electron capture detector (ECD). The separation was performed on a fused silica capillary column (DB-17, 30 m_ 0.250 mm internal diameter, and film thickness of 0.25 µm). The temperatures of the injector and detector were 250 °C and 290 °C, respectively. Oven temperatures program started from 150 °C and increased to 280 °C at 6 °C per minute. The injection was carried on a splitless injector, carrier gas was Helium at a flow rate of 2 mL/min and make up gas was nitrogen. The run time was 25 min. Quantification of the OCPs was based on external calibrations curves prepared from the standard solutions of each of the OCPs. Pesticide residues in the extracts were identified using the retention times of the reference standards as described by Oyeyiola et al. [9].

7.2.6 Quality control All reagents used during the analysis were exposed to the same extraction procedures. Solvents used were run to verify any interfering substances within the runtime. In all batches reagent blanks and samples were fortified with mixed OCP and OPP standards for quality control checks. All the samples were analysed in triplicates. Procedural recoveries were analysed concurrently with each batch of analytical extracts. Fortification level of 0.05 mg/kg was chosen based on the limit of determination. Organochlorine and organophosphate pesticide residues were recovered in the range of 69–119%. Calibration standards was prepared at different concentrations by dilution of the composite stock standard solution with hexane, corresponding to the expected range of concentrations found in the samples and it was used to calibrate (retention time and peak area) the instrument response with respect to analyte concentration. Calibration mixtures of concentration levels 0.005, 0.01, 0.05, 0.1, and 0.5 μg/kg were prepared in methanol: ammonium formate buffer 10 mM pH 4 (1:1) and kept at −4 °C, which produced a regression of range of 0.8975–1.0000. The LOD and LOQ were between 0.07 and 0.75 μg/kg and 0.3–2.3 μg/kg, respectively.

7.2.7 Statistical analysis All analysis was carried out in triplicates and Results were expressed as mean ± standard deviation. All the results obtained were explain using statistical package for social sciences (SPSS version 17.0) and Microsoft Excel 2018 Software.

7.2.8 Risk assessment Health and exposure risk assessment was determined using the United States Environmental Agency’s (USEPA) risk models that assess the health implication of

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7 Pesticide residuesgl in selected vegetables in Port Harcourt, Nigeria

organochlorine and organophosphate exposure to humans (Adults and children). The carcinogenic and noncarcinogenic health risk estimates for each of the organochlorine and organophosphate pesticides residues in vegetables was calculated as the estimated daily intake (EDI) and the health risk Index (HRI). The EDI was obtained by multiplying the mean residual pesticide concentration (m/kg) in each vegetable sample and the food consumption rate (kg/d) and dividing by body weight as shown in equation (7.1). 7.2.8.1 Noncarcinogenic assessment The non-carcinogenic health risk was assessed by calculating the hazard quotient (HQ) which was evaluated by dividing the EDI by their corresponding values of RFD as shown in equation (7.2). When the hazard quotient is >1, the food involved is considered unacceptable and could pose a health threat to the consumers; when the hazard quotient is 1 for children while p, p DDD (1.73 and 1.81 for eggplant and cabbage, respectively) and heptachlor epoxide (2.36 and 2.33 for cabbage and eggplant, respectively) posed noncarcinogenic risk to adult consumer of the studied vegetable sample. Thus, the estimated health risks of pesticide residues in the vegetables based on the HQ was relatively higher for children than adults (Tables 7.4 and 7.5). Only cucumber had HI values < 1 among the detected pesticide residue for the noncarcinogenic health risk. The presence of some residues assessed in this study that has HQs greater than 1 is suggestive of the noncarcinogenic risk associated with long time consumption of the study vegetables by adults and child population within the study area [13, 14]. Hence the organochlorine/organophosphate pesticide residue of vegetable plants in the present study may be considered to pose significant risk to the consumers [30]. The results of health risk obtained in adult and children groups from the analyzed vegetable samples corroborate with previous works on dietary exposure assessment of organochlorine pesticides in two commonly grown leafy vegetables in south-western Nigeria which revealed that the HI values for some of the pesticide residues were above the value of 1 for Adeleye et al. [4] and Adefemi et al. [31] who reported that heptachlor, aldrin, heptachlor epoxide, and endrin aldehyde detected in Senecio biafrae from Ekiti State, Nigeria, posed noncarcinogenic health risk to children. The wide use of pesticides in the world causes major health and environmental problems. Continuous monitoring is also essential from a government regulatory body to prevent indiscriminate use, misuse, and overuse of pesticides.

7.3.3 Carcinogenic risk assessment Carcinogenic risk was assessed for the organochlorine pesticides residue detected in the analyzed vegetable samples. Table 7.6 reveals the cancer benchmark concentrations (CBC) derived using cancer slope factors adopted from USEPA [13, 14] and their hazard ratios (HR) for adult and children consumers. For adult group, lindane (16.76) had HR > 1 in cabbage while delta HCH (1.23) had HR > 1 in eggplant which could pose carcinogenic

Pesticide residue

β-HCH Lindane Chlorothalonil Delta-HCH Heptachlor Aldrin Heptachlor epoxide Endosulphan I Dieldrin Endrin Endosulphan II p, pʹ – DDD

Adult Children Adult Children Adult Children Adult Children Adult Children Adult Children Adult Children Adult Children Adult Children Adult Children Adult Children Adult Children Adult Children

Cucumber

Carrot

Cabbage

Eggplant

EDI

HQ

EDI

HQ

EDI

HQ

EDI

HQ

.E- .E- .E- .E- .E- .E- .E- .E- .E- .E- .E- .E- NA NA .E- .E- NA NA .E- .E- NA NA .E- .E- .E- .E-

.E- .E- .E- .E- .E- .E- .E- .E- .E- .E- .E- .E- NA NA .E- . NA NA . . NA NA .E- .E- . .

.E- .E- .E- .E- .E- .E- .E- .E- .E- .E- .E- .E- NA NA NA NA .E- .E- NA NA .E- .E- .E- .E- .E- .E-

.E- . . . . . .E- . . . . . NA NA NA NA .E- .E- NA NA . . .E- . . .

.E- .E- .E- .E- .E- .E- .E- .E- .E- .E- .E- .E- .E- .E- .E- .E- NA NA NA NA .E- .E- NA NA .E- .E-

.E- . . . . . .E- . .E- . . . . . . . NA NA NA NA . . NA NA . .

.E- .E- .E- .E- .E- .E- .E- .E- .E- .E- .E- .E- NA NA .E- .E- .E- .E- NA NA NA NA .E- .E- .E- .E-

. . . . . . .E- . . . . . NA NA . . .E- . NA NA NA NA . . . .

7 Pesticide residuesgl in selected vegetables in Port Harcourt, Nigeria

α-HCH

Recipients

120

Table .: Noncarcinogenic health risk of organochlorine pesticide residues in vegetable samples.

Table .: (continued) Pesticide residue

Endosulphan sulfate p, pʹ – DDT

Cucumber

Recipients

Adult Children Adult Children

Carrot

Cabbage

Eggplant

EDI

HQ

EDI

HQ

EDI

HQ

EDI

HQ

.E- .E- .E- .E-

.E- .E- .E- .E-

.E- .E- .E- .E-

. . . .

.E- .E- .E- .E-

.E- . . .

.E- .E- .E- .E-

. . . .

NA, not applicable; E, exponent (×∧).

7.3 Results and discussion

121

Pesticide residue

Mevinfos Diazinon Dimethoate Diclofenthion Phosphamidon Pirimophos-methyl Chlorpyrifos Parathion Fenthion Isofenfos Bromophos Ethion

Adult Children Adult Children Adult Children Adult Children Adult Children Adult Children Adult Children Adult Children Adult Children Adult Children Adult Children Adult Children Adult Children

NA, no data; E, exponent (×∧).

Cucumber

Carrot

Cabbage

Eggplant

EDI

HQ

EDI

HQ

EDI

HQ

EDI

HQ

.E- .E- .E- .E- .E- .E- .E- .E- .E- .E- .E- .E- .E- .E- .E- .E- NA NA NA NA NA NA NA NA NA NA

.E- .E- .E- .E- .E- .E- .E- .E- .E- .E- .E- .E- .E- .E- .E- .E- NA NA NA NA NA NA NA NA NA NA

.E- .E- .E- .E- .E- .E- .E- .E- .E- .E- .E- .E- NA NA .E- .E- NA NA NA NA NA NA NA NA NA NA

.E- . . . . . .E- . . . . . NA NA . . NA NA NA NA NA NA NA NA NA NA

.E- .E- NA NA .E- . NA NA NA NA .E- .E- NA NA .E- .E- NA NA NA NA NA NA NA NA NA NA

.E- . NA NA . . NA NA NA NA . . NA NA .E- . NA NA NA NA NA NA NA NA NA NA

.E- .E- .E- .E- .E- .E- .E- .E- .E- .E- .E- .E- NA NA .E- .E- .E- .E- NA .E- .E- NA NA NA NA NA

. . . . . . . . . . . . NA NA . . .E- . NA . . NA NA NA NA NA

7 Pesticide residuesgl in selected vegetables in Port Harcourt, Nigeria

Diclorvos

Recipients

122

Table .: Noncarcinogenic health risk of organophosphate pesticide residues in vegetable samples.

Table .: Carcinogenic health risk estimation of organochlorine pesticide residues in vegetable samples. Pesticide residue

α-HCH β-HCH Lindane Chlorothalonil Delta-HCH Heptachlor Aldrin

Dieldrin p, pʹ – DDD p, pʹ – DDT

Adult Children Adult Children Adult Children Adult Children Adult Children Adult Children Adult Children Adult Children Adult Children Adult Children Adult Children

Cucumber

Carrot

Cabbage

Eggplant

CBC

HRI

CBC

HRI

CBC

HRI

CBC

HRI

. .E- . . . .E- . . . . . . NA NA . .E- . . . . . .

.E- .E- .E- .E- .E- . .E- .E- .E- .E- .E- .E- NA NA .E- .E- .E- .E- .E- .E- .E- .E-

.E- .E- .E- .E- .E- .E- . . .E- .E- .E- .E- NA NA NA NA NA NA . .E- . .E-

. . . . .E- . .E- .E- . . . . NA NA NA NA NA NA .E- . .E- .

.E- .E- .E- .E- .E- .E- . . .E- .E- .E- .E- .E- .E- .E- NA NA NA . .E- . .E-

. . . . . . .E- .E-E . . . . .E- . . . NA NA .E- . .E- .

.E- .E- .E- .E- .E- .E- . . .E- .E- .E- .E- NA NA .E- .E- NA NA .E- .E- .E- .E-

. . . . . . .E- . . . . . NA NA .E- . NA NA . .E- .E- .

7.3 Results and discussion

Heptachlor epoxide

Recipients

NA, not data; HRI greater than ; E, exponent (×∧).

123

124

7 Pesticide residuesgl in selected vegetables in Port Harcourt, Nigeria

risk to its consumers. For children group, delta HCH (1.56), β HCH (1.48), heptachlor (5.80), heptachlor, epoxide (3.108) and α HCH (9.95) detected in egg plant had HR > 1 while carrot and cabbage was observed with heptachlor(1.07) and α HCH (1.108), respectively, with HR > 1. It was observed that other detected pesticide residues had HR < 1. The hazard ratio (HR) > 1 means it could pose carcinogenic risk to its consumers (10, 28) The consumption of the contaminated pesticide could pose potential carcinogenic effect for children and adult consumers of the detected vegetable samples purchased from market with risks greater than 1 in a million people [30, 32]. The HRI for the entire pesticide residue detected in cucumber was however less than 1 for both adult and child consumers. Lindane recorded the highest HR of 16.76 in cabbage. These HR values observed in the present study indicate that the cancer benchmark concentrations exceeded the EDI for the respective organochlorine pesticide in the vegetable samples, thus raising serious concerns of possible carcinogenicity. The result of the present study was in line with the report of Adeleye et al. [4] but in contrast with the study of Bolor et al. [33] who reported that carcinogenic risk values for vegetables from all selected farms in Ghana were < 1. Consumption of vegetables contaminated with organochlorine/organophosphate pesticide residues could cause liver lesions and may also disrupt reproductive functions as well as carcinogenic risks [13].

7.4 Conclusions The study assessed the presence of organochlorine and organophosphate pesticide residue in four selected vegetables sold in major markets of Port Harcourt, Rivers state, Nigeria. Most of the samples (cucumber, carrot, cabbage, and eggplant) analysed in this study indicates presence of one or more of the pesticide residues, with most occurring at concentrations higher than their respective EU/UK maximum residue limits (MRLs). Exposure risk assessment of organochlorine and some of organophosphate pesticide residues in selected cereal crops indicate health threat, thus giving HQs and HRI values greater than 1. It indicates certain levels of noncarcinogenic/carcinogenic risk associated with the lifetime consumption of vegetables sold within the study area. The observed levels of pesticide residues in cucumber do not pose a serious risk to consumers, but pesticide contamination in carrot, cabbage, and eggplants may give rise to concern. Therefore, routine monitoring programs must be established to control the contamination of vegetables with pesticides as well as education to farmers on pesticides application.

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Supplementary Material: This article contains supplementary material (https://doi.org/10.1515/psr-20220317).

Rafia Azmat*, Rohi Bano, Sumeira Moin, Tahseen Ahmed, Ailyan Saleem and Waseem Ahmed

8 Detection of iodine in aqueous extract of plants through modified Mohr’s method Abstract: This article explores the extraction of iodine contents in Ipomoea pes-caprae plants using the modified Mohr’s method applied to a biological extract prepared in an aqueous solution. The plants were collected from three coastal regions of the Arabian Sea at Karachi coast, privileged as iodine resource areas. The size of the stem, leaves, and flowers of collected plants was measured after transportation into the laboratory before preparation of aqueous extract. It was found to be significantly different in size from each other. The electrical conductivity of the biological extract was recorded through a conductometer. For this purpose, the extract of different parts of the I. pes-caprae plants was prepared, followed by heating and filtration, while silver nitrate (AgNO3) was used as a precipitating agent. It was interesting to note that when filtrate was titrated with AgNO3, the precipitate started to settle down. Results showed the lowest iodine concentration in the flowers of all tested plants, followed by the highest in the leaves. Conductometric precipitation reaction was influential in determining iodine in herbal medicinal plants. It was observed that the size of the plants and collection sites impacted the iodine concentration. It was lowest in I. pes-caprae, collected from sea view Karachi, while highest in plants of Hawksbay. The endpoint of this biochemical reaction was taken when conductivity started rising. The standard curve of KI was prepared to determine the concentration of iodine in plant samples through conductometric titration. Moreover, the presence of Iodine was confirmed through a chemical testing method using HNO3, NH4OH, and H2SO4, after a complete precipitation reaction. The iodine quantification was done using a spectrophotometer through hexane solvent after being treated with H2SO4. Keywords: AgNO3; conductometric titration; iodine; plant; standard curve.

8.1 Introduction The role of the ocean is significant in the universal biogeochemical sequences of methyl halides as some halides like chloride and iodide are vital minerals for normal plant

*Corresponding author: Rafia Azmat, Department of Chemistry, University of Karachi, 75270 Karachi, Pakistan, E-mail: rafi[email protected] Rohi Bano and Sumeira Moin, Department of Botany, University of Karachi, 75270 Karachi, Pakistan Tahseen Ahmed and Ailyan Saleem, Department of Chemistry, University of Karachi, 75270 Karachi, Pakistan Waseem Ahmed, Department of Horticulture, The University of Haripur, Hatter Road, 22620 Haripur, Pakistan As per De Gruyter’s policy this article has previously been published in the journal Physical Sciences Reviews. Please cite as: R. Azmat, R. Bano, S. Moin, T. Ahmed, A. Saleem and W. Ahmed “Detection of iodine in aqueous extract of plants through modified Mohr’s method” Physical Sciences Reviews [Online] 2023. DOI: 10.1515/psr-2022-0291 | https://doi.org/10.1515/9783111328416-008

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growth and development. Among halides, ion iodine originated in numerous valence states in the landscape, including iodide (I−), iodate (IO3−), elemental iodine (I2), and organic iodine. This depends partially on pH and the redox status of the adjacent atmosphere. The marine organism in seawater is involved in the biosynthesis of CH3I [1, 2]. Primarily, soil I2 accumulates in the soil-plant structure through drizzling and dehydrated deposition derived from seawater’s volatilization of methylated forms [3]. Iodide is the predominant form of Iodine in freshwater resources; on the other hand, the ocean contains iodate as the most commonly existing state of I2 [4]. Yamada et al. [5] reported the occurrence of iodine in soil by the HPLC method. The marine water bodies are the principal pools of bioavailable iodine to the earth in land reservoirs [6]. Seafood, including fish, shrimps, and marine algae, is a rich resource of iodine. I2 is an essential nutrient element found in plants and some foods and a vital part of the thyroid hormone needed to control the metabolism of the body and other functions, including proper bone and brain development, specifically during pregnancy and infancy. It occurs in many states like iodate, iodide, and inorganic and organic iodine salts of sodium and potassium, where it is referred to as iodide, not as iodine. The soil in several regions of the world contains fluctuating amounts of Iodine that may affect the contents of Iodine in plants, which may act as a common threat factor to the people living in these regions based on plant diets [7, 8]. Therefore, in these regions, Iodine is recommended as a supplement to iodide salt or iodine supplement pills for health-associated issues. Furthermore, some land-based plants that grow in iodine-rich soil also contain high concentration of iodine. Iodine is substantial for human health due to its significance in metabolic functions related to human health, where deficiency of Iodine causes metabolic disorders, especially thyroid function [9]. The various areas of the earth facing iodine deficiency; are considered a first factor of the irregular distribution of Iodine on the earth’s crust [9]. The conductometric analysis is a reliable and valuable simple technique used to determine the (ions) nature of electrolytes in a solvent. Investigations on the movements of ions and their solvation in diverse solvent systems have attracted researchers in recent years due to their significant characteristics from basic to advanced levels [10–13]. In a mixture of solvents, especially in a 1:1 ratio, the conductance measurements are now abundant [3, 14] and reported in the literature in aqueous, nonaqueous [15], and mixed solvent systems [2, 16–18]. The electrical conductivity of the polyvinyl alcohol mixture with inorganic acids and water was investigated by several researchers [19, 20]. The conductivity measurements of transition metal (II) sulphates in binary solvents have been carried out, such as ZnSO4 and CuSO4 in dioxin + water, ethanol + water, acetone + water, and ethylene glycol + water [2]. Conductometric titration using silver nitrate has also been used for the determination of many drugs by Ayad et al. [1]. Current research aims to develop a simple, cost-effective analytical technique for monitoring and determining iodine in herbal plants under different environmental conditions, anticipating its importance in human life. Ipomoea pes-caprae- plant was selected for this purpose due to its medicinal significance as a resource of Iodine for

8.2 Materials and methods

129

regulating thyroid function. Generally, Iodine in plants is present in the form of methyl iodide. Therefore, an effort was made for the first time to detect Iodine through the conductometric chemical precipitation titration method in the aqueous extract of plants through modification in the Mohr method, where AgNO3 was used as a precipitating agent while precipitate was subjected to chemical testing using HNO3, NH4OH, and H2SO4 to confirm the presence of Iodine. An attempt has also been made to calculate the amount of iodide in the given plant sample of different locations concerning the growing region. Moreover, the iodine concentration was determined from a standard curve of KI constructed for the first time while molecular Iodine was separated using hexane solvent and analyzed through a spectrophotometer.

8.2 Materials and methods All chemicals like AgNO3, hexane, HNO3, H2SO4, and NH4OH were used without further purification; purchased from Sigma Aldrich.

8.2.1 Collection of Ipomoea pes-caprae from three coastal sites The matured stems, leaves, and flowers of fresh I. pes-caprae plants were plucked and collected from three coastal sites of the Arabian Sea, including Hawkes bay, Sea view, and Baluchistan, Pakistan, and transported to the Department of Chemistry, the University of Karachi, for measurement of their size and weight before analysis.

8.2.2 Preparation of plant samples for analysis of bioactive iodine An amount of 1 g of sample of fresh leaves, stems, and flowers crushed into a pistol and mortal with 20 mL of conductivity water followed by heating for 10 min. The crushed samples were then filtered and titrated with 0.02 M silver nitrate (AgNO3). The conductivity was plotted against the volume of AgNO3 to determine the iodine concentration by the first initial peak through the standard curve of KI.

8.2.3 Standard curve of KI The standard solution of KI was prepared through modification in the method described by Wu et al. [21], where KCl was replaced by KI. The conductivity of different dilutions of KI was noted, which was prepared through the standard solution (Figure 8.1).

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Figure 8.1: Standard curve of KI.

8.2.4 Chemical analysis of iodine The precipitate obtained by titrating the filtrate of an aqueous extract with AgNO3 was treated with HNO3, H2SO4, and NH4OH to validate the Iodine in different parts of the plants.

8.2.5 Separation of iodine The molecular Iodine was separated by treating the precipitate with H2SO4, where violet fumes were collected in hexane and subjected to spectral analysis at Schemadzo spectrophotometric 180 A.

8.2.6 Determining LOD and LOQ The limit of detection and quantification were determined by the method given in link [22].

8.3 Results and discussion The current investigation reports determining and quantifying Iodine through simple and cost-effective ways in the herbal plants’ I. pes-caprae for the first time. I. pes-caprae, tolerant to saline air, grows in upper parts of beaches usually called morning glory, also recognized as bishops or goat’s foot, and is a familiar creeping belonging to the family Convolvulaceae. It is widely distributed as one of the most common salt-tolerant plants and offers one of the best-known examples of oceanic dispersal. The size of the roots,

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131

stem, leaves, and flowers of all plants collected from three different regions showed that size and iodine contents related to the collection site (Figures 8.2–8.5). It was highest in plants I. pes-caprae, collected from Hawks Bay region, while minimal in the region of sea view site; however, average in plants collected from Balochistan coastal zone (Table 8.1). The principle used in this investigation is based on the solubility product (Ksp for Cl = 1.2 × 10−10 and I = 1.4 × 10−16) of halides ion. When a mixture of Cl− and I− titrated with AgNO3 as described by the Mohr method, AgI precipitated initially, almost completely, due to the lower solubility product of Iodine before AgCl began to precipitate. The volume (1.1 mL) of standard AgNO3 (0.02 M) added to the aqueous extract of various parts of the plant’s yield break appeared in the titration curve (Figure 8.1), which was used to calculate the concentration of I2. The iodine in plants is present as CH3I and converted into AgI after reaction with AgNO3, which acts as a potent reducing agent, thereby converting H2SO4 into hydrogen sulfide gas and the iodide ion oxidized to free Iodine according to the following equation.

Figure 8.2: The conductivity curve of flower extract samples.

Figure 8.3: The conductivity curve of stem extract samples.

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8 Detection of iodine in plants extracts

Figure 8.4: The conductivity curve of leaves extract samples.

Figure 8.5: The conductivity curve of root extract samples.

8I− + H2 SO4 + 8H+ == > 4I2 + H2 S + 4H2 O Results presented in Figures 8.2–8.5 showed that conductivity decreases with added amounts of AgNO3 in plant extract samples in which iodide ion has been precipitated in the form of AgI. The precipitate, after complete reaction, was separated by filtration and further treated with HNO3, NH4OH, and H2SO4 to validate the iodine contents in plants. It was observed that the precipitate remained undissolved during the reaction with HNO3, and NH4OH, while Iodine was liberated (purple colour) when the precipitate was treated with H2SO4, which was later on extracted from the reaction mixture by adding hexane. The Iodine dissolved in hexane was subjected to spectrophotometric analysis, where a peak was observed at a wavelength of 523, similar to that of Iodine in hexane as standard. Results showed that iodine contents were maximum in plants collected from the Hawkebay region, which may be due to the less pollution at this beach. The two regions of collection of plants, including Baluchistan and Karachi seaview, showed less content in all parts of the plants (Jopke et al. [14], which were related to the urbanization as sea view is an advanced, newly developed residential area with a thick population while Baluchistan coast is commercial zone). The conductometric analysis of Iodine indicates the impact of urbanization and commercialization on significant elements of human health. The iodine contents in the roots of the plants (Table 8.1) showed that soil which is the resource of any nutrient assembling in plants, is severely affected in the sea view region

Limit of detection (LOD) (μg kg−)

. . .

. . .

. . .

. . .

 ± .**  ± .**  ± .**

 ± .**  ± .**

 ± .**

 ± .**

 ± .**  ± .**

 ± .**

 ± .**  ± .**

Baluchistan Sea view Hawks bay

Baluchistan Sea view

Hawks bay

Baluchistan

Sea view Hawks bay

Baluchistan

Sea view Hawks bay

*

Reference value of other plants and food stuff

Dry fruits ranging from . to . mg vegetables Garlic (Allium sativum) . ± . µg/ g Leafy vegetables . μg kg−

. .

.

. .

.

.

. .

Roots

Stem

. ± . µg  g Garlic (Allium sativum) Freshwater fish . μg kg− Fresh fruits . μg kg− Water . μg kg−

 ± . µg/ g Cowpea (Vigna sinensis) Sea fish . μg kg− Dairy . μg kg− Meats . μg kg− Cereals . μg kg−

. ± . µg/ g Uha leaves (Pterocarpus spp.) Uziza (Piper guineense) . ± . µg/ g Leafy vegetables . μg kg−

Leaves

. . .

Flower

Limit of quantification (LOQ) (μg kg−)

Ujowundu et al. [] Fordyce []

Ujowundu et al. [] Fordyce []

Ujowundu et al. [] Fordyce []

Ujowundu et al. [] Fordyce []

Reference

± Standard deviation of three replicates, the significance difference is less than p = ., the main difference at significant is ***P < ., significance level = *P < ., **P < . and ***P < ..

Regions

I concentration in (μg kg−)

Table .: Values of Iodine in different Ipomoea pes-caprae plant samples through conductometrically in comparison of other techniques with LOD and LOQ.

8.3 Results and discussion

133

134

8 Detection of iodine in plants extracts

(with the lowest amount of iodine in it), where domestic pollution is at its highest level with no safety measurement of the environment. The current variation of I2 contents through conductometric analysis in I. pes-caprae plants, collected from three different zones, is in accordance with the reports of Fuge and Johnson [24]. They reported that the contents of I2 in plants varied with the type of soil, followed by the I2 content of herbage being lower than normal, showing irregularities in different plant species. Besides, this flower showed lower Iodine contents while the maximum was in the leaves, similar to the present investigation. Also, the current investigation results agree with previously published reports of Weng et al. [25], who reported that, generally, iodine concentration was found to be highest in the leaves (Table 8.1). Jerše, et al. [12] used ICP-MS method after alkaline microwave extraction to extract iodine in several plant samples. It was also reported that iodine contents were more and in an organic state in seaweeds while it was in an inorganic form in kelp, cabbage, tea leaf, and spinach [26]. Bellanger et al. [3] report a method for determining iodine in plants through the destruction of organic matter by alkaline incineration based on Sandell and Kolthoff’s reaction, followed by spectrophotometric determination of iodide. Jopke et al. [14] used photometry-based Sandell–Kolthoff–Reaction for the determination of iodine in plants and soil where the reaction was temperature and time-dependent, while the current method proves to be simple and easy, involving no complex material; and can be used for monitoring the environmental impact on iodine contents in herbal plants. In another study, Johansen and Steinnes [13] used radiochemical separation of iodine in dry plant material using I8 [14] with extraction and precipitation. Jerše et al. [12] established a method for determining iodine in plants through chromatography-inductively coupled plasma mass spectrometry (IC-ICP/MS). Alkaline extraction and IC-ICP/MS involve hightemperature pyrolysis absorption adopted as the pre-treatment method for total iodine analysis, which finally converted all the iodine species into iodide and measured as iodide. The current research results showed that the amount of Iodine collected was comparable with the reports of Yamada et al. [4]. They reported the iodine contents in plants by a high-performance liquid chromatography (HPLC) method through alkaline fusion. The amount of Iodine was determined spectrophotometrically at wavelength 230 nm where the amount of Iodine reaches to 1 mg kg− 1. In another report, Gwarzo [10], detected Iodine in the leaves of many vegetables, including Spinacea olarecea L. (spinach), Brassica olarecea V. (cabbage), Hibiscus sabdriffa L., and Lactuca sativa L. (lettuce) through chemical treatment based on the oxidation of iodide ions (I−) into molecular Iodine (I2). When I− is treated with concentrated H2SO4 and H2O2, I2 is liberated. The liberated I2 was extracted into tetra chloromethane, which developed a purple color whose intensity is proportional to the concentration of I− in the plants. He determined Iodine through a calibration graph prepared by 0.01 M iodine solution, while the current investigation determined iodide through KI standard curve. Similarly, Pauwels and Van Wesemael [10], also used H2SO4, HNO3 as well as perchloric acids for the determination of iodine contents in grass and crops using diluted digest, while the current method

References

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provides a simple analytical technique besides all over reported work for the detection of Iodine followed by environmental impact on important nutrient elements.

8.4 Conclusions It was concluded that detecting iodine in herbal plants using conductometric analysis proves to be a simple, accurate, and cost-effective method. It can be applied to monitor the environmental impact on other iodine-enriched plants, followed by monitoring medicinal plants as herbal plants. The results of detected Iodine through the conductometric chemical precipitation method are compared with the other advanced analytical techniques. The results showed that plants grow under different regions, impacting their size and elemental iodine uptake. Furthermore, the study has shown that herbal plants may be taken in place of iodine-supplemented salt to regulate thyroid function or to control iodine deficiency disorders (IDD) in humans. Acknowledgment: The Principal Author is thankful to the Dean, Faculty of Science, the University of Karachi, for financial support and HEC Pakistan for permanent equipment under Project No. No.20-2282/NRPU/R&D/HEC/12/5014.

References 1. Ayad MM, Abdellatef HE, Hosny MM, Sharaf YA. Conductometric titration method for determination of naftidrofuryl oxalate, propafenone HCl and sotalol HCl using silver nitrate. Eur J Chem 2012;3:332–6. 2. James JC. The electrolytic dissociation of zinc sulphate, copper sulphate, and zinc malonate in mixed solvents. J Chem Soc 1951;32:153–7. 3. Bellanger JR, Tressol JC, Piel HP. A semi-automated method for the determination of Iodine in plants. Ann Rech Vet 1979;10:113–8. 4. Tagami K, Uchida S, Hirai I, Tsukada H, Takeda H. Determination of chlorine, bromine and Iodine in plant samples by inductively coupled plasma-mass spectrometry after leaching with tetramethyl ammonium hydroxide under a mild temperature condition. Anal Chim Acta 2006;570:88–92. 5. Yamada H, Sugahara M, Kosaka H, Katayama A, Takahashi K, Yonebayashi K. Determination of total and water soluble Iodine in soil by high performance liquid chromatography. Soil Sci Plant Nutr 1996;42: 367–74. 6. Venturi S. Evolutionary significance of iodine. Curr Chem Biol 2011;5:155–62. 7. Duborská E, Matulová M, Vaculovič T, Matúš P, Urík M. Iodine fractions in soil and their determination. Forests 2021;12:1512. 8. Moin S. Asma. Iodine important health nutrient: sources and method of detection in search of natural iodine resources. J Adv Nutri Sci Technol 2022;2:25–9. 9. FAO. The state of food insecurity in the world. Rome: FAO; 2012. Available from: https://www.fao.org/ publications/card/en/c/d63542a7-6eb0-5284-9c7d-a960894b9183/. 10. Pauwels GB, Van Wesemael JC. A new routine method for determination of iodine in plant materials. Anal Chim Acta 1962;26:532–40.

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11. Gwarzo US. Determination of iodine content of some commonly utilized leafy vegetables: Spinacea oleracea Linn (spinach), Brassica oleracea Var (cabbage), Hibiscus sabdriffa Linn and Lactuca sativa L. (lettuce) found in Kano metropolis vegetable markets. Chem Search J 2012;3:11–3. 12. Jerše A, Jaćimović R, Maršić NK, Germ M, Šircelj H, Stibilj V. Determination of Iodine in plants by ICP-MS after alkaline microwave extraction. Microchem J 2018;137:355–62. 13. Johansen O, Steinnes E. Determination of Iodine in plant material by a neutron-activation method. Analyst 1976;101:455–7. 14. Jopke P, Bahadir M, Fleckenstein J, Schnug E. Iodine determination in plant materials. Commun Soil Sci Plant Anal 1996;27:741–51. 15. Schwehr KA, Santschi PH. Sensitive determination of iodine species, including organo-iodine, for freshwater and seawater samples using high performance liquid chromatography and spectrophotometric detection. Anal Chim Acta 2003;482:59–71. 16. Vargas MA, Vargas RA, Mellander BE. Phase behavior of a PVAL-based polymer proton conductor. Phys Status Solidi Basic Res 2000;220:615–24. 17. Vargas MA, Vargas RA, Mellander BE. New proton conducting membranes based on PVAL/H3PO2/H2O. Electrochim Acta 1999;44:4227–32. 18. Vargas RA, Zapata VH, Matallana E, Vargas MA. More thermal studies on the PVOH/H3PO2/H2O solid proton conductor gels. Electrochim Acta 2001;46:1699–702. 19. Stewart JC, Vidor GI. Iodine content of food. Br Med J 1976;2:757–8. 20. Ujowundu CO, Kalu FN, Nwosunjoku EC, Nwaoguikpe RN, Okechukwu RI, Igwe KO. Iodine and inorganic mineral contents of some vegetables, spices and grains consumed in Southeastern Nigeria. African J Biochem Res 2011;5:57–64. 21. Wu YC, Koch WF, Pratt KW. Proposed new electrolytic conductivity primary standards for KCl solutions. J Res Natl Inst Stand Technol 1991;96:191. 22. https://arts-sciences.und.edu/academics/chemistry/kubatova-researchgroup/_files/docs/determination_ of_lods_new.pdf. 23. Fordyce JA. Aggregative feeding of pipevine swallowtail larvae enhances hostplant suitability. Oecologia 2003;135:250–7. 24. Fuge R, Johnson CC. Iodine and human health, the role of environmental geochemistry and diet, a review. Appl Geochem 2015;63:282–302. 25. Weng HX, Weng JK, Yan AL, Hong CL, Yong WB, Qin YC. Increment of iodine content in vegetable plants by applying iodized fertilizer and the residual characteristics of Iodine in soil. Biol Trace Elem Res 2008;123: 218–28. 26. Teas J, Pino S, Critchley A, Braverman LE. Variability of iodine content in common commercially available edible seaweeds. Thyroid 2004;14:836–41.

Uche E. Ekpunobi*, Fabian M. Onyekwere, Rosemary U. Arinze, Daniel N. Enenche, Daniel O. Omokpariola and Victor U. Okechukwu

9 Appraisal and health risk assessment of potential toxic element in fruits and vegetables from three markets in Anambra state, Nigeria Abstract: The influence of anthropogenic activities has led to increase of potential toxic elements (PTEs) present in plant-based food sources, even in trace amounts; thus, it is known to pose a threat to human health over an extended period. The concentration levels of PTEs (Pb, Cu, Zn, Cd, Co and Ni) in soils and fruits and vegetables from three markets (Atani, Omor and Eke Awka) in Anambra state, Southeastern Nigeria were quantified and assessed using Atomic Absorption Spectrophotometer (AAS) instrument. The result of PTE (mg/kg) studied was in the order Zn > Cu > Pb > Ni > Co > Cd with the highest value for Zn (13.61 mg/kg) recorded in soil sample at Omor market. The mean concentrations of PTEs in soil of both evaluated studied areas were lower than the WHO permissible limits for PTEs in soil. Among the fruits and vegetables, Bitter leaf had highest PTE from Eke Awka followed by banana, fluted pumpkin, water leaf and onion. Fluted pumpkin had the highest PTE content from Omor followed by watermelon, water leaf, onion and cucumber, while water leaf had the highest metal concentration from Atani followed by bitter leaf, fluted pumpkin, cucumber, lettuce and carrot. Health risk assessment showed that hazard index (HI) in decreasing order was Eke – Awka market > Omor market > Atani market across all fruits and vegetables, as adverse health effect is not expected. The present study recommends containment measures of potential toxic elements in soils and fruits/vegetables to prevent excessive accumulation in food value chain. Keywords: fruits and vegetables hazard index; Nigeria; potentially toxic element; soil.

9.1 Introduction The increase of anthropogenic activities, especially with the application of modern technologies, environmental pollution and contamination of the human food chain, has

*Corresponding author: Uche E. Ekpunobi, Department of Pure and Industrial Chemistry, Nnamdi Azikiwe University, Awka, Anambra State, Nigeria, E-mail: [email protected]. https://orcid.org/0000-0003-2079-8470 Fabian M. Onyekwere, Rosemary U. Arinze, Daniel N. Enenche, Daniel O. Omokpariola and Victor U. Okechukwu, Department of Pure and Industrial Chemistry, Nnamdi Azikiwe University, Awka, Anambra State, Nigeria. https://orcid.org/0000-0002-8695-5574 (D.N. Enenche). https://orcid.org/0000-0003-1360-4340 (D.O. Omokpariola). https://orcid.org/0000-0003-2012-1646 (V.U. Okechukwu) As per De Gruyter’s policy this article has previously been published in the journal Physical Sciences Reviews. Please cite as: U. E. Ekpunobi, F. M. Onyekwere, R. U. Arinze, D. N. Enenche, D. O. Omokpariola and V. U. Okechukwu “Appraisal and health risk assessment of potential toxic element in fruits and vegetables from three markets in Anambra state, Nigeria” Physical Sciences Reviews [Online] 2023. DOI: 10.1515/psr-2022-0321 | https://doi.org/10.1515/9783111328416-009

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become unavoidable due to its complexities [1, 2]. Human exposure to toxic element has risen dramatically in the last decades because of an exponential increase with industrial processes and products [3]. Potential toxic elements are widely distributed in the environment bioavailable and are considered substantial chemical food contaminants that have been reported to have positive and negative functions in human life [4]. These elements in soil pose potential risks to the environment and can have an adverse effect on human health through various absorption pathways such as direct ingestion, dermal contact, diet through the soil–food chain, inhalation and oral intake [5, 6]. Fruits and vegetables play important roles in our daily diet as economic crops that are known to be rich sources of vitamins, minerals and fibres have significant antioxidative effects. As such, they hold essential mineralized water, calcium, iron, sulphur and potash [7]. However, various anthropogenic activities such as fast industrialization, disorganized urbanization, mining, smelting and extended-term use of substantial amounts of pesticides, composts, wastewater for irrigation and fertilizers are causing increased toxic metal concentrations in the environment [8]. Fruits and vegetables accept these toxic elements by absorbing them from contaminated soils, as well as from deposits on parts of the vegetables exposed to the air from contaminated environments [9]. A large number of plants have shown to bioaccumulate various metals (e.g., Cd, As, Cr, Cd, Cu, Pb and Fe) when grown near smelting or other industrial areas [10]. However, the uptake of these PTEs by plants from the soil depends on a number of factors, including soil type, plant species, application of agrochemicals, the solubility of toxic element and soil pH [11]. The crop-based contamination with high amount of lead, cadmium and arsenic has been known to change the biochemical functions of the kidney, lungs, liver and cardiovascular tissues in humans and may even cause gastrointestinal cancer [12]. In addition, manganese and nickel exposure may be associated with decreased intelligence quotient in children and increased incidences of allergic contact dermatitis and eczema [13]. Based on persistent nature and cumulative behaviour as well as the probability of potential toxicity effects of these elements because of consumption of fruits and leafy vegetables, they might be transported from soil to groundwaters and there is a need to evaluate and analyse these trace elements to meet the agreed international requirements. Thus, the research seeks to assess concentration level of potentially toxic element and health risk impact of cadmium (Cd), cobalt (Co), copper (Cu), zinc (Zn), lead (Pb) and nickel (Ni) in the soil and edible parts of different vegetable species in Anambra State, South-eastern Nigeria.

9.2 Materials and methods 9.2.1 Study area The study area was conducted in three different sites in Anambra state, South-eastern Nigeria: Atani, Omor and Eke Awka market. Atani lies between Latitude 6.0131°N and

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139

Longitude 6.7467°E, Omor lies between Latitude 6.517°N and Longitude 6.9612°E while Eke Awka market lies between Latitude 6.1223°N and Longitude 7.4536°E. The study areas are characterized by good vegetation and fertile lands to produce food crops such as rice, maize, yam, cassava and assorted fruits. The locations have two seasons (wet and dry); the wet season has longer duration from March to the end of October, whereas dry season has relative shorter duration from the last week of November or early December to early March dependent on climatic conditions. Based on its geographical location, the climate is tropical and rainy, and the region experiences abundant rainfalls with annual temperature ranges from 26 to 31 °C.

9.2.2 Sample collection Fruits and vegetable samples were collected from three market sites (Atani, Omor, and Eke Awka) in Anambra State, Nigeria, and soil samples were collected from two different farms in Atani and Omor Anambra State. The fruits and vegetable samples were washed and sliced into pieces, oven-dried at 80 °C and then crushed using a stainless-steel blender and passed through a 2 mm sieve. The resulting fine powder was kept at room temperature for further analysis.

9.2.3 Digestion of soil samples The soil samples were air-dried for 48 h, ground and sieved using a 0.5 mm mesh size sieve to have a uniform particle size. Each sample was labelled and stored in a dry plastic container that had been pre-cleaned with concentrated nitric acid before elemental spectral analysis with atomic absorption spectroscopy (AAS) as described by Wilson et al. [14].

9.2.4 Digestion of fruits and vegetables samples Around 1 g of each sample was transferred with 15 cm3 of aqua regia solution into 100 mL conical flask and heated with a hotplate in the fume cupboard for 20 min. Thereafter completion of sample digestion, the flask was allowed to cool and then 10 cm3 distilled water was added to the digested samples and the solution filtered. The filtrate was made up to 50 cm3 with distilled water and kept for AAS analysis as described by Sahito et al. [15].

9.2.5 Health risk assessment The health risks connected with PTEs consumed through fruits and vegetables consumption were assessed using the hazard quotient (HQ) as described by USEPA [16] and evaluated from the ratio of determined dose to the reference dose (RfD).

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9 Fruits and vegetables from three markets in Anambra state, Nigeria

HQ =

DIFV × EF × FD × Cmetal RfD × Bo × 365 days HI = ∑(HQ)

(9.1) (9.2)

where: HQ is likely probability that can lead to adverse health effects, DIFV is the daily intake of fruits and vegetables (kg/person/day), C-metal is the concentration of metal in the vegetable (mg/kg), EF is exposure frequency (days/week), FD is frequency duration (52 week/year), RfD is the oral reference dose for the metal (mg/kg/day), B0 is the human body mass (60 kg) and HI is hazard index, which is the sum-total of all hazard quotient (HQ). RfD approximates the daily exposure to which the human population is likely to be without any appreciable risk of harmful effects during a lifetime. The values of RfD for PTEs were taken from the Integrated Risk Information System (IRIS) [17]. The HI is a highly conservative and relative index, as HI is greater than one (>1), the cumulative PTEs may cause adverse health effects and vice versa that is less than one ( Cu > Pb > Ni > Co > Cd. The mean concentration mg/kg of Cd, Co, Cu, Zn, Pb and Ni in the soil at Atani is 0.353 mg/kg, 0.407 mg/kg, 3.537 mg/kg, 12.883 mg/kg, 2.044 mg/kg and 1.054 mg/kg, while that of Omor is 0.156 mg/kg, 0.321 mg/kg, 3.517 mg/kg, 13.61 mg/kg, 2.044 mg/kg and 1.054 mg/kg, respectively. The mean total concentrations of PTEs in the soil of both evaluated areas were lower than the WHO permissible limits for PTEs in soil [18]. The concentrations of PTEs in the

141

9.3 Results and discussion

Figure 9.1: Percentage stark column of PTEs in soil from Atani and Omor.

Table .: Mean PTEs concentration (mg/kg) in sampled soils. Location Atani Omor FAO/WHO

Cadmium

Cobalt

Copper

Zinc

Lead

Nickel

.b ± . .b ± . 

.a ± . .a ± . –

.a ± . .a ± . 

.a ± . .b ± . 

.b ± . .b ± . 

.b ± . .b ± . –

Values expressed as mean ± SD, astatistically significantly at p = ., bnot statistically significantly at p = ..

studied soil samples were highest for Zn followed by Pb, Cu, Ni, Co and Cd. The high observed Zn concentrations in the present study could be ascribed to the nature of the parental material of soils in the study area. Hence, Zn could result from phosphate fertilizers, use of pesticides, organic waste dumping, wastewater disposal, use of sludge, and fossil fuels releases in the area [19]. A high concentration of Zinc (Zn) in soil obstructs plants metabolic functioning, thereby results in retarded growth and causes senescence. Overexposure to Zn can lead to the metal fever, which is caused by oversensitivity. The presence of copper (Cu) in the study area is attributed to the disposal of batteries, smelting waste and soldering works. In high concentrations, copper can cause anaemia, liver and kidney damage and stomach irritation [20, 21]. Pb is attributed to gaseous substances from nearby industries and traffic activities while Cu is traced to the use of agrochemicals. The concentration of cadmium (Cd) was least across the study area. The low content of Cd might be due to the non-use of Cd-containing phosphate fertilizers. The Cd content

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9 Fruits and vegetables from three markets in Anambra state, Nigeria

obtained from this study was lower than that obtained by Yang et al. [22]. Cd is an ecotoxic element, with vastly undesirable effects on soil health, plant metabolism, humans and animal health [23, 24]. At extremely low levels, cadmium exposure results in anaemia, anosmia, cardiovascular diseases and renal problems [25]. Cadmium was generally low in the soil of the study areas. The application of agricultural inputs such as fertilizers, pesticides, and biosolids (sewage sludge), the disposal of industrial wastes increases the total concentration of Cd in soils, and the bioavailability of this Cd leading to increased plant Cd uptake to a significant degree. So, therefore, it can be stated that soil samples were not highly polluted by the six toxic elements, but containment measures are needed to prevent heavy metal accumulation from external pollutants.

9.3.2 Concentrations of potential toxic elements (mg/kg) in fruits and vegetables in Atani market Figure 9.2 and Table 9.2 reveal the results obtained for the mean PTE concentration for fruits and vegetables at Atani. The mean concentration of Cd ranged from 0.192 mg/kg in

Figure 9.2: Percentage stark column of PTEs in fruits/vegetables from Atani market.

143

9.3 Results and discussion

Table .: Mean PTEs concentration (mg/kg) in fruits/vegetables from Atani market. Fruits and vegetables Watermelon Pineapple Carrot Bitter leaf Water leaf F-pumpkin Tomatoes Onion Cabbage Lettuce Cucumber Banana a

Cadmium

Cobalt

Copper

Zinc

Lead

Nickel

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

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

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

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

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

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

a

a

a

a

a

Mean conc. of metal not significantly different at p = . level of significance.

banana to 0.046 mg/kg in pineapple, Co ranged from 0.087 mg/kg in onion to 0.002 mg/kg in watermelon, Cu ranged from 0.988 mg/kg in carrot to 0.131 mg/kg in cabbage, Zn ranged from 4.456 mg/kg in waterleaf to 1.817 mg/kg in banana, Pb ranged from 1.459 mg/kg in fluted pumpkin to 0.015 mg/kg in cucumber while Ni ranged from 0.525 mg/kg fluted pumpkin to 0.163 mg/kg watermelon.

9.3.3 Concentrations of potential toxic elements (mg/kg) in fruits and vegetables in Omor market Figure 9.3 and Table 9.3 reveal the results obtained for the mean PTEs concentration for fruits and vegetables at Omor. The mean concentration of PTEs in the fruits and vegetables was higher than the reference dosage. The mean concentration of Cd ranged from 0.373 mg/kg in onion to 0.099 mg/kg in tomatoes, Co ranged from 0.081 mg/kg in fluted pumpkin to 0.011 mg/kg in cucumber, Cu ranged from 1.133 mg/kg in banana to 0.149 mg/ kg in tomatoes, Zn ranged from 7.633 mg/kg in fluted pumpkin to 2.074 mg/kg in lettuce, Pb ranged from 1.394 mg/kg in watermelon to 0.057 mg/kg in tomatoes/onion while Ni ranged from 0.373 mg/kg in onion to 0.099 mg/kg in tomatoes.

9.3.4 Concentrations of potential toxic element (mg/kg) in fruits and vegetables in Eke Awka market Figure 9.4 and Table 9.4 reveal the results obtained for the mean PTE concentration for fruits and vegetables at Eke Awka market. The mean concentration of Cd ranged from 0.720 mg/kg in fluted pumpkin to 0.302 mg/kg in tomatoes, Co ranged from 0.470 mg/kg in bitter leaf

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9 Fruits and vegetables from three markets in Anambra state, Nigeria

Figure 9.3: Percentage stark column of PTEs from Omor Market. Table .: Mean PTEs concentration in fruits/vegetables at Omor. Fruits and vegetables Watermelon Pineapple Carrot Bitter leaf Water leaf F-pumpkin Tomatoes Onion Cabbage Lettuce Cucumber Banana a

Cadmium

Cobalt

Copper

Zinc

Lead

Nickel

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

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

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

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

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

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

Mean conc. of metal not significantly different at p = . level of significance.

to 0.190 mg/kg in watermelon, Cu ranged from 1.821 mg/kg in cucumber to 0.253 mg/kg in tomatoes, Zn ranged from 10.403 mg/kg in bitter leaf to 2.365 mg/kg in watermelon, Pb ranged from 3.132 mg/kg in carrot to 0.250 mg/kg in banana while Ni ranged from 0.720 mg/kg in fluted pumpkin to 0.302 mg/kg in banana.

9.3 Results and discussion

145

Figure 9.4: Percentage stark column of PTEs from Eke-Awka Market. Table .: Mean PTEs concentration in fruits/vegetables in Eke Awka market. Fruits and vegetables Watermelon Pineapple Carrot Bitter leaf Water leaf F-pumpkin Tomatoes Onion Cabbage Lettuce Cucumber Banana a

Cadmium

Cobalt

Copper

Zinc

Lead

Nickel

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

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

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

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

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

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

Mean conc. of metal not significantly different at p = . level of significance.

9.3.5 Implication of potential toxic element concentration in plants The incremental uptake and bioaccumulation of substances such as toxic elements by plants have public health implications over a period especially when the plants consumed by humans and livestock. The concentration of cadmium (Cd) in the fruits and vegetable samples analysed varied between 0.039 mg/kg and 0.192 mg/kg in Atani with the

146

9 Fruits and vegetables from three markets in Anambra state, Nigeria

least observed in bitter leaf and the highest in banana. In Omor, the Cd levels in the samples varied between 0.099 mg/kg and 0.373 mg/kg with the least observed in tomatoes and the highest observed in onion. In Eke Awka market, the Cd levels varied between 0.302 mg/kg and 0.720 mg/kg with the least observed in bananas and the highest observed in fluted pumpkin. The results of the present study were lower than the values reported by Divrkli et al. [26, 27], on the Cd level in Indian basil. Cadmium should not be neglected since the metal is mobile and highly toxic. Cd causes both acute and chronic poisoning and adverse effects on the kidney, liver, vascular, and immune systems [28]. The concentration levels of cobalt (Co) in the sampled vegetables/fruits varied between 0.002 mg/kg and 0.087 mg/kg with the least observed in watermelon and the highest observed in onion for Atani. In Omor, the Co levels in the samples varied between 0.011 mg/kg and 0.081 mg/kg with the least observed in cucumber and the highest observed in fluted pumpkin. In Eke Awka market, the Co level in the samples varied between 0.190 mg/kg and 0.470 mg/kg with the least observed in watermelon and the highest observed in bitter leaf. Inadequate evidence has been reported on its concentrations in food materials, as cobalt is used in preventing and treating pernicious anaemia, helps in red blood cell production as well supports normal nervous system functions [29]. The concentrations of copper (Cu) varied between 0.131 mg/kg and 0.988 mg/kg in Atani with the least observed in cabbage and the highest observed in carrot. In Omor, the Cu levels in the samples varied between 0.149 mg/kg and 1.133 mg/kg with the least seen in tomatoes and the highest observed in banana. In Eke Awka market, the Cu level in the samples varied between 0.253 mg/kg and 1.821 mg/kg with the least seen in tomatoes and the highest observed in cucumber. The results obtained here were seen to be lower compared to the result of Kacholi and Sahu [30] but in line with the quantitative analysis of Radwan and Salama [31] and Onjanwa et al. [32]. However, most plants hold a significant amount of copper, which is inadequate for normal growth, and is usually ensured through artificial or organic fertilizers. A high concentration of copper can cause metal fumes fever with flu-like symptoms, hair and skin discolouration, dermatitis, irritation of the upper respiratory tract, metallic taste in the mouth and nausea [33]. The concentration of zinc (Zn) in fruits and vegetable samples in Atani varied from 1.817 mg/kg to 4.456 mg/kg with the least in banana and the highest in waterleaf. In Omor, Zn levels varied between 2.074 mg/kg and 7.633 mg/kg with the least in lettuce and the highest in fluted pumpkin while in Eke Awka market, Zn levels varied between 2.365 mg/ kg and 10.403 mg/kg with the least in watermelon and the highest in bitter leaf. Zinc is an essential mineral due to its exceptional biological and public health significance, but excessive ingestion can have adverse effects on human health [32, 34]. Lead is a serious cumulative body poison that enters the body system through the air, water and food and cannot be removed by washing fruits and vegetables [26, 27].

9.3 Results and discussion

147

The lead (Pb) concentration in the fruits and vegetable samples in Atani varied between 0.015 mg/kg and 1.459 mg/kg with the least in cucumber and the highest in fluted pumpkin. In Omor, Pb levels varied between 0.057 mg/kg and 2.034 mg/kg with the least in tomatoes/onion and the highest in carrot. In Eke Awka market, Pb levels varied between 0.250 mg/kg and 3.132 mg/kg with the least in banana and the highest in carrot. The values were lower than Radwan and Salama [31] and Iwuanyanwu and Nganwuchu [35] assessments. The concentration of Pb in some plants could be as a result of increase in pollutants from irrigation water and soil or due to atmospheric deposition (dust resuspension) from traffic activities that impact the grown vegetables causing decolouration. Lead is known to cause both acute and chronic poisoning and thus poses adverse effects on kidney, liver, vascular and immune systems [36]. Nickel in plants could be attributed to cadmium–nickel batteries in the electrical gadgets and some paints used to polish the surfaces of the gadgets, which might have spread across study areas as it was also detected in the soil samples. Ni concentrations in fruits and vegetable samples analysed at Atani varied between 0.084 mg/kg and 0.525 mg/ kg with the least in carrot and the highest in fluted pumpkin. In Omor, Ni levels varied between 0.099 mg/kg and 0.373 mg/kg with the least in tomatoes and the highest in onion. In Eke Awka, Ni levels varied between 0.302 mg/kg and 0.720 mg/kg with the least in banana and the highest in fluted pumpkin. However, Ni levels of 0.067 mg/kg for Indian basil have been reported by Divrkli et al. [26], which is lower than the values seen in the present study. Nickel occurs naturally more in plants than in animal flesh. It activates enzyme in the human body in trace amounts, but its toxicity at higher levels is more conspicuous [27, 37].

9.3.6 Health risk assessment The degree of oral exposure was quantified using USEPA modelling to assess the health implication (non-cancer) of the continuous fruits and vegetables consumption for a period (exposure time) in tandem with exposure frequency, body weight and reference dose (RfD), respectively. The calculation was conducted using an on-the-spot assessment to decide the estimated hazard quotient for each fruit and vegetable consumed from Atani, Omor and Eke Awka market, Anambra State as shown in Table 9.5, Supplementary 1 and 2, respectively. The cumulative evaluation of all estimated hazard quotients of analysed PTE (cadmium, cobalt, copper, zinc, lead and nickel) gave the hazard index (HI) as shown in Figure 9.5, as a closer look at the graphical and data review (Tables 9.6–8) shows that all fruits and vegetables except carrot, cabbage, cucumber and banana in Eke Awka Market were less than one (1), which can be attributed to several reasons such storage condition, agricultural planting locations and style of planting (hydroponic and closed system) as it

148

9 Fruits and vegetables from three markets in Anambra state, Nigeria

Table .: Health risk parameters. Fruits and vegetables Watermelon Pineapple Carrot Bitter leaf Water leaf F-pumpkin Tomatoes Onion Cabbage Lettuce Cucumber Banana

Daily intake of fruits and vegetables

Exposure frequency (days/week)

Frequency duration (weeks/year)

Body weight

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

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

           

           

Figure 9.5: Hazard Index of fruits and vegetables in Atani, Omor, and Eke-Awka markets.

implies that consumption will not cause any adverse health issues and vice versa for others above one (1) [38]. Atani market was okay as the vegetables were relatively below the USEPA reference standard of less than one (1), as such, there is no need to be bothered about any adverse health issues except for fluted pumpkin (1.324). Omor was compared to that of Atani except for watermelon and carrot correspondingly. So, therefore, the reason

9.3 Results and discussion

149

Table .: Estimated hazard quotient of Atani market. Fruits and vegetables

Watermelon Pineapple Carrot Bitter leaf Water leaf F-pumpkin Tomatoes Onion Cabbage Lettuce Cucumber Banana

Hazard quotient

Hazard index (HI)

Cadmium

Cobalt

Copper

Zinc

Lead

Nickel

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

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

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

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

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

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

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

Table .: Estimated hazard quotient of Omor market. Fruits and vegetables

Watermelon Pineapple Carrot Bitter leaf Water leaf F-pumpkin Tomatoes Onion Cabbage Lettuce Cucumber Banana

Hazard quotient

Hazard index (HI)

Cadmium

Cobalt

Copper

Zinc

Lead

Nickel

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

. . . . . . . . . . . .

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

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

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

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

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

Atani market and Omor market had low HI values than Eke Awka market can be attributed to fewer automobile and transportation activities, low or absence of industrial activities in tandem with construction and urbanization, as they are both agrarian communities involved in full-time livestock and farming activities [21].

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9 Fruits and vegetables from three markets in Anambra state, Nigeria

Table .: Estimated hazard quotient of Eke Awka market. Fruits and vegetables

Watermelon Pineapple Carrot Bitter leaf Water leaf F-pumpkin Tomatoes Onion Cabbage Lettuce Cucumber Banana

Hazard quotient

Hazard index (HI)

Cadmium

Cobalt

Copper

Zinc

Lead

Nickel

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

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

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

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

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

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

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

9.4 Conclusions The present study was done to evaluate heavy metal concentrations in soil and commonly consumed fruits and vegetables at Atani, Omor and Eke Awka in Anambra state, Nigeria. The toxic element concentrations in the various soil and vegetables/fruits are all below the permissible limit of WHO/FAO. Health risk assessment showed that there is a need for containment measures on the soil to prevent higher concentrations of PTE in the future. Therefore, this study encourages environmentalists and public health workers to create public awareness to avoid the consumption of fruits and vegetables grown in contaminated soils, hence, reducing health threats.

Supplementary Material The supplementary materials have appendix 1 and 2. Acknowledgement: The authors acknowledge and extend their sincere gratitude to all who helped in the realization of this present work.

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Supplementary Material: This article contains supplementary material (https://doi.org/10.1515/PSR-20220321).

Liliana Mammino*

10 Complexes of a model trimeric acylphloroglucinol with a Cu2+ ion: a DFT study Abstract: Acylphloroglucinols (ACPLs, derivatives of phloroglucinol having at least one R−C=O group) are gaining increasing attention for their pharmacological potentialities. The presence of phenol OHs in their molecules confers antioxidant properties to ACPLs. Some ACPLs have already been identified as promising antioxidants for pharmaceutical purposes. Antioxidant properties may also be useful for a variety of other applications, including industrial ones. A viable option to verify and compare the antioxidant efficacy of compounds considers their ability to form complexes with a metal ion and reduce its charge. The present work considers a model structure maintaining all the identifying features of trimeric ACPLs (ACPLs containing three phloroglucinol moieties linked by methylene bridges) and studies the complexes of representative conformers with a Cu2+ ion, with the ion binding in turn to each of the available binding sites. Two series of calculations are performed at the DFT/B3LYP/ 6-31+G(d,p) level, without and with the Grimme’s D3 dispersion correction: the former series enables meaningful comparisons with previous calculations of complexes of other ACPLs, and the latter series is meant to evaluate the effect of taking dispersion into account on the estimation of the complexes’ properties. The results show that the Cu2+ ion is reduced to Cu+ ion. The molecule–ion interaction energy and the charge and spin density on the ion are comparable with those of complexes of known antioxidant ACPLs. Keywords: acylphloroglucinols; antioxidants; complexes of organic molecules with metal ions; effects of dispersion correction; intramolecular hydrogen bonding; molecule–ion affinity.

10.1 Introduction Acylphloroglucinols (ACPLs, Figure 10.1), are a broad class of compounds structurally derived from 1,3,5-trihydroxybenzene (phloroglucinol) and characterised by the presence of an acyl group R−C=O [1]. Most of them are of natural origin and exhibit a variety of biological activities: bactericide, antibiotic, antifungal, antidepressant, antioxidant, antimalarial, anticancer, antiparasitic and others. They are viewed as potential lead compounds for drug development [2, 3]. The stable conformers contain an intramolecular

*Corresponding author: Liliana Mammino, Faculty of Science, Engineering and Agriculture, 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 “Complexes of a model trimeric acylphloroglucinol with a Cu2+ ion: a DFT study” Physical Sciences Reviews [Online] 2023. DOI: 10.1515/psr-2022-0320 | https://doi.org/10.1515/9783111328416-010

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10 Complexes of a trimeric acylphloroglucinol with a copper ion

Figure 10.1: General structure of monomeric acylphloroglucinols and atom-numbering utilized for each monomer. The first atom of R^ is given the number 9, the first atom of R* is given the number 11 and the first atom of R is given the number 13. For trimeric acylphloroglucinols (Figure 10.2), the R^ of the first monomer, the R* of the third monomer, and both R^ and R* of the inner monomer are replaced by methylene bridges.

hydrogen bond (IHB) between the sp2 O of the acyl group and a neighbouring phenol OH; whether the H15⋯O14 IHB or the H17⋯O14 IHB is formed depends on the orientation of the OH groups [4]; either of them is here termed ‘first IHB’ – a term introduced in previous works on ACPLs (e.g., [4, 5]). The presence of phenol OHs in their molecules confers antioxidant properties to ACPLs, whose extent depends on the characteristics of individual molecules. Some naturally occurring ACPLs have already been identified as promising antioxidant for pharmaceutical purposes [1] because of their activity against excess reactive oxygen species (ROS). Excess accumulation of free radicals creates oxidative stress, which is implicated in physiological aging, the development of neurodegenerative diseases such as Parkinson’s and Alzheimer’s diseases, cardiovascular diseases and cancer [6]. Antioxidant properties may also be relevant for a variety of other applications, including industrial ones [7]. Antioxidant activities can be modelled and compared through the molecules’ ability to bind and reduce a Cu2+ ion [8]. Actually, the chelation of metal ions is one of the mechanism through which antioxidants may exert their activity, by inhibiting transition metal catalysed free radical formation [9, 10]. The determination of metal chelating properties becomes one of the experimental approaches to investigate the antioxidant

10.1 Introduction

155

ability of a compound [11]. The metal-chelating potential and reducing power of compounds depend on their structural features [12]. Computational studies of complexes with a Cu2+ ion had been performed for selected antioxidant ACPLs: hyperjovinol A [13, 14], arzanol [15], hyperguinones A and B [16] and furonewguinone B [17]. The studies showed that these ACPLs can bind and reduce the ion, highlighted the ion’s binding preferences, and confirmed the importance of additional O atoms and/or additional π bonds in substituents. A further study [18] confirmed the role of the presence of the three mutually meta phenol OHs in the phloroglucinol moiety for better antioxidant activity. ACPLs with more than one acylphloroglucinol units – ACPLs in which two or more acylphloroglucinol units are linked by methylene bridges – often show better biological activity than monomeric ACPLs [1]. In all their stable conformers, two consecutive units are also linked by IHBs on either side of the methylene bridge [19, 20]; they are here denoted as IMHBs (intermonomer hydrogen bonds) whenever it is relevant to recall that they form between monomers [19, 20]. The current work aims at verifying whether trimeric ACPLs (Figure 10.2) may have antioxidant activity comparable to that of monomeric ACPLs with known good activity. The expectation of good activity is supported by the fact that the other monomers provide the presence of additional O atoms and π systems in the vicinity of each monomer, i.e., provide the factors known to enhance antioxidant properties. Other aspects worthy of investigation refer to the likely influence of the presence of three aromatic rings on the binding preferences of the Cu2+ ion and on the effects of complexation on the molecular properties. The results show that trimeric ACPLs have antioxidant properties comparable with those of the ACPLs with known good properties, as indicated by the molecule–ion affinity, the reduction of the ion’s charge, and the fact that the spin density on the ion is close to zero. The main binding preferences of the Cu2+ ion are similar to those

Figure 10.2: General structure of trimeric acylphloroglucinols and atom numbering utilized in this work [20]. For each monomer, the atom numbering is the same as for monomeric ACPLs (Figure 10.1); the numbers are primed for second monomer and double primed for third monomer.

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10 Complexes of a trimeric acylphloroglucinol with a copper ion

identified for other ACPLs: simultaneous binding to two sites, followed by binding to the sp2 O14s. The effect of complexation on the IHBs is also similar to what observed for other ACPLs [21], with the difference that the effect can distribute across the monomeric units with a variety of patterns. The addition of the Grimme’s dispersion correction has significant effect in the estimation of energy-related quantities (relative energy, molecule–ion affinity) and often also on the estimation of the IHBs’ lengths.

10.2 Computational details The molecule selected for this study is the simplest model of a trimeric ACPL in which the acyl groups are not aldehydes, i.e., R, R′ and R″ (Figure 10.2) are methyl groups. This selection is the most suitable because it models the most general situation (R ≠ H). A methyl can adequately mimic an R ≠ H that does not contain H-bond donor or acceptor groups [4, 5, 22], including its effect on the first IHB, which is significantly stronger when R ≠ H than when R = H [4, 5]. This model molecule is henceforth denoted as T1, consistently with the way in which it is denoted in [20]. Calculations were performed in vacuo with fully relaxed geometry (in vacuo calculations are always the initial ones to be performed, as they enable initial comprehension of the characteristics of a molecular system, and their results are utilised as starting points for other types of calculations, including calculations in solution). Like in [20], two sets of calculations were performed at the Density Functional Theory (DFT) level, with the B3LYP functional [23–25] and the 6–31+G(d,p) basis set. The first set did not add any correction, and the results enable meaningful comparisons with previous calculations of antioxidant ACPLs [13–18] or other ACPLs for which complexes with a Cu2+ ion were considered [26]. The second set added the Grimme’s dispersion correction D3 [27–31], to evaluate the effect of explicit dispersion consideration on the estimation of computable molecular properties, expectedly resulting in better estimation of the quantities for which dispersion interactions are important. The fact that the combination of B3LYP and D3 is considered suitable for organic molecules [31] supports its selection. For the sake of conciseness, the two methods will respectively be denoted as DF and DF-D3 on discussing results in the rest of the text. All the calculations were performed with Gaussian-16 [32]. The visualization of molecular structures utilised GaussView [33] and Chem3D [34]. Besides the tables and figures included in the text, longer tables reporting all the obtained values (organised with diverse criteria to facilitate different types of comparisons), figures showing the geometries of all the calculated complexes, and figures with diagrams visualizing interesting comparisons, are included in the Electronic Supporting information (ESI). Their numbering is independent of the numbering of figures and tables in the text, and their numbers are preceded by an S to distinguish them from those in the text. They are cited in the text.

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10.3 Results 10.3.1 Selection and geometry of the calculated complexes 10.3.1.1 Input and optimised geometries The conformers of a trimeric ACPL may differ by the IHB patterns (the first IHB in each monomer, and the types of IMHBs in relation with the orientations of the OHs in the monomers), the mutual orientations of the monomeric units (identified by whether the acyl groups are on the same ‘rim’ with respect to the set of aromatic rings, or on different rims), and the mutual orientations of the methylene bridges [20]. The latter factor determines pairs of conformers with outstretched and half-bowl-shaped geometries for each combination of IHB patterns and mutual orientations of the monomers, where the outstretched geometry corresponds to opposite orientations of the two methylene bridges and the half-bowl-shaped geometry to same orientations [20]. The conformers of the uncomplexed T1 molecule had already been calculated [20]. The three lowest-energy conformer-pairs are selected for the current study; their geometries are shown in Figure 10.3; they are denoted with the same symbols as in [20], i.e., they are identified by numbers, and the letter ‘y’ is added to the number to distinguish the conformer with half-bowl-shaped geometry. The complexes are denoted with acronyms in which the symbols denoting the conformer are followed by ‘Cu’, in turn followed by its binding site(s) in the given complex. The initial ‘T1’ may be omitted

Figure 10.3: Geometries of the three lowest-energy pairs of conformers of the T1 molecule [20]. The acronym denoting the conformer is reported under each image on the left; the relative energies (kcal/mol) in the DF-D3 and DF results are reported on the right.

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from the complex acronyms, in the text or in tables, for space reasons and the sake of simplicity. For instance, 1-y-Cu–O14–O10″ denotes a complex of the T1-1-y conformer, in which the ion binds to O14 and to O10″; 3-Cu–O14″ denotes a complex of the T1-3 conformer, in which the ion binds to O14″; and so on. The potential binding sites in the T1 molecule are all the O atoms and the three aromatic rings. The inputs for the complexes of the selected conformers were prepared by positioning the ion in the vicinity of each potential binding site (Figure S1), considering also the steric accessibility of the sites in the given conformer. DF and DF-D3 calculations were performed independently on the same input (not one of them utilising the result of the other). In some cases, the DF and DF-D3 optimisations yielded complexes with the ion binding to different sites; then, each of them was used as input for a calculation with the other method, to have both complexes calculated with both methods. Figure 10.4 shows representative optimised geometries with the ion attached to different binding sites, and Figure S2 shows the optimised geometries of all the calculated complexes. Changes of binding sites on optimisation – with respect to the input – occur in several cases, mostly resulting in the ion binding simultaneously to more than one site; for instance, 1-y-Cu–O8′–O12″ and 3-y-Cu–O8–O12′ result from inputs with the ion in the vicinity of O8′ and O8, respectively. Inputs with the ion in the vicinity of O10′ mostly result in the ion binding to more than one site because its position implies that an ion initially placed in its vicinity is also sufficiently close to other O atoms to somehow interact with them (e.g., 2-Cu–O8–O12″, 2-Cu–O8–O10′, 3-y-Cu–O10–O10–O10″, 3-Cu–O10′-10″). Inputs with the ion in the vicinity of one of the aromatic ring, and positioned equidistantly from all the C atoms of that ring, optimise differently depending on the ring. If it is positioned in the vicinity of the central ring, the inputs may optimise to complexes in which the ion binds to two C atoms in different rings (e.g., 1-Cu–C3–C5′, 2-y-Cu–C3′–C5″) or to one C atom, not always in the central ring (e.g., 1-Cu–C3′, 2-Cu–C5′, 1-y-Cu–C5″). If it is positioned in the vicinity of the first ring, the inputs optimise to complexes in which the ion binds to C5; if it is positioned in the vicinity of the third ring, they optimise to complexes in which the ion binds to C3″ or C5″. 10.3.1.2 Analysis criteria for relevant properties The values of relevant quantities can be analysed from different perspectives: in terms of the binding site(s) of the ion, in terms of the conformers of the uncomplexed molecules, and considering the complexes according to their relative energy sequence. While the last two criteria are straightforward, the analysis in terms of the binding site/s of the ion requires some selections: because of the high number of sites and sites-combinations and the small number of complexes for several of the sites, some sites are grouped together. When the ion binds to more than one site, the following site-combinations are present: O14–O10″, O8–O12″, O8′–O12″, O8–O10′, O8′–C3″, O10–C5′, O10–O10′–O10″, O10′–10″, C4–O12′; each of them appears in one complex or, at most, in two complexes

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Figure 10.4: Representative complexes of the T1 model molecule with a Cu2+ ion, with the ion attached to different binding sites. Results from B3LYP/6-31+G(d,p)-D3 (DF-D3) calculations.

and, therefore, cannot identify a category; it was opted to group all the cases in which the ion binds to two or three sites, with at least one of them being an O atom, into one category. Similarly, the complexes where the ion binds to C atoms of aromatic rings – with the C3–C5′ and C3′–C5″ combinations and the C5, C3′, C5′, C3″ and C5″ individual sites – are grouped in one category. The categories for the complexes in which the ion binds to individual O atoms are selected by the atom type (O14, O8, O10 and O12). However, not all the potentially possible complexes for each site (six, corresponding to the considered six conformers of T1) are obtained. All the six complexes are obtained for O14, O14′, O14″, O8″, O10 and O12.

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Only two complexes are obtained for O8′, O10″ and O12′ and only one for O10′ and O12″. No complexes are obtained for O8. This situation is to be taken into account on considering diagrams meant to illustrate ranges of values. The ranges corresponding to the categories for the complexes with the ion binding to two sites, and for the ion binding to an aromatic C, are usually broader, with the former being the broadest because of the diversity of the binding site pairs it comprises. For the complexes in which the ion binds to individual O atoms, only the cases in which all the six complexes are obtained for that site are considered; therefore, the diagrams comprise ranges for O14, O14′, O14″, O8″, O10 and O12; these ranges are usually narrower because they correspond to only one site.

10.3.2 Energetics of the calculated complexes The energy aspects concerning the complexes comprise their relative energies and their molecule-ion affinity. Table 10.1 provides an overview of the ranges of these quantities in terms of the binding site(s) of the ion and Table S1 provides the values for all the calculated complexes, organising them according to each of the previously mentioned criteria, to facilitate the corresponding analyses. The lowering of the energy estimation deriving from the inclusion of the Grimme’s dispersion correction indicates the relevance of this correction; the values are reported in Table S2. Figure S3 presents Table .: Ranges of the relative energy (ΔE) and the magnitude of the molecule-ion affinity (∣MIA∣) of the calculated complexes of the T model trimeric acylphloroglucinol structure with a Cu+ ion. DFT/BLYP/+G(d,p) results with and without the Grimme’s D dispersion correction, respectively denoted as DF-D and DF in the columns’ headings. When only two values are available, they are both reported, separated by a comma. Binding site(s)

Ranges (kcal/mol) DF-D results

 or  O atoms O O′ O″ O′ O″ O O″ O O′ C in rings

DF results

ΔE

∣MIA∣

ΔE

∣MIA∣

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

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

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

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

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diagrams visualising all these analyses, to convey immediately-ready information about their outcomes; individual diagrams are concisely cited in the text with the number of the figure and the letter denoting the diagram; for instance, ‘diagram S3-d’ refers to diagram ‘d’ in Figure-S3. 10.3.2.1 The complexes’ relative energies The DF-D3 results identify one low-energy complex (1-y-Cu-14-O10″), and the energy of the next lower-energy one is 8 kcal/mol greater. After this, the energy increase is not so sharp: for instance, there are five complexes with relative energy between 9.1 and 9.6 kcal/mol. The DF results show 14 complexes with relative energy smaller than 8 kcal/mol, nine of which having relative energy smaller than 4 kcal/mol. These trends are visualised in diagram S3-a. The identification of a markedly lower energy conformer in the DF-D3 results is reflected in all the comparisons concerning energies. Table 10.1 and a comparison of diagram S3-f and diagram S3-g indicate similar binding site preferences in the DF-D3 and DF results, with the only exception of the markedly stronger preference for O14–O10″ in the former. The least preferred site is O12. Table 10.1 and diagrams S3-l and S3-m do not indicate marked preferences in terms of the conformers of the uncomplexed molecule, with conformer T1-2-y appearing somewhat less preferred. 10.3.2.2 The molecule-ion affinity As customary ([13–18] and references therein), the molecule–ion affinity (MIA) is evaluated as: MIA = (energy of the complex) − (energy of the uncomplexed conformer) − (energy of the ion) Like in [13–18], no basis set superposition error (BSSE) correction was considered because their influence is marginal for interaction energies of these complexes’ MIA magnitude, and practically negligible for studies primarily focused on comparisons [35] (this also entails that the BSSE influence of the estimation of the complexes’ relative energies is negligible). The MIA values of the calculated complexes are reported in Table S1 and their ranges in Table 10.1. Diagram S3-b compares the values from the two calculation methods utilised and highlights their trends. The MIA magnitude is nearly always greater in the DF-D3 results. It is greater for lower-energy complexes and decreases (although not uniformly) as the complex energy increases. The curve for the DF results in diagram S3-b would suggest less uniform trend than for the DF-D3 results; on the other hand, this diagram considers the complexes in order of their DF-D3 relative energies; diagram S3-c considers the DF MIA values listing the complexes in order of their DF increasing relative energy, and highlights a significantly more uniform trend.

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In terms of binding sites (diagrams S3-i and S3-j), the greatest MIA magnitude pertains to the lowest energy complex, followed by complexes with the ion binding to O8″ and to O14; the smallest magnitudes pertain to O12, and the second smallest to the C atoms in aromatic rings. In terms of the type of uncomplexed conformers (diagrams S3-o and S3-p), the greatest MIA magnitude pertains to T1-1-y, followed by T1-3 and T1-3-y in the DF-D3 results, while it pertains to T1-3-y and T1-3 in the DF results. 10.3.2.3 Influence of the addition of the Grimme’s dispersion correction on energyrelated estimations The addition of Grimme’s dispersion correction leads to lowering in the estimation of the energy of the complexes. The magnitude of this lowering is calculated as “DF-value minus DF-D3 value” and is comparable with that of the uncomplexed molecule [20]. Table S2-a reports its values and diagram S3-d visualises its trend. There is no steadytype relationship with the increasing relative energy of the complexes. Table S2-b and diagram S3-h show considerable similarity areas across the binding sites, with some differences for the largest and smallest values: the effect reaches its highest values for complexes where the ion binds to more than one sites, with at least one of them being an O atom, followed by O14 and by the cases in which the ion binds to C atoms of aromatic rings. Table S2-c and diagram S3-n show that the effect reaches greater magnitudes for the complexes of the half-bowl-shaped conformer than for the complexes of the conformer with outstretched geometry of the same conformers’ pair (e.g., for the complexes of T1-1-y than for the complexes of T1-1); this is consistent with the trends observed for the uncomplexed molecule [20], ascribable to the greater extent of stacking interactions when the mutual situation of the rings is closer to ‘facing each other’. The effect of the inclusion of the D3 dispersion correction on the MIA estimation is mostly marginal, likely because of the fairly close similarity of its influence on the estimation of the energies of the complex and of its uncomplexed conformer. Table S2-d reports the values of the effect and diagram S3-e shows their trend. The magnitude of the effect mostly remains below 5 kcal/mol, and the largest magnitudes correspond to complexes in which the ion binds to more than one site. Diagrams S3-d and S3-e highlight remarkable trend-similarity – although in association with different effectmagnitudes – thus supporting the inference that the small extent of the dispersion effect on the MIA estimation is related to the similarity of its effects on the estimations of the energies of the complex and its uncomplexed conformer. Table S2-e analyses the effect in terms of the binding site of the ion and diagram S3-k visualises corresponding ranges. Table S2-g analyses the effect in terms of the conformers of the uncomplexed molecule and diagram S3-q visualises corresponding ranges; the broadest ranges correspond to the complexes of conformers T1-2 and T1-2-y.

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10.3.3 Properties of the ion in the complexes The efficacy of the molecule’s antioxidant action is highlighted by the charge and the spin density (total spin per unit volume) on the ion. The formation of the complex entails the transfer of an electron from the molecule to the ion: the molecule acquires a positive charge and remains with an unpaired electron, and the ion is reduced from Cu2+ to Cu+, which has no unpaired electrons. The computed charge and spin density on the ion are indicators of the extent of these outcomes. 10.3.3.1 The charge on the ion in the complexes Both the Mulliken charge and the charge estimated with the natural bond orbital (NBO, [36–40]) approach are considered. Table S3 reports their values, and the diagrams in Figure S4 illustrate their trends. All the charges remain below 1, consistently with the reduction of the ion to Cu+. More accurate estimations yield comparatively greater values for the same complex. Thus, DF-D3 yields greater values than DF (diagrams S4-a and S4-b). The Mulliken charge is smaller than the natural charge (diagrams S4-c and S4-d), similarly to the results in [13–18]; the NBO charge can be considered more realistic. Analyses in terms of the binding site of the ion (Table S3-b, diagrams S4-f, S4-g, S4-h, S4-i) highlight narrow ranges of values when the ion binds to one O atom, and broad ranges for the cases where a variety of sites is included in the same category (two or more O atoms, C atoms pertaining to aromatic rings). The lowest charges correspond to some of the complexes in which the ion binds simultaneously to two O atoms, or simultaneously to two aromatic C atoms; these trends are consistent with those identified for complexes of monomeric ACPLs [13–18]. The charge-reducing abilities of individual O atoms are basically comparable. Analyses in terms of the conformers of the uncomplexed molecule (Table S3-c, diagrams S4-j, S4-k, S4-l, S4-m) highlight marginal dependence both for the Mulliken charge and for the NBO charge. The lowest values are reached for some complexes of conformer T1-3. 10.3.3.2 The Mulliken spin density on the ion in the complexes As already mentioned, the spin density on the ion provides indication of the effectiveness of its reduction to Cu+ (since Cu+ does not have unpaired electrons, its spin density is expected to be zero). Only the Mulliken spin densities were calculated. Within a previous study [16], a verification utilising NBO 6.0 [41] for the natural bond analysis of representative complexes had highlighted no major discrepancies, supporting the reliability of Mulliken spin densities, above all in works focusing on comparisons and trendsidentification.

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The values of the Mulliken spin densities are also reported in Table S3. Most values are close to 0 (the highest one remains below 0.03), which confirms the electron transfer from the molecule to the ion (absence of unpaired electrons in the ion). The differences between the DF-D3 and DF estimations are mostly marginal (diagram S4-e). Diagrams considering ranges in terms of the ion binding site (diagrams S4-j and S4-k) highlight much broader ranges for the cases when the ion binds to two atoms, one of which an O atom, or to aromatic C atoms; the range-width difference with the other binding sites is so large that it is necessary to remove these two ranges and expand the charge scale if one wishes to compare the widths of the other ranges (diagrams S4-j1 and S4-k1). These expanded diagrams show that the shortest range, also having the smallest spin densities for all the complexes, corresponds to O14, followed by O14″ and O14′. Diagrams considering ranges in terms of the conformers of the uncomplexed molecule (diagrams S4-p and S4-q) widths are comparable, although the ranges corresponding to T1-1-y and T1-3 are considerably broader.

10.3.4 How closely the ion approaches the molecule The distance between the ion and its binding site(s) can be expected to offer indications on the strength of their interaction, above all for comparisons among similar situations. Table S4 reports the distances for the calculated complexes; the complexes are considered in terms of the conformers of the uncomplexed molecule because, in this case, the binding sites constitute the column headings. The DF-D3 (Tables S4-a) and DF (Table S4-c) results are mostly very close. Figure S5 visualizes the ranges of these distances. Most distances from O atoms remain below 1.890 Å, and do not increase when the ion binds simultaneously to two sites (Tables S4-a and S4-c). The distance from O8′ and O8″ sometimes exceed 2.000 Å and the distance from O12 is mostly longer than 2.000 Å; these are also binding sites corresponding to comparatively smaller MIA magnitudes. A more detailed analysis of possible correspondences between ion–atom distance and MIA is facilitated by Table S4-e, which pairs the two quantities for complexes in which the ion binds only to an O atom, so that only one atom is responsible for the interaction. The comparison indicates significant correspondence between ion–atom distance and MIA, above all for the same O atom. Observed fluctuations are to be ascribed to the influence of the molecular context around the binding site, which is different in different cases and largely relates to the conformer type and its possible modifications caused by the vicinity of the ion. When the ion binds to a C atom in an aromatic ring, the ion-atom distance is 2.008–2.189 Å; the ranges are shown in diagrams S5-c and S5-d. Since the interaction is with a π system, the ion often comes sufficiently close to some of the C atoms neighbouring the atom to which it comes closest, to suggest slight interactions also with them; these distances are in the 2.573−2.694 Å range; they are written in red in Tables S4-b and S4-d.

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While it is easy to mentally visualize the approach of the ion to its binding site when it binds only to one atom, the cases when it binds to two or more sites may not be as immediate. Figure S6 shows how the ion ‘nests’ itself between two or more sites of the T1 molecule.

10.3.5 Effects of complexation on the intramolecular hydrogen bonds The most frequent phenomena caused by complexation on the IHBs of the ACPL molecule are the proton transfer from the donor to the acceptor atom and some changes in the IHB length and strength [21]. The study of complexes of monomeric ACPLs [13–18, 21] had highlighted that a proton transfer most often concerns IHBs whose acceptor is an sp2 O, and only rarely other types of IHBs; it also highlighted that it does not occur when the ion binds to the acceptor of a given IHB. In a trimeric ACPL, the variety of patterns for the proton transfer is much greater, and multiple proton transfers may occur in the same complex. The patterns are different for different conformers of the uncomplexed molecule and for different binding sites of the ion. The fact that IMHBs are often cooperative with the first IHB of one or another monomer may favour the transfer plurality, also in view of the fact that an OH donor which becomes an sp2 O through the transfer may acquire the role of acceptor for two cooperative IHBs. Figure 10.5 illustrates the transfer in representative complexes of T1-2, comparing their situations with that of the uncomplexed conformer. In 2-Cu–O14, the proton transfer concerns both the O8−H15⋯O14 and the O8′−H15′

Figure 10.5: Examples of proton transfer within intramolecular hydrogen bonds caused by the formation of a complex with the Cu2+ ion. In each row, the first image shows the T1-2 conformer of the uncomplexed molecules, and the other images show some of its complexes with a Cu2+ ion. In 2-Cu-O14, the proton transfer concerns both the O8−H15⋯O14 and the O8′−H15′⋯O14′ first IHBs; in 2-Cu-O8″, it concerns only the O8−H15⋯O14 first IHB; in 2-Cu-C3′, it concerns the O8−H15⋯O14 and O8′−H15′⋯O14′ first IHBs and the O10′−H16′⋯O8 IMHB. Both ball-and-stick and space-filling models are considered for each structure, because of their different visualization abilities.

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10 Complexes of a trimeric acylphloroglucinol with a copper ion

⋯O14′ first IHBs; both O8 and O8′ become sp2 O and act as acceptors to the first IHB of their monomer and to an IMHB. In 2-Cu–O8″, it concerns only the O8−H15⋯O14 first IHB: O8 becomes an sp2 O and is acceptor to the first IHB and to an IMHB. In 2-Cu–C3′, it concerns the O8−H15⋯O14 and O8′−H15′⋯O14′ first IHBs and also the O10′−H16′⋯O8 IMHB: O8′ becomes an sp2 O and is acceptor to the first IHB and to an IMHB; O10′ also become an sp2 O and is acceptor to two IMHBs. The proton transfers are evident for all the complexes from Figure S2, if one compares their IHB patterns to those of the corresponding uncomplexed conformers. Table S5 directly specifies all the cases in which a proton transfer occurs, by adding an asterisk to the values of the IHBs where it occurs. Tables S5-a and S5-c report the lengths of all the IHBs in all the calculated complexes, grouping the complexes in terms of the conformers of the uncomplexed molecule, and Table S5-b and S5-d report the changes in the IHB lengths in the complexes with respect to the uncomplexed conformers, respectively in the DF-D3 and DF results. In most cases, the length of the IHB undergoing a proton transfer increases, and the length of the cooperative IHB sharing the same sp2 O acceptor decreases. For instance, the length of H15⋯O8 increases with respect to the length of H15⋯O14 in the uncomplexed conformer, and the length of H16′⋯O8 decreases; similarly, the length of H17′⋯O12′ increases with respect to the length of H17′⋯O14′ in the uncomplexed conformer, and the length of H16⋯O12′ decreases. In 2-y-Cu–O8″, H15⋯O8 and H16′ ⋯O8 have the same length. In some cases, the ion inserts itself between the donor and the acceptor of an IMHB, preventing its formation. This entails changes in the geometry of the molecule: the H atom of the donor becomes perpendicular to the plane of the ring; the overall geometry may be distorted, and the planes of some rings might become nearly perpendicular to the plane of another ring (e.g., in 2-Cu–O8–O12″ and 2-Cu–O8–O10′); these changes are indicated in notes in Table S5.

10.3.6 Other molecular properties of the complexes 10.3.6.1 HOMO–LUMO energy gap of the complexes The HOMO–LUMO energy gap (difference between the energy of the LUMO and the energy of the HOMO) is an important property of molecules, related to their reactivity and to the ability to conduct electric current, and often utilised as descriptor in structure-activity (or structure-properties) relationships studies. The DFT model is known to considerably underestimate the HOMO–LUMO energy gap. On the other hand, comparisons with the results of ab initio methods – which provide more reliable results – have shown that – at least in the case of ACPLs – the trends highlighted by the two approaches remain similar, despite the large difference in the values-magnitude

10.3 Results

167

[4, 22]; therefore, analysing the trends obtained from the calculations used in the current study is meaningful. Table S6 reports the values of the HOMO–LUMO energy gap for the calculated complexes, and Figure S7 highlights relevant comparisons through diagrams. The values obtained from DF-D3 and DF calculations are mostly very close, and their trends are similar (diagram S7-a). An analysis of the ranges of the values in terms of the binding site(s) of the ion (diagrams S7-b and S7-c) shows that the gap has much larger values for the cases in which the ion simultaneously binds to two O atoms. An analysis of the ranges in terms of the conformers of the T1 molecule does not highlight important differences, and the largest values for each case correspond to the complexes of a given conformer in which the ion binds to two O atoms. It appears interesting to compare the values of the complexes with the values of the corresponding uncomplexed conformers. It has however to be taken into account that, in terms of electron distribution, the situation of the uncomplexed molecule and that of the complexes are different, because the molecule becomes a molecular ion in the complex and has an unpaired electron (so, the HOMO is only partially occupied); therefore, the comparison is useful only as far as it may indicate some factors influencing the difference. The differences (calculated as «HOMO–LUMO gap in the complex minus HOMO–LUMO gap in the uncomplexed conformer») are also reported in Table S6 and relevant comparisons are visualised in Figure S7. The estimated HOMO–LUMO gap is always smaller for the complexes than for the uncomplexed molecule, and the difference is large in most cases; substantially smaller differences correspond to the complexes in which the ion simultaneously binds to two O atoms (diagrams S7-g and S7-h). The dependence on the type of uncomplexed conformer is less marked (diagrams S7-i and S7-j); by excluding the complexes in which the ion binds to two sites for each conformer-type (thus excluding the smallest values), some dependence on the conformer of the uncomplexed molecule appears (diagrams S7-i′ and S7-j′). Thus, the binding site has the major role in determining the extent of the difference of the HOMO–LUMO gap in the complex and in the uncomplexed conformer, consequent to the fact that it determines the gap. 10.3.6.2 Dipole moment of the complexes The estimations of the dipole moment of the complexes are very similar in the DF-D3 and DF results (Table S7, diagram S8-a). The smallest values pertain to complexes in which the ion binds simultaneously to two O atoms or to two aromatic C atoms, and the highest values to complexes in which it binds to O12 (diagrams S8-b and S8-c). No significant trend-difference in relation to the conformers of the uncomplexed molecule is highlighted for conformers T1-1, T1-1-y, T1-2 and T1-2-y (diagrams S8-d and S8-e); the complexes of T1-3 and T1-3-y have the broadest ranges of dipole moment values and the highest dipole moments pertain to some complexes of T1-3.

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10 Complexes of a trimeric acylphloroglucinol with a copper ion

No comparison with the dipole moments of the uncomplexed conformers is carried out because the addition of a charge species (the Cu2+ ion) in the complexes makes this species and its binding site a determining factor of the dipole moment.

10.3.7 Discussion and conclusions The results confirm that the model trimeric ACPL structure considered in this study can be expected to have good antioxidant activity. The charge of the ion is reduced to close to 1 a.u., always remaining slightly below 1 a.u. in all the calculated complexes. The Mulliken spin density values on the ion approximate zero, indicating that no unpaired electron is present on the ion in the complex The molecule–ion affinity is comparable to that of complexes of ACPLs known to have good antioxidant activity [13, 15]. Most of the results are consistent with those of previous studies on the complexes of monomeric ACPLs with a Cu2+ ion [13–18]. The ion can bind to any of the available sites, and the energy of the complex is lowest when it binds to two sites simultaneously. The molecule–ion affinity depends on the binding site of the ion and – to a less extent – on the conformer types; it has greater magnitude when the ion binds to two sites simultaneously, followed by the sp2 O atoms of the acyl groups. The charge on the ion is smaller when the ion binds simultaneously to two sites; the complexes in which the ion binds to aromatic rings tend to have smaller charges, consistently with analogous effects observed for π bonds or systems in complexes of other ACPLs [13–18]. The distances of the ion from its binding sites are comparable with those in the complexes of other ACPLs. Since T1 is a good model for trimeric ACPLs, it can be expected that all trimeric ACPLs may have at least comparable antioxidant properties. The antioxidant activity of phenolic compounds appears to be enhanced by the presence of one or more C=C double bonds in a suitable position in a substituent [42, 43], and all ACPLs reported to have interesting antioxidant activity [1] contain either additional OH groups or C=C double bonds in some substituents. In the case of a trimeric ACPL, each monomer provides both additional OH groups and an additional aromatic system to the neighbouring monomer/s; therefore, good antioxidant activities can be expected. Furthermore, in many naturally occurring trimeric ACPLs, the outer OH ortho to the acyl group is replaced by a keto O in one or both outer monomers, providing an additional stronger binding site for the ion (an sp2 O being stronger than an sp3 O). It is concluded that the possibility of using naturally-occurring ACPLs as antioxidants for pharmaceutical or industrial applications deserves further explorations. In view of the importance of considering solvent effects for biologically active molecules, a study of the same model structure in water solution is in progress and will be the object of a separate work.

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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.

References 1. Singh IP, Bharate SB. Phloroglucinol compounds of natural origin. Nat Prod Rep 2006;23:558–91. 2. Verotta L. Are acylphloroglucinols lead structures for the treatment of degenerative diseases? Phytochemistry Rev 2002;1:389–407. 3. Kusumaningsih T, Prasetyo WE, Wibowo FR, Firdaus M. Toward an efficient and eco-friendly route for the synthesis of dimeric 2,4-diacetyl phloroglucinol and its potential as a SARS-CoV-2 main protease antagonist: insight from in silico studies. New J Chem 2021;45:7830–43. 4. Mammino L, Kabanda MM. Model structures for the study of acylated phloroglucinols and computational study of the caespitate molecule. J Mol Struct 2007;805:39–52. 5. Mammino L, Kabanda MM. A study of the intramolecular hydrogen bond in acylphloroglucinols. J Mol Struct 2009;901:210–9. 6. Rani R, Arora S, Kaur J, Manhas RK. Phenolic compounds as antioxidants and chemopreventive drugs from Streptomyces cellulosae strain TES17 isolated from rhizosphere of Camellia sinensis. BMC Compl Alternative Med 2018;18:82. 7. Breza M, Jelemenska I. Quantum-chemical studies of the antioxidant effectiveness of para-phenylene diamines. J Vinyl Addit Technol 2022;28:352–2665. 8. Alagona G, Ghio C. Antioxidant properties of pterocarpans through their copper (II) coordination ability. A DFT study in vacuo and in aqueous solution. J Phys Chem A 2009;113:15206–16. 9. Hudson BJF, Lewis LJ. Polyhydroxy flavonoid antioxidants for edible oils. Structural criteria for activity. Food Chem 1983;10:47–55. 10. Rice-Evans C, Miller N, Paganga G. Antioxidant properties of phenolic compounds. Trends Plant Sci 1997;2: 152–9. 11. Tan LTH, Chan KG, Khan TM, Bukhari SI, Saokaew S, Duangjai A, et al. Streptomyces sp. MUM212 as a source of antioxidants with radical scavenging and metal chelating properties. Front Pharmacol 2017;8:276. 12. Zhang H, Tsao R. Dietary polyphenols, oxidative stress and antioxidant and anti-inflammatory effects. Curr Opin Food Sci 2016;8:33–42. 13. Mammino L. Investigation of the antioxidant properties of hyperjovinol A through its Cu(II) coordination ability. J Mol Model 2013;19:2127–42. 14. Mammino L. Complexes in which two hyperjovinol-A molecules bind to a Cu2+ ion. A DFT study. In: Glushkov AV, Khetselius OY, Maruani J, Brändas EJ, editors. Advances in methods and applications of quantum systems in chemistry, physics and biology. Book series: progress in theoretical chemistry and physics. Cham: Springer; 2021, vol 33:249–66 pp. 15. Mammino L. Complexes of arzanol with Cu2+ ions: a DFT study. J Mol Model 2017;23:276. 16. Mammino L. Complexes of hyperguinones A and B with a Cu2+ ion: a DFT study. Adv Quant Chem 2019;78: 83–108. 17. Mammino L. Complexes of furonewguinone B with a Cu2+ ion. A DFT study. In: Mammino L, Maruani J, Ceresoli D, Brändas EJ, editors. Concepts, methods and applications of quantum systems in chemistry and physics. Book series progress in theoretical chemistry and physics. Cham: Springer; 2020, vol 32:158–82 pp. 18. Mammino L. Roles of the phenol OHs for the reducing ability of antioxidant acylphloroglucinols: a DFT study. In: Glushkov AV, Khetselius OY, Maruani J, Brändas EJ, editors. Advances in methods and applications of quantum systems in chemistry, physics and biology. Book series: progress in theoretical chemistry and physics. Cham: Springer; 2021, vol 33:219–47 pp.

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19. Mammino L. Intramolecular hydrogen bonding patterns, conformational preferences and molecular properties of dimeric acylphloroglucinols: an ab initio and DFT study. J Mol Struct 2019;1176:488–500. 20. Mammino L. Correlation effects in trimeric acylphloroglucinols. Computation 2021;9:121. 21. Mammino L. Effects of complexation with a metal ion on the intramolecular hydrogen bonds in acylphloroglucinols. Theor Chem Acc 2019;78:83–108. 22. Kabanda MM, Mammino L. The conformational preferences of acylphloroglucinols – a promising class of biologically active compounds. Int J Quant Chem 2012;112:3691–702. 23. Lee C, Yang W, Parr RG. Development of the Colle-Salvetti correlation-energy Formula into a functional of the electron density. Phys Rev B 1998;37:785–9. 24. Becke AD. A new mixing of Hartree-Fock and local density-functional theories. J Chem Phys 1993;98:1372–7. 25. Becke AD. Density functional thermochemistry III. The role of exact exchange. J Chem Phys 1993;98: 5648–52. 26. Mammino L. Complexes of 1-[3-geranyl-2,4,6-trihydroxyphenyl]-2-methylpropan-1-one with a Cu2+ ion. A DFT study. Theor Chem Acc 2019;138:15. 27. Grimme S. Semiempirical GGA-type density functional constructed with a long-range dispersion correction. J Comput Chem 2006;27:1787–99. 28. Grimme S, Antony J, Schwabe T, Mück-Lichtenfeld C. Density functional theory with dispersion corrections for supramolecular structures, aggregates, and complexes of (bio)organic molecules. Org Biomol Chem. 2007, 5, 741–58. 29. Schwabe T, Grimme S. Double-hybrid density functionals with long-range dispersion corrections: higher accuracy and extended applicability. Phys Chem Chem Phys 2007;9:3397−406. 30. Grimme S. Density functional theory with London dispersion corrections. Wiley Interdiscip Rev WIREs Comput Mol Sci. 2011;1:211–28. 31. Grimme S, Steinmetz M. Effects of london dispersion correction in density functional theory on the structures of organic molecules in the gas phase. Phys Chem Chem Phys 2013;15:16031–42. 32. Frisch MJ, Trucks GW, Schlegel HB, Scuseria GE, Robb MA, Cheeseman JR, et al. Gaussian 16, revision B.01. Wallingford CT: Gaussian, Inc.; 2016. 33. Dennington II R, Keith T, Millam J. GaussView 4.1. Pittsburgh: Gaussian Inc; 2006. 34. Chem3D. version 8.0.3, chemoffice, Cambridge software; 2003. 35. Alagona G, Ghio C. Plicatin B conformational landscape and affinity to copper (I and II) metal cations. A DFT study. Phys Chem Chem Phys 2009;11:776–90. 36. Reed AE, Weinhold F. Natural bond orbital analysis of near-Hartree-Fock water dimer. J Chem Phys 1983;78: 4066–74. 37. Reed AE, Weinstock RB, Weinhold F. Natural population analysis. J Chem Phys 1985;83:735–47. 38. Reed AE, Weinhold F. Natural localized molecular orbitals. J Chem Phys 1985;83:1736–41. 39. Reed AE, Curtiss LA, Weinhold F. Intermolecular interactions from a natural bond orbital, donor-acceptor viewpoint. Chem. Rev. 1988;88:899–926. 40. Carpenter JE, Weinhold F. Analysis of the geometry of the hydroxymethyl radical by the “different hybrids for different spins” natural bond orbital procedure. J Mol Struct 1988;169:41–62. 41. Glendening ED, Landis CR, Weinhold F. NBO 0.6: natural bond orbital analysis program. J Comput Chem 2013;34:1429–37. 42. Leopoldini M, Prieto Pitarch I, Russo N, Toscano M. Structure, conformation, and electronic properties of apigenin, luteolin, and taxifolin antioxidants. A first principle theoretical study. J Phys Chem A 2004;108: 92–6. 43. Leopoldini M, Marino T, Russo N, Toscano M. Density functional computations of the energetic and spectroscopic parameters of quercetin and its radicals in the gas phase and in solvent. Theor Chem Acc 2004;111:210–6.

Supplementary Material: This article contains supplementary material (https://doi.org/10.1515/psr-20220320).

Davor Margetić*

11 Mechanochemistry as a green method in organic chemistry and its applications Abstract: Activation of chemical reactions in the solid state by mechanical energy represents a novel method with a high potential to be used in organic chemistry and various applications. There are several advantages over the classical reactions which are carried out in solution. Green aspects are in the avoidance of organic solvents, which diminishes environmental impact, whereas shortening of reaction times and room temperature conditions reduce the energy input. Furthermore, mechanochemical reactions could lead to products which cannot be obtained by solution chemistry or are produced by higher atom efficiency. The realization of the simplicity of the method and its advantages by chemists has led to increased application. The basics of the method and selected reactions are illustrated, in order to introduce this environmentally friendly method and to widen its use by the organic science community. Keywords: green methods; mechanochemistry; organic synthesis.

11.1 Introduction The negative impact of organic reactions on the environment has increased with the expansion of chemical manufacture since the mid-20th century and the need to make it more sustainable was recognized by chemists [1–3]. Hence, innovative methodologies were developed in order to reduce the production of waste, save energy and increase the efficiency of synthetic processes. Environmentally more green synthetic methods are devised to replace classical synthesis in solution (with the application of heating) and include reactions promoted by microwave irradiation (in solution or solvent-free), photochemical reactions, enantioselective reactions, high-pressure reactions (at room temperature), use of green reagents and solvents (water, dimethyl carbonate (DMC), H2O2), and solvent-free grinding (mechanochemistry, high-speed vibrational milling, ball milling) [4]. The fact that most of the produced waste is inorganic salt by-products and organic solvents, and many of the

Based on the lecture delivered at Virtual Conference on Chemistry and its Applications (VCCA), 8–12 August 2022. *Corresponding author: Davor Margetić, Laboratory for Physical Organic Chemistry, Division of Organic Chemistry and Biochemistry, Ruđer Bošković Institute, Bijenička c. 54, 10000 Zagreb, Croatia, 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: D. Margetić “Mechanochemistry as a green method in organic chemistry and its applications” Physical Sciences Reviews [Online] 2023. DOI: 10.1515/psr-2022-0351 | https://doi.org/10.1515/9783111328416-011

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solvents are toxic, where solvent use accounts for approximately 80% of the waste generated during typical pharmaceutical processing [5], gives importance to the novel methods of reduction of the organic solvent use in organic synthesis. Hence, it is not surprising that the International Union of Pure and Applied Chemistry (IUPAC) has recognized solid-state mechanochemistry as one of 10 innovative technologies for a sustainable future [6, 7].

11.2 Mechanochemistry Mechanochemical synthesis encompasses the activation of reactions by mechanical energy, which was employed since ancient times in its rudimentary form of mortar and pestle grinding in inorganic reactions [8]. Automated ball milling has been applied to various organic reactions for over the last 25 years, firstly as the way to circumvent the reactants solubility problems and automate manual grinding [9] and then gradually to be used in reactions in solid state or with the minimal amount of solvent. Nowadays, mechanochemical organic synthesis has been accepted by the organic synthetic community and it is rapidly expanding as commercial ball milling equipment becomes available. The method is covered in review articles [10–13] and books [14–16] and thematic scientific conferences are organized. Usually, chemical reactions of solid reactants are performed by milling in stainless steel jars with one or more milling balls using automated shaker mills. Reaction jars made from other materials are sometimes applied and energy transfer depends on the hardness of the material. Ball milling equipment varies on the basis of the mode of operation of the mill (planetary ball mill, shaker mill, tumbler ball mill) and there are several reaction parameters which could influence the reaction outcome [17]. These are the rotational mode, rotational speed, reaction time, temperature, the material of reaction vessels and balls, size and number of balls, the

11.3 Synthetic applications

173

volume of the reaction vessel, filling degree (volume of reactants/volume of a vessel), grinding auxiliary, and small amount of solvent (liquid assisted grinding). The rapid advancement of the applications of mechanochemical synthesis is due to the advantages of the method: avoidance of the use of solvent, applicability to insoluble molecules, reduction of reaction time (energy consumption), operation at room temperature, applicability to a variety of reactions, atom efficiency, simplicity of the procedure and change in reactivity leading to novel reactions. These aspects will be illustrated by selected reaction examples in which only optimized reaction conditions are commented on.

11.3 Synthetic applications Applications of mechanosynthesis to molecules with low solubility in organic solvents by the avoidance of the use of solvent and change of the reactivity leading to novel reactions could be illustrated by fullerene reactions (Scheme 11.1) [18]. Transfer of mechanical energy to fullerene C60 in high speed vibrating mill (HSVM) in the presence of KCN led to the formation of unprecedented fullerene dimer C120 1, instead of known cyanodihydrofullerene C60H(CN) product 2 which was obtained previously in the classical solution conditions. This unusual reaction was rationalized by the change of reaction mechanism from cyanated C60 anion intermediate, C60(CN)−, to C60 radical anion intermediate. The procedure was subsequently applied in the preparation of fullerene trimer 3 with an angular structure (determined by scanning tunnelling microscopy) [19] and cross-dimer with fullerene C70 [20]. For this mechanochemical reaction, 4-aminopyridine (1 equivalent) was used as the catalyst.

Solid state conditions

Solution conditions

KCN (NaCN) 1 equiv. Ball milling

NaCN 1 equiv.

H

60 Hz 30 min RT

CN 1) ODCB/DMF 2 (29 %)

RT 2) TFA, RT

C60 BM 60 Hz 30 min RT

1 (30 %) NH2 1 equiv. N 1 (34 %) +

C70 C130 (2 %) 3 (4 %)

Scheme 11.1: Mechanochemical synthesis of fullerene oligomers.

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11 Mechanochemistry as a green method in organic chemistry

R2 O R2

Ball milling R

N R1

6

41-86 %

3

4

Pd(OAc)2

N R1

+ O

MnO2 SiO2 AcOH LAG RT 25 min

MnO2 DMF 100 oC overnight

R3 5

Ball milling

Solution conditions PdCl2

O R2

R3 N R1

6

30-42 %

PdCl2 MnO2 SiO2 RT 20 min O R3

R2

R2 N R1

N R1

7

69-87 %

Scheme 11.2: Reduction of reaction time and temperature by mechanochemical method.

Many organic reactions could be transferred from solution reaction conditions to solid-state ball milling conditions, and this change of reaction conditions could favourably lead to an increase in reaction yields. As an additional advantage, the reaction temperature is usually reduced and reaction time is shortened such as in the oxidative coupling of substituted indoles with acrylates (Scheme 11.2) [21]. Palladium-promoted C–H activation in conventional thermally conducted reaction in solution (DMF) requires heating of reaction mixture overnight at 100 °C, and 3-vinylindoles 6 were obtained in low to moderate yields (30–42%). By application of the same reagents (PdCl2 in conjunction with MnO2 as an oxidant) in a ball mill, with the addition of silica gel as solid grinding auxiliary, entirely different products were obtained, dimeric β,β-diindolyl propionates 7. This result was achieved by milling at room temperature within only 20 min and in high yields (69–87%). In order to obtain 3-vinylindoles 6 by mechanosynthesis, palladium chloride reagent was replaced by palladium acetate, Pd(OAc)2 and a small amount of acetic acid was added for liquid assisted grinding (LAG). Within 25 min of milling at room temperature (at 30 Hz frequency) substituted indoles 6 were produced in yields significantly higher than those obtained in solution reactions (41–86%). When the solvent which is used in the reaction is detrimental to the reaction outcome, its circumvention in solid-state mechanochemical conditions is a very effective way to make the reaction proceed. For instance, zinc/silver couple debromination of norbornenemaleimide-1,2-dibromide 8 leads to the formation of intermediate norbornadiene 9 in situ (Scheme 11.3) [22]. This is a very reactive dienophile which in given reaction conditions (tetrahydrofuran solution) reacts with THF via a radical mechanism affording exclusively product 10, whereas the expected Diels–Alder cycloadduct 11 with furan could not be obtained. When the reaction was transferred to solid-state ball milling,

175

11.3 Synthetic applications

O

Zn/Ag couple dry THF reflux, 2 h, N2 Br Br O O NMe 8

O

+

O

Zn/Cu ball mill 1 h RT O NMe O

O O NMe 10 (45 %)

+ O O NMe 11

+ O O NMe 12

10

neat grinding 1 : 0.2 : 0 LAG-THF 0.6 : 1 : 0.4 LAG-THF no furan 0 : 1 : 0.8

9 Scheme 11.3: Avoidance of solvent in mechanochemical reaction.

firstly a tedious preparation of activated Zn/Ag couple catalyst was simplified by simple in situ milling of zinc and silver dust. Further improvement of the synthetic 1,2-debromination procedure was achieved by the replacement of silver with copper dust. When Cu/Zn catalyst was generated in situ without any precaution from moisture and air, neat grinding in a short time (1 h) provided the desired cycloadduct 11 and the sideproduct 12 in a 1:0.2 ratio. The addition of a small amount of THF solvent (LAG) had a profound effect on the composition of reaction products, with the formation of unwanted side-product 10 arising from THF. Significant amounts of side-product 10 were obtained only when furan was not present in the ball milling vessel during the reaction. Synthetically very important copper(I)-catalyzed azide-alkyne cycloaddition (CuAAC) reactions, a 1,3-dipolar cycloaddition of terminal alkynes with azides were successfully transferred to ball milling conditions (Scheme 11.4) [23]. Variety of substituted reactants provided triazoles 15 in high yields (84–99%) and regioselectivity, when copper acetate (5 mol%) was used for ball-milling reactions and silica – SiO2 as grinding auxiliary. The whole solid-state reaction was driven to completion in a very short time (10 min). Furthermore, it was found that mechanochemical solid state reaction conditions are very favourable for CuAAC reactions even when the copper catalyst was replaced by a copper vial and copper milling balls, which indicates that catalytic amounts of copper generated from the reaction vessel are sufficient for the reaction to proceed. Further advance in CuAAC reaction conditions was achieved by developing a multicomponent variant of the reaction, composed of the in situ preparation of benzyl azide from benzyl bromide and sodium azide, although at the expense of prolongation of milling, from 15 min to 16 h [24]. Mechanochemistry is applicable to enantioselective reactions as well, which indicates that weak (noncovalent) bonding interactions operate in solid-state conditions

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11 Mechanochemistry as a green method in organic chemistry

Ball milling R1

+ 13 1.1

R N3 14

:

N

2

1

Cu(OAc)2 (5 mol %) SiO2 13 Hz, 10 min ZrO2 vial RT

N

2 N R

R1 15 84-99 %

R1=Ph, p-Me-C6H4, p-OMe-C6H4, p-FC6H4, 2-C5H4N,... R2=decyl, mesityl, benzyl, adamantyl

N3

17 15 mi n Ball milling

16

Cu vial /Cu ball RT

99 %

or Br + NaN3 18 16 h

N N

N

19

99 %

Scheme 11.4: Mechanochemical CuAAC reaction.

in a similar way as in the solution. In ball-milling conditions the execution of Michael reactions which are mediated by catalysts that involve hydrogen bonding in reactivity and stereo control effectively led to the formation of Michael adducts 22 from 2,4-dicarbonyl compounds 20 and trans-Nitrostyrenes 21 (Scheme 11.5) [25]. These reactions were catalysed by cinchona squaramide derivative (CAT1) with 0.5 mol% catalyst loading. High enantioselectivities obtained (91–99% ee) demonstrate that even without the possibility of stabilization of the hydrogen-bonding substrate/catalyst complex by solvent, the performance of the catalyst was not affected under mechanochemical conditions. Comparison of various reaction conditions for the nitro-Michael reaction of trans-Nitrostyrene 23 and acetylacetone 24 reveals that the Michael reaction performed in ball-milling conditions catalyzed by CAT1 afforded product 25 in high yield and excellent enantioselectivity (95% yield, 99% ee). This result is comparable to the reaction carried out in solution (95% yield, 98% ee), which required a much longer reaction time (8 h was reduced to 10 min). In literature, there is also an account of the same Michael reaction which was carried with different, thiourea fullerene catalyst CAT2 in neat conditions. Here product 25 was obtained in high yield and moderate to good enantioselectivity (87% yield, 84% ee), but with higher catalyst loading (2%) and prolonged stirring [26]. Weak non-bonding interactions which are crucial in the supramolecular chemistry are also observed in solid-state mechanochemical conditions and applied to supramolecular host-guest complexations in molecular cages or cyclodextrins, preparation of cocrystals and organic frameworks. It was demonstrated that mechanically interlocked

177

11.3 Synthetic applications

R2

R1

O

Ball milling

O

O

NO2

+ R4

20

CAT1 400 rpm 5-30 min, RT

21

H

O R2 NO2

R1 R3 22

63-99 % 91-99 % ee tBu R1=OMe, OEt, O R2= Me, OMe, Ph R3=Ph, p-Me-C6H4, p-OMe-C6H4, p-FC6H4, 3-Br-C6H4,...

up to 84:16 dr

O Ball milling CAT1 400 rpm 10 min, RT

O

O

Me

Me 23

Neat CAT2

+ NO2

Ph

4 h, RT

O Me Ph

F3C

O Me NO2

H

OMe N CAT1 (0.5 mol %) Me N O

24 Solution conditions CAT1 DCM 8 h, RT

N

HN

F3C

25 95 % 99 % ee

25 87 % 84 % ee

O

HN

O

N Me HN

S NH

F3 C 25 95 % 98 % ee

iPr

CF3 CAT2 (2 mol %)

Scheme 11.5: Enantioselective nitro-Michael reaction.

organic structures such as rotaxanes could be prepared by the employment of mechanochemistry [27]. Hetero[3]rotaxane 30 was prepared in two-step procedure, in which the final step of stoppering by cycloaddition reaction was carried by ball milling. Firstly, hetero[3]pseudorotaxane 29 was prepared by self-sorting of the bis-(ammonium) threadlike salt 26 through solvent-free heating and mixing with a dibenzo-24-crown-8 27 and 21-crown-7 28. Whereas crown 28 selectively encircles only the NH2+ center adjacent to the oct-7-ynyl terminus, dibenzocrown ether 27 complexes with the dibenzylic NH2+ center providing hetero[3]pseudorotaxane 29 (Scheme 11.6) [28]. In the following step, mechanochemical grinding of an equimolar mixture of the solid [3]pseudorotaxane 29, 1,2,4,5-tetrazine and 3,6-diphenyltetrazine for 9 h led to the regioselective Diels-Alder reaction of 1,2,4,5-tetrazine on the oct-7-ynyl end of the pseudorotaxane thread. Remarkably, pseudorotaxane has survived 9 h of milling. Stoppering on the phenylacetylene terminus was completed via inverse-electron demand Diels–Alder reaction of 3,6-diphenyltetrazine by heating for 3 days at 100 °C. This procedure afforded hetero[3] rotaxane 30 as hexafluorophosphate salt in 9% yield (crude yield 19%). Mechanochemistry offers possibilities to conduct chemical reactions in a more sustainable manner than conventional solution reactions, and more than one principle of

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11 Mechanochemistry as a green method in organic chemistry

O + N H2

+ N H2

O

O

O +

4

O DB24C8

O

2PF6

+

O O

26

O

O 21C7

O

O

O

O

O 28 27

self-sorting in threading

NN

N N

NN

heating and mixing

O O

N N

O

a) ball milling RT, 9 h b) 100 oC 3 d

2PF6

O

O

O

O

+ N O O H O O

+ O N H2 O O O

4

29

O O O N N 2PF6

O O

+ O N H2 O O O

O

O

+ N O O H O O

4

N

N

30 (9 %)

Scheme 11.6: Applications of mechanochemistry in synthesis of rotaxanes.

green chemistry could be satisfied [29]. The advancement of mechanochemical procedures in the synthesis of fine organic chemicals and synthetic precursors for the pharmaceutical industry leads to the development of more sustainable technologies and environmental aspects are illustrated by the Beckmann rearrangement reaction (Scheme 11.7) [30]. By this reaction, oximes are transformed into amides. In the mechanochemical procedure which was developed by Colacino et al., the ketoximes 32 are prepared by ballmilling from the corresponding ketones in two reaction steps. Ball-milling of ketones with hydroxylamine hydrochloride and imidazole led to the formation of ketoximes 32, which were treated in the same milling pot with 1-(p-toluenesulfonyl)imidazole and

179

11.3 Synthetic applications

HCl.NH2OH + (1.1 equiv) O 1

R

R

2

O

N N (1.1 equiv) H

OH N R

ball milling, 30 Hz Zirconia RT, 30-99 min

31

N

N

1

S O (1.1 equiv)

O R1

R

2

ball milling, 30 Hz Zirconia RT, 30-99 min

32

N H

R2

33 (18-92 %)

R1 = Ph, p-Me-C6H4, p-OMe-C6H4, o-Br-C6H4, p-NO2-C6H4,... R2 = Ph, Me, Bn, ... N

HCl.NH2OH + (1.1 equiv) O R1

O

N (1.1 equiv) H

34

ball milling, 30 Hz Zirconia

R1 = H, Cl, OMe

RT, 30 min

N

OH N

R1 35

N

S O (1.1 equiv)

N

O

Ts

R1

ball milling, 30 Hz Zirconia RT, 30 min

36 N

O NH2 Cl 38 (85 %)

C

NaOH (2 equiv) ball milling, 30 Hz Zirconia RT, 99 min

R1 37 (85-91 %)

N

N (1.1 equiv) H ball milling, 30 Hz Zirconia RT, 30 min

Scheme 11.7: Mechanochemical Beckmann rearrangement.

subjected to the second milling process. This one-pot, two-step mechanochemical procedure provided amides 33 in most cases in very high reaction yields. The same ecofriendly reagents were applied to solid state reactions of aromatic aldehydes 34 and onepot; three-step milling sequence provides a variety of nitriles 37, which could be subsequently transformed to amides such as 38. The environmental advantages of mechanochemical Beckmann rearrangement reaction over solution conditions are attested by the synthesis of acetanilide 33 (R1=Ph, R2=CH3) from phenylmethyl ketone and comparison with conventional reaction carried out in solution (reflux in ethanol, 1 h). Mechanochemically product 33 was obtained in 91% yield, whereas in solution yield is slightly lower (86.4%). When green chemistry metrics were estimated for both methods, eco-advantages become more obvious: atom economy AE (100 * mass of all products/mass of reactants) = 27% (solution 49.7%); environmental factor E (mass of waste/mass of product) = 100.65 (solution 242.78); reaction mass efficiency RME (100*mass of desired product/mass of reactants) = 23.8% (solution 29.53) and EcoScale score (100 - the sum of the penalty points) = 73 (solution 36). These metrics clearly indicate that the E-factor, as well as EcoScale score, are superior to solution conditions. In view of the need for the development of more sustainable organic processes, the aldehyde substrate used in Beckmann rearrangement was substituted by 1-phenylethanol 39, which could be obtained from renewable bio-resources. The copper catalyst required for oxidation was prepared firstly by mechanochemical grinding of four components and then alcohol 39 was introduced to the same milling jar (Scheme 11.8). In situ oxidation proceeded in high conversion and obtained ketone 40 was then ball-milled with

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11 Mechanochemistry as a green method in organic chemistry

[Cu(MeCN)4](OTf) (3 mol %), bpy (3 mol %) TEMPO (3 mol %) NMI (7 mol %), air

OH

ball milling, 30 Hz Zirconia RT, 2 min O Cu catalyst

39

ball milling, 30 Hz Zirconia RT, 2x10 min NaCl

40 >95 % conv

OH

HCl.NH2OH (1.1 equiv)

N

Im-H (1.1 equiv) ball milling, 30 Hz Zirconia RT, 30 min

H N

41 >95 % conv

p-Ts-Im (1.1 equiv) O

42 (71 %)

ball milling, 30 Hz Zirconia RT, 30 min

Scheme 11.8: Development of more sustainable mechanochemical synthesis of amides.

reagents in optimized conditions shown on Scheme 11.7. This two-step milling procedure for conversion of 40 to amide 42 was carried out by successive addition of reagents to the same milling jar and grinding, to afford acetanilide 42 in 71% overall yield starting from 39.

11.4 Future prospects One of the main challenges in future developments of mechanochemistry to increase its applicability in industrial settings is in the scale-up of processes from small laboratory scale milling (usually from 10 to 100 mg to several grams). This is a chemical engineering issue of converting batch mode of operation in ball mills to continuous industrial processes. One of the very promising advancements is organic synthesis by twin screw extrusion (TSE), which is the continuous, scalable and solvent-free method which could be applied for large-scale industrial production [31]. The extrusion technique allows heating of the reaction system, further promoting the reaction to 100% conversion. The applicability of this method in organic reactions is documented for Knoevenagel condensation (throughput rate 0.52 kg h−1), imine formation (0.03 kg h−1), aldol reaction (0.064 kg h−1), aldehyde reduction (4.53 kg h−1) and the Michael addition (0.07 kg h−1) [32].

11.5 Conclusions It has been demonstrated that mechanochemical solid-state organic synthesis has great potential and applicability in various organic transformations and in the synthesis of various fine organic chemicals, with diminished environmental impact in comparison to classical solution reactions.

References

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Acknowledgements: This work is funded by the Croatian Science Foundation (grant No. IP-2018-01-3298, Cycloaddition strategies towards polycyclic guanidines, CycloGu).

References 1. Ghosh SK. Advancement of organic chemistry and its impact on environment. Res J Chem Env Sci 2015;3: 44–7. 2. Zaharia C, Murăraşu I. Environmental impact assessment induced by an industrial unit of basic chemical organic compounds synthesis using the alternative method of global pollution index. Environ Eng Manag J 2019;8:107–12. 3. Naidu R, Biswas B, Willett IR, Cribb J, Singh BK, Nathanail CP, et al. Chemical pollution: a growing peril and potential catastrophic risk to humanity. Environ Int 2021;156:106616. 4. Ballini R, editor. Green synthetic processes and procedures. London: Royal Society of Chemistry; 2019. 5. Ritter SK. Reducing environmental impact of organic synthesis. Chem Eng News 2013;91. https://cen.acs. org/articles/91/i15/Reducing-Environmental-Impact-Organic-Synthesis.html. 6. Gomollón-Bel F. Ten chemical innovations that will change our world: IUPAC identifies emerging technologies in Chemistry with potential to make our planet more sustainable. Chem Int 2019;41:12–7. 7. Krämer K. IUPAC names 10 chemistry innovations that will change the world. RSC, London: Chemistry World; 2019. 8. Takacs L. The historical development of mechanochemistry. Chem Soc Rev 2013;42:7649–95. 9. Wang GW, Murata Y, Komatsu K, Wan TSM. The solid-phase reaction [60]fullerene: novel addition of organozinc reagents. J Chem Soc Chem Commun 1996:2059–60. https://doi.org/10.1039/cc9960002059. 10. Friščić T, Mottillo C, Titi HM. Mechanochemistry for synthesis. Angew Chem Int Ed 2020;59:1018–29. 11. Wang GW. Mechanochemical organic synthesis. Chem Soc Rev 2013;42:7668–700. 12. Margetić D. Mechanochemical organic reactions without the use of solvent. Kem Ind 2005;54:351–8. 13. Cuccu F, De Luca L, Delogu F, Colacino E, Solin N, Mocci R, et al. New tools to navigate the uncharted territory of “impossible” reactions. ChemSusChem 2022;15:e202200362. 14. Stolle A, Ranu B, editors. Ball milling towards green synthesis: applications, projects, challenges, RSC green chemistry, vol 31. Cambridge, UK: Royal Society of Chemistry; 2015. 15. Margetić D, Štrukil V. Mechanochemical organic synthesis. Amsterdam, Netherlands: Elsevier; 2016. 16. Colacino E, Ennas G, Halasz I, Porcheddu A, Scano A, editors. Mechanochemistry – a practical introduction from soft to hard materials. Berlin: De Gruyter; 2020. 17. Schmidt R, Burmeister CF, Baláž M, Kwade A, Stolle A. Effect of reaction parameters on the synthesis of 5-arylidene barbituric acid derivatives in ball mills. Org Process Res Dev 2015;19:427–36. 18. Komatsu K, Fujiwara K, Tanaka T, Murata Y. The fullerene dimer C120 and related carbon allotropes. Carbon 2000;38:1529–34. 19. Kunitake M, Uemura S, Ito O, Fujiwara K, Murata Y, Komatsu K. Structural analysis of C60 trimers by direct observation with scanning tunneling microscopy. Angew Chem Int Ed 2002;41:969–72. 20. Wang GW. Fullerene mechanochemistry: serendipitous discovery of dumb-bell-shaped C120 and beyond. Chin J Chem 2021;39:1797–803. 21. Jia KY, Yu JB, Jiang ZJ, Su WK. Mechanochemically activated oxidative coupling of indoles with acrylates through C–H activation: synthesis of 3-vinylindoles and β,β-diindolyl propionates and study of the mechanism. J Org Chem 2016;81:6049–55. 22. Štrbac P, Margetić D. Complementarity of solution and solid state mechanochemical reaction conditions demonstrated by 1,2-debromination of tricyclic imides. Beilstein J Org Chem 2022;18:746–53. 23. Thorwith R, Stolle A, Ondruschka B, Wild A, Schubert US. Fast, ligand- and solvent-free copper-catalyzed click reactions in a ball mill. Chem Commun 2011;47:4370–2.

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24. Cook TL, Walker JA Jr, Mack J. Scratching the catalytic surface of mechanochemistry: a multi-component CuAAC reaction using a copper reaction vial. Green Chem 2013;15:617–9. 25. Wang YF, Chen RX, Wang K, Zhang BB, Li ZB, Xu DQ. Fast, solvent-free and hydrogen-bonding-mediated asymmetric Michael addition in a ball mill. Green Chem 2012;14:893–5. 26. Andrés JM, González M, Maestro A, Naharro D, Pedrosa R. Recyclable chiral bifunctional thioureas derived from [60]Fullerene and their use as highly efficient organocatalysts for the asymmetric nitro-michael reaction. Eur J Org Chem 2017;2017:2683–91. 27. Friščić T. Supramolecular concepts and new techniques in mechanochemistry: cocrystals, cages, rotaxanes, open metal-organic frameworks. Chem Soc Rev 2012;41:3493–510. 28. Chen PN, Lai CC, Chiu SH. Self-sorting under solvent-free conditions: one-pot synthesis of a hetero[3] rotaxane. Org Lett 2011;13:4660–3. 29. Ardila-Fierro KJ, Hernández JG. Sustainability assessment of mechanochemistry by using the twelve principles of green chemistry. ChemSusChem 2021;14:2145–62. 30. Mocci R, Colacino E, De Luca L, Fattuoni C, Porcheddu A, Delogu F. The mechanochemical Beckmann rearrangement: an eco-efficient “cut-and-paste” strategy to design the “good old amide bond”. ACS Sustainable Chem Eng 2021;9:2100–14. 31. Crawford DE, Miskimmin CGK, Albadarin AB, Walker G, James SL. Organic synthesis by Twin Screw Extrusion (TSE): continuous, scalable and solvent-free. Green Chem 2017;19:1507–18. 32. Bolt RRA, Leitch JA, Jones AC, Nicholson WI, Browne DL. Continuous flow mechanochemistry: reactive extrusion as an enabling technology in organic synthesis. Chem Soc Rev 2022;51:4243–60.

Liliana Mammino*

12 Maximizing advantages and minimizing misinterpretation risks when using analogies in the presentation of chemistry concepts: a design challenge Abstract: Analogies are frequently used in chemistry education (and science education in general), above all when introducing a new concept or when a concept is perceived as too abstract by the teacher or by the learners. On the one hand, analogies can offer functioning opportunities for clarifications; on the other hand, they may risk engendering misinterpretations or misconceptions, because the terms of a given analogy may be perceived differently by the teacher and by the student, or may be too farfetched to have a clarifying role. In order to maximize the benefits and minimize the risks, the design of analogies needs to entail careful attention both to the nature of the analogy and to its ‘matching’ to the nature of the concept to which it refers. This involves vigilant analysis of all the details and of their implications, and the parallel design of a viable way to guide the student through the terms of the analogy; such guidance is actually meant to become an explanation component. The paper considers concrete examples from the author’s direct experience with general chemistry and physical chemistry courses, and analyses both the design of the details of the selected analogies and the corresponding guidance pathways. It also discusses related issues like the importance of limiting the resort to analogies to the cases where they can actually have a significant impact on students’ understanding, and the opportunity of replacing them with molecular models whenever feasible, as a model’s nature is closer to the mental images that it is desirable to promote through students’ perceptions. Comparisons of the types of guidance needed for analogies, for general-type visualization, and for visualization through models are also included. The take-home message reiterates the considerations on the nature of analogies as something to be designed, on the teacher’s active role in the design, and on the possibility of including students in the design process, when the concepts and corresponding analogies are suitable for such inclusion. Keywords: analogies; clarification roles of analogies; designing analogies in the classroom; misconceptions prevention; teacher’s guidance to students.

*Corresponding author: Liliana Mammino, Faculty of Science, Engineering and Agriculture, 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 “Maximizing advantages and minimizing misinterpretation risks when using analogies in the presentation of chemistry concepts: a design challenge” Physical Sciences Reviews [Online] 2023. DOI: 10.1515/psr-2022-0318 | https://doi.org/10.1515/ 9783111328416-012

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12.1 Introduction 12.1.1 Analogies as an expression, emphasising and clarification tool An analogy is a comparison between two items or two situations, intended to emphasize a certain feature or to provide clarifications. If A is the item that one wishes to emphasize or clarify and B is the analogy, then B is taken as commonly familiar, and/or capable of prompting immediately illustrative mental images or emotional responses. For a comparison to be an analogy, A and B must belong to different categories, i.e., must concern items of different nature. A is often called “target” in literature, while B is often referred to as the “vehicle,” “base,” “source,” or “analog” domain [1]. The present work prefers to use A and B, within a conceptual framework for which the person who builds an analogy knows all the details of A and builds B in such a way as to maximise the correspondence of its details to those of A, to pursue objectives that he/she considers desirable. In other words, A is the starting point and B is selected or designed in such a way as to clarify or emphasise the nature and characteristics of A, in view of desired understanding outcomes. This aspect is illustrated through examples in Section 12.2.3. Analogies are common in everyday life, as part of language and culture traditions. For instance, an analogy that is used in more than one language (e.g., both Italian and English) to convey the idea that one is really struggling to find a certain item for which one is searching, and that the search is objectively difficult, is the comparison with “finding a needle in a haystack”. In this case, A is the search for the given item and B is the concept of “finding a needle in a haystack”. Both needles and haystacks were broadly familiar objects up to recent past and, therefore, B prompted a mental image that clearly signified the difficulty of a search. It may also be noted that, while needles and haystacks were familiar objects, nobody would have actually tried to find a needle in a haystack; therefore, B conveys a perception of practical impossibility. Analogies have been used since ancient times and in many cultures. They have been part of poetry. Homer’s poems (8th century BC) contain many similitudes. There are about 180 in Iliad, mostly comparing the events on the battle-field to aspects of everyday life or nature. For instance, the battle-field scene of Agamemnon chasing and scaring the Trojan soldiers, who scatter away, and killing the ones that remains behind is compared to a lion chasing a group of heifers and killing the one that remains behind. The comparison thus builds two scenarios and stimulates two corresponding mental images in the reader [2]. The battle-field scene is A, the scene of the lion hunting the heifers is B; the image prompted by B emphasises the reality and the emotions of Agamemnon’s anger and chase on the one side, and the fear and anguish of the chased soldiers on the other. Thus B also contributes to enhance and expand the emotional response expected for A. Analogies have been widely used within philosophy. Plato (ca 427–347 BC) builds detailed allegories to convey the cores of crucial philosophical concepts. For instance, the allegory of the cave (Republic, 514 b–520 a) expresses a conception related to knowledge:

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human beings are like prisoners in a cave, who see the shadows (projections) of things on the back wall of the cave – the only one that they can face – and think that these shadows are the reality; when a person is freed and gets out of the cave, he can start the long process of seeing the actual things and learning the truth about them, up to attaining the knowledge of ideas, and this is the path of philosophy. Lucretius (c. 99 BC–c. 55 BC) uses convincing analogies to explain why it is reasonable to assume that matter is made of small particles, even if we cannot see them [3]. Galileo Galilei used the famous apologue of the shepherd boy discovering newer and newer items capable of emitting sounds, thus generating new music, to transmit the concept that we shall never be sure of knowing everything, that there will always be new areas and phenomena, of which we might not even have the faintest idea by now, but that we shall discover later [4]. In this apologue, each new item that can produce a new sound represents a new object of investigation and the consequent new piece of acquired knowledge. Analogies have been used to facilitate the acceptance and internalisation of concepts resulting from abstract reasoning. For instance, Arab mathematicians used an easily understandable analogy to explain the sign of multiplication results (A) by relating (B) the ‘friend’ concept to the positive sign and the ‘enemy’ concept to the negative sign (scheme 12.1).

Scheme 12.1: Analogies used by early Arab mathematicians to facilitate the understanding of the sign rules for multiplications. In the analogy, the positive sign corresponds to ‘friend’ and the negative sign to ‘enemy’.

Alchemy, continuing the ancient Egypt tradition for which the knowledge of substances is dangerous and should be reserved only to the initiated, used allegories to denote substances and to describe processes. In this case, only the initiated knew the actual keys and could interpret the similitude-correspondences between the allegory and the hidden ‘chemical’ meaning. The allegory was the B term of the comparison; the noninitiated did not have the key to recognise the nature of the A term. It has also sometimes happened that what would from hindsight be considered an analogy did actually ‘come first’. The structure of the benzene molecule constituted a headache for chemists in the XIX century, because only open chains were considered possible in those times, and there was no way of proposing an open-chain structure compatible with the C6H6 formula and the tetravalence of carbon. Then, one day Kekulé had a dream of a serpent biting its tail (a famous symbol within alchemy) and, when he

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12 Using analogies in the presentation of chemistry concepts

woke up, the image generated the intuition that the structure of the benzene molecule should be that of a ring; in 1865, he proposed the structure that has become so important in chemistry. From a chemistry point of view, the serpent is the B term and the structure of the benzene molecule is the A term.

12.1.2 Analogies in education and in chemistry education Within education, the comparisons entailed by analogies aim at facilitating clarifications of the less familiar concepts, including the nature or description of less familiar entities. Within chemistry education, and science education in general, analogies are used frequently, above all when introducing a new concept, or when a concept is perceived as too abstract by the teacher or by the learners. Examples are offered by some mathematical entities, or the entities of the molecular level (atoms and molecules) in chemistry. The strength of an analogy depends on how the features of A and B relate to each other [5]. Particularly successful analogies have become part not only of teaching but, in some respect, also of thinking. An example is the lock and key analogy for enzyme/ substrate complementarity [5, 6], where the terms’ correspondences are clear (complementarity of shapes, part of the key fitting inside the lock and part of the substrate fitting inside the active site of the enzyme) and no aspect is misleading. The last decades have witnessed active research and debate about the possible roles of analogies in science education and the best modes of using them. A variety of positions have appeared. This section quickly reviews the main questions that have been focuses of attention, whereas the next section will be based on the author’s direct experience and ensuing reflections. Particular attention is given to the works that analyse benefits and risks, with preference over works reporting examples of analogies without discussion of benefits and risks. Some major epistemological issues, such as the need to clearly distinguish between models, visualization and analogies, are discussed at the beginning of the next section; it is preferred to discuss them as an independent issue rather than to point out the confusions in this regard when recalling key contents of works in which such confusions appear. For the sake of conciseness, the LM acronym will henceforth be used in place of “the author of the present paper”. A number of works focus on the crucial issue of ensuring that analogies fulfil their clarification roles remaining auxiliary tools, without becoming dominant in the teaching activity or in students’ internalised concepts and images. Treagust et al. [7] investigated how textbook authors approach the issue of analogies by interviewing a number of authors. The authors’ answers are particularly enlightening and are recalled here rather extensively because they express concepts that will be important for further discussion in the present work. An author, commenting on a simple analogy for chemical equilibrium stated that “I would not mind using it myself if I had control of the situation in a classroom situation”; however, he “would not like to stick it in a text where everybody is going to use it” because he would not have control over it once the analogy is in textbook. Similarly,

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another author stated that he “would be reluctant to use an analogy in a textbook” because he would not be in a position to predict whether it would “provide an adequate representation for all students”, he also emphasised that “when you are teaching you can respond to those students who do not understand the concept by an analogy or further explanation”, but “you do not see the blank faces of the students you are writing the text for” and added that “analogy is a very personal thing, something you might deal with on a one-to-one basis”. Another author suggested that the analogies inserted in a textbook lose something with respect to how they can be presented in the classroom, and indicated that “analogy was something of a spontaneous exercise” and he was more likely to use them “when attempting to explain an abstract idea to students after the students had indicated that they did not clearly understand”. Another author recalled “the need to change or adapt analogies and models to suit the changing circumstances of the lesson and pupils”. Other comments included the fact that an analogy had been created spontaneously by the author as a result of a question by some students after a class, and this circumstance allowed him to push the analogy to lengths that can be sensible only within a teacherstudent conversation. In addition, [7] recalls the results of another study [8, 9], according to which a significant proportion of students did not understand the analogies in a sufficiently complete way. Some of the interviewed authors also expressed the opinion that an additional interesting component of using analogies in the classroom would be that of ‘breaking down’ an analogy after using it, to better highlight the roles of its terms, and that, when encountering a particularly problematic analogy (e.g., in textbooks), “maybe the way to deal with it is to point out what is wrong with it”. All these responses clearly emphasise the crucial aspect that analogies are meant for classroom work, where the teacher can use them in the way that best responds to the needs of the group of students present in the class; they also imply that the teacher needs to have good content knowledge to be in a position to handle analogies successfully. As a person who has repeatedly faced the challenge to try to counteract misconceptions about atomic structure engendered by unsuitable analogies provided in previous instruction, LM considers the suggested option [7] of dealing with problematic analogies by pointing out what is wrong with them as ideal. It is particularly suitable for some analogies regrettably still utilised in the presentation of the atomic structure, and it has the pedagogical efficacy of error analysis [10]. Some forms of visualization unintentionally engender perceptions for which the orbitals are viewed as the ‘rooms’ of electrons in the atom [11]. An analogy discussed in [12], instead of attempting to prevent such perception, encourages it openly, viewing the atom as an apartment block and establishing correspondences such as shell/storey, subshell/unit, orbital/room, state/bed, electrons/occupants and spin (+½, −½)/sex (male, female); it also speaks of the possibility “to look for a particular electron in an atom, analogous to a special occupant in a block”. Should students come across this analogy, it constitutes a perfect example for clarifying the concepts relevant to the structure of the atom by showing what is not correct in the analogy (including the fact that we cannot “look for a particular electron” because electrons are indistinguishable particles). Another analogy that has largely gone out-of-

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fashion, but still occasionally resurfaces, is that between the atom and the solar system, whose terms are presented in detail in [13]. The analogy refers to the planetary model of the atom, which is part of the history of chemistry and physics, but not part of modern science; it could be presented to students only as part of history, and then constructively utilised in terms of inviting students to identify and discuss all the aspects that are not compatible with the modern model of the atom (Scheme 12.2).

Scheme 12.2: Examples of differences to be highlighted in an error-hunting exercise concerning the analogy between the structure of the atom and the solar system.

A number of works explore benefits and risks in the use of analogies, and the conditions that make them useful and prevent risks. Several authors [6, 14, 15] recall that analogies may only be useful for teaching concepts that are conceptually difficult or abstract; if a concept is sufficiently simple to understand, an analogy may not be necessary, and risks turning into additional information that students are expected to remember, although not being inherent part of the concept [16]. The emphasis on “abstract” concepts as concepts needing analogies to make them more concrete appears to be dominant [17–23]. The emphasis on the dichotomy of possible outcomes – beneficial or confusing – is also frequent. A teacher may make a difficult concept easier to understand by using a suitable analogy; in other cases, the teacher’s analogy is the very item that students cannot understand: they may ignore it, but it may also happen that the analogy

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confuses instead of clarifying [6] . In addition, the use of an analogy (B) for a given concept (A) may limit a student’s ability to develop a deep understanding of A [6, 24–26]. Students may take B too far and fail to distinguish it from A, or focus on aspects of B that are extraneous to A [27]. As stated in [28] and recalled in [29], “analogies are double-edged swords, they can foster understanding, but they can also lead to misconceptions”. The concern about the risk that they engender misconceptions is also expressed in [26, 30, 31], with the additional concerns that some misconceptions may be difficult to identify [32, 33] and rectify [34, 35]. Simanek [36] considers that “analogies should never be used as arguments to reach a conclusion” and that “they encourage lazy and sloppy habits of thought”. Brown and Salter [31] clearly identify a critical cause of confusion and misconceptions “if student and teacher do not share a common understanding of analogy”. Several studies emphasize the role of the teacher for an effective use of analogies. The teacher needs to choose analogies that are familiar to the student; otherwise there will be no understanding [21, 27]. The teacher also needs to guide students through the terms and limitations of an analogy [6, 37–39]. Without adequate guidance, “students might fail to distinguish the analogy from the content to be learned, remember only the analogy rather than the content to be learned, focus on only irrelevant aspects of the analogy, and produce false results” ([40], reported in [41]). Care needs to be taken to prevent the rising of an impression that B is a true description of A [21, 42, 43]. The analogy in the title of [29] perfectly depicts the challenge of designing and using analogies with the objective of maximising the benefits and minimising the risks, by comparing it to the challenge of navigating between Scylla and Charybdis, the two monsters of ancient Greek mythology, facing each other across the narrow stretch of sea now known as the Messina Strait (a highly challenging but not impossible enterprise: according to Homer, Odysseus made it). The next section reports concrete examples from LM’s direct experience, within an operational perspective aimed at maximising the benefits and minimising the risks and a major focus on design.

12.2 Analogies in chemistry teaching: examples and reflections 12.2.1 Models, visualization and analogies A number of works appear to consider the model, visualization and analogy concepts as interchangeable and use the terms as synonyms. It is therefore important to clarify the differences between them, as categories having different nature. The model term may refer to mathematical models, or to a set of statements practically analogous to a theory, or to physical models. Mathematical models – when possible or already attained – express the most complete currently-available description of a

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given system or phenomenon. The quantum mechanical descriptions of atoms and molecules are typical examples of mathematical models. Physical models are models that are built as objects that can be handled. For instance, civil engineers may build small-scale models of complex buildings or other structures, to better study them before the actual construction; chemical engineers build pilot plants, producing few kg of products, to study all the details before building the actual plant which will produce tons of products. The wooden or plastic ball-and-stick models of molecules are physical models, enabling a hands-on perception of the 3D structure of molecules and of their geometrical characteristics; they are necessary to explain features such as the non-stackability of enantiomers, or the meaning of the definition of torsion angle. Models are not analogies, because they pertain to the same conceptual category as the item they model or represent. For instance, the stick-and-ball model of a molecule is a representation of a molecule, not of a different kind of item. Visualization involves the use of images, which show simplified versions of the object of interest (A) attempting to highlight its main characteristics; it does not need any comparison with an object of different nature (B) and, therefore, it is not a type of analogy. It may refer to a variety of aspects. It may be born from mathematics, like the diagrams of functions; these include images that have become familiar independently of their mathematical nature, like the shapes of atomic orbitals. It may represent physical models, like the images we use to represent molecules. Figure 12.1 shows two types of visualization models for the ammonia molecule: the ball-and-stick model and the spacefilling model. It is important to emphasise the model role of these images, to foster appreciation of the challenges of building an image of a molecule, incorporating full information. This is conveniently pursued by presenting more than one option and highlighting their advantages and limits. For instance, ball-and-stick models highlight the

Figure 12.1: Stick and ball (a) and space-filling (b) models of the ammonia molecule.

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Figure 12.2: Different ways in which ball-and-stick models (a) and space-filling models (b) highlight an intramolecular hydrogen bond. The intramolecular hydrogen bond is indicated by a dashed blue segment in (a) and by an arrow and the IHB acronym in (b).

geometrical arrangement of the nuclei and are the most suitable to explain geometric characteristics such as bond lengths, bond angles and torsion angles; on the other hand, they do not recall the fact that the atoms are in contact, actually overlapping. Space filling models give a better idea of a molecule as an entity with the atoms in contact and with a volume and shape, but the geometrical arrangement of the nuclei may not be so evident and concepts like bond lengths, bond angles and torsion angles are not so easy to highlight. The option of considering different types of visualization models automatically conveys the perception that these images are our models, not the reality; it is also important to explicitly explain that molecules are complex object and we use different visualization approaches according to what we wish to highlight [44]. The different visualization abilities can be further illustrated using examples like that in Figure 12.2: the ball-and-stick model does not automatically highlight an intramolecular hydrogen bond: we need to indicate it somehow (e.g., by adding a dashed segment, as in Figure 12.2a) if we want the reader to be aware of it; the space-filling model immediately highlights that a certain H atom is linked to both O atoms; on the other hand, the ball-andstick model enable clearer identification of the H-bond donor and acceptor.

12.2.2 When an analogy fails its purpose This section reports and discusses an example from LM’s direct experience. The analogy with a watermelon (B) was rather common to illustrate Thomson’s model of the atom (A),

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with the pulp of the watermelon corresponding to the evenly distributed mass and positive charge of the atom and the seeds corresponding to the electrons embedded in it. LM happened to be invited to a higher secondary school, and a pupil described Thomson’s model with reference to a watermelon. The description was so focused on the watermelon that LM had the perception that the pupil might take the analogy too literally, and asked “How big is an atom according to Thomson?”. The pupil had no hesitation and answered “as a watermelon” [45]. It is a typical case of students taking “the analogy too far” and becoming “unable to separate it from the content being learned” [21]. What might have gone wrong in this case? The teacher might have limited the terms of the analogy to the description (image) of the ‘internal situation’ of the object, without extending it to the size of the object, because of his/her internalised knowledge that atoms are very small. The pupil is not aware of this limitation and accepts all the possible terms of the analogy – actually replaces A by B. A number of questions arise easily. Is it really necessary to use an analogy for this model? No, because visualization can offer better illustration and clarifications. Is the watermelon a good choice for the B role? No, because the seeds are not distributed in all the regions within the watermelon and because of a thick skin which might transmit the impression that the atom is surrounded by some sort of thick shell. How could a teacher guide students through the analogy? By emphasising that they should only consider the way the seeds are embedded in the pulp of the watermelon, and not consider other features, because an atom is very small and is not enveloped by a shell.

12.2.3 Designing analogies in the classroom 12.2.3.1 Why designing analogies in the classroom? The need for suitable analogies arises in the classroom, when it appears evident that students experience challenges with a specific concept. Then, it may be important to design an analogy (B) that responds to characteristics of the concept (A) and to the characteristics of the students. Some crucial questions need to be given specific attention. – identifying whether the resort to an analogy can be beneficial for the understanding of A; – designing B in a way that it focuses students’ attention on the key aspects of A and that the comparison remains as rigorous as possible despite the different natures of A and B; – guiding students through the terms of the analogy, highlighting similarities and differences between A and B.

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These aspects will be illustrated by the consideration of selected concrete examples in the next subsections. The most convincing way – whenever possible – is that of guiding students to analyse the details of the relationships between A and B through questions, instead of providing them. In this way, the teacher’s guidance is guidance to and through reflections. 12.2.3.2 Analogies to explain the meaning of individual terms Some scientific terms were born from analogies; then the choice of B is straightforward. An example is the tunnel effect term. Then, B is a tunnel under a mountain in a mountain road; the role of the tunnel is that of enabling an object (e.g., a car) to go to the other side of the mountain without climbing to the top of the mountain and then descending from it. In terms of particles, we speak of tunnelling (A) when a certain entity goes from one state to another without passing through the top of the energy barrier between the two states. Visualization can show the parallelism between the two cases: two sides at the base of a mountain (denoted as state-1 and state-2), where the mountain represents an energy barrier, and a tunnel connecting the two states through the mountain. The meaning of the states and the barrier can be illustrated, e.g., by models of the two configurations of the ammonia molecule (pyramidal, with the N atom on one or the other side of the plane identified by the three H atoms) and the geometry corresponding to the top of the barrier (a trigonal planar geometry with N at the centre). Given the current decrease in the language mastery level of the young generation, it may happen that students do not know the meaning of some common language terms that are relevant in a given scientific discourse. The problem is unavoidably frequent in context using a second language as language of instruction. Then, the teacher needs to find examples illustrating the meaning of such terms. Examples often used by LM to illustrate the meaning of the adjective negligible refer to some familiar situations. For instance, students are asked whether, if they want to use a balance to know their body mass, they would remove a small hair clip before stepping on the balance, and whether they would remove a backpack. All students answer NO for the former question and YES for the latter one. Then the terms of the analogy are explained. The mass of the hair clip is negligible in the given situation because, whether we remove it or not, we shall read the same value on the scale. The mass of the backpack is not negligible because keeping it on the back or removing it will yield two different values on the scale. It may not always be easy to ‘invent’ an analogy when the need arises. The first time it became clear that a group of students was not following an explanation about the trend of the ionization energy from top to bottom down a group of the periodic table because they did not know the meaning of the shielding verbal form, LM tried to find an analogy in the surrounding. We could hear the voice of the lecturer in the next classroom, although smoothed. It was smoothed because the wall between the two rooms was exerting a shielding action on how we perceived the voice of that lecturer. The next step was that of

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asking how we would perceive it if there were two walls between us and that lecturer, and then three walls … In this way, not only the literal meaning of the shielding term was illustrated, but also the fact that the total shielding effect increases if there are more layers between the source of a certain action and those who perceive its effect. Then the transition to A was easy: a greater number of closed shells between the nucleus and the outer electron/s correspond to greater shielding of the nuclear charge as perceived by the outer electron/s. 12.2.3.3 Analogies meant to highlight operational routes Analogies may be used to highlight problem solving routes [46]. They may be particularly important for problems concerning entities of the atomic and molecular level. Careful guidance is needed for students to identify the terms of the analogy, at least when the concerned type of problem is new for them. For instance, a question (A) like “calculate the mass of a sodium sample containing 4.38 × 1024 sodium atoms” may be perceived as difficult by students because it refers to unfamiliar entities (atoms) and the number of these entities is expresses through a power of 10; this unfamiliarity prevents the recognition that it is actually a type of questions with which they are familiar. An analogy may consider a familiar type of question (B1) like “calculate the mass of a bag of potatoes knowing that it contains 40 potatoes and the mass of each potato is 300 g”. Students immediately answer that they need to multiply the mass of one potato by the number of potatoes. The parallelism of the physical situations between A and B1 is then highlighted, until students answer that the mass of the sample can be calculated by multiplying the number of atoms by the mass of each sodium atom. The next step needs to guide them to find how to calculate the mass of one sodium atom. A new analogy (B2) is proposed, of the type “Calculate the mass of one apple knowing that the mass of 20 apples is 1800 g and all the apples have the same mass”. The students know that they have to divide 1800 g by 20. Then they are asked whether they know the mass of a specific number of sodium atom, and the question stands until someone thinks of the mole. At this point, the mass of a sodium atom is calculated as the ratio between the molar mass of sodium and Avogadro’s constant. The result is the value needed for the calculation of the mass of the sample. Altogether, the analogies have the auxiliary role of helping identify the algorithm needed to solve the problem. The logic of the algorithm and the ‘interventions’ of the analogies to identify crucial steps are highlighted in Scheme 12.3. 12.2.3.4 Analogies meant to support the understanding of new concepts The need for analogies may arise when new concepts are introduced, if it becomes evident that students experience difficulties. Then, it is convenient to choose the analogies from something familiar. Few illustrative examples are provided below.

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Scheme 12.3: How the B1 and B2 analogies (Section 12.2.3.3.) provide guidance for the identification of the mathematical route to solve the given question (A). B1 corresponds to the question “calculate the mass of a bag of potatoes knowing that it contains 40 potatoes and the mass of each potato is 300 g”, B2 to the question “Calculate the mass of one apple knowing that the mass of 20 apples is 1800 g and all the apples have the same mass”.

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Example 12.1: Illustrating the concept that the number of particles’ collisions per unit time increases as temperature increases The concept (A) entails several conceptual components. The basic ones are introduced when introducing the ideal gas model: (i) a temperature increase means an increase in the average velocity of the particles; (ii) if the particles move faster, they collide more often. Additional components are needed to explain that the rate of a chemical reaction increases as temperature increases. They can be introduced after the effective collision concept is introduced: (iii) as the number of collisions per unit time increases, the number of effective collisions increases; (iv) as the velocity of the colliding particles increases, more collisions are sufficiently ‘violent’ to be effective. The analogy (B) utilised by LM has the features of an imagined experiment, articulated through the following suggestions to students: – Assume that you are all in an empty room; – Assume that you are moving around at low speed, along straight lines, and you do not take measures to avoid bumping into your friends. The cases of two people colliding with each other will have a certain rate. The collisions are gentle. – Now assume that everybody’s speed increases gradually. The number of collisions per unit time increases. The ‘bumping into each other’ becomes more energetic. After all the components of B are sufficiently clear, in-class interactions highlight the aspects relevant to the increase of reaction rate as temperature increase. If the total number of collisions increases, the number of effective collisions also increases. If the collisions become more energetic, the proportion of effective collisions increases. Thus, a temperature increase causes an increase in the reaction rate because it causes an increase of the number of effective collisions per unit time through two routes: increase of the total number of collisions, and increase of the proportion of collisions that are sufficiently “violent” to result in the reaction. The terms of the analogy are straightforward: relating the number of effective collisions per unit time to the velocity of the moving entities. There are no particular cautions to take care of. Similar imagined experiments are utilised as analogies to highlight the entropy increase on heating a substance or on mixing two substances [45]. Example 12.2: Illustrating the meaning of thermodynamic reversibility and irreversibility. The main concept (A) can be expressed in the terms that a process is reversible if its direction can be reversed at any moment by an infinitesimal modification of a variable [47]. The selected analogy B was particularly convenient in the given context because the

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science building has a staircase in a central position in a hall, not surrounded by walls. B entails an imagined experiment (with clear recognition that it cannot be realised in practice). It is illustrated in Figure 12.3. Let us suppose that one wants to go from the first floor to the ground floor. The reversible option is that of walking down the staircase: at any moment, one can decide to reverse direction. The irreversible option would be that of jumping from the first floor: after it starts, the process cannot be reversed. The validity and limitations of the correspondence between B and A need to be emphasised in detail. B provides a good illustration of the core ideas: the possibility that a process can change direction if it is reversible and the impossibility of changing direction if it is irreversible. On the other hand, a staircase is not a rigorous image because its steps have finite dimensions, whereas the steps entailed in the definition of reversible process are infinitesimal. After the presentation of the analogy in the just-described terms, students are invited to try to imagine a staircase in which the distance between consecutive steps becomes smaller and smaller, and the number of steps greater and greater, until the distance tends to be infinitesimal and the number of steps tend to be infinite. One can also draw more images like that in Figure 12.3, maintaining the same ground and first floor levels and with smaller distances between consecutive steps and greater number of steps in each subsequent image. We shall never be able to draw something with infinitesimal distances and infinite number of steps, but these subsequent images trigger a mental process leading to better understanding. Example 12.3: Illustrating the meaning of ‘quantization’. The definition (A) of quantisation can be given by saying that a quantity is quantised if it can take only discrete values. It is important to make the concept clear since early stages, as soon as the concept that the energy of the electron in the atom is quantised is introduced. This can be done through suitable analogies. The crucial feature requiring adequate emphasis in an analogy is the fact that quantisation concerns the possible values of a physical quantity. Therefore, one needs to identify a physical quantity that can

Figure 12.3: Analogy utilised to illustrate the meaning of thermodynamical reversibility and irreversibility.

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relate to everyday-life experience and that can only take discrete values. LM encountered this challenge first when preparing [48] and later [44]. The image of a staircase was sometimes being used in those times; however, the staircase by itself does not relate to any physical quantity and does not imply lack of continuity (we move on a staircase in a continuous manner, without having to miss some spaces); there is no clear connection between the features of the motion on a staircase and the situation of the energy of the electron in the atom. On the other hand, it is possible to associate a quantised physical quantity (B) to the image of a staircase: the distance from the ground of a person standing on one of the steps with both feet on the same step, which is tantamount to the distance of the upper surface of that step from the ground level (Figure 12.4). Then, it is easy to distinguish the ‘allowed values’ of this quantity (the distances for the cases when the person stands on one of the steps) and the non-allowed values (the values corresponding to intermediate distances between two steps); it is also easy to point out that the allowed values are discrete, as no intermediate values are possible between two consecutive allowed values. While drawing the image on the board, these concepts can be emphasised also by drawing a silhouette of a person standing on one of the steps and of another person standing mid-air between steps (which immediately appears impossible, i.e., not allowed). It is important to clearly emphasize the nature of the correspondence between B and A. B illustrates the meaning of a quantity being quantised, using a macroscopic-world quantity that we can easily understand; it does not illustrate some physical aspect of the atom. This clarification is important to prevent the risk of the generation of mental images of energy staircases located within an atom.

Figure 12.4: An example of ‘quantised’ physical quantity in the macroscopic world: The distance between the ground level and the feet of a person standing on a staircase can only take discrete values.

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Example 12.4: Illustrating the concept that molecules prefer to take the lowest energy conformations. The structural and geometrical properties of molecules are mostly visualised through 3D molecular models on a computer screen. On the other hand, there are aspects that are not suitable for this type of visualization, and the resort to analogies becomes an important route, although it is not always easy to find analogies that can have sufficient illustrative power without risks of misinterpretations. One of these aspects (A) is the fact that molecules prefer to take the lowest energy conformations (geometrical arrangements of their nuclei). An analogy (B) may refer to the possible ‘geometries’ that we (human beings) can take [49]. – The possible ‘geometries’ for human beings comprise sitting, standing, lying down, but also others like standing on one leg, standing with our arms raised high above our head, or outstretched from our bodies, and many others. We can characterise these geometries qualitatively, with the terms just used, but we could also give a quantitative characterisation for each person, like the distance between different parts of the body in the different ‘geometries’. – The ‘geometries’ that we choose more frequently are the ones with which we are more comfortable, i.e., the ones that we can maintain for long without particular efforts. – If we could take a snapshot of the inhabitants of a town at a certain time, we would find that most of them will be in the most comfortable ‘geometries’, like sitting or lying down. A very small proportion of persons (if any) would, e.g., be standing on one leg. – Calculations can tell us the possible geometries of a molecule, including the values of the parameters describing geometry (bond lengths, bond angles, torsion angles) and the energy corresponding to each geometry. An example of two different geometries of the same molecule is shown in Figure 12.5 [50]. – For the sake of relating B and A, we may consider that the low-energy geometries are the ones with which molecules are more ‘comfortable’. Therefore, these are the preferred geometries. – If we could take a snapshot of the molecules in a sample at a certain time, we would find that most of them will be in the preferred geometries, i.e., in the lowest energy conformers. Being the largest majority, these conformers determine the behaviour of a substance. The conceptual links between A and B are the possibility of taking many geometries and the preference for the most ‘comfortable’ ones. Example 12.5: Stressing the importance of following a logical plan when writing a text Students have to write a number of texts, from answers to specific questions for assessment to articulated texts like lab reports. When students have not been trained to

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Figure 12.5: Examples of conformers of one of the simplest trimeric acylphloroglucinols having different energy [50]. Conformer (a) has the lowest energy. The energy of conformer (b) is 17.15 kcal/mol greater than the energy of conformer (a). Most molecules will take the geometry of conformer (a), and only very, very few will take the geometry of conformer (b).

compose texts since early instruction stages, they find it difficult not only to build a text that has a logical framework, but even to simply understand why this is necessary. A convincing analogy for the latter point is the analogy with house-building – something whose univocal way of proceeding is self-evident. The analogy itself is developed through questions such as “when you are building a house, would you start from the window? Or from the ceiling?”. There is obvious agreement that we start from the foundations, and the interactions proceed step by step from there. The analogy is sufficient to create a mood for which students follow guidance, starting to examine the various pieces of information that had been randomly piled up in the initial answers, putting efforts to identify those that can have the role of foundations, then those that can be considered the next building blocks, and so on, until an articulated text is constructed.

12.2.3.5 Errors hunting: an ideal option to prevent or rectify misconceptions As already mentioned, some unfortunate analogies may become suitable grounds for the analysis of what is not correct in the A-B parallelisms that they suggest [7]. This has all the pedagogical advantages of error analysis: stimulating students’ active reflection and focusing their attention on individual details that might otherwise be missed [10]. Analyses of this type make sense only if the students come across some unfortunate analogy (e.g., in a textbook or on the internet) or if the teacher considers a specific errors-hunting exercise as particularly useful for the clarification of certain aspects. Scheme 12.2 outlines an exercise of this type with regard to the obsolete analogy between the structure of the atom and the solar system. It makes reference to the modern model of the atom, i.e., it considers this model as A. This does make sense because orbitals are already introduced at pre-university level in many contexts. The terms of the

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comparison may be selected according to the level at which the modern model is introduced, e.g., by leaving out some of the terms included in Scheme 12.2. Most terms of the modern views on the structure of the atom can be introduced at pre-university level by using sufficiently simple wording and taking care of avoiding rigour-loss [44, 48]. Other cases of unfortunate analogies may be much simpler to analyse. For instance, some old textbooks used to illustrate the meaning of “mole” by inviting students to consider a mole of horses, or of other macroscopic entities. Such analogies inherently contradict the definition of mole that is defined as a specific number of microscopic entities. The image of 6.02 × 1023 horses is misleading. A straightforward way to convey a perception of the enormous value of the number of particles present in one mole entails writing the Avogadro’s constant explicitly (602,000,000,000,000,000,000,000) and asking students to try to interpret it in terms of millions of millions of millions … or of billions of billions … 12.2.3.6 The teacher as designer All the aspects involved in the use of analogies pertain to the teacher: deciding when an analogy can be useful, choosing or designing the analogy, designing the guidance route to make the A-B comparison terms clear and to prevent the risk of misinterpretations. Interactive teaching approaches are the most suitable because they enable students’ active involvement in the identification of possible analogies and, simultaneously, realtime feedback to the teacher about students’ perceptions and interpretations. In suitable cases, students may be involved in the search for analogies through the following stages: making the features of A for which an analogy is searched sufficiently clear, searching for an apt B, identification and analysis of the A-B comparison terms and critical evaluation of their quality and limitations. The designer role entails good knowledge of the concepts involved, and the ability to recognise when an analogy can be useful and to build analogies that will be free from misconceptions’ generation risks. Analogies are useful when other options, such as models and visualization, would not bring sufficient help. This also entails full awareness of the distinction between analogy, visualization and models (Section 12.2.1.). Analogies may involve the use of images as part of their construction, like in examples 12.2, 12.3 and 12.4 (Section 12.2.3.4). On the other hand, any representation that does not involve entities having a different nature with respect to those pertaining to A is not an analogy. For instance, if a teacher draws a circle with 2 cm diameter on the board and asks the students how far she should go to draw a circle approximating the size of an atom and maintaining size proportions, this is a type of visualization, not an analogy, because it represents the nucleus with a circle and the atom with a bigger circle to be imagined (due to the practical impossibility of drawing the outer circle with the correct proportions). Students find it difficult to suggest the correct answer (1 km away from the centre of the circle drawn on the board) although the ratio (1 : 100,000) is usually recalled at the beginning of the exercise. The visualization of such proportion appears challenging

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because of the large size of the outer circle, and the fact that the proportion does not have any familiar correspondent in everyday life experience; on the other hand, the search for the answer, followed by the actual answer, conveys a perception of the enormous difference of the two sizes, as well as of the enormous difference between the world of atoms and the macroscopic world. Visualization is a powerful explanation and clarification tool. Imagery does not need to involve analogies: it is an independent explanation and communication tool. It is particularly important with reference to the world of atoms and molecules, to make it more concrete and familiar. The current extensive availability of computer-based images confers new strength to visualization, through the ability of offering visualizations of 3D structures, of the vibrations of bonds, and many other aspects. The teacher will select the most suitable visualizations for each topic. In addition, the teacher may conveniently design images that respond to particular questions or challenges encountered by students.

12.3 Discussion The information and examples presented in the previous sections suggests a variety of reflections. The crucial issue is whether to use analogies within chemistry teaching (or science teaching in general) and how to use them. Although it is understandable that “some teachers choose not to use analogies at all” [21] to avoid the risk of misinterpretations and misconceptions, it may be worthwhile not to miss their benefits when the benefits are evident. As stated in [25] and recalled in [51], the learning outcomes of an analogy depend on how the analogy is used. When and how using analogies is the main focus of the present section. First of all, the use of analogies should not be considered a sort of ‘duty’ with which a teacher needs to comply, simply because some educational works suggest a nearly allpervasive usage of them. The first priority is conceptual understanding. Therefore, an analogy is useful if it facilitates conceptual understanding. On the other hand, excessive use of analogies risks to blur the thread through which the content develops, or to blur the content itself. There is no need to find analogies for everything, and analogies whose terms are too far-stretched are to be avoided. When B has little connection to A and the BA correspondences that may exist are not sufficiently straightforward, it becomes necessary to devote considerable time to analyse and clarify them; then B does not have a clarification role for A: the analogy has no pedagogical use, it likely entails misconception risks, and it becomes a burden for the students, instead of being an aid. An analysis of toofar-stretched analogies from literature would go beyond the scope of the current work. It can just be mentioned that there is abundance of analogies that appear to respond more to an author’s wish to ‘invent’ whichever analogy for every bit of content rather than to respond to the key pedagogical criterion of being beneficial while simultaneously

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preventing misunderstandings. In some extreme cases, B is more complex than A, and understanding the B-A correspondences is more challenging than understanding A. A statement like “Understanding analogies can be challenging for students because the nature of the relationship may not be immediately obvious” [24] prompts questions and concerns. If it is not easy for students to understand certain analogies, then those analogies are not useful and are likely harmful. They demand time and efforts without corresponding benefits (in a way, they are not cost-effective). Experimenting with analogies whose scientific rigour is not warranted is not ethical. It engenders incorrect conceptions that student will later have to put efforts to get rid of, because of the spontaneous tendency to trust what has been taught at the first encounter with a new concept or theme. The most informative way of verifying the benefits or damages of specific analogies would be that of checking how the perceptions that they have engendered help or hinder understanding in more advanced courses; this would include the verification of how easy or difficult it will be for students to get rid of an incorrect analogy, or the perceptions it generated, or its implications, in further stages of their study career. For instance, LM has repeatedly witnessed the difficulties associated with getting rid of the planetary model of the atom after it has been embedded into students in earlier stages of instruction through both analogies and visualization; it is one of the phenomena that clearly document the need to eliminate obsolete analogies from educational approaches at any level. The conceptual range between useful and harmful (taken as the ends of the range) comprises the unnecessary concept. Analogies can be used when they are really useful. Unnecessary analogies would only complicate the learning process by taking time without bringing benefits. Chemistry education needs to recall Lavoisier’s attitude: he did not reject the phlogiston hypothesis because of it being wrong, but because of it being unnecessary. Unnecessary components are not accepted within science theories and models; they are also not needed in science education. Analogies should be limited to the role of clarification resources. They should never become part of assessment, because this would favour confusion between A (the chemistry or science concept) and B (the analogy), blurring the perception of the learning focus (A). If an analogy becomes part of assessment, this automatically conveys the message that the analogy is part of the science concept (B gets perceived as an integral component of A). Students should never be ‘left alone’ with analogies. The teacher’s guidance is needed for the student to get the benefits expected from the use of a certain analogy without risking to turn it into a component of the science concept. This is also an additional reason why analogies should not be part of assessment tools, because an analogy embedded in a test or exam question would be an analogy with which the student is ‘left alone’ when taking the test or the exam. The search for suitable analogies (identifying and building an analogy, and critically analysing its terms) may become a tool for classroom interactions, in the same way as visualization can [52]. For instance, in the case of analogies meant to clarify the meaning

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of a term (Section 12.2.3.2), students may initially be invited to find analogies on their own; if they fail and the teacher provides one, they can be invited to suggest others, and their suggestions provide feedback on their actual understanding of the concept. There may also be a spontaneous tendency from teachers to repeat analogies that they had been taught, or that had been taught (handed down) through some generations of teachers and students. Counteracting this tendency requires independent verification, comparison with modern views and, ideally, availability of corresponding information in educational literature. A typical example is offered by the analogy of the electron as a sphere rotating around its axis (B), often accompanying the introduction of the spin (A) concept, although the electron does not rotate around its axis (in English speaking contexts, the analogy is also favoured by the common-language meaning of the “spin” term [53]). As background attitude-generating information, students need to acquire the awareness that there are concepts that we introduce because of observed behaviours. This is the case, for instance, of the electric charge concept. Observations (at a macroscopic level) have led to the inference that there is a property of matter that determines certain types of behaviour (attraction, repulsion); the property has been called electric charge and the observations indicated that there are two types, that we distinguish as positive and negative. Similarly, it has been observed that a beam of electrons entering an area between the two poles of a magnet splits into two beams, with direction towards one or the other pole; this indicated that electrons have a property that determines their behaviour in a magnetic field, and this property has two ‘types’; we call the property spin; mathematics has shown that we can identify its two types by the two values +½ and −½. In this way, electric charge takes the role of B in an analogy meant to present the spin (A) without introducing analogies or images that do not correspond to modern models. The fact that the behaviour effects of electric charge can be observed at a macroscopic level makes it easier for students to accept that there might be other properties determining opposite behaviour types. It is also convenient to emphasise that electrons (like the other elementary particles) have three properties: mass, charge and spin. This example also highlights an important message: when it is necessary to avoid an obsolete or misleading analogy, the teacher may need to develop an explanation expressed in sufficiently simple terms to be understandable by the given group of learners while remaining rigorous. It is part of the challenges of the teacher-as-designer leitmotiv. It may also be recalled that imagery trying to represent abstract concepts may entail risks similar to those of analogies. A typical example is the diagram presenting atomic orbitals and energy levels. Frequent options represent each orbital with a small box or a circle ( , ), often with arrows inserted within them to represent how the electrons fill the orbitals. This is functional, but it requires explicit warnings to ensure that students do not develop the image that an atom contains small cubic or spherical cells that are the ‘rooms’ of the electrons. This image of an atom consisting of cells inhabited by electrons has been communicated by students more than once; therefore, caveats to prevent its development are important. Some authors (including LM) prefer to represent individual

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orbitals in the diagram using something that does not recall the idea of small cells, e.g., small segments (—), above which the arrows visualizing orbitals-filling can be drawn. The meaning of the arrows also needs to be clarified (representing the spin of electrons, to show when they are parallel and when they are antiparallel), because several students developed the idea that electrons have the shape of arrows. These last considerations further emphasise the importance to clearly stress the correspondence terms both when analogies are used and when graphical representations that are not models of an object are used, to ensure the prevention of misinterpretations whose variety often exceed the prediction-abilities of educators. Finally, it is important to recall that one of the roles of science education is that of facilitating the development of abstract thinking abilities. Should analogies replace the actual conceptual thinking, the latter might not develop sufficiently. It is the risk highlighted in [54], in a scenario where the use of small sticks as analogies to segments replaces the actual reasoning in terms of segments, thus hampering the development of the ability of thinking in geometry terms.

12.4 Conclusions The inferences from the content of the previous sections can be summarised as follows. Analogies can be relevant clarification tools for some concepts or features, above all for concepts that are perceived as abstract and cannot be illustrated through common visualization options involving models of objects. Their use needs to be limited to the cases in which they can actually have significant and constructive impact on students’ understanding. The terms of the correspondence between an analogy (B) and the concept (A) need to be easily identifiable for an analogy to have an educational meaning and not be misleading. Adequate teacher’s guidance is needed to ensure that the terms of the analogy are clear and no space for misinterpretations appears. It is important to ensure that the selected analogies are suitable for the ‘real’ students in a given class, with whom the teacher is interacting, and that analogies do not become an additional burden for students, instead of a learning aid. Analogies referred to obsolete models (e.g., the planetary model of the atom) or entailing non-rigorous comparisons (e.g., the idea of a “mole of horses’) must be carefully avoided because they are bound to generate misconceptions. Analogies are objects of design, often within (or in response to) in-class contextual situations. The design needs to take into account the nature of the concept to be clarified and the ways in which its details can match the details of the proposed analogy, as well as the specificities of a given group of students (e.g., what can be familiar to them).

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Index 1,2-debromination 175 1,3,5-trihydroxybenzene 153 2-nitrophenol 93 AA8 87 abstract concepts 188 abstract thinking 205 activation maps 56 acylphloroglucinols 153 adsorbents 94 adsorption 94 adsorption capacity 100 adsorption-desorption cycles 104 aggregation 83 agiliti-ketu 70 Alchemy 185 alunite 83 amide 179 analogies 186 analogies and assessment 203 analogies benefits and risks 186, 188, 189, 203 analogies in a classroom situation 186, 187, 192, 205 analogies in a textbook 186, 187 analogies to explain the meaning of individual terms 193 analogy definition 184 analysis of Iodine 132 analysis of what is not correct in an analogy 187, 192, 200 anatase 82 anthropogenic 137, 138 antioxidant activity 168 antioxidant properties 154 application 106 aquatic system 75 aqueous solution 106 area 54, 59, 60, 64–66 assay 81 atomic and molecular level 194 azide alkyne cycloaddition 175 B3LYP functional 156 ball milling 171, 172 basis set 156 Beckmann rearrangement 178 bentonite 84, 87 binding site preferences 161 binding sites 158

https://doi.org/10.1515/9783111328416-013

bioaccumulation 75 biological activities 153 biomass 106 biomaterials 94 biosorbents 104 biosorptive 107 biosynthesis 128 blood oxygenation level dependent 53, 54 BOLD 53–56, 59, 64–67 BOLD effects 53, 55, 59, 64, 66, 67 brain 53 building a text having a logical framework 200 CaCO3 85 cadmium 74 cambridge structural database (CSD) 1 carcinogenic 114, 119 catalyst 175 cellulose 97 cerebral blood volume 66 cerebral water 60, 62, 67 characterization 106 chelation of metal ions 154 chemical precipitation 135 chemistry education 186 CHESS 57 choline 53, 59, 62, 65, 66 cinchona 176 classroom interactions 203 CNTs 89 coastal zone 131 comparison 104 complexes of monomeric ACPLs 168 computable molecular properties 156 conformational preferences of molecules 199 ConQuest 3 contact time 100 contaminated 76 copper 72 cost-effective 135 creatine 53, 59, 62, 64–66 cristobalite 83, 84 crosslinking 96 CuAAC 175 cyclodextrin 93 cytotoxicity 81 deadtime 57

210

Index

density functional theory 156 designing analogies in the classroom 192 destruction 134 determining factor 103 dibromide 174 Diels-Alder reaction 177 different regions 135 diffraction 85 dipole moment 167 dispersion interactions 156 EcoScale score 179 effective collision 196 efficiency 104 electric charge 204 electrical conductivity 70 electron transfer 164 enantioselective reaction 175 environment 94 environmental factor 179 epistemological issues 186 equilibrium 101 equilibrium isotherm 95 error analysis 187 experiments 53, 53, 56, 57, 59, 60, 62, 64, 66, 67 field strength 64–66 first initial peak 129 fixation cross 55, 57 flame atomic absorption spectrophotometer 72 fMRI 53–57, 59, 64–66 formazan 86 fourier transform infrared 96 free Iodine 131 frequency domain 54, 59 freundlich 103 fruits and vegetables 137–140, 142, 143, 146, 147, 150–152 full interaction maps 11 fullerene 173 Galileo Galilei 185 Gaussian-16 156 glutamate 53, 59, 62, 64, 66 goethite 84 green chemistry 178 Grimme’s dispersion correction 156, 162 guidelines 89 half-bowl-shaped geometry 157 halides 127 halloysite 83, 88 hazard index 110, 137, 140, 147

hazards 80 head coil 56 height 53, 59, 60, 62, 65, 66 hemicellulose 97 hexamethylene diisocyanate 94 highlighting problem solving routes 194 Homer’s poems 184 HOMO–LUMO energy gap 166 hydrogen bonding 99 hydrophobic interaction 99 IC50 86 IHB patterns 157 indole 174 industrial activities 75 industrial effluent 73 INT 89 interleaved 53, 57, 59, 62, 64–67 intermolecular interactions 10 intramolecular hydrogen bond 154 ion binding site 164 iron 72 ISO 80, 89 isocyanide 17, 21–23, 26–29, 31, 32, 34–39, 45, 47, 51, 52 JCPDS 81 kaolinite 83 kekulé 185 ketoximes 178 kinetics 95 knoevenagel condensation 180 lagos lagoon 74 langmuir 103 lead 74 least squares minimization 59 length of intramolecular hydrogen bond 166 lengths of all the IHBs 166 lens ensemble 56 levenberg–marquardt method 59, 60, 67 lignin 97 lignocellulose materials 96 lock and key analogy 186 lorentzian lineshapes 59 low cost 105 lucretius 185 magnetic field 54 magnetic resonance spectroscopy 53, 67, 68 manganese 72 manual grinding 172 markets 109, 111, 118, 124

Index

mathematical entities 186 MCF-7 89 mechanism 108 mechanochemical organic synthesis 172 mechanochemistry 171 mercury 4 metabolites 53, 55, 65, 67 metal contaminants 77 metals 70 methylene bridge 155 metrology 89 Michael reaction 176 micropollutants 94 micropowders 82 microscale 88 misconceptions counteracting 187 misconceptions preventing misconceptions about atomic orbitals 187, 188 modeling 107 models 186, 189, 190 models:mathematical 189 models:physical 190 molecular level 186 molecule ion affinity 160, 161, 164 molecules’ collisions and temperature 196 mole:meaning 201 monolayer capacity 103 montmorillonite 84, 87 MTT 81 Mulliken charge 163 Mulliken spin densities 163 multicomponent reactions 15, 46, 52 municipal run-offs 74 mutual orientations of the methylene bridges 157 mutual orientations of the monomeric units 157 MWCNTs 89 myo-inositol 59, 62, 64, 66 NAA 53, 59, 62, 64–67 N-acetyl aspartate 53, 59 nanoclay 83 nanomaterials 79 nanometer 84 nanoparticles 80, 87 nanosafety 79, 80 nanostructures 87 nanotechnology 79, 80 natural bond analysis 163 natural bond orbital 163 natural charge 163

211

negligible 193 neural activity 54, 66 neurons 54, 57 NEX 53–55, 57, 59, 60, 62, 64–66 nickel 72 Nigeria 109, 111, 117, 119, 124–126, 137 NIH ST3 89 non-bonding interactions 176 nonlinear form 101 NSA 54, 54, 57, 59, 64 number of signal averages 54, 64 ogun river 70 ogun river catchments 74 optimum 99 organic matter 134 organochlorine 109–112, 114, 115, 119, 124–126 organometallic chemistry 2 organophosphate 109, 110, 112–115, 118, 119, 124 ortho-nitro-phenol 108 outstretched geometry 157, 162 oxidative coupling 174 oxidative stress 154 paired t-test 60 palladium chloride 174 paradigm 53, 59, 60, 62, 64–66 parameter 100 passerini 15, 17, 21–23, 26, 32, 45, 46, 50 pesticides 109–112, 114, 115, 118, 119, 124–126 pH 71 pH-dependent 99 phenolic compounds 168 Philippines 79, 80 philosophy 184 photocatalytic 87 pine bark 93 plato 184 point of zero charge 96 policy 80 pollutant 70, 106 polluter pays principle 77 pollution sources 76 port harcourt 109, 111 potential toxic elements: heavy metals 137, 140 PRESS 57, 65, 67 proton transfer 165 pseudo-first order model 101 pulse sequence 54, 57, 59, 64 purity 80 quantisation 197

212

Index

quartz 83 QuEChERS 109, 112, 125 radiochemical separation 134 ranges of values 160 rate constant 101 RAW264.7 88 reduction of the ion 163 reduction of the ion’s charge 155 regeneration 95 relative energies 160 remediation 107 removal 106 repetition time 57 rise time 57 risk 109, 110, 114, 119, 124–126 risk assessment 110, 119 roadmap 80 role of the teacher 189, 203 rotaxane 177 rutile 82 salt-tolerant plants 130 scattering 89 scherrer 81 sediments 71 SEM 89 shielding:meaning 193 shipping 76 silver 85 similitude 184 simple 135 single stimulation 53, 60, 64 SiO2 88 site-combinations 158 size 80 solid addition method 96 solid state 172 solid waste 74 solution pH 98 solvent free 171 spectra 53–55, 59, 64 spectral analysis 59 spectral averaging schemes 64, 66 spectral lines 54, 54, 57, 59, 64 spectral peaks 59, 60, 62 spherical 81 spin 204 spin density 155, 163 square-planar geometry 6

stacking interactions 162 standards 80 stimulation 53, 55, 57, 59, 60, 62, 64–68 strength of an analogy 186 structural chemistry 1 structure of the benzene molecule 185 supramolecular chemistry 176 susceptibility 54, 66, 68 synthesis 172 tank farms 73 teacher-as-designer 204 techniques 135 TEM 89 temporal resolution 65 terms and limitations of an analogy 189 tetrazine 177 tetrazolium 86 textbook authors 186 the half-bowl-shaped conformer 162 thermal gravimetric 97 thermodynamic reversibility and irreversibility 196 thyroid 128 time course 56, 59, 60, 62 TiO2 82, 87 tolerance 64 total dissolved solids 71 toxicity 80 trimeric ACPL 168, 155 tunnel effect 193 twin screw extrusion 180 ugi 15, 17, 22, 27–29, 31, 32, 45, 46, 50, 50 untreated wastewater 74 vascular architectures 66 vegetable 109–112, 114, 115, 118, 119, 125 visual activation 56 visual cortex 67 visual perception 54 visualization 186, 190, 201, 202 voxel 56, 59, 67 VSEPR 2 WST-1 89 XRD 81 XTT 88 zinc 75 zinc/silver couple 174 ZnO 88 zwitterion 17, 22, 34–36, 38–40, 45, 46, 52

Index

VSEPR 2 water 107 width 54, 59, 60, 65 world health organization’s permissible limits 74 WST-1 89 XRD 81

XTT 88 zinc 75 zinc/silver couple 174 ZnO 88 zwitterion 17, 22, 34–36, 38–40, 45, 46, 52

213