Representative green technologies for the GO reduction.
\\n\\n
Released this past November, the list is based on data collected from the Web of Science and highlights some of the world’s most influential scientific minds by naming the researchers whose publications over the previous decade have included a high number of Highly Cited Papers placing them among the top 1% most-cited.
\\n\\nWe wish to congratulate all of the researchers named and especially our authors on this amazing accomplishment! We are happy and proud to share in their success!
\\n"}]',published:!0,mainMedia:null},components:[{type:"htmlEditorComponent",content:'IntechOpen is proud to announce that 179 of our authors have made the Clarivate™ Highly Cited Researchers List for 2020, ranking them among the top 1% most-cited.
\n\nThroughout the years, the list has named a total of 252 IntechOpen authors as Highly Cited. Of those researchers, 69 have been featured on the list multiple times.
\n\n\n\nReleased this past November, the list is based on data collected from the Web of Science and highlights some of the world’s most influential scientific minds by naming the researchers whose publications over the previous decade have included a high number of Highly Cited Papers placing them among the top 1% most-cited.
\n\nWe wish to congratulate all of the researchers named and especially our authors on this amazing accomplishment! We are happy and proud to share in their success!
\n'}],latestNews:[{slug:"intechopen-authors-included-in-the-highly-cited-researchers-list-for-2020-20210121",title:"IntechOpen Authors Included in the Highly Cited Researchers List for 2020"},{slug:"intechopen-maintains-position-as-the-world-s-largest-oa-book-publisher-20201218",title:"IntechOpen Maintains Position as the World’s Largest OA Book Publisher"},{slug:"all-intechopen-books-available-on-perlego-20201215",title:"All IntechOpen Books Available on Perlego"},{slug:"oiv-awards-recognizes-intechopen-s-editors-20201127",title:"OIV Awards Recognizes IntechOpen's Editors"},{slug:"intechopen-joins-crossref-s-initiative-for-open-abstracts-i4oa-to-boost-the-discovery-of-research-20201005",title:"IntechOpen joins Crossref's Initiative for Open Abstracts (I4OA) to Boost the Discovery of Research"},{slug:"intechopen-hits-milestone-5-000-open-access-books-published-20200908",title:"IntechOpen hits milestone: 5,000 Open Access books published!"},{slug:"intechopen-books-hosted-on-the-mathworks-book-program-20200819",title:"IntechOpen Books Hosted on the MathWorks Book Program"},{slug:"intechopen-s-chapter-awarded-the-guenther-von-pannewitz-preis-2020-20200715",title:"IntechOpen's Chapter Awarded the Günther-von-Pannewitz-Preis 2020"}]},book:{item:{type:"book",id:"5722",leadTitle:null,fullTitle:"Graphene Materials - Structure, Properties and Modifications",title:"Graphene Materials",subtitle:"Structure, Properties and Modifications",reviewType:"peer-reviewed",abstract:"Graphene is, basically, a single atomic layer of graphite, an abundant mineral that is an allotrope of carbon that is made up of very tightly bonded carbon atoms organized into a hexagonal lattice. 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Kyzas is an Associate Professor in the Department\nof Chemistry at the International Hellenic University (IHU).\nHe was born in Drama (Greece) and studied Chemistry in the\nDepartment of Chemistry at the Aristotle University of Thessaloniki (AUTh). He obtained his BSc, MSc, and PhD from the\nDepartment of Chemistry (AUTh) specializing in Chemical\nTechnology and Materials Science. He then worked as a PostDoc\nResearcher in the Department of Chemistry (AUTh) on many research projects,\nwhile at the same time he was an Adjunct Assistant Professor at Eastern Macedonia and Thrace Institute of Technology (Greece). He was then officially assigned\nas Associate Professor in the Department of Chemistry (International Hellenic\nUniversity), being the Head of the Department. 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by",editors:[{id:"196544",title:"Prof.",name:"Angel",middleName:null,surname:"Catala",slug:"angel-catala",fullName:"Angel Catala"}],productType:{id:"1",title:"Edited Volume",chapterContentType:"chapter",authoredCaption:"Edited by"}}},ofsBook:{item:{type:"book",id:"7675",leadTitle:null,title:"Viscoelastic and Viscoplastic Materials",subtitle:null,reviewType:"peer-reviewed",abstract:"\r\n\tWhen materials undergo stress, two classic extreme responses can be expected: they can be elastic, that is to say that once the applied stress is removed they recover their original form, or they can have a viscous response, which enables the material to flow during the induced stress. However, there are materials that have complex responses when subjected to stress or strain and present a combination of both responses; such compounds have viscoelastic or viscoplastic responses. The term viscoelasticity is used to describe materials that when subjected to a deformation, have a combination of responses and can flow and at the same time have an elastic response, indicating that such materials can recover from deformation. This type of response in materials is most commonly represented by macromolecular materials. A material which exhibits viscoplastic response, when subjected to stress, can no longer recover its original form. In this book we seek to compile works on materials that exhibit both viscoelastic and viscoplastic responses when subjected to stress or defined strain, and present new methodologies for the evaluation of these types of materials.
",isbn:null,printIsbn:"979-953-307-X-X",pdfIsbn:null,doi:null,price:0,priceEur:null,priceUsd:null,slug:null,numberOfPages:0,isOpenForSubmission:!1,hash:"68c9f6836be98c144b843db45dd48f3c",bookSignature:"Dr. Jose Luis Rivera Armenta and Dr. Beatriz Adriana Salazar-Cruz",publishedDate:null,coverURL:"https://cdn.intechopen.com/books/images_new/7675.jpg",keywords:"Viscoelasticity, Viscoelastic Materials, Fractional Viscoelastic Model, Pseudoplastic Fluid, Damping, Viscoelastic Phenomena, Viscoelastic Models, Linear Viscoelasticity, Viscoplasticity, Kernels Model, Finite Element Model, Rheological Test, Viscoleastic Tests, Deformation Stress Tests, Stress Relaxation, Creep Test, Rheology Of Reinforced Composites",numberOfDownloads:null,numberOfWosCitations:0,numberOfCrossrefCitations:0,numberOfDimensionsCitations:0,numberOfTotalCitations:0,isAvailableForWebshopOrdering:!0,dateEndFirstStepPublish:"March 11th 2019",dateEndSecondStepPublish:"April 1st 2019",dateEndThirdStepPublish:"May 31st 2019",dateEndFourthStepPublish:"August 19th 2019",dateEndFifthStepPublish:"October 18th 2019",remainingDaysToSecondStep:"2 years",secondStepPassed:!0,currentStepOfPublishingProcess:5,editedByType:null,kuFlag:!1,biosketch:null,coeditorOneBiosketch:null,coeditorTwoBiosketch:null,coeditorThreeBiosketch:null,coeditorFourBiosketch:null,coeditorFiveBiosketch:null,editors:[{id:"107855",title:"Dr.",name:"Jose Luis",middleName:null,surname:"Rivera Armenta",slug:"jose-luis-rivera-armenta",fullName:"Jose Luis Rivera Armenta",profilePictureURL:"https://mts.intechopen.com/storage/users/107855/images/system/107855.jpeg",biography:"José Luis Rivera-Armenta was born in Tampico, Mexico, in 1971. He earned his BSc in Chemical Engineering in 1994, an MSc in Petroleum Technology and Petrochemicals in 1998, and a Ph.D. in Chemical Engineering in 2002 at the Technological Institute of Madero City (ITCM). Since 2003 he has been a full-time professor in postgraduate programs at ITCM and a project manager of several developments sponsored by the National Technologic of Mexico and CONACYT. He has been a member of the National Research System at CONACYT level 1 since 2005. His responsibilities include injection and extrusion and thermal analysis at the laboratory at the Petrochemical Research Center at ITCM. He has advised on nine Ph.D\\'s, 16 master’s degrees, and four bachelor theses and also supervised three post-doctorate students. He has published 50 articles and five book chapters.",institutionString:"Instituto Tecnológico de Ciudad Madero",position:null,outsideEditionCount:0,totalCites:0,totalAuthoredChapters:"4",totalChapterViews:"0",totalEditedBooks:"2",institution:{name:"Instituto Tecnológico de Ciudad Madero",institutionURL:null,country:{name:"Mexico"}}}],coeditorOne:{id:"171043",title:"Dr.",name:"Beatriz Adriana",middleName:null,surname:"Salazar-Cruz",slug:"beatriz-adriana-salazar-cruz",fullName:"Beatriz Adriana Salazar-Cruz",profilePictureURL:"https://mts.intechopen.com/storage/users/171043/images/system/171043.jpeg",biography:"Beatriz A. Salazar Cruz attained her Ph.D. in 2014, and has been an associate member of MATCO since 2016 and an associate professor of the Technological Institute of Madero City since 2012. From 1995 to 2008 she gained experience in the chemical process industry (Dynasol Elastomers) developing projects with SBS, SBR and SEBS polymers and characterizing and innovating the quality of products in a wide range of applications: asphalts, adhesives, and compounds. Dr. Salazar-Cruz is the author or coauthor of several scientific publications in English and Spanish, and author or coauthor of several book chapters. She has taught several rheology courses and has also collaborated in several projects supported by the National Technologic of Mexico and CONACYT. She has been an advisor for master degree thesis and has also supervised engineering students.",institutionString:"Technological Institute of Ciudad Madero",position:null,outsideEditionCount:0,totalCites:0,totalAuthoredChapters:"3",totalChapterViews:"0",totalEditedBooks:"0",institution:null},coeditorTwo:null,coeditorThree:null,coeditorFour:null,coeditorFive:null,topics:[{id:"14",title:"Materials Science",slug:"materials-science"}],chapters:null,productType:{id:"1",title:"Edited Volume",chapterContentType:"chapter",authoredCaption:"Edited by"},personalPublishingAssistant:{id:"177731",firstName:"Dajana",lastName:"Pemac",middleName:null,title:"Ms.",imageUrl:"https://mts.intechopen.com/storage/users/177731/images/4726_n.jpg",email:"dajana@intechopen.com",biography:"As a Commissioning Editor at IntechOpen, I work closely with our collaborators in the selection of book topics for the yearly publishing plan and in preparing new book catalogues for each season. This requires extensive analysis of developing trends in scientific research in order to offer our readers relevant content. Creating the book catalogue is also based on keeping track of the most read, downloaded and highly cited chapters and books and relaunching similar topics. I am also responsible for consulting with our Scientific Advisors on which book topics to add to our catalogue and sending possible book proposal topics to them for evaluation. Once the catalogue is complete, I contact leading researchers in their respective fields and ask them to become possible Academic Editors for each book project. Once an editor is appointed, I prepare all necessary information required for them to begin their work, as well as guide them through the editorship process. I also assist editors in inviting suitable authors to contribute to a specific book project and each year, I identify and invite exceptional editors to join IntechOpen as Scientific Advisors. I am responsible for developing and maintaining strong relationships with all collaborators to ensure an effective and efficient publishing process and support other departments in developing and maintaining such relationships."}},relatedBooks:[{type:"book",id:"6702",title:"Polymer Rheology",subtitle:null,isOpenForSubmission:!1,hash:"c24234818cd4b2ce3ed569c2b29f714c",slug:"polymer-rheology",bookSignature:"Jose Luis Rivera-Armenta and Beatriz Adriana Salazar Cruz",coverURL:"https://cdn.intechopen.com/books/images_new/6702.jpg",editedByType:"Edited by",editors:[{id:"107855",title:"Dr.",name:"Jose Luis",surname:"Rivera Armenta",slug:"jose-luis-rivera-armenta",fullName:"Jose Luis Rivera Armenta"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"6522",title:"Modified Asphalt",subtitle:null,isOpenForSubmission:!1,hash:"3f759084429ece2b3f7ec329b8242459",slug:"modified-asphalt",bookSignature:"Jose Luis Rivera-Armenta and Beatriz Adriana Salazar-Cruz",coverURL:"https://cdn.intechopen.com/books/images_new/6522.jpg",editedByType:"Edited by",editors:[{id:"107855",title:"Dr.",name:"Jose Luis",surname:"Rivera Armenta",slug:"jose-luis-rivera-armenta",fullName:"Jose Luis Rivera Armenta"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"6188",title:"Solidification",subtitle:null,isOpenForSubmission:!1,hash:"0405c42586170a1def7a4b011c5f2b60",slug:"solidification",bookSignature:"Alicia Esther Ares",coverURL:"https://cdn.intechopen.com/books/images_new/6188.jpg",editedByType:"Edited by",editors:[{id:"91095",title:"Dr.",name:"Alicia Esther",surname:"Ares",slug:"alicia-esther-ares",fullName:"Alicia Esther Ares"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"6802",title:"Graphene Oxide",subtitle:"Applications and Opportunities",isOpenForSubmission:!1,hash:"075b313e11be74c55a1f66be5dd56b40",slug:"graphene-oxide-applications-and-opportunities",bookSignature:"Ganesh Kamble",coverURL:"https://cdn.intechopen.com/books/images_new/6802.jpg",editedByType:"Edited by",editors:[{id:"236420",title:"Dr.",name:"Ganesh Shamrao",surname:"Kamble",slug:"ganesh-shamrao-kamble",fullName:"Ganesh Shamrao Kamble"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"6517",title:"Emerging Solar Energy Materials",subtitle:null,isOpenForSubmission:!1,hash:"186936bb201bb186fb04b095aa39d9b8",slug:"emerging-solar-energy-materials",bookSignature:"Sadia Ameen, M. Shaheer Akhtar and Hyung-Shik Shin",coverURL:"https://cdn.intechopen.com/books/images_new/6517.jpg",editedByType:"Edited by",editors:[{id:"52613",title:"Dr.",name:"Sadia",surname:"Ameen",slug:"sadia-ameen",fullName:"Sadia Ameen"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"6320",title:"Advances in Glass Science and Technology",subtitle:null,isOpenForSubmission:!1,hash:"6d0a32a0cf9806bccd04101a8b6e1b95",slug:"advances-in-glass-science-and-technology",bookSignature:"Vincenzo M. 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Churchill, Maja Dutour Sikirić, Božana Čolović and Helga Füredi Milhofer",coverURL:"https://cdn.intechopen.com/books/images_new/8812.jpg",editedByType:"Edited by",editors:[{id:"219335",title:"Dr.",name:"David",surname:"Churchill",slug:"david-churchill",fullName:"David Churchill"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"7960",title:"Assorted Dimensional Reconfigurable Materials",subtitle:null,isOpenForSubmission:!1,hash:"bc49969c3a4e2fc8f65d4722cc4d95a5",slug:"assorted-dimensional-reconfigurable-materials",bookSignature:"Rajendra Sukhjadeorao Dongre and Dilip Rankrishna Peshwe",coverURL:"https://cdn.intechopen.com/books/images_new/7960.jpg",editedByType:"Edited by",editors:[{id:"188286",title:"Associate Prof.",name:"Rajendra",surname:"Dongre",slug:"rajendra-dongre",fullName:"Rajendra Dongre"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}}]},chapter:{item:{type:"chapter",id:"54318",title:"Green Routes for Graphene Oxide Reduction and Self- Assembled Graphene Oxide Micro- and Nanostructures Production",doi:"10.5772/67403",slug:"green-routes-for-graphene-oxide-reduction-and-self-assembled-graphene-oxide-micro-and-nanostructures",body:'\nGraphene, one-atom thick layer of densely packed carbon atoms into a honeycomb crystal lattice, is considered the key building block of graphite, carbon nanotubes, and fullerenes [1]. It is of current interest due to its remarkable physical and chemical properties, which makes it useful for theoretical studies for several technological applications. Current applications of graphene include flexible electronics, batteries, and so on [2]. Diverse methods have been proposed to produce high-quality single and few layer graphene films. Among them, graphite micromechanical cleavage, chemical vapor deposition, and graphitization of SiC have been the most utilized methods [3]. Although these methods produce high-quality graphene in a controlled way, they suffer from mass production scaling.
\nIn the past years, graphene-derived materials, such as graphene oxide (GO), graphane (the hydrogenated version of graphene), graphene fluoride, and so on [4, 5] have been paid special interest because of their potential applications. Particularly, GO and its reduced version, reduced-graphene oxide (rGO), have emerged as a technologically important material by its their own right [6].
\nGO is mainly prepared through chemical methods and therefore achieves unique and useful physiochemical properties to prepare a variety of functional materials for a range of advanced applications, such as rGO self-assembled microstructures [7, 8] and, rGO-based composites with inorganic nanoparticles (metals, semiconductors, metal oxides). These GO-derived materials have successfully been tested in the technological areas of nanomedicine, electronics, environmental remediation, energy conversion, and others [7–9].
\nThe chemical methods to prepare single-layer GO use graphite as the raw material, which is exfoliated either using strong oxidants in aqueous medium (based on Hummers’ method) or using organic solvents (based on the solution-phase technique), among others [10]. During the graphite oxidation process, oxidative species intercalate into graphite galleries provoking the partial disruption of the graphene sp2-hybridization and the covalent attachment of oxygen-rich species. This results on the weakening of the interlayer attractive force, so that single-layer GO sheets are easily obtained upon application of low power sonication in water [8].
\nFrom a structural point of view, GO is considered as a graphene sheet comprising in-plane undisturbed π-conjugated domains, and functionalized ones with covalently attached hydroxyl and epoxy groups, and additional carboxyl and carbonyl groups located at the sheet edge [11]. This chemical structure gives GO an amphiphilic character and then makes it dispersible in polar or nonpolar solvents [12]. This amphiphilic character preserves in rGO because it is obtained after the partial remotion of these functional groups by a reduction process.
\nInterestingly, rich oxygenated groups attached to the graphene structure makes GO and rGO highly hydrophilic and susceptible for further functionalization. Therefore, pristine or reduced GO can conveniently be functionalized to facilitate the interfacial interaction between GO and other materials including polymers, metal oxides, and inorganic nanoparticles to form GO-based composite materials, or to link the sheets together and then lead to macroscopic GO-based materials [13, 14].
\nDue to its multiple applications, GO is produced at an industrial level. Nowadays, worldwide research groups are looking for ways to find cost-effective and environment-friendly methods for graphene-derived materials’ mass production. These include electrochemical, mechanical, and chemical exfoliation of graphite [15]. In general, these methods produce GO-like materials, i.e., functionalized graphene, and they may be further processed to produce rGO with multiple functionalities. To date, the phase solution graphite exfoliation-based methods have demonstrated their high versatility to fabricate bulk amounts of graphene-derived materials at relatively low cost [16].
\nThere are diverse methods for GO reduction, such as thermal reduction, chemical reduction using toxic or green reductive reagents, and multistep reduction (either by combining chemical and thermal processes or by combining green and toxic reducers to get an effective reduction). Dangerous and toxic reagents such as hydrazine, oxalic acid, sodium hydrosulfite, and sodium borohydride were reported to reduce GO efficiently. On the other hand, GO environment-friendly reduction routes include flash photo reduction, hydrothermal dehydration, solvothermal reduction, catalytic reduction, and photocatalytic reduction. Furthermore, green reductants have also been essayed including vitamin C, alcohols, bovine serum albumin, ging-seng, bacteriorhodopsin, bacteria, and polyphenols (present in green tea and caffeic acid, among others) [17, 18].
\nThis work presents an overview on the environmental-friendly methods to reduce GO and produce GO-based nanocomposites. A survey of their applications is also presented.
\nIn addition, we present the mechanistic aspects on GO-based nanocomposites, as well as those associated in the formation of GO nano- and microstructures by self-assembly process.
For applications where the exceptional electrical conductivity and transparency of graphene are demanded, GO can be subjected to an additional chemical treatment to detach the covalently oxygenated groups on graphene basal plane and restoring the sp2-hybridization. As previously mentioned, the detected drawbacks for the chemical reductants, such as hydrazine, hydroquinone, and sodium borohydride [19], have fuelled the search for both environment-friendly methods and chemicals for GO reduction. The so-called “green technologies” satisfy both criteria, and the most reported green technologies may be classified, selected reducing agent, into four groups, as indicated in Table 1.
Bacteria are living beings capable of surviving under the most extreme conditions, i.e., in severe temperature and chemical composition. Bacteria have been found in the most warmed underwater pools, where tectonic plates emanate pernicious gasses and incandescent material or in lakes of extreme saline composition, surrounded by an environment that is highly concentrated in arsenic, such as those found in Mono Lake, California [20]. To survive, bacteria can take organic and inorganic molecules from the surrounding environment and transform them into the substance required to start the cellular process in which oxidation-reduction mechanism is employed to obtain an energy source [21, 22]. The overall redox process carried out by bacteria has been used in GO reduction by means of Shewanella [23], Bacillus subtilis [24], Extremophiles bacteria [25], Escherichia coli [26], and Gluconacetobacter xylinus [27].
\nGreen technologies | Reducer agent sources |
---|---|
Bioreduction | •Bacteria •Plants •Commercial biomolecules |
Photoreduction | •Electromagnetic irradiation |
Reduction by polymers | •Polyelectrolytes |
Aided-metal reduction (or reduction by metals) | •Transition metals |
Mechanochemical reduction | •Ball milling system |
Electrochemical reduction | •Supporting electrolyte |
Representative green technologies for the GO reduction.
Note that the involved reaction mechanisms depend on the bacteria cell structure, which determines the capacity for directly or indirectly hydrolyzing the acidic groups attached to the GO molecular structure, particularly, the groups that comprise oxygen atoms. Wang et al. [23] used Shewanella for reducing GO (Figure 1) through a mechanism that consists segregating the heme group proteins such as c-type cytochromes, through the membrane and these proteins act as electron intercessor [28].
\nAs bacteria, Shewanella oneidensis utilizes terminal electron acceptors during its respiratory metabolism. It transfers electrons from cell surface to any extracellular acceptor such as metal oxides or graphene oxide. It was proposed that GO reduction by Shewanella procceds via an electron exchange among MtrB, MtrC and OmcA cytochromes to finally transfer an electron to GO and then reduce it.
Zhang et al. [24] reported that depending on the bacteria type, it is possible to select a process to efficiently reduce GO for specific applications of the final nanomaterial. It was also proposed that, in the bacteria-based reduction processes, the parallel action of different bacteria could increase the effectiveness of reduction process. Based on Zhang’s observations, Raveendran et al. [25] achieved reducing GO using extremophiles bacteria, obtaining graphene with excellent conductive properties.
The chemical compounds naturally existing in plants (phytochemicals) have been used for years as nutrients, drugs, etc. In the past few years, phytochemicals, such as vitamins, amino acids, saccharides, alkaloids, proteins, and enzymes [29, 30], have been studied as reductant precursors for metals and GO. The reported attempts for the GO reduction by using phytochemicals go into the employment of either laboratory-extracted (plant extracts) or commercial-purchased phytochemicals.
\nHerein, we present some relevant results emphasizing on the reductant chemical source.
To date, the GO reduction by means of plant extract is intensively studied [31]. In this approach, the plant is chosen considering the antioxidant compound contents. For preparing the plant extract leaves, flowers, stems, and/or roots are refluxed in water, alcohol, or water-alcohol mixtures as solvents.
\nGreen tea has proven to be an excellent source of antioxidant biomolecules. For example, it was successfully used for reduction of graphene oxide [32]. The reducing capacity of green tea is based on the antioxidant biomolecules extracted from emulsion, mainly polyphenols.
\nExtracts of chrysanthemum flower and lycium barbarum plants, used in the traditional Chinese medicine, were recently reported for GO reduction by Hou et al. [33, 34]. The extracts were obtained in aqueous media at boiling temperature and then filtered. Afterward, the extract was poured into the GO dispersion at the water boiling point for 24 h. The authors reported that the chemical composition of extracts, namely, chrysanthemum extract and flavonoids (diosmetin, luteolin, apigenin, and glucoside), were the predominant phytochemicals. Whereas the lycium barbarum extract comprised flavonoids, phenols, carotenoids, and polysaccharides as dominant phytochemicals.
\nThe authors suggested that polyphenols present in chrysanthemum and lycium barbarum extracts transform to quinone releasing H+ ions that interact with GO for reducing it. Importantly, chrysanthemum and lyceum barbarum plants hold promise to effectively reduce GO, because the C/O ratio values obtained by X-ray photoelectron spectroscopy (XPS) were 1.35, 4.96, and 6.5 for pristine GO, rGO-chrysanthemum, and rGO-lycium barbarum, respectively.
Vitamin C (L-ascorbic acid) has been widely used in GO reduction because of its reducing effectivity and is comparable to that of hydrazine, besides promoting highly stabilized dispersions of rGO sheets in water. It has been observed that oxidized L-ascorbic acid is unreactive and stable and does not provoke damage to living cells [31].
\nIn some GO reduction reactions, L-tryptophan (an aromatic amino acid) has been considered as a stabilizing agent to prepare highly stable rGO aqueous dispersions [35]. It effectively prevents against agglomeration of the rGO sheets because it readily adsorbs on undisturbed π–π domains at the basal plane of the rGO chain, which minimizes the attractive π–π interaction. Furthermore, the remaining terminal carboxylate anion of L-tryptophan has provided an electrostatic repulsion between the individual graphene sheets.
\nThe L-tryptophan-stabilized rGO dispersion prepared with vitamin C exhibited good electrical conductivity of 14.1 S/m (pristine GO: 5.72 × 10−10 S/m). The mechanistic aspects for the GO chemical reduction remains unknown, but a plausible reduction mechanism was proposed as comprising two-step SN2 nucleophilic reactions. That is, L-ascorbic acid oxidizes into the dehydroascorbic anion releasing electrons and protons, which react with oxygenated groups on the GO sheet to reduce it.
\nThe free-stabilizing agents including vitamin C-reduced rGO dispersions were also prepared by Zhang, who reported high stability for all the prepared samples. The electrical conductivity with a value of 800 S/m was obtained in the sample prepared for 48 h [36]. Fernandez-Merino et al. [37] reported that the reduction capability of vitamin C could be improved by increasing the alkali concentration into reducing solution; using this approach the reduction time was shortened to 15 min. Furthermore, rGO showed good dispersibility in polar organic solvents, with high C/O ratio (~12.5) as well as high electrical conductivity (7700 S/m). In addition, riboflavin (vitamin B2), phosphate salt of vitamin B2, and pyridoxine (vitamin B6) were used to reduce GO. These bioreductants have also been proven to successfully reduce GO [38].
Saccharides are nutrients that may be used as reducing agents; these are classified into four chemical groups: mono/di/oligo/polysaccharides. Monosaccharides, glucose, and fructose have demonstrated mild reductive ability and nontoxic property in GO reduction experiments. In general, their potential for reduction is closely related to the ease to form open-chained structures [31]. In the GO reduction, it was found that their oxidized products play an important role to stabilize rGO sheets in aqueous dispersions, i.e., they may act as capping agents. Both saccharides and their oxidized products are environmental friendly. Zhu et al. [39] used glucose, fructose, and sucrose in aqueous ammonia solution for the reduction of GO. They determined that the ammonia solution is useful for both completion and enhancement of the GO deoxygenation reaction rate. In addition, they found that the reduction capability of sucrose was weaker than that for the glucose and fructose, under similar reaction conditions. The resulting rGO powder was biocompatible and highly dispersible in water. Likewise, Akhavan et al. [40] found that glucose increases its power to reduce GO in the presence of an iron catalyst under neutral condition.
\nOn the other hand, dextran (a polysaccharide) was tested as a GO reducer in aqueous ammonia [41]. However, the as-reduced rGO exhibited a rather low electrical conductivity (1.1 S/m) that can be notably improved (10,000 S/m) upon thermal annealing (500°C under Ar atmosphere).
L-Cysteine is a thiol-containing amino acid that is liable to oxidate to cystine. It inhibits oxidative properties because thiol groups can suffer redox reactions. Chen et al. [42] synthesized rGO using L-cysteine as reducing agent under mild conditions. They proposed the reduction pathway for GO by L-Cysteine might be like that observed in the GO reduction by vitamin C. That is, at first, the reactions comprehend nucleophilic attack by thiol groups, which develop upon proton releasing during the L-Cysteine oxidation process. Afterward, the released protons react with the oxygenated groups producing water and byproducts, inducing the GO reduction. The rGO suspension conductivity increases by about 106 times in comparison to that of pristine GO. Bose et al. [43] used other amino acid such as glycine for reducing GO. They found that glycine not only reduces the GO but also functionalized it, as a result amine group can covalently bound to a GO network. In other work, L-Lysine was successfully used for reduction of graphene oxide in the presence of carboxymethyl starch (CMS) as stabilizing agent. The rGO suspension exhibited good dispersion stability in water [44]. Furthermore, L-aspartic acid has been employed for synthesizing rGO, the product obtained by this process also presents uniform separation in water as well as good electrical conductivity of ~700 S/m [45]. Other studies have revealed that some amino acids such as tryptophan, arginine, and histidine reduce the GO and also augmented the consolidation of rGO–metal nanoparticles [46]
Gallic and citric acids are natural organic acids that have been tested as GO reductants. It was found that both acids could play the dual role as a reducing agent and a surfactant. Li et al. [47] found that the GO can be significantly reduced by gallic acid in aqueous ammonia, either at room temperature or under heating condition. Although, the reduction mechanism of GO by gallic acid has not been explored, it is expected that its three adjacent hydroxyl groups (pyrogallol moieties) interact with the GO in-plane oxygenated groups. The prepared rGO suspensions displayed excellent dispersibility in various solvents such as H2O, N-Methyl-2-pyrrolidone (NMP), dimethylsulfoxide (DMSO), dimethylformamide (DMF), and methanol, probably due to adsorbed oxidized gallic acid.
\nOn the other hand, citric acid has extensively been studied for the synthesis of silver and gold nanoparticles. Recently, Ortega-Amaya et al. [18] used the one pot approach to produce highly dispersible functionalized rGO by using citric acid. This process was made in aqueous medium at room temperature, under Ar atmosphere. To explain the dual role of citric acid as a reducer and a stabilizer, the authors assumed that protons released by the citric acid dissociation bind to epoxy or hydroxyl groups to form water molecules and an active carbocation at the GO network. Afterward, a di-ionized citrate HCit2− anion covalently binds to the carbocation to stabilize it. The whole effect was one of reduction by protons, and functionalization by HCi2− anion. Last one being the predominant specie in the aqueous solution at pH 4.
UV, microwave, or ultrasound irradiation have been used for transforming colloidal GO to graphene with a similar quality as that produced by means of hydrazine. In acidic GO colloids, Lu et al. [48] obtained free contaminants rGO by microwave heating. First, an acidic GO colloid at pH 1, 3, or 5 was separately prepared by dropping a NaOH solution. Afterwards, each mixture was heated at 150°C under microwave irradiation, employing a power of 80 W, for 10 min. They monitored the GO reduction advance by visual observation of the color changes from brownish-yellow to black [49, 50].
\nA different method for the GO reduction based on electromagnetic irradiation was reported by Ding et al. [51]. The authors reported clean reduction of colloidal GO using the strong UV absorption property of water [52]. The UV radiation dissociates the water molecule into three radicals (hydrogen H2, hydroperoxyl HO2, and hydrated electrons e−), each one retaining one of the earlier bounded electrons [53]. Then, hydrated electrons behave as a reducer to form rGO (Figure 2). Although the reduction process takes a long reaction time, it is possible to monitor the formation of rGO dispersions through UV-vis spectroscopy [54].
\nSchematic representation of the reduction of GO under UV irradiation.
Another green processing by irradiation was published by Nyangiwe et al. [53], which is a very simple method and is described for the reduction of GO solution. By irradiating a GO sample dispersed in water with sunlight, the most oxygenic functional groups in GO were removed. The authors considered that photoreduction of GO by sunlight can be explained by a model proposed by Ji et al. [55], where the absorbed UV radiation in solvent excites the water molecule near its photoionization threshold (6.5 eV), generating solvated electrons, which will act like reducers. The complete process is described by the following equations [56]:
There are scarce reports on the GO reduction by polymers. Zang et al. [57] reported the GO reduction using poly(diallyldimethylammonium chloride) (PDDA) polyelectrolyte [57]. It has been reported that the addition of PDDA to a GO aqueous dispersion triggers a chemical reaction that promotes a color change in the GO dispersion, indicating that GO transforms to rGO. Although the mechanistic aspects of the GO reduction were not clearly explained, the PDDA-functionalized rGO exhibited an excellent dispersion in water. Therefore, polyelectrolyte might be used as a reducing agent as well as a stabilizer to prepare a colloidal suspension of graphene. This method is based on the Yang et al. report [56], where PDDA was adsorbed on the external surface of carbon nanotubes through π–π and electrostatic interactions [56, 58]. It was assumed that repulsive electrostatic interaction dominates to produce well dispersed PDDA-functionalized carbon nanotubes in water.
An interestingly eco-friendly approach toward the GO reduction consists of using transition metals (e.g., Fe, Zn, Cu, and Co) as GO reducing agents. In this case, the reduction mechanism strongly depends on the experimental conditions (mainly pH and temperature) and it follows a frequently complex pathway. Some examples are described below.
\nGO was reduced by iron in aqueous medium by Fan et al. [59]. They studied the GO reduction by powdered iron (10 µm average size) in an acidic HCl-water mixture at room temperature. They proposed that H+ interacts with the iron surface particle to bring forth the Fe/Fe2+ core/shell structure (iron particle with a thin sheet of charged Fe2+ ions). These positively charged Fe/Fe2+ species interacts with the functional groups on the GO sheets and after electron transport from Fe/Fe2+ to GO, the reduction of GO was achieved.
\nExperiments on GO reduction using Zn powder were essayed by Yang et al. [60]. To evaluate the Zn reduction capability and how it is affected by the solution pH and temperature, they prepared aqueous GO colloids with and without sodium hydroxide at room temperature and 100°C. They obtained lower reduction levels for all cases other than alkaline in 100°C conditions. The proposed reduction mechanism consists of an electron exchanging between Zn and GO to produce rGO and by products. The GO reduction using Zn powder were also carried out by Mei and Ouyang [61] and Liu et al. [62] under acidic conditions.
\nThe formation of Cu2O/rGO nanostructures during the GO reduction by Cu nanoparticles was described by Wu et al. [63]. They mixed polydisperse Cu nanoparticles with water-dispersed GO under neutral conditions. After sonication and heating at 95°C a composite comprising of rGO sheet decorated by Cu2O nanoparticles was observed. Authors claimed that GO was reduced through a redox reaction between Cu an GO, in which Cu nanoparticles transformed to nanozised Cu2O and GO was reduced. In addition, they reported that the GO reduction strongly depended on the Cu particle size, because experiments involving fine grained Cu powder was unable to effectively reduce GO.
\nGO reduction experiments using metal foils as a substrate were done by Cao et al. [64]. A number of metal foils (Cu, Ni, Co, Fe, and Zn) were separately immersed in a GO aqueous dispersion at pH = 6. After taking out and drying at ambient temperature, the metal foil was coated with a rGO film. It was assumed that the rGO film was developed by a self-assembly process of rGO nanosheets and that the GO was spontaneously reduced by direct transference of electrons from metal ions to GO. Some metal ions were found in the GO layers’ galleries.
\nHu et al. [65] used a GO dispersion at pH = 4 to immerse metallic foils (Cu, Fe, Zn, Co, and Al) and also a nonmetallic (carbon) film supported by Cu, and after (1–12 h) immersion, the metal foil was covered by self-assembled rGO multilayers and was dried at ambient conditions or freeze dried. They found that there had been electron transfer between the metal and GO propitiated for the acid condition (Figure 3). A significant difference is that they found metal oxide nanoparticles decorating the rGO; another important result was that the electron exchange to reduce GO had taken place even when a conductive layer (carbon or Au, Pt, Ag) covers the Cu substrate.
\nScheme of GO reduction and metal oxidation. A hydroxyl group present at GO was protonated in acid conditions and then an electron transfer between metal GO took place, rGO was obtained, and oxidized metal.
The mechanical reduction of GO into graphene was tested by a hydrogen-assisted ball-milling process [66]. The ball-milling process was carried out in a planetary micro ball-milling machine with a stainless steel chamber and stainless steel 5 mm diameter balls. First, 2.0 g of previously prepared GO powder by a modified Hummers’ method [67] was loaded into the ball-milling chamber, and then filled with hydrogen gas. The chamber was rotated at 900 rpm for different times in the 30- to 240-min interval, to obtain a variety of ball-milled rGO samples. The GO reduction process with the milling time was visually verified by observing the GO color change from a brownish-yellow to black. The final powder, as analyzed by transmission electron microscopy, XPS, and infrared absorbance spectroscopy, consisted of well-exfoliated oxygen-free single-layer graphene [68, 69].
The electrochemical technique is widely used in thin film deposition on conductive substrates. The electrochemical reduction of GO develops either during the film deposition process or a preformed film as described in the comprehensive review [70]. The one-step and two-step approaches are usually employed to produce GO, and the reduction level can be controlled by varying the processing time, electrode material, on-off cycles, electrolyte type, and potential values. A variety of nontoxic electrolytes, such as NaCl, KCl, NaHPO4, Na2SO4, KNO3, and phosphate buffer solution (PSB), have been used. Furthermore, glass carbon, Au, Pt, Ag, and 3-aminopropyltriethoxysilane (APTES) have been tested as electrode materials [70].
\nIn the one-step approach, GO sheets are dispersed into a mixture of electrolyte and buffer solution, and the power source is turned on and the GO thin film deposition and reduction occur simultaneously at the cathode surface material.
\nIn the two-step approach, a thin film is deposited by some technique (drop-casting, spray pyrolysis, layer-by-layer, etc.) on an electrode of a three-electrode system (reference, working, and auxiliary electrodes), and then immersed into the electrolyte solution. Under controlled conditions of electrolyte temperature and composition, as well as electrodes potential, a rGO thin film is obtained. Recently, Fang et al. [71] used the two-step system to produce large area rGO and rGO/silk fibroin composites. They used a reference electrode of Ag/AgCl, auxiliary electrodes of ITO, Ag wire, and titanium and were tested individually and the working electrode GO materials, electrolytes of NH4Cl, KCl, or [dmin][BF4] were used. After the reduction process, the large area GO were tested for electrical properties, having 28,200 S/m conductivity after the reduction.
A summary of the above-mentioned green reducing methods and its reduction rate is presented in Table 2.
\nReducing agent of GO | Reducing grade reported, based on XPS measurements | References |
---|---|---|
Shewanella | C–C bonds increased from 28% in GO to 90–95% in rGO | [23] |
Gluconacetobacter xylinus | C/O ratio increased from 1.8 in GO to 3.1 in rGO | [27] |
Camellia sinensis (green tea) | C–C bonds increased from 58.9% in GO to 74.0% in rGO | [32] |
Vitamin C | C/O ratio increased from 2.3 for GO to 12.5 for rGO. Samples reduced with (hydrazine was 5.5) | [37] |
Microwave irradiation | C/O ratio is ∼9.12, closed to value for rGO obtained by conventional hydrazine reduction ∼10 | [48] |
UV irradiation | Functional groups that contain oxygen in the GO nanosheets were successfully removed. However, complete elimination of oxygen groups must be due to longer UV irradiation | [51] |
Poly(diallyldimethylammonium chloride) (PDDA) | C–C bonds increased from 24.5% in GO to 52.7% in rGO | [57] |
Metallic zinc | C/O atomic ratio increased from 1.19 in GO to 7.19 in rGO | [60] |
Ball milling | In the presence of H2, a dramatic decrease in the oxygen-bonded carbon components is observed | [66] |
Summary of reductive green methods.
Due to their amphiphilic character GO sheets are valuable building blocks for preparing a variety of carbon-based nano- and microsized nanostructures by a self-assembly process. Because GO sheets are few nanometers in size, their self-assembly hierarchy proceeds to develop 1D, 2D, and3D nano- and microsized materials. It thus enables to use templates for directing the self-assembly of GO sheets into complex structures with the specific shape and morphology for a given application.
\nIt is noteworthy that the GO sheets’ self-assembly was observed to occur at interfaces such as solid-liquid and air-liquid [12], and hence some hybrid metal- or metal oxide-GO nanocomposites comprise GO-coated inorganic nanoparticles.
\nRegarding GO self-assembled micro- and nanostructures, the presence of functional groups on the GO sheet surface promotes the assembly of nanoscale GO sheets into macroscopic 2D structures (films or fibers) and 3D bulk graphene by GO solution filtration or hydrothermal treatment. The forces that drive the self-assembly process are quite like those forces that participate in the self-assembly process of colloidal nanocrystals. Potential applications for these emergent structures are flexible fiber-type actuators, robots, motors, photovoltaic cells, and supercapacitors.
Graphene and derivative materials are widely used to develop novel nanocomposites when combined with polymers and/or nanoparticles (semiconductors, metals, or metal oxides) [72]. These materials display superior physicochemical properties than those of their individual components and are currently essayed for water remediation, sensing, catalysis, photovoltaic films, materials reinforcement, and biomedical applications.
\nAt present, a number of nanocomposites have been prepared by diverse methods and with specific physicochemical properties for biomedical [73], energy conversion, environmental and electrochemical storage [12], and miscellaneous [9] applications, as reported in recent review articles.
\nAmong the variety of chemical and physical synthesis methods reported in the literature, we just include representative examples of the four classes of nanocomposites as described below.
\nAccording to their final morphology, rGO hybrid nanocomposites are broadly classified as supported, encapsulated, incorporated, and multilayered composites [7, 13]. Schematic representation of nanocomposites is presented in Figure 4.
\nKinds of metal-rGO nanocomposites. (a) Supported rGO surface is decorated by metal nanoparticles. (b) Encapsulated nanocomposites, few or multilayers of rGO are wrapping individual or clusters of MNP. (c) Incorporated layers of rGO are intercalated by metal layers. (d) rGO sheets are present in a metal matrix.
The primary nanostructure GO sheets and nanoparticles (metal, metal oxide, or semiconductor) to develop nanocomposite materials are mainly processed by chemical methods.
\nThe supported-rGO nanoparticles can be prepared by either direct synthesis of inorganic nanoparticle in the rGO dispersion or by mixing of previously prepared rGO and nanoparticles colloids. In the first approach, precursors are first dissolved in a convenient solvent, and then poured into the rGO dispersion. For preparing rGO-metal or rGO-metal oxide nanocomposites, the preferred method consists in adding the metal precursors (chlorides, nitrates, etc.) and a reducing agent (vitamin C, citric acid, L-lauric acid, etc.) into a previously pristine GO colloid. The whole processing can be performed using a range of synthesis systems such as microwave oven, hydrothermal, electrodeposition, sonication, and so on.
\nFor instance, Kim et al. [74] used ascorbic acid to simultaneously reduce GO, Pd, Pt, Au, and Ag. An aqueous GO dispersion was kept at 100°C, then metal precursor and ascorbic acid solution were sequentially added. The final product consisted of rGO-supported nanosized noble metals. They used the rGO/Pd nanocomposite as catalytic material for Suzuki coupling reaction and observed that the nanocomposite catalytic activity was almost fully restored after five cycles.
\nAn interesting version of supported-rGO nanoparticles approach is one in which rGO wraps the nanoparticle. In some cases, rGO sheets conformally enwrap the nanoparticle developing a hermetically sealed multilayer coating. The resultant composite comprises GO sheets decorated with a number of nanoparticle/rGO structures. These core/shell nanostructures are of current interest, since they are protected against oxidation or degradation. Chemical methods in solution and chemical vapor deposition have successfully been used to prepare such nanocomposites. They are used as lithium storage electrodes, high performance anodes, and biomedical applications.
\nOther interesting approach to obtain coated-rGO nanoparticles is the aerosol encapsulation technique reported by Chen et al. [75], to coat citric acid-stabilized Ag nanoparticles. These workers used an ultrasonic system to generate an aerosol composed of GO and Ag nanostructures, which was transported into a furnace at 600°C by using N2 as the carrier gas. After draying, the individual drops transformed into a sample composed of Ag/rGO microstructures. This composite could be of interest for applications in tissue engineering, magnetic resonance imaging, X-ray computed tomography, and bioimaging contrast agent.
\nThe layer-by-layer method is a thin film deposition technique in which alternating layers are successively deposited, and a film with a multilayered structure is obtained. Techniques such as immersion, spray coating, spin coating, and electrochemical are suitable ones to deposit multilayered nanocomposite films with large interfacial area. These kinds of nanocomposites are ideal for energy storage and generation.
\nJang et al. [76] reported rGO/maghemite multilayered nanocomposite preparation. The GO was exfoliated by thermal expansion in vacuum at 200°C and then heated up to 300°C for 5 h. The powder of exfoliated GO, iron acetylacetonate, and oleic acid were mixed together and mechanically ground with a pestle and mortar. After heating at 600°C for 3 h, they obtained intercalated rGO-maghemite nanocomposite, and studied its performance as an anode material of Li ion batteries. In contrast with individually tested rGO and iron oxide samples, the nanocomposite displayed enhanced cycling stability and rate performance.
\nIncorporated nanocomposites have a low GO content (less than 1% vol) and are usually prepared by the ball-milling technique and a postsintering process. In this case, the properties to be exploited are mechanical and electrical properties, since they are applied in structural elements and implants.
\nRecently, the preparation of rGO incorporated in Al, Ni, Mg, and Cu matrices was reported [77]. It is expected that rGO sheets replace carbon nanotubes as a reinforcement material because rGO can be produced at large scale and at a lower cost. Zhang and Zhan [78] reported rGO-reinforced copper by ball milling and spark plasma sintering. They found that the presence of 0.1–1% vol rGO greatly enhances its mechanical properties (yield and tensile strengths) compared to those of pure Cu.
Currently, the GO self-assembled micro- and nanostructures are being essayed as semiconductor in thin film transistors, transparent electrode of solar cells, active material for chemical sensing, etc. These applications require paper-like and thin film of self-assembled GO nanosheets.
\nThere are plenty of reports on the GO self-assembly into 2D and micro- and nanostructures [79, 80]. Shao et al. [14] did an exhaustive description of the mechanistic aspects of the GO self-assembly at diverse interfaces. Herein, we present the more recent findings on 2D microstructures, including thin films.
\nUntil now several self-assembly mechanisms to form GO thin films have been proposed. The GO thin films formation frequently occurs at liquid-air-type interfaces by evaporation and Langmuir-Blodgett assembly [81, 82]. The techniques employed are simple in almost all cases and also let the assembly on the suspension surface, for instance, dip-coating, drop-casting, spin-coating, and spray-coating for mentioning some of them.
\nLangmuir-Blodgett assembly leads to the formation of GO very thin films and GO single layer, so when GO is obtained, it can be dispersed in a highly volatile organic solvent in the presence of little amounts of water, and then as the solvent evaporates, the GO begins to aggregate at the interface of water-air, forming a GO monolayer. The assembled material at the interfaces can be collected later by dipping a substrate [83]. The main advantage lies in the collected GO very thin films on the substrate, and they are a source to reduce the GO into graphene sheets by either chemical or thermal treatment [19, 84].
\nThe resulting GO films have high transmittance, high surface chemical activity, and low sheet resistance. The morphology and dispersion degree can be modified by the pH of the synthesis solution; so, the pH modulates the amphiphilic nature of GO layers as evidenced by Cote et al. [85]. The self-assembled GO films are dominated by attractive forces as van der Waals forces and π–π interaction which lead to stacking of single GO layer.
\nIn the evaporation case, the solvent is heated to accelerate its evaporation and the agglomeration of GO sheets in the interface of water-air, any solvent with relatively low evaporation point can be used to disperse and afterward evaporate it to promote the self-assembly on the solvent surface. In this way, it is possible to obtain both thin films and membranes. Similarly, the Langmuir-Blodgett assembly and evaporation induced lead to the formation of GO membranes by staking layer-by-layer (Figure 5). Cote and Shao [85, 86] provide examples of self-assembly using the Langmuir-Blodgett and evaporation-induced mechanisms.
Self-assembly process into GO thin layers at the interface of liquid-air. The assembly is assisted by the solvent evaporation.
As started below, the GO membrane can be considered as 2D microstructures with a morphology that depends either on the attractive or repulsive interaction among individual GO layers.
The GO sheet self-assembly can produce thin films and 2D membranes as aforementioned; however, this would occur at a liquid-air interface. If the interface is now liquid-solid type, a variety of microstructures of 2D and 3D can be obtained by self-assembly of single GO layers in the presence of a solid. The GO sheets’ interactions with the solid surface involve π–π interaction, hydrogen bonding, electrostatic forces, and surface tension.
\nGO self-assembly driven by electrostatic forces produce several morphologies of 2D and 3D such as thin films, membranes, and capsules [87]. In this case, the ionization of COOH groups provides a negative charge distribution at the GO sheet edge. This charge distribution can be controlled by the pH of GO dispersion. An electric field applied in the GO solution is able to drift the negatively charged GO layers toward the positive electrode (solid element) dipped in the dispersion. GO sheets are agglomerated on the electrode surface and forced to assemble by staking. An interesting effect is presented during the GO sheets drift, since the GO layers that assemble are electrochemically reduced. So the electric field can remove the oxygen-based functional groups [88] and promote the assembly only by π–π interaction.
\nThe presence of particles or nanoparticles in a GO dispersion can also drive the self-assembly, in this case foreign particles act as agglomeration centers, which destabilize the GO dispersion. This can be considered as a GO colloid in a disperse state [83, 89]. Therefore, attractive forces originated from particles overcome the electrostatic repulsion (colloidal stabilization) and lead to agglomeration and assembly.
\nThree-dimensional GO structures with polyhedral-like morphology were reported in Ref. [90]. In this case, the self-assembly process was observed at microscale. GO is synthesized by Hummers’ method and the material is patterned on a silicon wafer. The patterned 2D GO membranes become the building blocks. A metallic frame is deposited around the GO membranes; it drives the folding of each membrane by surface tension forces. Considering the membrane size, the attractive forces as van der Waals are not manifested at this scale; therefore, surface tension forces are dominant here. These 3D cubes can be used as microcontainer of liquids and gases. Different assembly stages are shown in Figure 6, initially any material can be patterned as isolated blocks, Figure 6(a), and then the metallic frame is deposited by a photolithographic process, Figure 6(b). The metallic frame is constituted by two different metals, which linked the assembly blocks and allow the self-folding [91].
Self-assembly process of 3D microcubes, schematizing different stages by self-folding.
1D GO microfibers have been obtained by self-assembly of single GO layers; however, this is an example of microstructures’ self-assembly at a liquid-air-type interface. As reported by Tian et al. [92], which these 1D fibers are formed by two forces combination, π–π interaction and van der Waals attractive forces are gradually manifested in the GO dispersion. These forces drive the single GO layers agglomeration toward the GO dispersion surface and staking layer by layer. Then, progressive accumulation of GO sheets produces these 1D GO microfibers. The fibers are annealed later to obtain rGO. The authors in this case do not provide further information about the rolling up process that give place to the 1D fibers.
Synthesis, reduction, and advanced application of graphene oxide (GO) are fast growing research areas because there exist a great variety of preparation techniques for mass production, the chemical-based ones being the most promising. For its chemical richness, chemically obtained GO is an extraordinary product in various aspects. First, it can be obtained by means of scalable, simple, and low-cost techniques, which is important for gram- or kilogram-scale applications (e.g., rGO-metal-based composites for the lithium battery anode, rGO-based foams, water cleaning, etc.). Second, it has demonstrated to be an excellent precursor material for developing advanced materials, such as graphene, graphane when treated under hydrogen atmosphere, and Teflon-like materials when fluorinated.
\nThis chapter presents an overview on the GO reduction by green methods, on the production methods of carbon-based structures by GO sheets self-assembly, and on preparation methods of GO-based metal nanocomposites.
\nThe so-called green methods for GO reduction demand that both, starting chemicals and byproducts, are safe to handle and environmentally friendly. Technologies such as bioreduction, photoreduction, reduction by polymers, reduction by metals, mechanochemical reduction, and electrochemical reduction fulfill both criteria.
\nOn the other hand, the amphiphilic character of GO sheets make them valuable as building blocks for preparing a variety of carbon-based structures produced by their self-assembly, as well as hybrid nanocomposites when combined with metal semiconductor nanoparticles. The self-assembled carbon structures and hybrid nanocomposites are currently essayed for water remediation, sensing, catalysis, photovoltaic films, materials reinforcement, and biomedical applications.
It has been becoming a key factor for artificial intelligent computers, which are composed of modern style machine learning system, how they are able to get involved with human.
Then, in our study, we have conducted experiments over a decade so that we can clarify human information processing, aiming to improve their interaction of AI doctor or support robot with human being by predicting their behavior from finding out their individual cognitive traits [1].
Specifically, we have predicted that their traits concerning with information processing would become clearer by comparing response time to short sentences between presenting with sound voice and letters. Those short sentences which are 120 questionnaires of psychological testing (YGPI) ask subjects whether they are the same or not, comparing with their daily ordinary behavior [2]. In other words, those questionnaires are concerning autobiographical memory [3], which are not effects of their knowledge or academic ability, but personality of 12 factors which divided into two factors, emotional and non-emotional [4, 5, 6].
From the results of our previous study, correlation coefficient between individual response time and the criteria of measurement (duration of each reading questions or the number of words in one question) in the experiment by sound voice (listening) was higher than those of by letters (silent reading). And more, there was greater dispersion of response time among subjects in presenting letters experiment than former ones. From these reasons, we predicted that there would be differentiation of individual traits of information processing for letters than those of sound voice [6, 7].
We have therefore examined response time by silent reading individually and found out that there were persons of Visual type (N = 12 of 98, r < 0.3) whose correlation coefficients were much lower than those of Auditory type (N = 31 of 98, r > 0.5). In addition to this, the average of response time of Visual type persons was significantly shorter than those of Auditory type [6, 7].
Moreover, we have inspected reaction time of silent reading, especially among Intermediate type (N = 55), and found out there were another pattern of information processing between Emotional and non-Emotional questionnaires [6, 7].
In this paper, we have categorized two types, Eidetic type and Adjusting type, whose correlation coefficients and response time patters were different with each other. From these viewpoints, we had formulated a hypothesis (dual loop theory) and verified them by the experiments of practical collaborative learning in nursing class. One loop might be concerning positive feedback control (PFC) and other one might be negative feedback control (NFC) [8, 9]. Epidemic type persons might have tendency of PFC while they are solving problems. On the other hand, Adjustive type might tend coordinating two cycles (PFC and NFC) [10, 11]. We had revealed differentiations between the two types of behaviors.
Consequently, we would like to propose that the results of this study might help AI computer to learn machinery, thereby analyzing Big Data of various students’ results and predicting their individual pattern of behavior so that it can support for personalized education, for instance, optimizing combination for collaborative learning.
Our purpose of this study is to clarify human information processing in order to optimize machine learning for AI computer, which is intended to communicate interactively with human being.
At first, there were problems in collaborative learning of practical nursing class at university and we needed to find the solution. After investigating them in 2014, we have found that there was the main cause of those problems which were failing at a relationship among team members. Then, we have developed the Personalized Education and Learning Support System (PELS) in 2015 [1], which helps instructors and learners to work interactively with each other by optimizing combinations of team members from the viewpoint of personality (Figure 1).
Local search for solution of combinatorial optimization.
The main system of PELS is Big Data processing system (1) (Figure 2), [11], which gathers students’ various data, for instance. measuring their traits (2), recording their behavior, results of their performance, questionnaires, and so on, and analyzes them (3), then inform them to instructors (4) so that they can make plans for instructions included teaming members for collaborative learning interactively.
Local search for solution of combinatorial optimization.
The result of students’ performances at the first semester (Figure 3, upper) has been improving after introducing PELS to nursing classes, comparing the average scores with the conventional form in 2014; on the other hand, it has been dealing from 2015 to 2017 at second semester (Figure 3, right). We have supposed that the reason of those phenomena might be influenced by not only their personality but also their cognitive traits [12], especially concerning with language information processing, because our lifestyle has been changed dramatically in digital society even in educational field [6, 7].
Changing scores over the years.
From these reasons, we have been examining PELS from the viewpoints of optimizing combination for teaming members, through comparing performances and individual differences between successful and unsuccessful teams. Combinatorial optimization, however, is considered that it is difficult to find out precise solution because of discrete and non-contiguous data structure; therefore, we have decided to find solution of interactive problems by introducing the method of scaling up [13, 14, 15], which needs to be revised in the field of education. As this scaling up method should not change the current education system at their university, we have asked instructors and students to participate in experimental practical nursing class and agree to investigate their problems and solutions continuously [16].
Before starting those practical experiments, we had been developing the measuring system for individual traits [12], regarding human information processing. This system is simulated interactive communication between an instructor (A) and a learner (B) with using ICT (a) → (b) → (c) → (d) (Figure 4). In the field of educational technology studies, they call this interaction as learning process. When the learner responses to the instructor (A) after the information or instruction for assignments from the instructor (A) conveyed to her or him, the one session of activity has been considered as coming into effect of learning (Figure 5).
Learning processing.
Human information processing.
From this theory of learning processing, we predicted that language information processing might be the same as each other (Figure 5, ①). Then, instead of the instruction or assignment, we decided to use questionnaires of YGPI (Yatabe-Guilford Personality Inventory), which is consisted of 120 short sentences and 12 factors (10 of 120 each), and more, they are composed of two main factors, emotional and non-emotional factors. Subjects are required to choose responses to questionnaires among “yes,” “no,” or “either,” comparing with their daily activities or behavior. The system also measures their response time from the start of presenting the questionnaire to subjects’ replies (Figure 5, ② and ③). Card has introduced the theory that the perceptional system (τp), cognitive system (τc), and motor system (τM) are involved in simple reaction time [17, 18].
As questionnaires would be the same between those presented by sound voice and letters, differences of their response time should be the same, except the duration of comprehension for problem solving (τc2) and decision making of intention (τc3), which are considered working as high-order functions. Hence, response time, which is measured in this study, is not the same as simple reaction time but same as complex reaction time. According to the theory of information processing by Card [17, 18], reaction time for encoding by perceptive organs (τc1) is correlated with the number of words, because of cycling for processing with each elements of the word.
The results of our exploratory experiments (over 100 subjects aged from 13 to 64) have been shown, however, that the system of encoding might not be the same among subjects. Especially, encoding system [19] for letters might be different individually, and the results of preliminary experiments which have been conducted in the same conditions (age, sex, history of education, and environment of experiments) have imprecated the individual differentiation of cognitive system, included encoding.
From these perspectives, we had introduced the model of human information processing (Figure 5) into our research. Specifically, it was predicted that there might be individual differences of information processing, depending on contents of questionnaires, between emotional and non-emotional factors [4] because of the encoding system or image schema system (Figure 5; A2, V2) [20], which is concerning with conceptualization. Those might have effects on their comprehension (Figure 5; A3, V3) or decision making (Figure 5; A4, V4) strongly.
Consequently, the model of information processing had been reviled to Figure 6 which shows two types of cycle: (4) and (5). Along with previous examinations, the criteria would be decided for discriminating each other by analyzing correlation coefficient between response time and duration of reading (listening) or the number of words, depending on contents; emotional and non-emotional factors. In this chapter, we will examine hypotheses of “dual loop theory” as below.
Model of language information processing system.
There might have existed two loops for human information processing: one might be a positive feedback control (PFC) and the other might be a negative feedback control (NFC). Depending on students, which they might choose one during the problem solving would be different and it might be clarified by analyzing the response time, regarding the context of questionnaires.
In the case of PFC, Loop of (1)(2)(4)(5), (Figure 6), encoded words into symbols might be feedback directly to perceptive organs in order to comprehend the next word along with the context of given each questionnaire. Therefore, this type might have a tendency toward eidetic with short-term memory (STM) to make their decisions in a short time without phonologically silent reading.
In the case of NFC, Loop of (1)(2)(4)(6)(7)(3)(4)(8), (Figure 6), encoded words into symbols might be feedback control after phonologization with image schema and matching meanings of the words with sound voice by long-term memory (LTM). If there are conflicts between them, s/he might need to modify either one of them; then, the results would be conveyed to the cycle of feedforward control (Figure 6; (3)). In this case, they need time to make decision.
Most of the students might use both loops to solve problems and make decisions for replies. How they might choose one, depending on questionnaires, would be effects on their performances.
And more over, this tendency might have effects on their personality.
The purpose of prototype experiments is to calibrate the measurement system.
Twenty-eight university students participated in this experiment.
The experiment took place from January to March in 2015.
The participants were divided into two groups for a counterbalance depending on orders of the way of presentation by sound voice or letters. Prototype experiments are implemented twice to the same participants in the same way and conditions in January and March, for example, the arrangement of laptop displays on the desks and seats in the same room.
Each comparative experiment plans to obtain 240 responses and response time per person. Total amount of data should be 6720 for each element.
Under the condition of optimized combinations of team members at this time by considering inter personality which is predicted by the result of YGPI and instructors’ experiences, the aim is to find out problems remaining in collaborative learning class in order to improve students’ performance from another factor.
Ninety-eight new students at university participated in this experiment.
The experiment took place from April in 2015 to March in 2016.
Beforehand, the instructors had been introduced how to optimize combination of team members in teacher training by using personality types and their experiences. At first, students were explained about the practical experiment and collaborative learning. After obtaining their agreements, they had participated in activities of this experiment, for instance, taking personality testing before starting class, answering questionnaires, collaborative learning in practical nursing class with optimized team members, and so on. Students were required to wear the saddlecloth so that observers and instructors can survey their behavior individually in class.
The results of performance in class; both low and high stakes assignment;
YGPI (response, response time, and evaluation (profile));
questionnaires and interviews to instructors and students;
interaction among students while they are using LMS (learning management system);
record of video in class; and
participatory fieldwork.
The calibration is done by comparing the average of response time to questionnaires by presenting sound voice or letters obtained in prototype experiments in the first and the second time, divided by the number of words.
The calibration is done by comparing the average of response time to questionnaires by presenting sound voice or letters obtained in practical experiments in Visual and Auditory types (Table 1), divided by the number of words.
Criteria type I.
The calibration is done by comparing scatter diagrams of response time to questionnaires by presenting letters and standard reading time (sound voice) obtained in practical experiments in Eidetic and Adjusting types (Table 2), dividing into emotional and non-emotion context (Table 3).
Criteria type II.
Comparison of elements between emotion and non-emotion.
After processing parallel distributions of individual records of performance, low and high stakes assessments, and traits of information processing (Tables 1 and 2), a table will be made in order to analyze and evaluate by comparing performances of teams between success and ill-successes team (Team B and Team C).
After processing parallel distributions of individual traits of information processing (Tables 1 and 2), descriptions of answering questionnaires about psychological testing will be compared between two types of presentation and interpersonal communication in class or practical training (Team B and Team C).
Then, their differences will be discussed in order to clarify the effectiveness of collaborative learning.
Twenty-eight participants were the same members as the first and the second implementation on the same seat and the same display for each person. The experiments were conducted by representing counter-balanced by order. The results were obtained by analyzing the average of reaction time divided by number of words in a short sentence (Figure 7); both sound voice and letters were not significantly different between the first and the second experiments. The total average (first, second) of sound voice was (=2.69, =2.58) and letters (=2.32, =2.20). The correlation coefficients between response time and the number of words were not significantly different between the first and the second experiments, both representing questionnaires by sound voice and letters (Table 4).
Comparison of response time between the first and the second experiments (left: presented by sound voice; right: presented by letters).
Examination of comparison between the first and the second response time.
From these results, it has been proved with reliability that the level of calibration was high enough to reproduce scientifically, regarding our measuring system. Concerning standard deviation, however, letters (SD = 0.93, SD = 0.85) was larger than sound voice (SD = 0.64, SD = 0.64) (Figure 5). Specifically, when the number of words was higher, the standard deviation of reaction time to letters became larger. This means that there might be individual differences of information processing among students.
As we have mentioned in Section 3.1, from the results of prototype experiments, we have proved the reliability and the reproducibility of our measurement system. Then, in a practical experiment, we have used them and gathered data, with the similar way of procedures and conditions applied in the prototype experiments. As the standard deviation of response time presented by letters was larger than those of sound voice, we have checked individual differences of the correlation coefficients between response time and the number of words. Along with the categorization of those correlation coefficients, we have divided students’ types as traits of information processing I, Visual type and Auditory type. And then, comparing the average of reaction time between Visual and Auditory type (Figure 8), in the case of letters, Visual type (=2.01, SD = 0.92, N = 13) responded significantly faster than Auditory type (=2.65, SD = 0.98, (N = 31)) (Table 5) (t = −21.05, r < 0.001).
Comparison of reaction time between Visual and Auditory types (left: presented by sound voice; right presented by letters).
Results of tests, the significant differences of reaction time between visual and auditory types.
Figure 9 shows the different patterns of distributed response time (intermediate type of information processing I) between eidetic (N = 8 of 11) and adjusting type (N = 6 of 10), which were categorized traits by the differences of correlation coefficients between emotional and non-emotional contexts (eidetic type; X ≤ μ-σ, Adjusting type; X ≥ μ + σ) (Table 2). In the case of Adjusting type (N = 10), the average of response time of emotional contexts was significantly faster than those of non-emotional contexts. This tendency is found in the patterns of the scatter diagram, which shows distributions of each response time how they diff between emotional and non-emotional contexts. On the other hand, in the case of Eidetic type (N = 11), there are no differences between them.
Comparison of reaction time between emotion and non-emotion (upper: Eidetic type; lower: Adjusting type).
Figure 10 shows the quantitative interaction between two types of students, comparing their scores between the first and the second semesters (F = 5.3, p<0.01). The average of Eidetic type in the first semester was better than that of Adjusting type; however, in the second semester, it was reversed.
Comparison of scores first and second semester.
This phenomenon should be examined in detail, checking whether the statistical results are right or not by seeing individual performances practically. Therefore, we have chosen team members whose team was success or ill-success in low- and high-stakes’ assessments. In the case of low-stakes assessments, Team B members’ records were shown the best improvement among teams, comparing pre-post test scores. On the other hand, in the case of Team C, their records were the worst in class. Those tests conducted in the first semester, and the average of Team C (=77.5) was lower than Team B (=87.3). In the second semester, traits of the whole tendency of teams were the same; however, looking into individual performances, their tendencies were also the same as Figure 10. For instance, both scores of eidetic type; SubB-2 and SubC-2 in the second semester were lower than in the first semester, on the other hand, in the case of Adjusting type, SubB-1 and SubC-3, their scores in the second semester, became much better than those of the first semester.
In order to check them from another viewpoint practically, their descriptions of answering questionnaires were compared among types of information processing (Appendix 1 and 2).
Appendix 1 shows descriptive answers to the questionnaire about the comparison between auditory and visual presentation of testing. Two of four students, who are visual type, said that it was easier for them to decide responses or image by sound voice than by letters. On the other hand, all three students of Adjusting type have described their responses through self-evaluation by testing.
In Appendix 2, regarding interpersonal communication, which students are required to obtain in practical field for nursing, all three Adjusting type students have described that they think it is important. The others have described about the interactive communication a little more subjectively.
All four members of Team B were interviewed on September 9th in 2018. SubB-2, however, did not appear at the appointment time. After getting appointment again, she appeared for the interview. She said that similar cases have repeatedly happened because it was nothing unusual to make misread message (which caused missing appointment). Concerning interpersonal communication, it has been difficult for her in collaborative working in the practical field and it was the best condition in 2015 with Team B members.
In the case of SubB-1 and SubB-4, they both have talked about their strategies to communicate interactively in collaborative working, even at the specialized treatment department. It seemed that they were able to cope with any persons and cases.
There are a significant number of studies, which have been conducted about human information processing in the world [17, 18]. Every study is very important for us; on the other hand, most of them are still vague and unclear, because we need to observe real time while it is working, from outside. It should be difficult, however, to see inside of our mind directly. Therefore, we have developed the measurement of individual traits from cognitive aspects so that we can clarify human information processing and predict their behaviors. I would like to make it a meaningful measurement; however, it is still exploratory research and data analysis.
Although there might be a lot of methods to find out the mechanism of human information processing [21, 22], there should be different approaches from each other to achieve a goal, depending on their own purposes. The end of this study is to improve personalized education, however, both the environments in society and educational field have been changing, which must be a lot of elements and always impact on our cognitive system, in other words, on the way of human information processing. This means that we always need to find out the problems which might be courses of ill-success in education.
For instance, in our study case, we have supported collaborative learning in nursing class, which has been introduced for cutting age electronic equipment. It must help students when they start to work at hospital, coping with electronic equipment. On the other hand, they are required to obtain the skill of interactive communication with patients and coworkers. For this reason, the instructors have introduced the method of collaborative learning, which needs to divide students into teams with four members in each. It seems cumbersome to decide the members of teams, if instructors seek for effective learning, because they would be required to predict students’ behaviors by analyzing their data, for instance, individual traits and their needs. Hence, we have begun to support optimizing combination; however, there is no exact solution for it [23]. For those reasons, we have developed the support system or personalized education and learning. This has the measuring system to provide students’ data to instructors before starting classes.
As I have mentioned above, however, it has been becoming complicated to combine members of teams. Therefore, if AI doctor or machine would solve this problem by optimizing combination, personalized education and learning would be improved. To achieve this meaningful goal, we need to clarify information processing for interactive communication. This must have synergic effect on AI doctor, care assisting robots and so on, because they need to obtain the ability of interactive communication with people by machine learning.
From these viewpoints, this study and the measuring system for clarifying human information processing must be meaningful to achieve our goal.
We have planned to examine dual loop theory, which I have proposed as hypotheses and implemented experiments, gathered data, and analyzed them. Those ideas were hinted by Card’s Model Processor [18], which is a “cognitive model of the user to be employed by the designer in thinking about the human interaction with computer at the interface” and “the Recognize-Act Cycle of the Cognitive Processor,” from the view of LTM and STM as a simple reaction time. Although they have introduced this model, they have tried to propose another one (GOES: Goals, Operations, Methods, and Selections) for tasks which can be taken from the half-second level to the two-second level. Approximately, dual loop theory model (Figure 6) might be a combination of those two models and we can predict subjects’ behavior. Many of such models have been introduced; however, there might be a few to find out individual differences in human information processing.
The idea of this dual loop theory might be similar to the others, however, we seek for finding out individual differences which patters would indicate some types of trait concerning with cognitive behavior.
Although having said that, when the model is examined, we need to use previous studies as references. For instance, by comparing processing between sound voice and letters [24] and cycle reaction time which is proposed by Card [18], we have examined calibration of measuring instrument. From the results of analysis for response time by presenting sound voice have been shown the high level of the calibration from the viewpoints of reliability and the reproducibility (Figure 7, left), considering the high correlation coefficient with the number of words which means cycle of response time. On the other hand, in the letter presentation case, it was recognized reproducibility; however, its correlation coefficient with the number of words was not shown high.
From this result, it was predicted that individual differences clearly among students concerning the way of silent reading. Then, categorized types of trait (visual or auditory type) by strengthening of the correlation coefficient between response time and the number of words or duration of reading aloud. There are no differences between the two types of reaction time represented questionnaires by sound voice, but recognized significantly differences by letters (Figure 8 under Table 5). Students of Auditory type have needed time longer than those of Visual type from starting to silent reading to making decision (Figure 5). This means that the auditory type might tend to process a word and a sentence with phonologization, using LTM or NFC loop; conversely, the visual type tends to process directly encoding symbol using STM or PFC loop.
From these results of analyses, the hypotheses [a] and [b] have been proved, and next hypothesis [c] should be examined. It was predicted that depending on the context of sentences, we might process them with different ways, PFC or NFC Loop. One hundred twenty psychological questionnaires were used as a task for one session, but they consisted of mainly two types of contexts, emotion and non-emotion. From the previous studies, when the emotional context is processed, it is considered that we tend to use STM because the effect of emotion on hippocampal-dependent memory consolidation [25, 26]. Then, the categorization of types concerning contexts is performed, Eidetic [27] and Adjusting type, depending on the differentiation of correlation coefficients between emotional or non-emotional contexts (Table 2). In the case of adjusting type, the differentiation of response time was clear, and the average of response time to emotional contexts is significantly faster than non-emotional ones. This means that students of Adjusting type might change their strategies to read silently and make decision depending on contexts. In the case of emotional contexts, they might use STM or PFC; on the other hand, in non-emotional contexts, their correlation coefficient is higher and much longer time spent from starting silent reading to making decision [28, 29]. This means that they might read silently with phonologization of words, referring concepts of words meaning by sound voice with image schema. This information processing might help them to reflect on their comprehension is right or not, which is considered negative feedback control (NFC).
From these results, we have proved hypothesis (c); however, we would like to examine more details for this hypothesis.
Two teams were selected from the aspect of low stakes assessments (highest and lowest teams, assessing for ability of conceptual metaphor and collaboration), in order to examine more in detail from the aspect of individual differences (Table 7). It is easy to compare the improvement performances among students’ traits and records or between teams by parallel processing and analyses. The result of the comparison of the average scores between Eidetic and Adjusting types and between first and second semesters has been examined this parallel processing and analyses, which have shown matching with each other.
Moreover, the comparison of those examinations between results of scores and descriptions of students (Appendix 1 and 2) by parallel processing has shown their matching. From this viewpoint, whether those results are matching with the evaluation of personality, regarding the factors of lack of objectivity (O Factor) and lack of cooperativeness (Co Factor) among 12 factors (Appendix 3). Students of Adjusting type (SubB-1, SabB-4, and SubC-3) have taken low scores for both factors; in other words, they are evaluated as objectivity and cooperativeness are strong. On the other hand, students of eidetic type (SubB-3 and SubC2) have taken high score in both factors, comparing with the former students, which means they are subjective and a little bit uncooperative.
Consequently, we might be also able to predict their behavior from traits of information processing. Though the results of our experiments have been proven useful, they are complicated for us. In addition to it, instructors must be busy to prepare other instructions for students. From these reasons, AI machine or doctor which might be able to obtain machine learning is expected and prospected for matching members of team by optimizing combinations.
In this chapter, dual loop theory, which consisted of two kinds of feedback control, concerning with human information processing, was proposed (Figure 6) and examined by analyzing the results of experiments. The data were gathered students’ response time, using psychological questionnaires (Figures 4 and 5) and their records of performances in collaborative learning class and analyzed by the way of parallel distributed processing. The results were as follows:
The prototype experiments were conducted by representing counter-balanced by order. The results of analyzing the average of reaction time divided by number of words in a short sentence (Figure 7) in both sound voice and letters were not significantly different between the first and the second experiments. Therefore, it has been proved with reliability that the level of calibration was high enough to reproduce scientifically, regarding our measuring system.
The response time to questionnaires of sound voice presentation was strongly correlated to the number of words which consist of a short sentence of questionnaires. In presenting letters case, the average of correlation coefficients was weaker and dispersed than those of sound voice (Figure 7). From these results, it was supposed that there were individual differences during information processing while students were reading silently. Then, their response time was categorized by the strength of correlation coefficients with the number of words (Tables 1 and 2).
It was found out that the average of response time depending on types was different between each other. In the case of Auditory type, the average of response time was significantly longer than those of Visual type (Table 5 and Figure 8).
Next, when the sentences were divided into two categories, emotion and non-emotion, there were found different phenomena among students, regarding traits of information processing type (Figure 9). In the case of Adjusting type, the average of response time for emotional contexts was significantly faster than that of non-emotional contexts (Table 6).
Therefore, the average scores of students’ records were compared between Eidetic and Adjusting types. The result has shown the quantitative interaction between them (Figure 10).
Moreover, we have examined whether those individual differences are connected to other students’ performances (Table 7, Appendix 1 and 2), and then, checking the verification of the criteria which classified traits both of personality and cognitive features (Appendix 3).
Finally, we have discussed on hypotheses (2.3), from three aspects: meaning of clarifying human information processing, the examination of dual loop theory, and the relevance between individual differences and personality. In conclusion, the feature of Adjusting type has been shown their way of information processing by both positive and negative feedback controls, comparing the other type of students, depending on the context. In addition to this result, we have checked their performances, descriptions, and interviews practically.
Results of tests, the significant differentiation of reaction time between emotion and non-emotion context for Adjusting type.
Comparison scores between teams.
We need to examine this theory furthermore and optimize the combination of members in order to communicate interactively among students and instructors. Eventually, those results would help the modern style machine learning of artificial intelligent to predict human behavior depending on types and consequently improve their interactive communication with human beings.
In conclusion, dual loop theory would be expected to help us to understand the system of human information processing and predict our behavior according to its patterns. It would be also applicable widely to the machine learning system, for instance, AI doctor and assistive robots which requires the interactive communication with human.
The author is grateful to Dr. Kiyoko Tokunaga and participants for collaboration in our practical research.
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