High School mathematical and science background of incoming students by enrollment into MISTEM, OSTEM and NonSTEM. All values are rounded to the nearest decimal place.
\\n\\n
IntechOpen Book Series will also publish a program of research-driven Thematic Edited Volumes that focus on specific areas and allow for a more in-depth overview of a particular subject.
\\n\\nIntechOpen Book Series will be launching regularly to offer our authors and editors exciting opportunities to publish their research Open Access. We will begin by relaunching some of our existing Book Series in this innovative book format, and will expand in 2022 into rapidly growing research fields that are driving and advancing society.
\\n\\nLaunching 2021
\\n\\nArtificial Intelligence, ISSN 2633-1403
\\n\\nVeterinary Medicine and Science, ISSN 2632-0517
\\n\\nBiochemistry, ISSN 2632-0983
\\n\\nBiomedical Engineering, ISSN 2631-5343
\\n\\nInfectious Diseases, ISSN 2631-6188
\\n\\nPhysiology (Coming Soon)
\\n\\nDentistry (Coming Soon)
\\n\\nWe invite you to explore our IntechOpen Book Series, find the right publishing program for you and reach your desired audience in record time.
\\n\\nNote: Edited in October 2021
\\n"}]',published:!0,mainMedia:{caption:"",originalUrl:"/media/original/132"}},components:[{type:"htmlEditorComponent",content:'With the desire to make book publishing more relevant for the digital age and offer innovative Open Access publishing options, we are thrilled to announce the launch of our new publishing format: IntechOpen Book Series.
\n\nDesigned to cover fast-moving research fields in rapidly expanding areas, our Book Series feature a Topic structure allowing us to present the most relevant sub-disciplines. Book Series are headed by Series Editors, and a team of Topic Editors supported by international Editorial Board members. Topics are always open for submissions, with an Annual Volume published each calendar year.
\n\nAfter a robust peer-review process, accepted works are published quickly, thanks to Online First, ensuring research is made available to the scientific community without delay.
\n\nOur innovative Book Series format brings you:
\n\nIntechOpen Book Series will also publish a program of research-driven Thematic Edited Volumes that focus on specific areas and allow for a more in-depth overview of a particular subject.
\n\nIntechOpen Book Series will be launching regularly to offer our authors and editors exciting opportunities to publish their research Open Access. We will begin by relaunching some of our existing Book Series in this innovative book format, and will expand in 2022 into rapidly growing research fields that are driving and advancing society.
\n\nLaunching 2021
\n\nArtificial Intelligence, ISSN 2633-1403
\n\nVeterinary Medicine and Science, ISSN 2632-0517
\n\nBiochemistry, ISSN 2632-0983
\n\nBiomedical Engineering, ISSN 2631-5343
\n\nInfectious Diseases, ISSN 2631-6188
\n\nPhysiology (Coming Soon)
\n\nDentistry (Coming Soon)
\n\nWe invite you to explore our IntechOpen Book Series, find the right publishing program for you and reach your desired audience in record time.
\n\nNote: Edited in October 2021
\n'}],latestNews:[{slug:"webinar-introduction-to-open-science-wednesday-18-may-1-pm-cest-20220518",title:"Webinar: Introduction to Open Science | Wednesday 18 May, 1 PM CEST"},{slug:"step-in-the-right-direction-intechopen-launches-a-portfolio-of-open-science-journals-20220414",title:"Step in the Right Direction: IntechOpen Launches a Portfolio of Open Science Journals"},{slug:"let-s-meet-at-london-book-fair-5-7-april-2022-olympia-london-20220321",title:"Let’s meet at London Book Fair, 5-7 April 2022, Olympia London"},{slug:"50-books-published-as-part-of-intechopen-and-knowledge-unlatched-ku-collaboration-20220316",title:"50 Books published as part of IntechOpen and Knowledge Unlatched (KU) Collaboration"},{slug:"intechopen-joins-the-united-nations-sustainable-development-goals-publishers-compact-20221702",title:"IntechOpen joins the United Nations Sustainable Development Goals Publishers Compact"},{slug:"intechopen-signs-exclusive-representation-agreement-with-lsr-libros-servicios-y-representaciones-s-a-de-c-v-20211123",title:"IntechOpen Signs Exclusive Representation Agreement with LSR Libros Servicios y Representaciones S.A. de C.V"},{slug:"intechopen-expands-partnership-with-research4life-20211110",title:"IntechOpen Expands Partnership with Research4Life"},{slug:"introducing-intechopen-book-series-a-new-publishing-format-for-oa-books-20210915",title:"Introducing IntechOpen Book Series - A New Publishing Format for OA Books"}]},book:{item:{type:"book",id:"3673",leadTitle:null,fullTitle:"Advances in Risk Management",title:"Advances in Risk Management",subtitle:null,reviewType:"peer-reviewed",abstract:"Risk management is an important part of governance sciences and has applications in several domains ranging from enterprise risk management to environmental surveillance. The ideas and approaches described in the book deal with general aspects of risk management as well as the peculiarities arising from given application domains. With contributions from researchers and practitioners in different fields, Advances in Risk Management will provide you with valuable insights into the evolution of models, methodologies and technologies necessary for an effective implementation of risk management systems.",isbn:null,printIsbn:"978-953-307-138-1",pdfIsbn:"978-953-51-5946-9",doi:"10.5772/270",price:119,priceEur:129,priceUsd:155,slug:"advances-in-risk-management",numberOfPages:272,isOpenForSubmission:!1,isInWos:null,isInBkci:!1,hash:null,bookSignature:"Giancarlo Nota",publishedDate:"August 17th 2010",coverURL:"https://cdn.intechopen.com/books/images_new/3673.jpg",numberOfDownloads:35441,numberOfWosCitations:11,numberOfCrossrefCitations:1,numberOfCrossrefCitationsByBook:0,numberOfDimensionsCitations:4,numberOfDimensionsCitationsByBook:0,hasAltmetrics:0,numberOfTotalCitations:16,isAvailableForWebshopOrdering:!0,dateEndFirstStepPublish:null,dateEndSecondStepPublish:null,dateEndThirdStepPublish:null,dateEndFourthStepPublish:null,dateEndFifthStepPublish:null,currentStepOfPublishingProcess:1,indexedIn:"1,2,3,4,5,6,7",editedByType:"Edited by",kuFlag:!1,featuredMarkup:null,editors:[{id:"10232",title:"Prof.",name:"Giancarlo",middleName:null,surname:"Nota",slug:"giancarlo-nota",fullName:"Giancarlo Nota",profilePictureURL:"https://mts.intechopen.com/storage/users/10232/images/1961_n.jpg",biography:"Giancarlo Nota is an Associate Professor of Software Engineering at the Department of Computer Science, University of Salerno. \nSince 1982 he has been developing an intense scientific activity in the following fields: programming and specification languages, workflow management systems, project and risk management systems, as well as distributed knowledge management.\nHe was a chairman of various international workshops and is the author of many technical papers in refereed international journals and conferences.\nIn recent years, his research interest about methodological, organizational and technological issues has been directed toward pursuing innovation in the fields of Project and Risk Management in the context of Virtual Enterprises and Networks of Organizations. \nNota has been the project and risk manager of many business process reengineering projects. He is also involved in developing actions to promote process innovation and quality management especially in universities and local governments.",institutionString:null,position:null,outsideEditionCount:0,totalCites:0,totalAuthoredChapters:"1",totalChapterViews:"0",totalEditedBooks:"2",institution:null}],equalEditorOne:null,equalEditorTwo:null,equalEditorThree:null,coeditorOne:null,coeditorTwo:null,coeditorThree:null,coeditorFour:null,coeditorFive:null,topics:[{id:"807",title:"Safety Engineering",slug:"safety-engineering"}],chapters:[{id:"11535",title:"The Role of Standardization in Improving the Effectiveness of Integrated Risk Management",doi:"10.5772/9893",slug:"the-role-of-standardization-in-improving-the-effectiveness-of-integrated-risk-management",totalDownloads:4274,totalCrossrefCites:1,totalDimensionsCites:4,hasAltmetrics:0,abstract:null,signatures:"Carmen Nadia Ciocoiu and Razvan Catalin Dobrea",downloadPdfUrl:"/chapter/pdf-download/11535",previewPdfUrl:"/chapter/pdf-preview/11535",authors:[null],corrections:null},{id:"11552",title:"A Model for Process Oriented Risk Managenent",doi:"10.5772/intechopen.83880",slug:"a-model-for-process-oriented-risk-managenent",totalDownloads:3449,totalCrossrefCites:0,totalDimensionsCites:0,hasAltmetrics:1,abstract:null,signatures:"Giancarlo Nota and Maria Pia Di Gregorio",downloadPdfUrl:"/chapter/pdf-download/11552",previewPdfUrl:"/chapter/pdf-preview/11552",authors:[null],corrections:null},{id:"11553",title:"Quantitative Operational Risk Management",doi:"10.5772/intechopen.83881",slug:"quantitative-operational-risk-management",totalDownloads:3614,totalCrossrefCites:0,totalDimensionsCites:0,hasAltmetrics:0,abstract:null,signatures:"Aleksandra Brdar Turk",downloadPdfUrl:"/chapter/pdf-download/11553",previewPdfUrl:"/chapter/pdf-preview/11553",authors:[null],corrections:null},{id:"11554",title:"Trends, Problems and Outlook in Process Industry Risk Assessment and Aspects of Personal and Process Safety Management",doi:"10.5772/intechopen.83882",slug:"trends-problems-and-outlook-in-process-industry-risk-assessment-and-aspects-of-personal-and-process-",totalDownloads:3555,totalCrossrefCites:0,totalDimensionsCites:0,hasAltmetrics:0,abstract:null,signatures:"Bruno Fabiano and Hans Pasman",downloadPdfUrl:"/chapter/pdf-download/11554",previewPdfUrl:"/chapter/pdf-preview/11554",authors:[null],corrections:null},{id:"11555",title:"Managing Requirements Risks: A Value-Based Process",doi:"10.5772/9905",slug:"managing-requirements-risks-a-value-based-process",totalDownloads:2555,totalCrossrefCites:0,totalDimensionsCites:0,hasAltmetrics:0,abstract:"The technological, cost, people and schedule issues faced during software development, make it vulnerable for several types of risks. The requirements’ related risks are one of the most occurring risks. If remain unnoticed or unmanaged, the requirements related risks can cost a project greatly, financially and otherwise. It is extremely important to manage requirements related risks efficiently and effectively. Moreover, a project can never be successful if stakeholders do not get their “valued” things. Every requirement contributes towards some value for stakeholder(s). Therefore it is important to manage requirements risks to the satisfaction of stakeholders. Different stakeholders have different perception of risk, it is therefore necessary to have a process that not only manages requirements’ risks but it also fulfills the values of stakeholders as well. This chapter presents a Value-Based Requirements Risk Management (VRRM) process that is designed (Samad et al., 2008) to manage requirements related risks in a value based manner.",signatures:"Naveed Ikram, Mohammad Usman, Javeria Samad and Abdul Basit",downloadPdfUrl:"/chapter/pdf-download/11555",previewPdfUrl:"/chapter/pdf-preview/11555",authors:[null],corrections:null},{id:"11556",title:"Risk Management for Ag Families: An Outreach Education Model for Improving Family Business Success",doi:"10.5772/intechopen.83883",slug:"risk-management-for-ag-families-an-outreach-education-model-for-improving-family-business-success",totalDownloads:2022,totalCrossrefCites:0,totalDimensionsCites:0,hasAltmetrics:0,abstract:null,signatures:"Christopher Bastian, Amy Nagler, Randolph Weigel and John Hewlett",downloadPdfUrl:"/chapter/pdf-download/11556",previewPdfUrl:"/chapter/pdf-preview/11556",authors:[null],corrections:null},{id:"11557",title:"Improving Quality and Risk Management in Outpatient Surgery",doi:"10.5772/intechopen.83884",slug:"improving-quality-and-risk-management-in-outpatient-surgery",totalDownloads:2508,totalCrossrefCites:0,totalDimensionsCites:0,hasAltmetrics:0,abstract:null,signatures:"Hubert Le Hetet",downloadPdfUrl:"/chapter/pdf-download/11557",previewPdfUrl:"/chapter/pdf-preview/11557",authors:[null],corrections:null},{id:"11558",title:"Risk Management in Acute Pulmonary Embolism",doi:"10.5772/intechopen.83885",slug:"risk-management-in-acute-pulmonary-embolism",totalDownloads:2804,totalCrossrefCites:0,totalDimensionsCites:0,hasAltmetrics:0,abstract:null,signatures:"Luca Masotti and Roberto Cappelli",downloadPdfUrl:"/chapter/pdf-download/11558",previewPdfUrl:"/chapter/pdf-preview/11558",authors:[null],corrections:null},{id:"11559",title:"Multi-Level Geosimulation of Zoonosis Propagation: a Multi-Agent and Climate Sensitive Tool for Risk Management in Public Health",doi:"10.5772/9906",slug:"multi-level-geosimulation-of-zoonosis-propagation-a-multi-agent-and-climate-sensitive-tool-for-risk-",totalDownloads:2055,totalCrossrefCites:0,totalDimensionsCites:0,hasAltmetrics:0,abstract:null,signatures:"Mondher Bouden",downloadPdfUrl:"/chapter/pdf-download/11559",previewPdfUrl:"/chapter/pdf-preview/11559",authors:[null],corrections:null},{id:"11560",title:"Risk Management of Water Resources in a Changing Climate",doi:"10.5772/intechopen.83886",slug:"risk-management-of-water-resources-in-a-changing-climate",totalDownloads:1999,totalCrossrefCites:0,totalDimensionsCites:0,hasAltmetrics:0,abstract:"Fresh water is a critical resource for human survival. However, conflicting demands and threats on fresh water supplies constantly arise, jeopardizing the sustainability of these resources. Decisions made today regarding water resources have an impact not only on current water usage, but also on that of years to come. Thus, both surface and groundwater resources should be managed with sensitivity to present needs, as well as consideration for future threats. The risks concerning water resources are either natural risks that may be difficult or impossible to be controlled and prevented, or risks resulted from human actions. The risk management method is most commonly used in the planning and developing phases of complex industrialized projects. The current paper demonstrates that this methodology is applicable with some modifications to help manage projects, organizations, and even monetary risks of water resources. Traditionally, risk events are measured by two main criteria: impact and probability of occurrence. However, some of the risks associated with water resource management may contain high expected costs and be beyond the control of human society. Therefore, an additional criterion is proposed for the assessment of risks – the controllability. The introduction of this criterion adds a third dimension to the risk evaluation process. To explore the applicability of the risk management methodology and its modification, it is applied to the risk management of water resources in Israel. For this specific empirical setting, the addition of the controllability criterion alters the order of severity of some of the perceived risks. Keywords: Risk management, Water resources, Risk controllability",signatures:"Amnon Gonen and Naomi Zeitouni",downloadPdfUrl:"/chapter/pdf-download/11560",previewPdfUrl:"/chapter/pdf-preview/11560",authors:[null],corrections:null},{id:"11571",title:"Model for Geological Risk Management in the Building and Infrastructure Processes",doi:"10.5772/intechopen.83892",slug:"model-for-geological-risk-management-in-the-building-and-infrastructure-processes",totalDownloads:3761,totalCrossrefCites:0,totalDimensionsCites:0,hasAltmetrics:0,abstract:"The geologic risks management is a process that requires to follow the tendencies of the new models of technological innovation. Nowadays it becomes necessary to elaborate an specific model for the management of the geologic risks, that is adapted to the peculiarities of the current development of the building and infrastructure systems; and allow the use of the current tools as the GIS, Wombs, Analysis Cost Benefit, etc., for the organization and the control of the knowledge management and final quality of the executed works. To model with the processes management could be an alternative form before this task. Proposing in this occasion a variant to negotiate from this perspective the management of geologic risks in the building and infrastructure processes.Keywords: Model, risks, management, geological hazards, process management, buildings, infrastructures.",signatures:"Liber Galban",downloadPdfUrl:"/chapter/pdf-download/11571",previewPdfUrl:"/chapter/pdf-preview/11571",authors:[null],corrections:null},{id:"11580",title:"Transnational Collaboration in Natural Hazards and Risk Management in the Alpine Space - A Flexible Response Network",doi:"10.5772/intechopen.83897",slug:"transnational-collaboration-in-natural-hazards-and-risk-management-in-the-alpine-space-a-flexible-re",totalDownloads:2847,totalCrossrefCites:0,totalDimensionsCites:0,hasAltmetrics:0,abstract:null,signatures:"Andreas Paul Zischg",downloadPdfUrl:"/chapter/pdf-download/11580",previewPdfUrl:"/chapter/pdf-preview/11580",authors:[null],corrections:null}],productType:{id:"1",title:"Edited Volume",chapterContentType:"chapter",authoredCaption:"Edited by"},subseries:null,tags:null},relatedBooks:[{type:"book",id:"146",title:"Risk Management Trends",subtitle:null,isOpenForSubmission:!1,hash:"b787e4bce18617fb8b4778d72d78e25b",slug:"risk-management-trends",bookSignature:"Giancarlo Nota",coverURL:"https://cdn.intechopen.com/books/images_new/146.jpg",editedByType:"Edited by",editors:[{id:"10232",title:"Prof.",name:"Giancarlo",surname:"Nota",slug:"giancarlo-nota",fullName:"Giancarlo Nota"}],equalEditorOne:null,equalEditorTwo:null,equalEditorThree:null,productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"2271",title:"Reliability and Safety in Railway",subtitle:null,isOpenForSubmission:!1,hash:"dc878cf0bd0cccdb16386293622ba7de",slug:"reliability-and-safety-in-railway",bookSignature:"Xavier Perpinya",coverURL:"https://cdn.intechopen.com/books/images_new/2271.jpg",editedByType:"Edited by",editors:[{id:"111217",title:"Dr.",name:"Xavier",surname:"Perpiñà",slug:"xavier-perpina",fullName:"Xavier Perpiñà"}],equalEditorOne:null,equalEditorTwo:null,equalEditorThree:null,productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"1591",title:"Infrared Spectroscopy",subtitle:"Materials Science, Engineering and Technology",isOpenForSubmission:!1,hash:"99b4b7b71a8caeb693ed762b40b017f4",slug:"infrared-spectroscopy-materials-science-engineering-and-technology",bookSignature:"Theophile Theophanides",coverURL:"https://cdn.intechopen.com/books/images_new/1591.jpg",editedByType:"Edited by",editors:[{id:"37194",title:"Dr.",name:"Theophile",surname:"Theophanides",slug:"theophile-theophanides",fullName:"Theophile Theophanides"}],equalEditorOne:null,equalEditorTwo:null,equalEditorThree:null,productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"3161",title:"Frontiers in Guided Wave Optics and Optoelectronics",subtitle:null,isOpenForSubmission:!1,hash:"deb44e9c99f82bbce1083abea743146c",slug:"frontiers-in-guided-wave-optics-and-optoelectronics",bookSignature:"Bishnu Pal",coverURL:"https://cdn.intechopen.com/books/images_new/3161.jpg",editedByType:"Edited by",editors:[{id:"4782",title:"Prof.",name:"Bishnu",surname:"Pal",slug:"bishnu-pal",fullName:"Bishnu Pal"}],equalEditorOne:null,equalEditorTwo:null,equalEditorThree:null,productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"3092",title:"Anopheles mosquitoes",subtitle:"New insights into malaria vectors",isOpenForSubmission:!1,hash:"c9e622485316d5e296288bf24d2b0d64",slug:"anopheles-mosquitoes-new-insights-into-malaria-vectors",bookSignature:"Sylvie Manguin",coverURL:"https://cdn.intechopen.com/books/images_new/3092.jpg",editedByType:"Edited by",editors:[{id:"50017",title:"Prof.",name:"Sylvie",surname:"Manguin",slug:"sylvie-manguin",fullName:"Sylvie Manguin"}],equalEditorOne:null,equalEditorTwo:null,equalEditorThree:null,productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"371",title:"Abiotic Stress in Plants",subtitle:"Mechanisms and Adaptations",isOpenForSubmission:!1,hash:"588466f487e307619849d72389178a74",slug:"abiotic-stress-in-plants-mechanisms-and-adaptations",bookSignature:"Arun Shanker and B. 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Current biomass estimates are derived primarily from ground-based samples, as compiled and reported in inventories and ecosystem samples. By using remote sensing technologies, we are able to scale up the sample values and supply wall to wall mapping of biomass. Three separate remote sensing technologies are available today to measure ecosystem biomass: passive optical, radar, and lidar. There are many measurement methodologies that range from the application driven to the most technologically cutting-edge. The goal of this book is to address the newest developments in biomass measurements, sensor development, field measurements and modeling. The chapters in this book are separated into five main sections.",isbn:null,printIsbn:"978-953-51-0313-4",pdfIsbn:"978-953-51-6177-6",doi:"10.5772/696",price:139,priceEur:155,priceUsd:179,slug:"remote-sensing-of-biomass-principles-and-applications",numberOfPages:336,isOpenForSubmission:!1,isSalesforceBook:!1,isNomenclature:!1,hash:"c93637da5d1c8fcd07eda02777afab83",bookSignature:"Temilola Fatoyinbo",publishedDate:"March 28th 2012",coverURL:"https://cdn.intechopen.com/books/images_new/169.jpg",keywords:null,numberOfDownloads:38224,numberOfWosCitations:118,numberOfCrossrefCitations:55,numberOfDimensionsCitations:140,numberOfTotalCitations:313,isAvailableForWebshopOrdering:!0,dateEndFirstStepPublish:"October 12th 2010",dateEndSecondStepPublish:"November 9th 2010",dateEndThirdStepPublish:"March 16th 2011",dateEndFourthStepPublish:"April 15th 2011",dateEndFifthStepPublish:"June 14th 2011",dateConfirmationOfParticipation:null,remainingDaysToSecondStep:"12 years",secondStepPassed:!0,areRegistrationsClosed:!0,currentStepOfPublishingProcess:5,editedByType:"Edited by",kuFlag:!1,biosketch:null,coeditorOneBiosketch:null,coeditorTwoBiosketch:null,coeditorThreeBiosketch:null,coeditorFourBiosketch:null,coeditorFiveBiosketch:null,editors:[{id:"11875",title:"Dr.",name:"Lola",middleName:null,surname:"Fatoyinbo",slug:"lola-fatoyinbo",fullName:"Lola Fatoyinbo",profilePictureURL:"https://mts.intechopen.com/storage/users/11875/images/system/11875.jpg",biography:"Research Physical Scientist, Biospheric Sciences Laboratory, NASA GSFC \n\nDr. Lola Fatoyinbo studies forest ecology and ecosystem structure at the NASA Goddard Space Flight Center. Dr. Fatoyinbo’s current research focus is the fusion of optical, Synthetic Aperture Radar and lidar data to quantify forest structure, biomass, extent and degradation. Dr. Fatoyinbo has carried out extensive field and remote sensing research in tropical forest ecosystems of continental Africa, Madagascar and Latin America. She received her Bachelors in Biology in 2003 and her PhD in Environmental Sciences in 2008 from the University of Virginia. She then completed a NASA Postdoctoral Fellow within the Radar Science and Engineering Section at the Caltech - Jet Propulsion Laboratory, where her primary research focus was on using interferometric SAR data to quantify tropical forest extent, height and biomass through the development of radar-lidar fusion algorithms. Dr Fatoyinbo is now a research physical scientist at the NASA Goddard Space Flight Center in the Biospheric Sciences Laboratory.",institutionString:null,position:null,outsideEditionCount:0,totalCites:0,totalAuthoredChapters:"0",totalChapterViews:"0",totalEditedBooks:"1",institution:null}],coeditorOne:null,coeditorTwo:null,coeditorThree:null,coeditorFour:null,coeditorFive:null,topics:[{id:"793",title:"Sustainable Energy Engineering",slug:"sustainable-energy-engineering"}],chapters:[{id:"33849",title:"Lidar Remote Sensing for Biomass Assessment",slug:"lidar-remote-sensing-for-biomass-assessment",totalDownloads:3010,totalCrossrefCites:4,authors:[{id:"28463",title:"Dr.",name:"Jacqueline",surname:"Rosette",slug:"jacqueline-rosette",fullName:"Jacqueline Rosette"},{id:"39879",title:"Dr.",name:"Juan",surname:"Suárez",slug:"juan-suarez",fullName:"Juan Suárez"},{id:"39880",title:"Dr.",name:"Sietse",surname:"Los",slug:"sietse-los",fullName:"Sietse 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CNTs have highly unique electronic, mechanical, catalytic, adsorption, and transport properties, making them interesting for a variety of applications. It is very important for the use of CNTs to prepare CNTs with various constructs in large scale and ideal quality. Up to now, CNTs have been produced by various kinds of strategies, mainly including Arc discharge, laser ablation, and chemical vapor deposition (CVD). Compared with the other two methods, CVD is considered as the most promising method for easily scaled-up to batch-scale production duo to simplicity and economy. At present, a lot of modified CVD were studied and explored for preparing pure CNTs in large scale, especially single-walled CNTs (SWCNTs), such as plasma-enhanced CVD (PECVD) and thermal CVD. A vapor–liquid–solid (VLS) mechanism is generally accepted for CNTs growth, and catalyst plays an important role in the CVD synthesis of CNTs, which is needed to be removed in follow purity of CNTs for further use. To avoid the problem, some modified CVD methods without metal particles as catalyst were also carried out for producing relative pure CNTs.
CNTs possess large specific surface areas due to their high aspect ratio, while their structural integrity and chemical inertness support relatively high oxidation stability in comparison with graphite. For these reasons, CNTs are promising building blocks for hybrid materials, which could endow CNTs more excellent performances for widely potential fields. CNTs-based hybrid materials, including CNTs-inorganic species and CNTs-organic species, were synthesized by numerous diverse strategies, which can be classed roughly into two kinds: ex situ approach (attaching nanoparticles to CNTs) and in situ synthesis (Directly on the CNTs surface). CNTs/metal oxide heterostructures are an important kind of CNTs-inorganic composites. The synergistic effect of CNTs and metal oxide makes CNTs/metal oxide heterostructures possess the properties of the both and have more widely potential application. The electrochemical properties of the CNTs/metal oxide heterostructures have attracted researchers’ interest duo to their excellent performances on electrochemical catalyst, electrochemical sensor, supercapacitors and batteries. Over the past few years a great number of CNTs/metal oxide heterostructures, such as TiO2/CNTs, Co3O4/CNTs, Au/CNTs, Au/TiO2/CNTs, Co/CoO/Co3O4/CNTs (Fig. 1) [1] etc, were synthesized and their electrochemical properties were investigated as well.
Numerous synthetic methodologies have been developed for the preparation of CNTs and their composites. In this chapter, besides the preparation of nanocomposites based on CNTs, we also focus on investigating the electrochemical applications of CNT/metal-oxide or hydroxide composites.
a) and b) a large view and a detailed view on the Au/TiO2/CNTs nanocomposites prepared by the photo-assisted method, (c) a detailed view on the Au/TiO2/CNTs prepared by the self-assembly method, and (d) a detailed view on the TiO2/Co3O4/CNTs nanocomposite prepared by the self-assembly method. (Courtesy of J. Li et al. [
CNTs with tubular structures are made entirely of rolled-up layers of interconnected carbon atoms [2,3], and with diameters ranging from about one nanometer to tens of nanometers and lengths up to centimeters. CNTs can be open-ended or closed by a hemispherical fullerene-type cap, depending on their synthesis method [4]. CNTs have highly unique electronic, mechanical, catalytic, adsorption, and transport properties, making them interesting for a variety of applications [5-13]. The scale-up preparation of CNTs with high purity and homogeneity is essential for the use of CNTs in reality. In several past decades, a lot of effort was made to investigate the synthesis strategies of CNTs and modify constantly the promising methods. By far, various strategies, maily including Arc discharge, laser ablation, and various chemical vapor depositions (CVD), have been found to produce SWCNTs and MWCNTs with different merit and demerit. In this section, a brief discussion about the progress in preparation of CNTs is divided into two parts (synthesis of SWCNTs and synthesis of MWCNTs) and made.
SWCNT can be considered to be a unique molecule, with different physical properties, depending on their chirality, which determines that SWCNTs are primarily metallic or semiconducting. Although obtaining a single aliquot of one (
SWCNTs were first synthesized by the coevaporation of a cobalt catalyst and graphite in an electric arc [3,14]. In the early arc-generated nanotube material, the SWCNT fibers typically consisted of 7-14 bundled SWCNTs, with the individual tubes being 1.0-1.5 nm in diameter. The early arc-generated materials also contained relatively low weight percent densities of SWCNTs. An improvement in the nanotube yield was observed by changing the metal catalysts employed in the arc-technique [15]. Fig. 2a displays a transmission electron microscope (TEM) image of a typical arc SWCNT material generated with an iron catalyst [16]. Note that the nanotube bundles are relatively small (containing~3-5 nanotubes). SWCNTs were later produced at much higher yield by the method of laser vaporization [17]. Crystalline ropes micrometers in length containing 10-100 s of individual SWCNTs were easily obtained [18]. Fig. 2b shows a typical TEM image of crude laser-generated material produced with an Alexandrite laser operating at ~45 W/cm2, with Co and Ni at 0.6 % each and an external furnace temperature of 1200 °C [19]. Note the large, very long bundles of SWCNTs present in Fig. 2b. Although there is a high density of SWCNTs (30-40 wt %), non-nanotube carbon impurities, as well as metal catalyst particles, are still clearly visible in the TEM image. Laser-generated SWCNTs typically have low defect concentrations [20], making them easier to purify, since they are not as likely to be destroyed by the acids generally employed to remove metal catalyst particles. For example, Laser-genrated SWCNTs were purified with dilute HNO3 to remove the metal catalyst particles and then burned in air at 550 °C to remove the non-nanotube carbon [21]. However, the laser technique is often considered to be too expensive to be industrially scalable.
Consequently, numerous research groups have turned to the development of CVD as a potentially low-cost scalable technique for the production of SWCNTs. In 1996, SWCNT growth employing CVD on a supported catalyst was demonstrated as a promising route to carbon nanotube production [22]. Multiple reports quickly followed, further establishing CVD as a viable large-scale production process [23-32]. Typically, CVD materials contain more metal and often smaller and shorter SWCNT bundles than those produced in the laser processes. Fig. 2c shows a TEM image of commercially available CVD material produced by a high pressure carbon monoxide (HiPCO) process [29]. CVD production of isolated nanotubes has been achieved on oxidized silicon substrates using an iron catalyst [33]. Additionally, isolated SWCNTs have been generated in the gas phase by the technique of hot wire chemical vapor deposition (HWCVD) [34]. Fig. 2d shows high-resolution TEM images of isolated SWCNTs produced by this HWCVD process. For comparison, a high resolution TEM image of a bundle of purified laser-generated SWCNTs is also provided in Fig. 2e [9]. A multistep process for the purification of HiPCO CVD-generated nanotubes was reported in 2001 [35]. Recently, K. E. Hurst reported a simple“laser cleaning” method than can be employed to purify a host of CNT materials [36]. The unavoidable metal species remaining in the SWCNT products would result in obvious disadvantages for both intrinsic property characterization and application exploration of SWCNTs. Despite sustained efforts, it has been until now an intractable problem to remove metal catalysts completely from SWCNT samples without introducing defects and contaminations. Surprisingly, in 2009, Liu et al. proposed a simple and effective method for growing SWCNTs via a metal-catalyst-free CVD process on a sputtering deposited SiO2 film. Metal-free, pure, and dense SWCNTs were obtained [37]. The successful growth of SWCNTs using a nonmetal catalyst can provide valuable implications for understanding the growth mechanism of SWCNTs in-depth, which accordingly will facilitate the controllable synthesis and applications of carbon nanotubes.
TEM images of bulk SWCNT materials showing (a) typical arc-material generated with a Co/Ni catalyst mixture. (b) Alexandrite laser-generated crude material produced with ~45W/cm2 and a Co/Ni catalyst mixture at 1200 °C. (c) commercially available CVD material produced by the HiPCO process. (d) Isolated SWCNTs produced by a continuous HWCVD process where ferrocene is employed to supply the metal catalyst. (e) Bundle of purified laser-generated SWCNTs. (Courtesy of A. C. Dillon et al. [
MWCNTs were discovered by Iijima in 1991 while vaporizing carbon in an electric arc [2] and were then produced at higher yield by increasing the pressure of the helium gas atmosphere [38]. MWCNTs typically have inner diameters of ~3-20 nm and are then surrounded by concentric graphene sheets with an interstitial spacing between the sheets of ~3.4 Å. The number of concentric graphene sheets can range from 2 to ~100. High resolution TEM images of MWCNTs are provided in Fig. 3 [39]. In Fig. 3a the high-resolution TEM image clearly shows that the distance between layers of the MWCNTs measures ~3.4 Å. Fig. 3b depicts a MWCNT with ~20 concentric shells. MWCNTs have electronic properties similar to those of graphite and are thus semimetals. They are promising for multiple applications including strong composite materials, field emission displays, and adsorbents for gas separation or storage [40-47].
TEM images of MWCNTs (a) showing that the distance between layers of the MWCNTs measures ~0.34 nm and (b) depicting a MWCNT with ~15 concentric shells. (Courtesy of A. C. Dillon et al. [
Similar to SWCNTs, a continuous low-cost production method producing MWCNTs that are easily purified is required for MWCNTs to be incorporated in emerging technologies. In 1997 [48] and 1998 [49], ferrocene was utilized as a gas-phase catalyst in a CVD process for continuous MWCNT formation from methane at 1150 °C. However, the 1997 study reported materials containing more amorphous carbon than arc-generated MWCNTs, presumably due to a lower synthesis temperature than that achieved in the arc process [48]. In the 1998 study, the outer layers of the tubes were not graphitic [49], making them more difficult to purify. Later, ferrocene and ethylene were employed in CVD of MWCNTs between 650 and 950 °C [50]. Again, carbon impurities were observed at high-density. The authors concluded that further work was necessary to improve the nanotube microstructure and yield [50]. High-purity aligned, graphitic MWCNTs were synthesized via decomposition of a ferrocene-xylene mixture at ~675 °C. However, although the catalyst was supplied in the gas-phase, nucleation of tube growth occurred only for iron species deposited on a quartz substrate, resulting in a surface growth mechanism and limiting yields to available surface area [51]. CVD techniques employing benzene pyrolosis [52] and the decomposition of ethylene [53] and acetylene [54] on supported metal catalysts have been demonstrated as viable large-scale production methods. MWCNTs have also been grown on supported metal particles or films via CVD [55-59], plasma-enhanced CVD [60-70], hot-wire chemical vapor deposition [71,72], and plasma-enhanced HWCVD methods [73,74]. One HWCVD report employed evaporation of the Fe-Cr filament to supply a gas-phase catalyst, resulting in MWCNTs with a high density of structural defects and significant carbon impurities [75]. Although more research was deemed necessary, this method had potential for large-scale production, since it was not substrate dependent. In 2003, the first continuous high-density MWCNT formation with minimal non-nanotube carbon impurites was demonstrated with HWCVD employing methane as the carbon source and ferrocene as a gas-phase catalyst [39]. The metal catalyst impurities were simply removed via refluxing in dilute HNO3 [39]. Surprisingly, Multi-walled carbon nanotubes has been synthesized using acetylene as carbon sources with a metal-free mild chemical vapor deposition process by Du et al. [76]. The authors not only gave a simple and facile way to synthesize MWCNTs without metal but also provided valuable information for a deeper understanding of CNT formation in CVD.
The transition metal oxides are an important family of inorganic nanomaterials with abundant properties in optics, electronics, magnetics and catalysis. The property of metal oxide nanomaterials can be further tuned by varying their composition, structure and morphology [77].
Composite materials based on CNTs and metal oxide nanomaterials integrate the unique characters and functions of the two types of components and may also exhibit some new properties caused by the cooperative effects between the two kinds of materials [78–82]. Therefore, these composite materials have shown very attractive potential applications in many fields. This section summarizes the handling of CNTs and the preparation of CNT-based nanocomposites.
Generally, the carbon nanotubes as-prepared are grown as mixtures of carbon nanotubes and impurities such as amorphous carbon, metal catalyst particles and carbon nanoparticles. These impurities significantly influence the properties of CNTs and limit their applications. Consequently, development of economical purification methods has become an important issue to the development and practical applications of the CNTs. A commonly used purification approach consists of two steps. The thermal or acid oxidation treatment on raw CNTs can effectively remove the amorphous carbon, carbon nanoparticles and carbon layers coated on the metal particles [83,84]. The following acid refluxing treatment removes the residual naked metal-oxide particles [85]. This method is time consuming and has the disadvantage of low yield and damaging the nanotubes. Some extraordinary methods such as magnetic filtration, microwave irradiation, electrochemical oxidation, surfactant-assisted purification and C2H2F4 or SF6 treatment have shown higher efficiency in removing the contaminants in the CNT samples and are less damaging to the nanotube structure [86–89].
Due to the strong - stacking interactions between the neighboring CNTs, they tend to aggregate into bundles, making it very difficult for them to be dispersed in water and organic solvents. Their insolubility has become a great obstacle to the manipulation and application of CNTs. Dispersing nanotubes at the individual nanotube level is critical for a better performance of CNTs in most applications, especially the preparation of CNT-based composites. Therefore, many strategies have been explored to improve the solubility of CNTs in solvents. They can be classified mainly into two types, one is the sidewall covalent functionalization, and the other is the noncovalent modification using guest molecules as solubilizers. By sonication in mixtures of sulfuric and nitric acids or sulfuric acid and hydrogen peroxide, carbonyl and carboxylic groups can be introduced onto the nanotubes [90]. This is one of the most prevalently used covalent modification methods. The modified CNTs can be further functionalized by esterification or amidation reaction [91,92]. Other methods such as carbene cycloaddition, diazonium reaction and grafting of polymers have also been successfully used in the functionalization of CNTs [93,94]. The covalent methods convert the carbon atom hybridization from sp2 to sp3, leading to a disruption of electronic structure of CNTs. The noncovalent modification methods, such as polymer and DNA wrapping, - stacking interactions with aromatic molecules and surfactant-assisted dispersion, are based on van der Waals or - stacking between CNTs and solubilizer molecules [95–98]. The noncovalent strategy offers the advantage of remarkably improving the CNTs’ solubility without disrupting the electronic structure of the tubes. Therefore it is more attractive than the covalent method for the maintenance of pristine structure and properties of CNTs. SWCNTs can be directly dispersed in the imidazolium-based ionic liquids simply by mechanical milling. The concentration of SWCNTs can be as high as 1wt%, which is remarkably higher than that of conventional covalent and noncovalent approaches [99]. The prosperities of SWCNTs are well preserved, since only weak van der Waals force exists between SWCNTs and ionic liquids [100,101]. Therefore, imidazolium-based ionic liquids are superior solvents for the handling of CNTs [100,101].
The earliest attempt toward CNT-inorganic hybrids (in 1993) was the filling of MWCNTs with metal oxides (PbO [102] and Bi2O5 [103]). Because of their larger inner diameter (5-50 nm) compared with SWCNTs (1-1.5 nm), most efforts had been spent on filling MWCNTs, and it was not until five years later that Sloan et al. reported the filling of SWCNTs with RuCl3 [104]. Recently, a few of researchers reported that magnetic nanoparticles or nanowires, such as cobalt, iorn and corresponding oxides, filled carbon nanotubes by various technologies and their properties were investigated as well [105-107]. However, carbon nanotube is difficult to be filled fully without few impurities duo to capillarity of itself.
Initially, the motivation arose mainly from the prospect of forming encapsulated or (upon oxidation) freestanding inorganic nanowires with new crystal structures or novel properties. Although both MWCNTs and SWCNTs have been filled with a vast number of compounds, little is known about their properties and potential in applications. This has been frequently blamed on high impurity levels in the synthesized hybrids, the lack of bulk quantities, and the need for specifically designed measurement devices [108]. In general, the distinction should be made between the intrinsic properties of the filler, the altered properties of the encapsulated material due to the confined-space, and novel properties arising from interactions between the filler and CNTs.
In this ex situ or building block approach, metal oxide nanoparticles are attached to the CNTs via linking agents that utilize covalent, π-π Stacking, or electrostatic interactions. In this approach, either the metal oxide nanoparticles or the CNTs (or the both) require modification with functional groups. The type of functionalization and, thus, the strength of interaction determine the distribution and concentration of the metal oxide nanoparticles on the CNT surface.
Carboxyl groups on the surface of acid-treated CNTs are often used to attach amine-terminated or mercapto-terminated metal oxide nanoparticles via amide bonds [109]. This can be achieved either by directly linking amine-terminated or mercapto-terminated nanoparticles with the carboxyl groups or by modifying these carboxyl groups into thiol groups, which then anchor to colloidal nanoparticles. In a similar way, QDs have been linked by first stabilizing them with a mixed monolayer of trioctylphosphine oxide (TOPO) and 2-aminoethanethiol [110]. The resulting amino-functionalized QDs then reacted with the carboxylic groups of the acid-treated CNTs to form amide bonds.
Metal oxides can be attached to the carboxyl groups without any linking agent due to their hydrophilic nature, as recently demonstrated for MnO2 [111], MgO [112], and TiO2 [113]. However, the authors observed only weak interactions between the oxides and the acid-terminated CNTs, resulting in rather nonuniform distributions of the nanoparticles. Better adhesion was observed when capping agents were used. For instance, Sainsbury and Fitzmaurice produced capped TiO2 via a standard sol-gel process using titanium tetraisopropoxide (TTIP) as precursors with cetyltrimethyl ammoniumbromide (CTAB) as the capping agent [114]. MWCNTs were modified with 2-amino-ethylphosphoric acid and then mixed with the oxide nanoparticles. The authors showed that the phosphonic acid groups on the CNTs were well-distributed and provided an excellent driving force for the attachment of TiO2 nanoparticles (Fig. 4).
Example of an ex situ attachment of SiO2 and TiO2 nanoparticles to functionalized CNTs via silane and phosphonic acid bonds, respectively. (Courtesy of T. Sainsbury et al. [
This approach uses the moderately strong interactions between delocalized π-electrons of CNTs and those in aromatic organic compounds, such as derivatives of pyrene [115-117], porphyrins [118-120], phthalocyanines [121], or combinations thereof [122], as well as benzyl alcohol or triphenylphosphine (Fig. 5) [123]. These molecules are often modified with long alkyl chains that are terminated with thiol, amine, or acid groups, which can then connect to metal oxide nanoparticles and enable their attachment to pristine CNTs via π-π stacking. For example, Li et al. used pyrene derivatives with a carboxylic termination to anchor magnetic nanoparticles such as Co or Fe3O4 [124].
One of the major advantages of this approach is that the pyrene compounds remain strongly adsorbed on the CNT surface after workup steps (e.g., washing, filtration) and thus provide enhanced solubility and allow continuous redispersing of the modified CNTs in various aqueous and organic solvents. Furthermore, spectroscopic experiments on CNT/Pt [125] and CNT/porphyrin hybrids[118,126] revealed an enhanced charge transfer from inorganic nanoparticles to the CNTs, mediated by the aromatic compound. This was also observed for attached Co and Fe3O4 nanoparticles [124], whose magnetic and electronic properties were altered due to a strong electron transfer. Moreover, this effect is tunable by the length of the chain.
Examples of linking agents and ligands used to attach metal (oxide) nanoparticles to pristine CNTs via π-π interactions: pyrene derivatives. (Courtesy of V. Georgakilas et al. [
The approach utilizes electrostatic interactions between modified CNTs and metal oxide nanoparticles. Among the known examples, the deposition of ionic polyelectrolytes to attract charged nanoparticles is the most common route [127-131]. These polyelectrolytes typically bond covalently to the functional groups on the CNT, in contrast to polyethyleneimine (PEI), which interacts with CNTs via physisorption [132]. For instance, Sun et al. deposited Al2O3, ZrO2, and TiO2 nanoparticles on charged CNTs in a slightly modified way [133]. CNTs were pretreated in NH3 at 600 °C to induce a positive surface charge. The addition of PEI increased the positive charges even further and enabled a better dispersion. Commercially available α-Al2O3 and 3Y-TZP were then dispersed in poly(acrylic acid) (PAA), which provided a negative surface potential over a wide range of pH values. Upon mixing, the Al2O3 and ZrO2 nanoparticles formed strong electrostatic attractive interactions and covered the CNT surface completely.
Sun et al. attached nanocrystals of TiO2 to acid-treated SWCNTs, also using PEI as a modifier (Fig. 6) [134]. In the first step, TiCl4 was mixed with PEI, whose charged amino groups were protonated at a pH of 8. The positively charged amino groups of PEI accelerated the hydrolysis of TiCl4 into TiO2-nanoparticles and stabilized them electrosterically. These amine-terminated TiO2-nanoparticles with positive charge then attached to acid-treated SWCNTs, either via amide linkage or through electrostatic interaction. Another route has been suggested by Jerome et al. [135], who grafted MWCNTs with poly-2-vinylpyridine (P2VP) to form carboxylate terminated alkyl chains, onto which positively charged magnetic Fe3O4 nanoparticles were anchored. The main advantage of the polymer route is that the polymers provide a very dense, uniform distribution of either negative or positive charges over the entire CNT surface, which enables very dense assemblies of metal oxide nanoparticles.
Two-step process for the attachment of TiO2 nanoparticles to CNTs: (1) modification of the nanoparticles with positively charged PEI, containing primary, secondary, and tertiary amines, and (2) attachment via amide linkage or electrostatic interactions. (Courtesy of J. Sun et al. [
In summary, these few examples demonstrate the simplicity and feasibility of the ex situ approach, which remains the method of choice for the deposition of metal oxide nanoparticles. The main advantage is the possibility of using prepared nanoparticles with controlled morphology, structure, shape, and size, and, therefore, a good structure property relationship. The downside of this route, however, is the need to chemically modify either the CNTs or the metal oxide. This process is often work-intensive, and functionalization alters both the surface chemistry of the CNTs and also, particularly for SWCNTs, their physical properties. Furthermore, the use of predefined building blocks restricts the synthesis of novel hybrid materials and, thus, the development of new physical properties.
The metal oxides or hydroxides can also be formed directly on the surface of pristine or modified CNTs. The main advantage of this route is that the metal oxide or hydroxide can be deposited as a continuous amorphous or single-crystalline film with controlled thickness, or as discrete units in the form of nanoparticles, nanorods, or nanobeads. Furthermore, the CNTs may act as a support to stabilize uncommon or even novel crystal phases or prevent crystal growth during crystallization and phase-transformation processes. Finally, a variety of chemical and physical synthesis techniques can be applied. The deposition can be carried out either in solution via electrochemical reduction of metal salts, electro- or electroless deposition, sol-gel processing, and hydrothermal treatment with supercritical solvents, or from the gas phase using chemical deposition (CVD or ALD) or physical deposition (laser ablation, electron beam deposition, thermal evaporation, or sputtering).
Electrochemistry is a powerful technique for the deposition of various metal oxide nanoparticles, as it enables effective control over nucleation and growth [136,137]. Most research has been conducted on the deposition of metal oxides, as they are the metal oxides of choice for applications like heterogeneous electrocatalyst, sensors, supercapacitors and so on. In general, metal oxide nanoparticles are obtained via reduction of metal complexes by chemical agents (chemical reduction), or by electrons (electrodeposition). The size of the metal oxide nanoparticles and their coverage on the sidewalls of CNTs can be controlled by the concentration of the metal salt and various electrochemical deposition parameters, including nucleation potential and deposition time [138].
These techniques involve reactions, in which the reduction of the precursor is carried out with liquid or gaseous reducing agents with the aid of heat, light, ultrasound, microwave, or supercritical CO2 [139,140]. As an example, Sivvakkumar et al. deposited MnO2 via chemical reduction of KMnO4 [141]. The authors suspended the CNTs in Na-p-toluene sulfonate and pyrrole, which polymerized with the aid of ultrasonication. KMnO4 was then slowly added and reduced with acetonitrile to form hydrous MnO2. A very elegant variation of this process is shown in Fig. 7 and uses KMnO4 as both the oxidizer and reactant [142]. In detail, pristine MWCNTs were first oxidized under reflux with KMnO4, which introduced exclusively hydroxyl groups on the sidewalls of CNTs, in contrast to other oxidation treatments, e.g., with oxidizing acids. These hydroxyl groups then acted as anchors for Mn7+ ions, which subsequently were reduced to Mn4+ with citric acid to form a coating of -MnO2. For comparison, a pretreatment of the CNTs in strong acids, which induces the formation of carboxyl groups, resulted instead in γ-MnO2 [143]. Therefore, this work provides an interesting example of the effect of the CNT surface chemistry on the crystal structure of the metal oxide coating. In contrast to MnO2, the deposition of other metal oxides typically requires oxidizing rather than reducing processes. For instance, Huang et al. added acid-treated CNTs to a solution containing ammonium metavanadate [144]. The adsorbed VO2+ ions were then oxidized with oxalic acid to V2O5. A different approach combined the reduction of cationic precursors by hydrogen with a water-in-oil microemulsion technique for the deposition of metal oxide nanoparticles [145]. Sun et al. used a slightly modified process [146]. The CNTs was mixed first with an aqueous solution of sodium dodecylbenzene sulfonate (NaD-DBS) and then with a mixture of Triton-X and cyclohexane, which resulted in very small water droplets on the CNT surface. Upon adding zinc acetate as the metal precursor, the Zn2+ ions concentrated in the aqueous phase and then reacted with NH3 or LiOH to form spherical, hollow ZnOH nanoparticles. Subsequent calcinations oxidized them to create small and dense ZnO nanoparticles. In all cases, the microemulsion technique produced fine dispersions of small nanoparticles.
Example of the deposition of MnO2 on CNTs via chemical reduction. The multistep process involves (1) the oxidation of MWCNTs with KMnO4 to form hydroxyl groups, (2) the precipitation of permanganate ions, and (3) their reduction with citric acid to MnO2. (Courtesy of X. Xie et al. [
Many of the above-mentioned reduction/oxidation techniques are very time-consuming and as such allow impurities in the bath solution to be incorporated into the inorganic phase. In contrast, electrodeposited nanoparticles, exhibit higher purities as well as a good adhesion to the CNT surface [138].
In most cases, simple van der Waals interactions between the CNTs and the metal oxide nanoparticles seem to be sufficient to provide strong enough adhesion. Although most research currently concentrates on the electrodeposition of metal nanoparticles, there have also been a few reports on electrodeposited metal oxides. As an example, TiCl3 was used as a precursor and electrolyte and was kept at pH 2.5 with HCl/Na2CO3. The deposition was then carried out via galvanostatic oxidation with 1 mA/cm2 and resulted in a rather irregular and partial coating of a mixture of anatase and TiO2-B [147]. The galvanostatic approach (3 mA/cm2) has also been applied to codeposit Ni and Co oxides from their nitrates, with Pt and saturated calomel as the counter electrodes and reference electrodes, respectively [148]. This was also demonstrated by Kim et al., who produced a continuous 3 nm thick coating of RuO2 via the potential cycling method while varying the potential from 200 to 1000 mV with a scan rate of 50 mV/s [149]. They observed that the gas atmosphere during the postannealing process had a significant effect on the morphology of RuOx [150].
One advantage of this technique is that the electrodeposition occurs to the same extent on both the sidewalls of the tubes and the tips [151]. Consequently, the presence of carboxyl or hydroxyl groups as activators is not required. However, the major drawback of electrodeposition is that it is difficult to produce bulk quantities.
The sol-gel process is a versatile, solution-based process for producing various ceramic and glass materials in the form of nanoparticles, thin-film coatings, fibers, or aerogels and involves the transition of a liquid, colloidal “sol” into a solid “gel” phase [152]. The sol-gel process is a cheap and low-temperature technique that allows fine control of chemical composition and the introduction of lowest concentrations of finely dispersed dopants. One of the major drawbacks is that the product typically consists of an amorphous phase rather than defined crystals and, thus, requires crystallization and postannealing steps.
In general, the sol-gel process has emerged as the most common technique to synthesize CNT/metal oxide hybrids. Early attempts concentrated on the dispersion of CNTs within a matrix of metal oxide nanoparticles. Vincent et al. synthesized TiO2 nanoparticles using metal organic precursors and acetic acid as a gelator [153]. They observed that the dispersion of pristine CNTs was more stable when the TiO2 nanoparticles had been produced in the presence of the CNTs (in situ) compared with the simple mixing of the two materials. Upon reducing the amount of TiO2 with respect to CNTs, Jitianu et al. obtained a thin but rather irregular and partial coating of TiO2 on CNTs [154,155]. Typically, the thickness of the coating can be controlled by various parameters, such as the reaction time [156], the reaction composition, and the choice of metal precursor [157]. For instance, in the case of TiO2, the use of titanium tetraisopropoxide (TTIP) produced irregular coatings [154,158], while tetraethoxy orthotitanate (TEOT) [154] or tetrabutoxy orthotitanate (TBOT) [157,159] enabled a more uniform deposition. The sol-gel process was sometimes carried out under reflux [156], or with the aid of ultrasonication [160], microwave [161], or magnetic agitation [162], in order to enable faster and simultaneous nucleation resulting in a more homogeneous coating.
These early works used pristine CNTs, whose hydrophobic nature provides little attractive interaction with the metal oxide and thus limits the quality of the coating. Similar to the ex situ approach, the most common approach to change the surface chemistry of CNTs is to treat them in strong oxidizing acids (H2SO4/HNO3). This process introduces a variety of organic groups, with limited control over their number, type, and location, and causes surface etching and shortening of the tubes. Consequently, the metal oxide coatings on acid-treated CNTs were often nonuniform, although they provided better interaction in comparison with pristine CNTs. Despite these drawbacks, most researchers have used such acid-treated CNTs for various metal oxide coatings, including SnO2 [160,161,163-165], TiO2 [159,166-168], RuO2 [169,170], CeO2 [162], NiO [171], and mixed oxides [172].
In a similar way to the ex situ approach, noncovalent attractions and π-π interactions can be used to grow the metal oxide on the surface of CNTs. Cao et al. modified CNTs with surfactants such as sodium dodecylsulfate (SDS) [173]. The hydrophobic aliphatic chain interacted with the surface of the CNTs, while the hydrophilic end attracted the metal ions of the RuCl3 precursor, which then reacted to form RuO2. Recently, this author has developed a nondestructive, simple process to coat pristine CNTs with TiO2 by using benzyl alcohol as a surfactant [174]. Benzyl alcohol adsorbs onto the CNTs’ surface via π-π interactions with the alcohol’s benzene ring, while simultaneously providing hydrophilic hydroxyl groups for the hydrolysis of the titanium precursor (TBOT) (Fig. 8). In contrast to the sample without benzyl alcohol (Fig. 8A), the addition of small amounts of benzyl alcohol resulted in a very uniform coating that covered the whole CNT surface. The work further showed that benzyl alcohol strongly affected the phase transition from anatase to rutile, providing very small and uniform rutile nanocrystals with very high specific surface areas (60-100 m2/g) without too great a hindrance of the anatase to rutile transformation.
Top) Scheme of the beneficial role of benzyl alcohol in the in situ coating of pristine CNTs with TiO2. The benzene rings of the alcohol adsorb onto the CNT surface via π-π interactions and at the same time provide a high density of hydroxyl groups for the hydrolysis of the titanium precursor directly on the CNT surface. (Bottom) SEM images of pristine MWCNTs (A) without the use of benzyl alcohol and (B) with a titanium-to-benzyl alcohol molar ratio of 5. (Courtesy of D. Eder et al. [
In contrast to the ex situ approach, the use of electrostatic interactions for the in situ sol-gel route has been demonstrated for a few metal oxides. As an example, Hernadi et al. used CNTs that had been pretreated with SDS, dried, and redispersed in 2-propanol [175]. Using metal halides as precursors, the authors could successfully produce coatings of Al2O3 and TiO2. Vietmeyer et al. used acid-treated CNTs to attract Zn2+ ions (using zinc acetate), which then reacted with LiOH in an ice-water bath to form ZnO nanoparticles [176]. While some metal oxide nanoparticles were attached to the CNT surface and tips, most of the nanoparticles formed large clusters rather than a continuous coating. For comparison, Jiang et al. enhanced the negative charge on the surface of acid-treated CNTs by simply depositing a thin layer of SDS and carried out the same reaction with zinc acetate and LiOH [177]. This surface modification significantly enhanced the attractive interactions with the metal ions and led to a more uniform coating with ZnO crystals.
In recent years, many organic-inorganic hybrids have been produced by hydrothermal techniques [178]. In contrast to standard sol-gel routes, the hydrothermal method typically enables the formation of crystalline particles or films without the need for postannealing and calcinations. Furthermore, the forced crystallization enables the formation of metal oxide nanowires and nanorods [179].
In this simplest case of hydrothermal synthesis, pristine or acid-treated CNTs were added to an aqueous solution of the precursor and treated in an autoclave at temperatures between 100 and 240 °C to produce crystalline films of ZnO [180], TiO2 [181], or Fe2O3 [182]. These works consistently produced dense coatings of spherical or slightly elongated nanoparticles. Zhang et al. used aligned CNTs, which they precoated with a thin, amorphous layer of ZnO via magnetron sputtering [183]. Then they dissolved a fine ZnO powder in NaOH at a pH of 10-12, which provided a saturated solution of Zn(OH)42-. The modified CNT carpet was then placed top-down in an autoclave, floating on the precursor solution. By keeping the reaction at a temperature of 100 °C for several hours, the Zn precursor nucleated on the CNT-ZnO film to grow ZnO nanowires perpendicular to the CNTs, with a thickness of 30-70 nm and lengths of up to 0.5 µm (Fig. 9).
In general, specific molecules provide efficient control of the crystal size of the nanoparticles, which hinder the crystal growth by steric configuration (capping agents). For example, Yu et al. dissolved copper acetate in water and diethylene glycol and added acid-treated CNTs [184,185]. Upon heating in an autoclave at 180 °C, the diethylene glycol-capped copper species condensed to CuOx and nucleated to form small Cu2O crystals. After a reaction time of 2 h, the crystals were 5-10 nm in diameter and covered by an amorphous layer. Similarly, small crystals of Fe3O4 were produced by Jia et al., who used polyethylene glycol (PEG) and FeCl3 and a reaction temperature of 200 °C [186]. However, the 5 nm crystals agglomerated to about 180 nm aggregates (nanobeads), which attached preferably to the carboxyl groups on the surface of acid-treated CNTs. Consequently, the magnetite nanobeads were concentrated at the tips of the CNTs and acted as a glue to form heterojunctions between the CNTs (Fig. 10).
Example of the hydrothermal synthesis: growth of ZnO nanowires perpendicular to MWCNTs. (Courtesy of W. D. Zhang et al. [
Example of a polyol-assisted hydrothermal deposition of Fe3O4 on acid-treated MWCNTs. Polyethylene glycol (PEG) was used to reduce FeCl3 and to control the size of the magnetite nanoparticles, which formed large aggregates (nanobeads) near the carboxyl groups on the CNT surface. (Courtesy of B. P. Jia et al. [
A very important hydrothermal method involves the use of supercritical CO2 as an antisolvent that reduces the solvent strength of ethanol, resulting in the precipitation of the oxide due to high saturation. Using metal nitrates or halides, this method has been applied to deposit Eu2O3 [187], CeO2 [188], La2O3 [188], Al2O3 [188], SnO2 [189,190], and Fe2O3 [191] onto pristine CNTs. Sun et al. used supercritical ethylenediamine as a solvent to produce thin coatings of RuO2 [192]. They also observed various morphologies and structures of cerium oxide by simply changing the reaction temperature [188]. For instance, the authors could alter the composition of cerium oxide from preferentially Ce2O3 at 120 °C to CeO2 at 150 °C. In contrast, a reaction temperature of 120 °C was needed to obtain a coating of SnO2, while at 35 °C the oxide was only encapsulated [190].
Chemical and physical vapor deposition techniques are among the most common methods to produce metal oxide nanomaterials, as they provide excellent control over the size, shape, and uniformity of the oxide material. Furthermore, it is possible to deposit thin, continuous films on carbon substrates, without altering the 3D integrity of vertically aligned CNTs. This section provides examples of the synthesis of various CNT/metal oxide hybrids using physical techniques, such as evaporation, sputtering, and pulsed laser deposition (PLD), and chemical methods, including chemical vapor deposition (CVD) and atomic layer deposition (ALD).
Physical vapor deposition involves the evaporation of material in a crucible under high vacuum, using either resistive heating (thermal evaporation) or electron bombardment (electron beam deposition). In contrast, sputtering (magnetron and radio frequency, RF) relies on plasma (typically argon) to bombard the target material, which can be kept at a relatively low temperature. Reactive sputtering involves a small amount of oxygen, which reacts with the sputtered material to deposit oxides. The deposition of metal oxides via thermal evaporation has been demonstrated by Kim et al., who mixed annealed CNTs with Zn powder in a ratio of 1:12 [193]. Depending on the reaction temperature, the Zn particles reacted with oxygen impurities in argon to form a coating on the CNTs consisting either of spherical particles (450 °C), nanowires (800 °C), or short nanorods (900 °C). RF or magnetron sputtering has been used to deposit RuOx, [194] and ZnO [195]. Furthermore, Jin et al. have cosputtered Ba and Sr in an oxygen atmosphere to obtain a BaO/SrO coating [196]. In most of these works, the coating around the CNTs was generally conformal, although in the case of vertically aligned CNTs (“carpet”), the metal oxide material was deposited predominantly along the top of the carpet. Interestingly, Fang et al. observed that the distribution of RuO2 particles on vertically aligned CNTs was significantly better on nitrogen-doped CNTs compared with pure CNTs [197]. It is well-known that the incorporation of nitrogen into the sp2-type walls of CNTs causes many structural defects (pyridine-like bonding) due to the different valence of nitrogen ions. It seems that these defects are more reactive toward the adsorption of RuO2 than the graphitic walls of pure CNTs and allow a uniform dispersion along the sidewalls of nitrogen-doped CNTs (Fig. 11).
In summary, sputtered films typically have a better adhesion to the substrate than evaporated films and a composition closer to that of the source material. Sputtering also enables the deposition of materials with very high melting points and can be performed “top-down”, while evaporation must be operated “bottom-up”. On the other hand, evaporation techniques typically offer better structural and morphological control and more flexible deposition rates.
Scheme of the deposition of RuO2 nanoparticles on aligned CNTs (top) and nitrogen-doped CNTs (N-CNTs, bottom) via magnetron sputtering. The pyridine-like defects in the N-CNTs provide a stronger interaction with RuO2 and a more uniform dispersion along the sidewalls. (Courtesy of W. C. Fang et al. [
Pulsed laser deposition (PLD) is related to the evaporation techniques but utilizes a high-power pulsed laser beam. The directed laser pulse is absorbed by the target, vaporizes the material, and creates a plasma plume containing various energetic species, such as atoms, molecules, electrons, ions, clusters, particulates, and molten globules, which then expand into the vacuum and deposit on a typically hot substrate to nucleate and grow as a thin film. This process can occur in ultrahigh vacuum or in the presence of a background gas, such as oxygen, which is commonly used when depositing oxides. Ikuno et al. used individual MWCNTs (grown by arc discharge), which were attached to a molybdenum plate via electrophoresis. A pulsed Nd:YAG (yttrium aluminum garnet) laser with a wavelength of 355 nm was focused onto a target at a repetition rate of 10 Hz. A single-crystalline Si wafer and pellets of Zr, Hf, Al, ZnO, Au, and Ti were used as targets for the coating of SiOx, ZrOx, HfOx, AlOx, and ZnOx [198,199]. The advantages of this technique over other thin-film deposition methods include the relatively simple basic setup and the operation at room temperature. On the other hand, PLD has a lower average deposition rate than other deposition techniques, such as CVD or evaporation/sputtering techniques, but is faster than ALD.
Chemical vapor deposition (CVD) is a versatile technique often used in the semiconductor industry that involves the growth of a solid material from the gas phase via chemical reaction at the surface of a substrate. In contrast to high-pressure/high-temperature synthesis, the CVD technique typically operates at medium temperatures (600-800 °C) and at slightly reduced atmospheric pressures. Because CVD utilizes reagents of very high purity, the technique is capable of synthesizing crystals with controlled purity and composition. Other advantages of CVD include the high deposition rate, good degree of control (purity and composition), and easy scalability. However, due to the fast deposition, it is difficult to achieve uniform and defect-free coatings when scaling down to a few nanometers. Several groups have used CVD to synthesize CNT-metal oxide hybrids with SnO2 [200] and RuO2 [201]. Kuang et al. deposited acid-treated MWCNTs on a Si waver and heated them to 550 °C in a SnH4-containing N2 atmosphere [200]. At this temperature, the precursor decomposed, attached to the functional groups of the CNTs, and reacted with the oxygen impurities to produce SnO2. In a similar way, CNTs/RuO2 [150], CNTs/Co3O4 [202] and CNTs/ZnO [203-205] have been synthesized by directly coating of prepared CNTs. On the other hand, An example of the in situ growth of CNTs is the work of Liu et al., who used a water-assisted CVD process to produce CNTs with a ZnO coating [206]. Most of the ZnO coating was observed at the tip of the CNTs. An interesting alternative to ZnO was demonstrated by Ho et al. [207]. The authors used Ni catalyst particles, supported on MgO, which were deposited on a Si wafer and placed inside a tube furnace. ZnO powder was put inside a thin ceramic tube, whose ends were covered with Al foil, and placed inside the reactor tube, close to the catalyst powder. Upon reaching a certain temperature (300-350 °C), assisted via a plasma-enhanced CVD technique, CNTs began to grow on the Ni particles and kept growing as the temperature was steadily increased to 700 °C over a period of about 6-7min. At this temperature, the Al foil melted and exposed the ZnO powder, which was instantaneously transformed to Zn vapor via carbothermal reduction and deposited on the still-growing CNTs as a thin coating of ZnO.
In contrast to CVD, the precursors for atomic layer deposition (ALD) are kept separate and exposed sequentially. Ideally, each precursor forms a monolayer on the substrate, and the excess vapor is removed before the next precursor is introduced. This process is then repeated until the deposited film reaches the desired thickness. Hence, ALD film growth is self-limiting and based on surface reactions, which enables deposition control on the atomic scale. As an example, Gomathi et al. used metal chloride precursors to coat acid-treated MWCNTs with TiO2 and Al2O3 [208]. Similarly, Javey et al. demonstrated coatings of ZrO2 as thin as 8 nm covering the top of horizontally attached SWCNTs for device applications [209]. ALD has unique advantages over other thin-film deposition techniques, as it can be operated at low temperatures and allows exact control over the thickness of the deposited coating. However, because of the sequential exposure of the precursors, the technique has the lowest deposition rate compared with CVD and PLD.
The improved performances of CNT/metal oxide hybrids are demonstrated in many applications, such as photocatalysis, electrocatalysis, and environmental catalysis, gas sensors and chemical sensors, supercapacitors and batteries, and field emission devices. In the section, some applications of electrocatalysis, electrochemical sensors, supercapacitors and batteries are highlighted and summarized as follow.
Electrocatalyst is a new catalyst depending on electric energy. Which accelerate the reactions on the interface between electrode and electrolyte. It has significance in research work and also has important application value on organic wastewater treatment and degradation, electrolytic desulfurization, and electroreduction study. As an electrocatalyst, two basic conditions are necessary: (a) electrical conductivity and transfer free electron well; (b) efficient catalytic activity towards target substrate. Metal, especially noble metal, and some metal oxide or hydroxide are widely used as electrocatalysts [210]. In contrast, some common transition metal oxides or hydroxides, such as TiO2, SnO2, Ni(OH)2, have many advantages including low cost, facile preparation, and thus are more interesting for wide study. CNTs endows CNT/metal oxide or hydroxide composites enhancement on electrocatalysis duo to electric conductivity and fast electron transfer.
For example, Qiao et al. reported that the MWCNTs/Ni(OH)2–Ni electrode showed better catalytic effect on the oxidation of CH4 in 1.0 M NaOH than Ni(OH)2–Ni electrode [211]. ZnO-NWs/MWCNTs nanocomposite had been prepared via a hydrothermal process by Mo et al., and showed remarkable electrocatalytic activity towards H2O2 by comparing with bare MWCNTs [212]. Subseguently, Ma et al. discovered that ZnO-MWCNTs/Nafion film showed fast and excellent electrocatalytic activity to not only H2O2, also trichloroacetic acid [213]. Other metal oxide or hydroxide, such as FeOOH [214] and cobalt-coordinated polypyrrole [215], have been reported have obvious electrocatalysis towards oxygen reduction reaction. Recently, we synthesized CNTs/nickel glycolate polymer core-shell heterostructures and found that the composite have significant electrocatalysis towards glucose in 0.5 M NaOH, and catalytic activity dependent obviously on the thickeness of the nickel glycolate polymer on MWCNTs (Fig. 12).
The role of CNTs is often attributed to their ability to stabilize highly dispersed catalyst nanoclusters, resulting in higher specific surface areas. So, There is also important significant for fuel cell and biosensor to study further electrocatalysis of CNTs/metal oxide or hydroxide composites.
A) CVs of MWCNTs (a,b) and MWCNT/nickel glycolate polymer core-shell heterostructures in 0.5 M NaOH with absence of (a,c) and presence of (b,d) 1 mM glucose. B) The electrocatalysis activity difference of the nanocomposites with different thickness of glycolate polymer shell towards glucose.
Metal oxide semiconductors (MOS) are prominent examples of sensing materials in gas sensors, as their electrical properties are highly affected by the surrounding gas environment. For instance, tungsten trioxide (WO3) shows sensitivity to pollutants such as SO2, H2S, NO, and NH3 [216,217], while SnO2 is sensitive to NOx, CO, ethanol, and C2H4 [218]. However, their sensing properties often suffer degradation due to growth and aggregation. In contrast with metal oxide sensors, CNTs exhibit excellent adsorption properties due to their high specific surface area, which provides a large number of active surface sites [219-221]. As the CNTs’ electric properties are effectively altered by very small amounts of adsorbed gas molecules, the CNT gas sensor can be operated at temperatures close to room temperature. However, as MWCNTs are not very sensitive to ambient gas, the use of CNTs as gas sensors is mainly restricted to SWCNTs. Furthermore, due to their long recovery times, CNT-based sensors typically need reactivation.
CNTs/MOS hybrid composite sensors have been fabricated and investigated widely. These sensors don’t noly improve the efficiency of the metal oxide based sensors, such as enhanced sensitivity and selectivity to target gases, reduced response and recovery times, but also are operated at lowered temperature, even room temperature. Similar to core/shell heterostructures like MOS/CdS, CNT/MOS hybrids have shown improved photoluminescent quantum efficiencies and enhanced gas-sensing properties including reduced response and recovery times [163,222]. So far, CNT/SnO2 has been tested for detection of CO [172], NO2 [201,203], NH3 [223], formaldehyde [224], and ethanol [225]. Wei et al. used a sol-gel process to coat pristine SWCNTs with SnO2 and investigated the gas-sensing performance for NO2 at room temperature [165]. They observed considerably enhanced sensitivities (∆R/∆C, Fig. 13) compared with the pure SnO2 sensor. Because the morphology and surface area of the hybrid sensors were similar to those of the pure SnO2, and the observed sensitivities increased with increasing CNT loading, the authors concluded that the advanced sensing behavior originated from a common interface with CNTs. In contrast to conventional SnO2 sensors, which typically operate at temperatures between 200 and 500 °C, the SWCNT/SnO2 hybrid gas sensors could indeed be operated at room temperature. When the NO2 gas molecules adsorb on the surface of pure SnO2, they extract electrons, leaving the oxide surface positively charged. This leads to the formation of a depletion zone and to an increase in the sensor resistance. In the CNT/SnO2 hybrid sensor, the electric properties of the oxide are strongly enhanced by the highly conducting CNTs. Consequently, the sensor resistance is dominated by the Schottky barrier at the interface between the n-type SnO2 grains and the p-type CNTs, causing the formation of additional depletion layers, which then amplifies the increase in resistance upon NO2 adsorption and enables the operation of the gas sensor at room temperature.
As CNTs/metal oxide hybrid sensors emerges, some of which were investigated to applied in liquid sensing, especially biosensors including glucose sensor, hydrogen peroxide, hydrazine sensor and nitrite detection etc. The sensors have significant potential application in biology and medicine because of unavoidable drawbacks of enzyme sensor, such as temperature, PH, and unicity. More and more CNTs/metal oxide hybrids were reported to be used as various sensors in liquid.
For instance, Jiang et al. reported a highly sensitive nonenzymatic glucose sensor based on CuO nanoparticles-modified carbon nanotube electrode by sputtering deposition method [226]. The CuO/MWCNTs electrode exhibits an enhanced electrocatalytic property, low-working potential, high sensitivity, excellent selectivity, good stability, and fast amperometric sensing towards oxidation of glucose, thus is promising for the future development of nonenzymatic glucose sensors. Glucose was also detected with CNT/Fe3O4 hybrids electrochemically doped glucose oxidase reported by Liu et al. [227] The PA–Fe3O4–CNT-based glucose sensor shows much higher sensitivity and linearity than the PA–Fe3O4-based sensor, indicating that CNTs significantly enhance the performances of the biosensor. A novel hydrazine electrochemical sensor based on a carbon nanotube-wired ZnO nanoflower-modified electrode was reported via a simple process (ammonia-evaporation-induced synthetic method) by Fang et al. [228]. The authors observed that an optimized limit of detection of 0.18 M at a signal-to-noise ratio of 3 and with a fast response time (within 3 s).
Example of CNT-SnO2 hybrid in gas sensors. (a) Relative resistances versus NO2/air gas concentrations of SnO2 and hybrids with low (A) and high (B) concentrations of CNTs at room temperature. The sensitivities (∆R/∆C) increase considerably with increasing CNT concentration. (b) Scheme of the presence of depletion zones near the CNT oxide interface. (Courtesy of B.Y. Wei et al. [
Reliable and affordable electricity storage is a prerequisite for optimizing the integration of renewable energy systems. Energy storage, therefore, has a pivotal role to play in the effort to combine a future, sustainable energy supply with the standards of technical services and products. For both stationary and transport applications, energy storage is of growing importance as it enables the smoothing of transient and/or intermittent loads and the downsizing of base-load capacity with substantial potential for energy and cost savings. The extended lifetime of batteries in handheld devices is credited not only to higher energy densities but also to a simultaneous reduction of energy consumption of the portable devices. In contrast, the electric vehicle has failed to become the accepted mode of transportation mainly because of the battery. Short distances between recharging and a limited service life of the battery are to blame, but also the incredible weight and volume of the batteries. Electrochemical capacitors (ECs) are energy-storage devices that possess higher energy and power density than conventional dielectric capacitors and batteries and are used in applications including electric vehicles, noninterruptible power supplies, dc power systems, lightweight electronic fuses, memory backups, and solar batteries [229]. The challenges for these applications concern limitations in volumetric/gravimetric power densities and RC time, long life, safety, simplicity of design, cost, and the possibility of recharging, and can only be accomplished by specially designed materials.
According to the mechanism of energy storage, ECs can be categorized into two classes [229]: (a) electrochemical double layer capacitors (EDLC), based on double-layer capacitance due to charge separation at the electrode/electrolyte interface, which thereby need materials with high specific surface area (e.g., activated carbon, CNTs), and (b) pseudocapacitors or supercapacitors, based on the pseudocapacitance of faradaic processes in active electrode materials such as transition metal oxides and conducting polymers.
Because of their exceptional electronic properties, which allow ballistic transport of electrons over long nanotube lengths, CNTs have been considered a most promising candidate for electrochemical capacitors [230,231]. However, pure CNTs possess a rather low specific capacitance, typically about 10-40 F/g, which depends on the microtexture, purity, and electrolyte [231]. A considerable enhancement can be expected from the combination of CNTs with an electroactive material, which provides the additional pseudocapacitance while each tube acts as a minute electrode. the electrochemical stability of transition metal oxides makes them a better choice, provided they are highly conducting. A wide range of oxides has been investigated for use in CNT hybrids, including NiO [171,232,233], MnO2 [234-237], V2O5 [238,239], and RuO2 [169,192,194]. In all of these studies, the synergistic effects of CNT oxide hybrids have shown a considerable improvement of the otherwise poor electric properties and deficient charge transfer channels of the pure oxide.
Among these transition metal oxides, ruthenium oxides are regarded as the most promising electrode material for supercapacitor applications due to their high specific capacitance, highly reversible redox reactions, wide potential window, and very long cycle life [192]. Park et al. reported a specific capacitance for CNT/RuO2 (13 wt %) of 450 F/g at a potential scan rate of 20 mV s-1, which could be further enhanced, by using hydrous ruthenium oxide and functionalized CNTs, to 800 F/g [170]. Reducing the thickness of the RuO2 layer to 3 nm led to an even higher capacitance (1170F/g at a potential scan rate of 10 mV s-1) [149]. All of these studies also demonstrated longer lifetimes and greater stabilities and rate capabilities, while the specific capacitances were in general considerably higher than those observed for pure MWCNTs (<80 F/g) or pure RuO2 (<400 F/g).
In view of the rather high costs and environmental issues concerning RuO2, and despite the lower specific capacitances, there has been a clear trend toward the use of hydrous manganese oxides in the past few years. Xie et al. produced a CNT/MnO2 hybrid by in situ reduction of KMnO4 on MWCNTs with citric acid and observed that the specific capacitance increased from 18 to 190 F/g [140]. Zhang et al. electrodeposited MnO2 “nanoflowers” onto the surface of vertical CNT arrays and obtained capacitances of about 200 F/g, as well as excellent rate capabilities (50.8%, capacity retention at 77 A/g) and a long cycle life (3% capacity loss after 20 000 charge/discharge cycles) [236]. The authors proposed a model for the improved electrochemical storage characteristics based on high electronic conductivity in CNTs. In this model, the geometry of the MnO2 nanoflowers and their hierarchical localization at CNT junctions limits the diffusion length of ions within the MnO2 phase during the charge/discharge process and enhances the ionic conductivity of the hybrid.
To maximize the electrochemical utilization of the supercapacitor, it is desirable to (a) use a low loading of metal oxide and (b) increase the interfacial area between CNTs and metal oxide. Consequently, the preparation of a thin, uniform, connected coating on CNTs is expected to improve the specific capacitance significantly. This was confirmed by Nam et al., who observed increasing capacitances with decreasing NiO content and thus coating thickness [233].
A promising approach to enhance the capacitive performance has recently been reported by Sivakkumar et al., who synthesized a ternary hybrid of CNT/polypyrrole/hydrous MnO2, which showed a significantly higher specific capacitance (280 F/g) compared to the binary CNT/MnO2 (150F/g) [141]. The increase in capacitance is believed to be due to the better dispersion of hydrous MnO2 in the composite electrode. However, the presence of the polymer also accounts for the poor cyclability behavior. Fang et al. chose a different approach by depositing RuO2 on N-doped CNT [240], for which they measured considerably higher specific capacitances. The authors showed that the RuO2 particles were considerably better dispersed on N-doped CNTs than on pure CNTs, due to the presence of structural defects in N-CNTs. Consequently, the hybrids with N-doped CNTs had an enhanced interface, which resulted in their better performance as a supercapacitor (Fig. 14).
Capacitance voltage diagrams of hybrids with RuO2 using undoped and nitrogen-doped CNTs; (a) light (RF power 100 W), (b) moderate (RF power 140 W), and (c) heavy (RF power 180 W) loading of RuO2. (d) Capacitance of hybrids as a function of RF sputtering power. In contrast to the undoped CNTs, the N-doped CNTs are coated completely with RuO2, resulting in a larger interfacial area and an improved capacitance. (Courtesy of A. L. M. Reddy et al. [
A good cyclability is crucial to a successful application as an anode material for lithium-ion batteries, as demonstrated by Chen et al. for CNT/SnOx hybrids [164]. Pure CNTs typically show good cycle stability, but also a large and irreversible capacity loss in the first cycle. The authors showed that well-dispersed SnOx on CNTs enhanced the electrochemical performance significantly as the capacity fade of the hybrid electrode was strongly reduced compared to unsupported SnOx or pure CNT. The authors attributed the improvement in performance to the enhanced conductivity and ductility of the hybrid. This is in line with Zheng et al., who observed a 5-fold increase in electronic conductivity and thus enhanced cycling rate and stability in CNT/CuO hybrids compared to pure CuO.
In summary, electrochemical devices have been among the first applications for which CNT/metal oxide hybrids were tested. In the long run, CNT hybrids are expected to strongly enhance the batteries’ intrinsic capacities, stabilities, and lifetimes, as well as the charging/discharging characteristics. Furthermore, the substitution of part of the heavy oxide material (e.g., in car batteries) with the lightweight CNTs will significantly reduce the weight of the batteries.
As reviewed in the former publications, numerous synthetic methodologies have been developed for the preparation of CNTs and composites. In this chapter, besides the preparation of nanocomposites based on CNTs, we also focus on investigating the electrochemical applications CNT/metal oxide or hydroxide composites. There is a wealth of opportunities for nanoscale electrochemical devices based on carbon materials. Furthermore, when the electrode dimension approaches the double layer thickness, interesting and unusual kinetic and electrostatic effects occur. It is likely that carbon nanotubes electrodes will be key players in the investigation of such phenomena.
Financial support from Zhejiang Provincial Natural Science Foundation of China (Y4110304), Zhejiang Qianjiang Talent Project (2010R10025), the Educational Commission of Zhejiang Province of China (Z200909406) are gratefully acknowledged.
Gender disparities in STEM enrollment in college continue to receive a significant amount of attention [1]. Research investigating reasons for this disparity in enrollment by gender highlights three consistent themes: a) females who are less academically prepared in math and sciences than males are less likely to enroll in STEM; b) despite comparable academic preparation, females, on average, enroll in STEM majors in smaller percentages than males, and c) females’ participation varies by major, with some majors, such as biology seeing a higher proportion of females than in math intensive fields (MI STEM) such as engineering or computer science [2, 3, 4, 5]. Despite the abundance of research on this topic, there lacks a more detailed yet cohesive look at the interrelationships among high school academic preparation, ACT scores, and STEM enrollment.
This study utilized hierarchical agglomerative clustering to analyze high school and college data from a cohort (n = 3104) of students at a large, public research institution in the Midwest. Students entering this institution tend to arrive with a broad academic background and varying levels of readiness to pursue a STEM degree. Students receive guidance from professional academic advising staff and faculty about course enrollment and academic trajectories. Frequently these decisions are made by considering one or two individual data points such as a students’ GPA or ACT score, yet research in this area consistently demonstrates that it is a combination of several of these factors that more accurately represents students’ preparedness and ability to succeed [6, 7, 8]. By clustering, our data analysis will provide more nuanced insights than traditional statistical analyses. Traditional analyses tend to focus on averages and distributions of individual student characteristics across all students. Clustering algorithms seek to identify natural groupings (clusters) of students such that students within a cluster are academically more similar to each other regarding their pre-collegiate training than they are to students from any other cluster. Thus, clustering better accounts for the interrelationships of several factors and offers much more robust information than the more common approach to examining individual variables.
As it relates to our study, we were interested in the potential for this technique to provide a more in-depth understanding of female students’ enrollment in STEM based on several factors of academic preparation. This approach has been used to study gender inequality in the STEM workforce (see, for example, [9]); we apply a similar method to examine gender disparities in STEM choice.
We begin by investigating the potential of identifying unique groups of students using hierarchical agglomerative clustering. We then examine enrollment in STEM by gender and clusters. The following questions guide our analyses:
What are pre-collegiate grades, academic rank, and academic mathematical and science courses, of males and females in NonSTEM, math-intensive STEM (MISTEM), and other STEM (OSTEM)?
Can hierarchical agglomerative clustering meaningfully identify unique groups of students based on precollege student characteristics? If yes, do gender differences in academic background exist?
Given cluster membership, are there gender differences in enrollment into STEM, specifically into MISTEM and OSTEM majors?
The results of our study have implications for secondary and postsecondary education. A more robust understanding of the interrelationships among variables that contribute to enrollment in STEM areas can be used to develop strategies that can enhance STEM enrollment. Postsecondary institutions can gain a better and more sophisticated understanding of the academic readiness of a cohort of incoming students. This information can be used strategically by institutions and departments to tailor course offerings to the immediate academic needs of students allowing for possibly critical adjustments in the present time. This approach can also be used to better understand not only enrollment, but also retention and completion of STEM degrees. Further, using the clusters to measure the pursuit of STEM degrees, retention, and completion of STEM degrees permits a novel use of clusters as a statistical predictive model in STEM education. Our data analysis approach is innovative and differs from traditional statistical analyses by using hierarchical agglomerative clustering that allows students with similar characteristics to be grouped together. Through the use of clustering, this study more closely examines gender differences in academic preparation, STEM interest, and enrollment in STEM.
Females have been narrowing the gaps in math and science achievement and have seen more participation in STEM enrollment and careers [10]. However, the rate of participation is still drastically disproportionate, with only 27% of all STEM careers being occupied by women [11]. These statistics also vary based on major. For example, women represent over half of all bachelor’s degree recipients in biology but are significantly underrepresented in physical sciences, engineering, mathematics, and computer sciences (MISTEM) [12, 13, 14]. Even at selective institutions with a large pool of interested students willing to enroll, females represent between 15 and 28% for Bachelor’s degrees and only 13–20% for Ph.D.s in math departments [15]. We drew upon past literature to better understand differences in STEM and guide our selection of variables.
According to Weeden, Gelbgiser, and Morgan [16], between 19% and 32% of the gender gap for STEM degree completion can be attributed to the gender gap in STEM career interest in high school. Additionally, only 13% of female high school graduates expressed an intent to pursue a STEM career compared to 26% of their male counterparts [16]. This gap in interest as early as high school indicates that the STEM career gap is not solely caused by attrition in college or women exiting STEM careers post-graduation alone.
In high school, females consistently outperform males in their core classes, including math and science [17, 18, 19]. Despite earning high grades, females do not perform quite as well on high stakes standardized tests in math and science, scoring 0.7, 0.2, and 0.4 fewer points than males on the math, science, and STEM portions of the ACTs, respectively [20]. In spite of earning better grades in math and science, course selection among males and females shows some discrepancies. For example, female graduates took roughly the same number of advanced math courses as their male counterparts, experiencing an overrepresentation only in Algebra II. Science, however, shows more variations, with ten percent more females taking Advanced Biology, about six percent more females taking Chemistry, about 6 percent more males taking Physics, and males enrolling in engineering courses five times higher than females [21]. Furthermore, correlational research supports that participation in Advanced Placement (AP) STEM courses and STEM career interest are associated [22] and that students with high math abilities and exposure to rigorous courses were more likely to enroll in STEM majors [23].
GPA is positively correlated with the pursuit of a STEM major [24], and it is a better predictor for college success than the ACTs [25]. The rigor of math and science courses is a better predictor for enrollment in a STEM major than the number of courses alone [26]. Class rank, on the other hand, is more complex. When ranks are calculated by subject (i.e., math and reading) and communicated to students, ranking can have statistically significant effects on students’ career choices. For example, a study conducted in Ireland found that students ranked highly in math had a positive association with STEM career choice and a negative association with careers in the arts and social sciences, while those who were highly ranked in English had a positive association with arts and social sciences and a negative association with STEM careers [27]. A study performed in Florida found that high school class rank and GPA, which are higher for females, were the best predictors of collegiate GPA and the number of credits earned in college [28]. But, as detailed above, males and females experience similar pre-collegiate STEM preparation in many respects with small differences in math, science, and STEM test scores and some discrepancy in enrollment of advanced science courses. Despite these similarities, males are twice as likely to intend to declare a STEM major than females. A closer look at STEM enrollment is required.
In high school, females are less likely to be interested in STEM and more likely to lose interest over time [29]. Controlling for math achievement and aptitude, females are still less likely than males to be interested in STEM [30]. In fact, one of the best predictors for enrollment and persistence through a STEM major is an individual’s desire to pursue a STEM career in high school, with those expressing interest in high school completing degrees at three times the rate of those who do not express this interest in high school [31]. Among females who intend to major in a STEM field in college, nearly half of them switch majors to non-STEM fields compared with only a third of males [21].
That is not to say women are not enrolling in STEM majors; in fact, women earned 53% of STEM degrees (short of their 58% share of all degrees that would be proportional to their overall makeup of the workforce) [32]. However, there are significant disparities among the types of STEM degrees women choose to pursue. For example, women are overrepresented in health-related STEM careers with, 85% of Bachelor’s degrees being awarded to women, but they are awarded less than 45% in Mathematics and Physical Science and less than 25% in Engineering and Computer Science [32]. Because interest in STEM begins before students enroll in postsecondary education and gender gaps in STEM still persist, we consider new ways to understand reasons for these gaps based on high school preparation.
Data for this study were provided by the institution’s Office of the Registrar at one large public, research-intensive institution. To ensure our research conformed to standards and guidelines of ethical research practice, we received approval from the Institutional Review Board at the study’s institution. Per written agreement with the Office of the Registrar, all students enrolled in an introductory level mathematics or statistics course in the Spring 12, Fall 12, or Spring 13 semester were eligible for the parent study. Students were given the opportunity to opt out of the study, and of 16,401 eligible students, 32 chose to opt out. We focused on these courses as they often serve as gatekeeper courses to a STEM degree.
Because we were interested in the relationship between high school preparation and enrollment in a STEM major, we focused only on first-semester, degree-seeking students who entered the institution directly from high school and were enrolled in an introductory level math course. We excluded students who transferred from another post-secondary institution because we did not have access to pre-college data for many of these students. Students who were classified as non-degree-seeking or international were also removed prior to the analysis as these students are likely to differ in their academic background or degree goal. Of the initial 3219 eligible students, we had complete data on the variables of primary interest for 3104 students.
Using the STEM Designated Degree Program List (2012 revised list) provided by the Department of Homeland Security, we categorized students into STEM and NonSTEM majors. We further split STEM majors into math-intensive STEM (MISTEM) and Other STEM (OSTEM) majors. For the purpose of this study, a STEM major was considered math-intensive if it required at least one semester of science or engineering calculus. This definition is similar to the definitions used by Ceci and Williams [33] and by Bressoud [34]. This differentiation served distinct purposes: (a) the gender gap has historically been more pronounced in MISTEM majors such as engineering, computer science, or physics, whereas fields like biology or chemistry (OSTEM) have increasingly grown the proportion of women to the extent that women are now in the majority of degree earners [35]; (b) definitions of STEM vary greatly and can range from more inclusive by considering fields such as psychology, dietetics majors or kinesiology as STEM fields to less inclusive lists, which consider mainly engineering, mathematics, physics, natural sciences, and computer sciences. Distinguishing MISTEM majors define majors represented in most STEM field definitions.
Our analysis included the following variables: gender, major, high school rank (HS Rank), grade point average (GPA), number of high school credits earned in mathematics courses, including algebra, geometry, trigonometry, and calculus, and credits earned in biology, chemistry, and physics, ACT composite, ACT English, and ACT Math scores. The ACT is a national standardized test commonly used in college admissions decisions.
Using student demographic characteristics and pre-college academic background variables, we conducted an agglomerative hierarchical cluster analysis. “Cluster analysis is a data-mining technique that allows researchers to cluster a set of observations into similar (homogeneous) groupings based on a set of features” [36]. It accounts for the different high school experiences and preparation with which students enter their first year of college and can provide a more complex description of students than a comparison on a variable-by-variable basis. Clustering students reduces the focus on mean comparisons, which captures a population’s average behavior, but less on how students compare at the individual level. Although cluster analysis has been used in a variety of academic settings, its use to investigate female enrollment discrepancies in STEM vs. non-STEM fields is novel. Cluster analysis has been used to develop classroom observation tools [36], reveal different learner profiles based on motivation, achievement, needs satisfaction, etc. [37], and the differences between females and males who succeed within higher technical education [38]. Using similar methods as these studies, we will compare clusters of similar students to determine any trends among factors such as gender, preparation, and STEM enrollment.
All statistical analyses were run in SAS/STAT software, Version 9.4 of the SAS system and RStudio Version 1.3.1073. To address the first research question, we calculated the means and standard deviations of each of the variables used in the cluster analysis by type of major (NonSTEM, MISTEM, OSTEM) and by gender. To address the second research question, we utilized PROC Cluster and Ward’s minimum-variance method [39]. Ward’s minimum-variance method is based on the total error sum of squares that arises by grouping observations into distinct clusters where the total sum of squares corresponds to the sum of the within-cluster sum of squares [40]. Merging a set of observations into a cluster can be considered a loss of information. Ward’s method seeks to minimize the loss of information from merging any two clusters at a given step in the clustering algorithm. That is, the two clusters whose merging will lead to the smallest increase in the total error sum of squares will be combined into a new cluster [40]. Initially, each student represents a single cluster. At each step of the algorithm, two existing clusters are merged until only one cluster remains. The number of clusters is unknown prior to the analysis and an appropriate cluster solution is typically based on a set of clustering criteria such as the cubic clustering criterion (CCC), the Pseudo-F, and Pseudo T2 statistic [41, 42].
In order to see if the gender disparity in STEM enrollment is associated with gender or merely high school preparation, we clustered students according to their high school science, mathematics, and standardized test score data. Taking calculus in high school is a strong predictor of STEM interest and success [43]. Therefore, we separated students into two groups prior to clustering: students with calculus in high school (Calc group) and students without (NonCalc group). We then ran a separate cluster analysis based on high school rank, ACT English, ACT Math, and the sum of high school science credits in biology, physics, and chemistry. We decided on an initial number of clusters in each group based on the CCC, Pseudo-F, and Pseudo-T2 clustering criteria. To address the final research question, we examined the proportion of males and females in each cluster that chose a major in NonSTEM and STEM. We then limited our sample only to those who chose STEM and examined the proportion of males and females in each cluster who chose MISTEM or OSTEM.
We wish to acknowledge some methodological limitations. To be included in the sample, a student had to take a mathematics or statistics course during their first semester in college. We are therefore missing students who may have transferred credits into college or postponed taking a mathematics or statistics course in their first semester. Our sample also only included students who chose to major in STEM in their first semester. Additional research that examines students who may decide to major in STEM after their first semester would provide additional insights.
A cluster analysis using different variables would likely result in different clusters. For example, if we were to treat the numbers of biology credits, chemistry credits, and physics credits as separable variables rather than use their sum, clusters would likely form around differences between students with respect to the individual variables such as students with many biology credits versus students with few biology credits. We chose the sum of all science credits for two reasons. From a methodology point of view, the sum of science credits is more preferable as a variable because it has a greater range of values. Secondly, the choice of variables depends on the characteristics deemed meaningful to identify differences between students.
Additionally, we wish to acknowledge the limitations and ethical considerations of using quantitatively techniques to group students and subsequent interpretations of these efforts. Quantitatively analysis affords an opportunity to see patterns that may otherwise be unclear, yet this approach can also minimize nuances within clusters and overlook significant implications of variables that were not included. For example, our study focused on gender but did not consider variables such as socioeconomic status, nationality or race, or secondary school quality. Our results also should not be used to imply causality or judgment [44, 45]– we seek to understand possible associations between variables but cannot conclude that one set of patterns causes a specific outcome or that one is qualitatively better than others.
Across all fields, NonSTEM, MISTEM, and OSTEM, female students are equally prepared as men in the mathematics and sciences courses (see Table 1). Females have consistently higher high school ranks and GPA scores compared to their male peers, which is consistent with the results of the American Association of University Women Educational Foundation [17], Degol et al., [18], and Voyer and Voyer [19]. Females who enroll in MISTEM also score on average as well as their male peers and slightly outperform them on the English ACT placement test. Men enrolling in NonSTEM and OSTEM majors show a slight advantage on the Math ACT placement test, which is consistent with prior research [20].
Variable | NonSTEM NF=537, NM=542 | MISTEM NF=273, NM=1385 | OSTEM NF=216, NM=151 | |||
High School | Female | Male | Female | Male | Female | Male |
GPA | 3.62 (0.36) | 3.38 (0.42) | 3.81 (0.38) | 3.63 (0.4) | 3.65 (0.34) | 3.5 (0.4) |
Rank | 77 (14.89) | 68.1 (16.23) | 83.9 (14.36) | 76.6 (16.17) | 77.9 (14.46) | 72.3 (17.17) |
Calculus Cr | 0.4 (0.8) | 0.5 (0.91) | 1.3 (1.17) | 1.2 (1.18) | 0.6 (0.98) | 0.5 (0.89) |
Algebra Cr | 4 (0.26) | 4 (0.28) | 4 (0.35) | 4 (0.21) | 4 (0.26) | 4 (0.27) |
Geometry Cr | 2.6 (0.51) | 2.6 (0.5) | 2.7 (0.46) | 2.8 (0.45) | 2.6 (0.49) | 2.6 (0.51) |
Trigonometry Cr | 1 (0.68) | 1 (0.66) | 1.2 (0.57) | 1.2 (0.6) | 1 (0.63) | 1 (0.67) |
Statistics Cr | 0.3 (0.64) | 0.4 (0.78) | 0.4 (0.77) | 0.3 (0.69) | 0.3 (0.67) | 0.2 (0.57) |
Adv. Math Cr | 1.3 (1.05) | 1.2 (0.99) | 1.8 (0.95) | 1.7 (0.90) | 1.3 (1.31) | 1.3 (1.12) |
Physics Cr | 1 (1.03) | 1.3 (1.05) | 1.9 (1.09) | 2 (1.05) | 1.1 (0.99) | 1.5 (1.01) |
Biology Cr | 2.7 (1.07) | 2.5 (0.95) | 2.6 (1.19) | 2.4 (0.91) | 3.1 (1.36) | 2.8 (1.32) |
Chemistry Cr | 2.1 (0.63) | 2 (0.71) | 2.4 (1.03) | 2.3 (0.95) | 2.2 (0.69) | 2.1 (0.94) |
Science Cr | 5.8 (1.6) | 5.9 (1.47) | 6.9 (2.01) | 6.7 (1.66) | 6.4 (1.83) | 6.4 (1.92) |
ACT | ||||||
Math | 23.3 (3.71) | 24.3 (3.68) | 27.3 (4.27) | 28.1 (3.89) | 23.3 (4.1) | 24.8 (4.12) |
English | 24 (4.56) | 22.9 (4.28) | 26.7 (5.06) | 25.4 (4.6) | 23.6 (4.57) | 23.4 (4.8) |
Composite | 23.8 (3.48) | 24.1 (3.47) | 26.9 (4.01) | 26.9 (3.63) | 23.9 (3.77) | 24.6 (3.62) |
High School mathematical and science background of incoming students by enrollment into MISTEM, OSTEM and NonSTEM. All values are rounded to the nearest decimal place.
Based on the clustering criteria, four or five clusters were reasonable choices for students with and without calculus. To arrive at the most meaningful number of clusters for each group, we plotted each clustering variable using side-by-side boxplots (see Figure 1). Each boxplot shows the distribution of the variables for all students in the respective cluster, while the horizontal line inside the box represents the median value observed for these students. We based our decision on the final number of clusters for each group of students on what we considered to be meaningful differences in the distribution and median value for each cluster in the context of our research questions [46]. Due to the agglomerative nature of the clustering procedure, a solution consisting of four clusters arises from the merging of the two closest clusters in the solution consisting of five clusters while the remaining clusters remain unchanged. Thus, we will decide, for example, on four clusters if merging the two closest clusters in the five-cluster solution does not result in a sufficiently large loss of information but changing from four to three clusters would. In essence, we are looking for a solution that is both inclusive and parsimonious. For this reason, we included the three- and six-cluster solutions in the decision-making process. For simplicity, we discuss the different cluster solutions in terms of one cluster being split as opposed to two clusters being merged.
Finalized cluster solution.
We begin with the interpretation of the three-cluster solution and describe which of the three clusters is divided in the transition from three clusters to four.
For students with calculus (N = 1280), a three-cluster solution identifies three types of students. A first cluster consists of 627 students with noticeably lower HS ranks, ACT English and ACT Math scores compared to the students in the remaining two clusters. Among the latter, the more academically prepared students, two groups emerge; a cluster with students who have substantially more science credits and a slightly better but noticeable ACT Math score (N = 335) compared to the students in the second group (N = 318). Both groups have comparable ACT English scores and HS Ranks.
The four-cluster solution arises from splitting the lower performing group of 627 students into two distinct groups based on differences in ACT scores and HS Ranks. Students in both groups have the lowest HS ranks out of all calculus students but the first group (N = 213) performs substantially better on the ACT English and ACT Math exams than the second group (N = 414). We deemed this split meaningful. A plausible interpretation could be that the second group (N = 414) consists of students who tend to underperform on standardized tests for a variety of reasons. Alternatively, the smaller group of students (N = 213) excels on standardized exams relative to their overall high school performance as reflected by high school rank. Due to the hierarchical nature of the clustering algorithm, the other two clusters remained unchanged.
In a five-cluster solution, the group of 414 students is broken up into students with slightly higher ACT scores, noticeably more science credits, and better HS ranks (N = 99) compared to the second group (N = 315). Although this distinction might be relevant, it was not relevant for our research questions, and we decided against this additional split; both groups of students maintained their relative, below-average performance on the ACT exams. Consequently, we determined four clusters to be the most appropriate number of clusters for students with calculus. For this reason, we did not consider the six-cluster solution.
For students without calculus (N = 1284), a three-cluster solution distinguishes three groups. Students in group 1 (N = 663) have overall the lowest high school ranks but perform otherwise similar to students in a second group (N = 423) with respect to the number of science credits taken in high school and scores on the ACT exams. The third group (N = 738) outperforms the first and second group in the number of science credits and on the ACT components but shares HS ranks similar to those in the group of 423 students.
In a four-cluster solution, the third group of 738 students is divided into two separate groups. The first group (N = 603) includes the best students with respect to HS rank and ACT English and Math scores, but students in this cluster tend to have taken fewer science credits. The remaining students (N = 135) have the most science credits among all students without calculus. Their ACT scores and HS ranks are, however, much lower compared to the top students, and they also tend to have lower HS ranks compared to the second group (N = 423) in the three-cluster solution.
To move from four clusters to five the group of students with below average ACT scores and lower HS ranks (N = 663) are divided. This split reflects the same pattern we saw in the group of students with calculus. The 663 students are separated into a group of students (N = 258) who have overall the low high school ranks but who score better on the ACT English and Math placement tests and a second group, whose students have higher high school ranks but do not perform below average on the placement tests. Because we already saw a similar distinction among the calculus students, we consider the five-cluster solution meaningful and retain it over the four-cluster solution.
The six-cluster solution focused on the cluster previously consisting of the overall most prepared students (N = 603). These students are broken up into two clusters. The smaller of the two clusters retains the best students overall (N = 228), while students in the second cluster (N = 375) perform slightly worse than the top students, they still do better than the cluster of N = 423 students. Because both groups still outperform any of the remaining clusters overall, we decided against this additional split and retain the 5-cluster solution as the final number of clusters. A description of the finalized cluster solution is given in Table 2.
Cluster | Name | N | Cluster description |
1 | Calc. Strong, Less Science | 318 | Highest ACT English and HS Rank, Second highest ACT Math scores, below average (less than 6) science credits |
2 | Calc, Average | 414 | Typically, about average, showing slightly above average HS Rank and science credits but tend to fall short of average ACT English and Math scores |
3 | Calc, Strong Overall | 335 | Best students overall with all students having taken above average number of science credits |
4 | Calc, Low HS Rank | 213 | Students with far below average HS Ranks that have above average ACT English and Math scores |
5 | No Calc, Average | 423 | Above average HS Rank, very few science credits, below average ACT English and Math scores |
6 | No Calc, Low ACTs | 405 | About 50% of students have HS rank 1 standard deviation below average, lowest ACT English and Math scores |
7 | No Calc, Strong Overall | 603 | Strongest performers out of all non-calculus groups but students do not score as high as two top calculus clusters |
8 | No Calc, Low HS Rank | 258 | Lowest HS Rank, below average science credits but almost all students have ACT English and Math scores within 1 standard deviation below average |
9 | No Calc, Average, More Science | 135 | Worst performers on ACT English and Math, low HS Rank but take many science credits, credits comparable to the top cluster (Cluster 3) |
Description of clusters and respective sample sizes.
Our analysis revealed that we could use clustering to find meaningful differences in groups. We then examined the proportion of female students in each cluster. Out of 3104 students, 1026 are female representing 33% of the students in the sample. Assuming that there are no systematic differences in the academic background between females and males, we expect to see about 33% of each cluster to be female students. Figure 2 shows a visualization of the proportion of females in each cluster. In Cluster 7 (No Calculus, Low ACTs), the proportion is close to the target value of 33% with 34.3%; females are slightly below 33% in Clusters 1–3 (Calculus, Strong Less Science – Strong Overall). On the other hand, females are overrepresented in three out of the five non-calculus clusters and underrepresented in Clusters 4 (Calculus, low HS rank) and 8 (No Calculus, low HS rank). Although these students had lower HS ranks they still scored well on the ACT English and Math tests relative to students in clusters that proportionally contain more female students (Clusters 5, 6, and 9). Students in Cluster 8 (No Calculus, Strong Overall) are very similar to students in Clusters 1–3 when it comes to high school rank and performance on the ACT, except they did not have calculus in high school. The proportion of female students in a calculus cluster being average or below average shows that proportionally, fewer female students take calculus in high school (33%) than their male peers (45%).
Distribution of gender across clusters.
In the overall sample, 48% of females chose to major in STEM and 74% of males chose to major in STEM. Of those who majored in STEM, 90% of males and 56% of females enrolled in MISTEM.
Using the cluster solution identified in Table 2, we examine the proportion of female students by cluster. As mentioned previously, if differences in enrollment by gender are within natural variation, we can expect about one-third of the students to be female in each cluster.
Even though the proportion of females in Clusters 1–3 and 7 are similar and close to average (see Figure 2), the enrollment in STEM majors is strikingly different. For example, females with a calculus background are consistently more likely than females without a calculus background to choose a STEM major. This is evident when comparing Cluster 7 to Clusters 1–4: female students with no calculus background are less likely than male students to enter STEM.
Within the same cluster, thus with similar academic background, a smaller proportion of female students enroll in STEM than male students (see Figure 3). When we compare across gender and clusters, we see differences in this gap. For example, for Cluster 3, there is less than a 13% difference between males and females (94% vs. 81%); however, in Cluster 7, this gap increases to 31%.
Enrollment into STEM by gender.
The lower enrollment rate for females is especially evident for students in the NoCalc, Strong Overall cluster. We mentioned before that this NoCalc cluster is similar in background to the calculus clusters, the only difference being that the students did not have calculus in high school. Males in this cluster, however, enroll in STEM at a similar proportion to their female peers who did have calculus.
We then restricted ourselves to students enrolled in STEM in each cluster. Among those, Figure 4 shows that a higher proportion of male STEM students choose MISTEM than female STEM students. The trend is very apparent in the NoCalc clusters but is also present in the calculus clusters. Another way of saying this is that more female STEM students choose OSTEM than male students, especially the NoCalc Students. There are two interesting clusters to contrast: the NoCalc, Strong Overall and NoCalc, Average More Science. They have the same proportion of females enrolling into STEM; however, a much higher proportion of females in NoCalc, Strong Overall choose MISTEM than the NoCalc, Average More Science cluster.
Proportion of STEM students enrolled in MISTEM by gender.
Most male students in STEM, independently of their cluster membership, chose to go into MISTEM majors. Female students with a calculus background are more likely to go into MISTEM than female students without calculus in high school. The percentage of females in MISTEM is high for the NoCalc, Low ACT cluster, but because we limited ourselves to females who chose STEM within the cluster and the females are underrepresented in this cluster, we find an even smaller proportion of females chose STEM.
Despite significant efforts to minimize the gender gap in STEM, differences in interest and enrollment between men and women still exist. Ensuring a globally skilled workforce that meets the needs of the 21st century requires a post-secondary education in STEM fields, yet the interest to pursue STEM begins prior to enrolling in college. Therefore, efforts examining pre-collegiate preparation and STEM enrollment are critical.
We first examined the individual pre-collegiate variables of males and females. Similar to other research in this area [17, 18, 19], we found that overall, females and males have similar preparation although males are more likely to take calculus than females [16]. Examining these individual variables may lead to an assumption that these similarities in preparation are consistent across individuals and groups. However, by employing a more advanced statistical technique such as cluster analysis, we notice that when accounting for several pre-collegiate factors simultaneously uncovers (or reveals) marked differences in enrollment patterns within and across gender. For example, if we were to investigate just one variable, such as ACT scores, we get a different picture than when we combine variables such as standardized test scores with rank and/or GPA. For example, in our findings the combination of other factors such as enrollment in science courses, standardized test scores and high school rank results in variations in enrollment in STEM. Relatedly, the grouping of variables reveals differences in STEM participation more broadly and in the types of STEM, i.e., MISTEM and OSTEM.
Females consisted of 33% of our overall sample, yet, their representation in each cluster varies from 12% in Cluster 4 to 49% in Cluster 5 (Figure 2). Females never reached 33% in any of the calculus clusters. Enrollment in STEM and type of STEM (MISTEM, OSTEM) also varies by cluster. The results in Figure 3 add/continue the pattern of female underrepresentation in STEM. Although males and females follow overall a similar trend across clusters, female enrollment in STEM is consistently below male enrollment, confirming previous results that females chose STEM at lower rates than their male peers even when they are equally prepared [3]. The consistency across clusters also confirms that this holds true for all levels of preparedness in terms of academic high school background. Patterns for males and females, however, were not entirely parallel and equal. While Cluster 3 shows the highest, and Clusters 5 and 8 the lowest enrollment in STEM for both genders differences can be found in Clusters 6 and 7. For men there is little difference between Clusters 5 and 6 and larger differences between Clusters 6 and 7. For females, these percentages increase steadily from Cluster 5 to Cluster 7. Cluster 7 tells us that, despite being strong students, females who do not have Calculus in high school are much less likely to choose STEM than male students with the same background.
Further differences exist in enrollment by type of STEM. Figure 4 shows percentages for enrollment into MISTEM above 60% for both genders in all calculus clusters. But females in non-calculus clusters show much more variation. Although females in Clusters 5 and 8 are equally unlikely to enroll into STEM having the lowest enrollment rate overall, there is a clear difference in the rate at which females in Cluster 8 enroll into MISTEM compared to Cluster 5. Interestingly, females in Cluster 8 enroll into MISTEM in rates comparable to females in any of the calculus clusters. Female students in Clusters 5, 6 and 9 are more likely to enroll into OSTEM than MISTEM while 75% or more of male students choosing STEM still enroll into MISTEM in those clusters. Overall, male students in STEM overwhelmingly choose MISTEM ranging between 73% in Cluster 6 to 94% in Cluster 8.
Our analysis limits our ability to understand why these differences occur. However, past research may lend some insights. For example, females are more likely to possess both high verbal and high math skills, whereas males are more likely to possess solely high math skills [47]. Due to the discrepancy between math and verbal skills, males tend to choose STEM careers, whereas females, who have a choice between verbal and math-centric careers, tend to choose non-STEM-related fields, opting instead for challenging fields that are more applied and practical rather than theoretical [46]. Of course, there are also work and lifestyle factors to consider; women are looking for work-family balance and value it more highly than men [48]. In addition to lifestyle values, there are also differences between social and moral career preferences with women tending toward occupations with a social, community, or altruistic component and men tending toward careers that require working with objects [49].
Although our study cannot account for the reasons differences exist, the results have implications for research and practice. Our study illustrates a method that can be adopted by institutional leaders for use on their own campuses. Although this study was limited to one institution, we utilized commonly collected pre-collegiate data. Because of the availability of this data on most campuses, this study can be replicated in a variety of campus contexts. Institutions vary in their enrollment criteria and student populations; cluster analysis techniques afford the ability to select relevant variables and determine if unique groups emerge. Researchers have noted the importance of variables such as students’ race, ethnicity, and nationality and non-cognitive variables such as self-efficacy [50]. Future research could incorporate these additional variables.
Identifying these clusters is an important first step in a more comprehensive understanding of STEM interest and success. Once established, future efforts could examine the persistence and graduation rates of students in these clusters. The research on the relationship between individual measures of academic preparation and persistence and graduation in STEM has produced mixed results. Examining the combination of these measures through cluster analysis would lead to a more nuanced understanding of the role of academic preparation in STEM enrollment. For example, if there was a consistent relationship found between completing calculus and graduation in STEM, regardless of other factors, the availability and enrollment in calculus courses in high school should be encouraged.
Qualitative research methodologies could help address the questions of “why?” Individual interviews or focus groups with students in each cluster could be conducted to understand student choices in academic preparation or what aspects of their academic preparation contributed to their enrollment and success in STEM.
Minimizing the gender gap in STEM fields continues to be necessary to meet the needs of the global workforce. Academic preparation prior to enrolling in a post-secondary institution influences students’ intent to pursue STEM; yet research efforts that investigate this relationship often are limited by focusing on individual variables. Our study uses an advanced statistical technique - hierarchical agglomerative clustering - that considers multiple factors simultaneously. This technique groups students into distinct categories based on a combination of academic preparation measures and by doing so, paints a different picture of the relationship between academic preparation and STEM enrollment than simply examining individual variables. Subsequently, these inconsistencies reaffirm that narrowing the gap requires a multi-faceted approach that consider academic preparation and non-cognitive factors. In addition to its research significance, there are valuable practical implications from this work. We demonstrate how this technique can be applied to institutional data; thus, we provide a valuable tool that can be utilized in postsecondary institutions for postsecondary leaders to utilize in understanding enrollment patterns within their institutions. Summarily, our study contributes to both research and practice through its use of a robust yet accessible technique that can be widely applied to quantitative data to uncover unique patterns largely overlooked by other approaches.
This material is based upon work supported by the National Science Foundation under Grant No. HRD 1036791.
The authors declare no conflict of interest.
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The performance of these filters will be compared exploitation the applied mathematics parameter Peak Signal-to-Noise Ratio (PSNR).",book:{id:"6144",slug:"high-resolution-neuroimaging-basic-physical-principles-and-clinical-applications",title:"High-Resolution Neuroimaging",fullTitle:"High-Resolution Neuroimaging - Basic Physical Principles and Clinical Applications"},signatures:"Hanafy M. Ali",authors:[{id:"213318",title:"Dr.",name:"Hanafy",middleName:"M.",surname:"Ali",slug:"hanafy-ali",fullName:"Hanafy Ali"}]},{id:"46296",doi:"10.5772/57398",title:"Physiological Role of Amyloid Beta in Neural Cells: The Cellular Trophic Activity",slug:"physiological-role-of-amyloid-beta-in-neural-cells-the-cellular-trophic-activity",totalDownloads:5907,totalCrossrefCites:19,totalDimensionsCites:32,abstract:null,book:{id:"3846",slug:"neurochemistry",title:"Neurochemistry",fullTitle:"Neurochemistry"},signatures:"M. del C. Cárdenas-Aguayo, M. del C. Silva-Lucero, M. Cortes-Ortiz,\nB. Jiménez-Ramos, L. 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Luna-Muñoz and M.A.\nMeraz-Ríos",authors:[{id:"42225",title:"Dr.",name:"Jose",middleName:null,surname:"Luna-Muñoz",slug:"jose-luna-munoz",fullName:"Jose Luna-Muñoz"},{id:"114746",title:"Dr.",name:"Marco",middleName:null,surname:"Meraz-Ríos",slug:"marco-meraz-rios",fullName:"Marco Meraz-Ríos"},{id:"169616",title:"Dr.",name:"Maria del Carmen",middleName:null,surname:"Cardenas-Aguayo",slug:"maria-del-carmen-cardenas-aguayo",fullName:"Maria del Carmen Cardenas-Aguayo"},{id:"169857",title:"Dr.",name:"Maria del Carmen",middleName:null,surname:"Silva-Lucero",slug:"maria-del-carmen-silva-lucero",fullName:"Maria del Carmen Silva-Lucero"},{id:"169858",title:"Dr.",name:"Maribel",middleName:null,surname:"Cortes-Ortiz",slug:"maribel-cortes-ortiz",fullName:"Maribel Cortes-Ortiz"},{id:"169859",title:"Dr.",name:"Berenice",middleName:null,surname:"Jimenez-Ramos",slug:"berenice-jimenez-ramos",fullName:"Berenice Jimenez-Ramos"},{id:"169860",title:"Dr.",name:"Laura",middleName:null,surname:"Gomez-Virgilio",slug:"laura-gomez-virgilio",fullName:"Laura Gomez-Virgilio"},{id:"169861",title:"Dr.",name:"Gerardo",middleName:null,surname:"Ramirez-Rodriguez",slug:"gerardo-ramirez-rodriguez",fullName:"Gerardo Ramirez-Rodriguez"},{id:"169862",title:"Dr.",name:"Eduardo",middleName:null,surname:"Vera-Arroyo",slug:"eduardo-vera-arroyo",fullName:"Eduardo Vera-Arroyo"},{id:"169863",title:"Dr.",name:"Rosana Sofia",middleName:null,surname:"Fiorentino-Perez",slug:"rosana-sofia-fiorentino-perez",fullName:"Rosana Sofia Fiorentino-Perez"},{id:"169864",title:"Dr.",name:"Ubaldo",middleName:null,surname:"Garcia",slug:"ubaldo-garcia",fullName:"Ubaldo Garcia"}]},{id:"41589",doi:"10.5772/50323",title:"The Role of the Amygdala in Anxiety Disorders",slug:"the-role-of-the-amygdala-in-anxiety-disorders",totalDownloads:9707,totalCrossrefCites:4,totalDimensionsCites:28,abstract:null,book:{id:"2599",slug:"the-amygdala-a-discrete-multitasking-manager",title:"The Amygdala",fullTitle:"The Amygdala - A Discrete Multitasking Manager"},signatures:"Gina L. Forster, Andrew M. Novick, Jamie L. Scholl and Michael J. Watt",authors:[{id:"145620",title:"Dr.",name:"Gina",middleName:null,surname:"Forster",slug:"gina-forster",fullName:"Gina Forster"},{id:"146553",title:"BSc.",name:"Andrew",middleName:null,surname:"Novick",slug:"andrew-novick",fullName:"Andrew Novick"},{id:"146554",title:"MSc.",name:"Jamie",middleName:null,surname:"Scholl",slug:"jamie-scholl",fullName:"Jamie Scholl"},{id:"146555",title:"Dr.",name:"Michael",middleName:null,surname:"Watt",slug:"michael-watt",fullName:"Michael Watt"}]},{id:"26258",doi:"10.5772/28300",title:"Excitotoxicity and Oxidative Stress in Acute Ischemic Stroke",slug:"excitotoxicity-and-oxidative-stress-in-acute-ischemic-stroke",totalDownloads:7183,totalCrossrefCites:6,totalDimensionsCites:25,abstract:null,book:{id:"931",slug:"acute-ischemic-stroke",title:"Acute Ischemic Stroke",fullTitle:"Acute Ischemic Stroke"},signatures:"Ramón Rama Bretón and Julio César García Rodríguez",authors:[{id:"73430",title:"Prof.",name:"Ramon",middleName:null,surname:"Rama",slug:"ramon-rama",fullName:"Ramon Rama"},{id:"124643",title:"Prof.",name:"Julio Cesar",middleName:null,surname:"García",slug:"julio-cesar-garcia",fullName:"Julio Cesar García"}]},{id:"62072",doi:"10.5772/intechopen.78695",title:"Brain-Computer Interface and Motor Imagery Training: The Role of Visual Feedback and Embodiment",slug:"brain-computer-interface-and-motor-imagery-training-the-role-of-visual-feedback-and-embodiment",totalDownloads:1455,totalCrossrefCites:13,totalDimensionsCites:24,abstract:"Controlling a brain-computer interface (BCI) is a difficult task that requires extensive training. Particularly in the case of motor imagery BCIs, users may need several training sessions before they learn how to generate desired brain activity and reach an acceptable performance. A typical training protocol for such BCIs includes execution of a motor imagery task by the user, followed by presentation of an extending bar or a moving object on a computer screen. In this chapter, we discuss the importance of a visual feedback that resembles human actions, the effect of human factors such as confidence and motivation, and the role of embodiment in the learning process of a motor imagery task. Our results from a series of experiments in which users BCI-operated a humanlike android robot confirm that realistic visual feedback can induce a sense of embodiment, which promotes a significant learning of the motor imagery task in a short amount of time. We review the impact of humanlike visual feedback in optimized modulation of brain activity by the BCI users.",book:{id:"6610",slug:"evolving-bci-therapy-engaging-brain-state-dynamics",title:"Evolving BCI Therapy",fullTitle:"Evolving BCI Therapy - Engaging Brain State Dynamics"},signatures:"Maryam Alimardani, Shuichi Nishio and Hiroshi Ishiguro",authors:[{id:"11981",title:"Prof.",name:"Hiroshi",middleName:null,surname:"Ishiguro",slug:"hiroshi-ishiguro",fullName:"Hiroshi Ishiguro"},{id:"231131",title:"Dr.",name:"Maryam",middleName:null,surname:"Alimardani",slug:"maryam-alimardani",fullName:"Maryam Alimardani"},{id:"231134",title:"Dr.",name:"Shuichi",middleName:null,surname:"Nishio",slug:"shuichi-nishio",fullName:"Shuichi Nishio"}]}],mostDownloadedChaptersLast30Days:[{id:"29764",title:"Underlying Causes of Paresthesia",slug:"underlying-causes-of-paresthesia",totalDownloads:192987,totalCrossrefCites:3,totalDimensionsCites:7,abstract:null,book:{id:"1069",slug:"paresthesia",title:"Paresthesia",fullTitle:"Paresthesia"},signatures:"Mahdi Sharif-Alhoseini, Vafa Rahimi-Movaghar and Alexander R. Vaccaro",authors:[{id:"91165",title:"Prof.",name:"Vafa",middleName:null,surname:"Rahimi-Movaghar",slug:"vafa-rahimi-movaghar",fullName:"Vafa Rahimi-Movaghar"}]},{id:"63258",title:"Anatomy and Function of the Hypothalamus",slug:"anatomy-and-function-of-the-hypothalamus",totalDownloads:4596,totalCrossrefCites:6,totalDimensionsCites:12,abstract:"The hypothalamus is a small but important area of the brain formed by various nucleus and nervous fibers. Through its neuronal connections, it is involved in many complex functions of the organism such as vegetative system control, homeostasis of the organism, thermoregulation, and also in adjusting the emotional behavior. The hypothalamus is involved in different daily activities like eating or drinking, in the control of the body’s temperature and energy maintenance, and in the process of memorizing. It also modulates the endocrine system through its connections with the pituitary gland. Precise anatomical description along with a correct characterization of the component structures is essential for understanding its functions.",book:{id:"6331",slug:"hypothalamus-in-health-and-diseases",title:"Hypothalamus in Health and Diseases",fullTitle:"Hypothalamus in Health and Diseases"},signatures:"Miana Gabriela Pop, Carmen Crivii and Iulian Opincariu",authors:null},{id:"57103",title:"GABA and Glutamate: Their Transmitter Role in the CNS and Pancreatic Islets",slug:"gaba-and-glutamate-their-transmitter-role-in-the-cns-and-pancreatic-islets",totalDownloads:3523,totalCrossrefCites:3,totalDimensionsCites:9,abstract:"Glutamate and gamma-aminobutyric acid (GABA) are the major neurotransmitters in the mammalian brain. Inhibitory GABA and excitatory glutamate work together to control many processes, including the brain’s overall level of excitation. The contributions of GABA and glutamate in extra-neuronal signaling are by far less widely recognized. In this chapter, we first discuss the role of both neurotransmitters during development, emphasizing the importance of the shift from excitatory to inhibitory GABAergic neurotransmission. The second part summarizes the biosynthesis and role of GABA and glutamate in neurotransmission in the mature brain, and major neurological disorders associated with glutamate and GABA receptors and GABA release mechanisms. The final part focuses on extra-neuronal glutamatergic and GABAergic signaling in pancreatic islets of Langerhans, and possible associations with type 1 diabetes mellitus.",book:{id:"6237",slug:"gaba-and-glutamate-new-developments-in-neurotransmission-research",title:"GABA And Glutamate",fullTitle:"GABA And Glutamate - New Developments In Neurotransmission Research"},signatures:"Christiane S. Hampe, Hiroshi Mitoma and Mario Manto",authors:[{id:"210220",title:"Prof.",name:"Christiane",middleName:null,surname:"Hampe",slug:"christiane-hampe",fullName:"Christiane Hampe"},{id:"210485",title:"Prof.",name:"Mario",middleName:null,surname:"Manto",slug:"mario-manto",fullName:"Mario Manto"},{id:"210486",title:"Prof.",name:"Hiroshi",middleName:null,surname:"Mitoma",slug:"hiroshi-mitoma",fullName:"Hiroshi Mitoma"}]},{id:"35802",title:"Cross-Cultural/Linguistic Differences in the Prevalence of Developmental Dyslexia and the Hypothesis of Granularity and Transparency",slug:"cross-cultural-linguistic-differences-in-the-prevalence-of-developmental-dyslexia-and-the-hypothesis",totalDownloads:3609,totalCrossrefCites:2,totalDimensionsCites:7,abstract:null,book:{id:"673",slug:"dyslexia-a-comprehensive-and-international-approach",title:"Dyslexia",fullTitle:"Dyslexia - A Comprehensive and International Approach"},signatures:"Taeko N. Wydell",authors:[{id:"87489",title:"Prof.",name:"Taeko",middleName:"N.",surname:"Wydell",slug:"taeko-wydell",fullName:"Taeko Wydell"}]},{id:"58597",title:"Testosterone and Erectile Function: A Review of Evidence from Basic Research",slug:"testosterone-and-erectile-function-a-review-of-evidence-from-basic-research",totalDownloads:1349,totalCrossrefCites:2,totalDimensionsCites:2,abstract:"Androgens are essential for male physical activity and normal erectile function. Hence, age-related testosterone deficiency, known as late-onset hypogonadism (LOH), is considered a risk factor for erectile dysfunction (ED). This chapter summarizes relevant basic research reports examining the effects of testosterone on erectile function. Testosterone affects several organs and is especially active on the erectile tissue. The mechanism of testosterone deficiency effects on erectile function and the results of testosterone replacement therapy (TRT) have been well studied. Testosterone affects nitric oxide (NO) production and phosphodiesterase type 5 (PDE-5) expression in the corpus cavernosum through molecular pathways, preserves smooth muscle contractility by regulating both contraction and relaxation, and maintains the structure of the corpus cavernosum. Interestingly, testosterone deficiency has relationship to neurological diseases, which leads to ED. Testosterone replacement therapy is widely used to treat patients with testosterone deficiency; however, this treatment might also induce some problems. Basic research suggests that PDE-5 inhibitors, L-citrulline, and/or resveratrol therapy might be effective therapeutic options for testosterone deficiency-induced ED. Future research should confirm these findings through more specific experiments using molecular tools and may shed more light on endocrine-related ED and its possible treatments.",book:{id:"5994",slug:"sex-hormones-in-neurodegenerative-processes-and-diseases",title:"Sex Hormones in Neurodegenerative Processes and Diseases",fullTitle:"Sex Hormones in Neurodegenerative Processes and Diseases"},signatures:"Tomoya Kataoka and Kazunori Kimura",authors:[{id:"219042",title:"Ph.D.",name:"Tomoya",middleName:null,surname:"Kataoka",slug:"tomoya-kataoka",fullName:"Tomoya Kataoka"},{id:"229066",title:"Prof.",name:"Kazunori",middleName:null,surname:"Kimura",slug:"kazunori-kimura",fullName:"Kazunori Kimura"}]}],onlineFirstChaptersFilter:{topicId:"18",limit:6,offset:0},onlineFirstChaptersCollection:[{id:"82319",title:"Traumatic Optic Neuropathy",slug:"traumatic-optic-neuropathy",totalDownloads:2,totalDimensionsCites:0,doi:"10.5772/intechopen.104731",abstract:"Traumatic optic neuropathy (TON) is a specific neurological sequence of traumatic brain injury (TBI). It has a different mechanism than other most neurologic complications of head trauma and its consequences can be devastating. The damage can be from direct penetrating trauma or bone fracture injuring the optic nerve directly or secondary to indirect blunt trauma (usually causing traction). The diagnosis of TON is based on the clinical history and examination findings indicative of optic neuropathy, especially the presence of defective pupillary light response. TON can cause only mild vision loss but, in some cases, severe vision loss is present. Imaging findings can support the diagnosis, and provide information on the mechanism as well as treatment options. The treatment options include observation alone, systemic steroids, erythropoietin, surgical decompression of the optic canal, or combination. The evidence base for these various treatment options is controversial and each treatment has its side effects and risks. Poor prognostic factors include poor visual acuity at presentation, loss of consciousness, no improvement in vision in the first 48 hours, and evidence of optic canal fractures on neuroimaging.",book:{id:"11367",title:"Traumatic Brain Injury",coverURL:"https://cdn.intechopen.com/books/images_new/11367.jpg"},signatures:"Ainat Klein and Wahbi Wahbi"},{id:"82203",title:"Resting-State Brain Network Analysis Methods and Applications",slug:"resting-state-brain-network-analysis-methods-and-applications",totalDownloads:19,totalDimensionsCites:0,doi:"10.5772/intechopen.104827",abstract:"Resting-state fMRI has been widely applied in clinical research. Brain networks constructed by functional connectivity can reveal alterations related to disease and treatment. One of the major concerns of brain network application under clinical situations is how to analyze groups of data to find the potential biomarkers that can aid in diagnosis. In this paper, we briefly review common methods to construct brain networks from resting-state fMRI data, including different ways of the node definition and edge calculation. We focus on using a brain atlas to define nodes and estimate edges by static and dynamic functional connectivity. The directed connectivity method is also mentioned. We then discuss the challenges and pitfalls when analyzing groups of brain networks, including functional connectivity alterations, graph theory attributes analysis, and network-based statistics. Finally, we review the clinical application of resting-state fMRI in neurorehabilitation of spinal cord injury patients and stroke patients, the research on the mechanism and early diagnosis of neurodegenerative diseases, such as multiple system atrophy, as well as the research on brain functional network alteration of glioma patients.",book:{id:"11742",title:"Neurophysiology",coverURL:"https://cdn.intechopen.com/books/images_new/11742.jpg"},signatures:"Yunxiang Ge and Weibei Dou"},{id:"82099",title:"Recent Advances in the Development of Biofluid-Based Prognostic Biomarkers of Diffuse Axonal Injury",slug:"recent-advances-in-the-development-of-biofluid-based-prognostic-biomarkers-of-diffuse-axonal-injury",totalDownloads:13,totalDimensionsCites:0,doi:"10.5772/intechopen.104933",abstract:"Even though head injury is a silent pandemic of the century producing immense social and economic impact, predictive models have not been established to develop strategies promoting the development of reliable diagnostic tools and effective therapeutics capable of improving the prognosis. Diffuse axonal injury (DAI) is a type of traumatic brain injury (TBI) that results from a blunt injury to the brain. Discovering biomarkers for DAI have been a matter of debate and research. A number of studies have reported biomarkers that are correlated with severity of TBI but no conclusive and reproducible clinical evidence regarding the same has been put forward till now. Additionally, many DAI biomarkers have limitations so that they cannot be generalized for universal applications. The properties of these biomarkers should be extensively researched along with the development of novel biomarkers to aid important clinical decisions for the benefit of the society. This chapter summarizes the existing biofluid-based biomarkers, critically examines their limitations and highlights the possibilities of a few novel biomolecules as prognostic biomarkers of DAI.",book:{id:"11367",title:"Traumatic Brain Injury",coverURL:"https://cdn.intechopen.com/books/images_new/11367.jpg"},signatures:"Vinu V. Gopal, Rinku Raj Mullasseril and Goutam Chandra"},{id:"81998",title:"Understanding the Neuropathophysiology of Psychiatry Disorder Using Transcranial Magnetic Stimulation",slug:"understanding-the-neuropathophysiology-of-psychiatry-disorder-using-transcranial-magnetic-stimulatio",totalDownloads:5,totalDimensionsCites:0,doi:"10.5772/intechopen.103748",abstract:"Transcranial magnetic stimulation (TMS) is a safe and non-invasive tool that allows researchers to probe and modulate intracortical circuits. The most important aspect of TMS is its ability to directly stimulate the cortical neurons, generating action potentials, without much effect on intervening tissue. This property can be leveraged to provide insight into the pathophysiology of various neuropsychiatric disorders. Using multiple patterns of stimulations (single, paired, or repetitive), different neurophysiological parameters can be elicited. Various TMS protocol helps in understanding the neurobiological basis of disorder and specific behaviors by allowing direct probing of the cortical areas and their interconnected networks. While single-pulse TMS can provide insight into the excitability and integrity of the corticospinal tract, paired-pulse TMS (ppTMS) can provide further insight into cortico-cortical connections and repetitive TMS (rTMS) into cortical mapping and modulating plasticity.",book:{id:"11742",title:"Neurophysiology",coverURL:"https://cdn.intechopen.com/books/images_new/11742.jpg"},signatures:"Jitender Jakhar, Manish Sarkar and Nand Kumar"},{id:"81646",title:"Cortical Plasticity under Ketamine: From Synapse to Map",slug:"cortical-plasticity-under-ketamine-from-synapse-to-map",totalDownloads:17,totalDimensionsCites:0,doi:"10.5772/intechopen.104787",abstract:"Sensory systems need to process signals in a highly dynamic way to efficiently respond to variations in the animal’s environment. For instance, several studies showed that the visual system is subject to neuroplasticity since the neurons’ firing changes according to stimulus properties. This dynamic information processing might be supported by a network reorganization. Since antidepressants influence neurotransmission, they can be used to explore synaptic plasticity sustaining cortical map reorganization. To this goal, we investigated in the primary visual cortex (V1 of mouse and cat), the impact of ketamine on neuroplasticity through changes in neuronal orientation selectivity and the functional connectivity between V1 cells, using cross correlation analyses. We found that ketamine affects cortical orientation selectivity and alters the functional connectivity within an assembly. These data clearly highlight the role of the antidepressant drugs in inducing or modeling short-term plasticity in V1 which suggests that cortical processing is optimized and adapted to the properties of the stimulus.",book:{id:"11374",title:"Sensory Nervous System - Computational Neuroimaging Investigations of Topographical Organization in Human Sensory Cortex",coverURL:"https://cdn.intechopen.com/books/images_new/11374.jpg"},signatures:"Ouelhazi Afef, Rudy Lussiez and Molotchnikoff Stephane"},{id:"81582",title:"The Role of Cognitive Reserve in Executive Functioning and Its Relationship to Cognitive Decline and Dementia",slug:"the-role-of-cognitive-reserve-in-executive-functioning-and-its-relationship-to-cognitive-decline-and",totalDownloads:30,totalDimensionsCites:0,doi:"10.5772/intechopen.104646",abstract:"In this chapter, we explore how cognitive reserve is implicated in coping with the negative consequences of brain pathology and age-related cognitive decline. Individual differences in cognitive performance are based on different brain mechanisms (neural reserve and neural compensation), and reflect, among others, the effect of education, occupational attainment, leisure activities, and social involvement. These cognitive reserve proxies have been extensively associated with efficient executive functioning. We discuss and focus particularly on the compensation mechanisms related to the frontal lobe and its protective role, in maintaining cognitive performance in old age or even mitigating the clinical expression of dementia.",book:{id:"11742",title:"Neurophysiology",coverURL:"https://cdn.intechopen.com/books/images_new/11742.jpg"},signatures:"Gabriela Álvares-Pereira, Carolina Maruta and Maria Vânia Silva-Nunes"}],onlineFirstChaptersTotal:13},preDownload:{success:null,errors:{}},subscriptionForm:{success:null,errors:{}},aboutIntechopen:{},privacyPolicy:{},peerReviewing:{},howOpenAccessPublishingWithIntechopenWorks:{},sponsorshipBooks:{sponsorshipBooks:[],offset:0,limit:8,total:null},allSeries:{pteSeriesList:[{id:"14",title:"Artificial Intelligence",numberOfPublishedBooks:9,numberOfPublishedChapters:89,numberOfOpenTopics:6,numberOfUpcomingTopics:0,issn:"2633-1403",doi:"10.5772/intechopen.79920",isOpenForSubmission:!0},{id:"7",title:"Biomedical Engineering",numberOfPublishedBooks:12,numberOfPublishedChapters:104,numberOfOpenTopics:3,numberOfUpcomingTopics:0,issn:"2631-5343",doi:"10.5772/intechopen.71985",isOpenForSubmission:!0}],lsSeriesList:[{id:"11",title:"Biochemistry",numberOfPublishedBooks:32,numberOfPublishedChapters:318,numberOfOpenTopics:4,numberOfUpcomingTopics:0,issn:"2632-0983",doi:"10.5772/intechopen.72877",isOpenForSubmission:!0},{id:"25",title:"Environmental Sciences",numberOfPublishedBooks:1,numberOfPublishedChapters:12,numberOfOpenTopics:4,numberOfUpcomingTopics:0,issn:"2754-6713",doi:"10.5772/intechopen.100362",isOpenForSubmission:!0},{id:"10",title:"Physiology",numberOfPublishedBooks:11,numberOfPublishedChapters:141,numberOfOpenTopics:4,numberOfUpcomingTopics:0,issn:"2631-8261",doi:"10.5772/intechopen.72796",isOpenForSubmission:!0}],hsSeriesList:[{id:"3",title:"Dentistry",numberOfPublishedBooks:8,numberOfPublishedChapters:129,numberOfOpenTopics:2,numberOfUpcomingTopics:0,issn:"2631-6218",doi:"10.5772/intechopen.71199",isOpenForSubmission:!0},{id:"6",title:"Infectious Diseases",numberOfPublishedBooks:13,numberOfPublishedChapters:113,numberOfOpenTopics:3,numberOfUpcomingTopics:1,issn:"2631-6188",doi:"10.5772/intechopen.71852",isOpenForSubmission:!0},{id:"13",title:"Veterinary Medicine and Science",numberOfPublishedBooks:11,numberOfPublishedChapters:106,numberOfOpenTopics:3,numberOfUpcomingTopics:0,issn:"2632-0517",doi:"10.5772/intechopen.73681",isOpenForSubmission:!0}],sshSeriesList:[{id:"22",title:"Business, Management and Economics",numberOfPublishedBooks:1,numberOfPublishedChapters:19,numberOfOpenTopics:3,numberOfUpcomingTopics:0,issn:"2753-894X",doi:"10.5772/intechopen.100359",isOpenForSubmission:!0},{id:"23",title:"Education and Human Development",numberOfPublishedBooks:0,numberOfPublishedChapters:5,numberOfOpenTopics:1,numberOfUpcomingTopics:1,issn:null,doi:"10.5772/intechopen.100360",isOpenForSubmission:!0},{id:"24",title:"Sustainable Development",numberOfPublishedBooks:0,numberOfPublishedChapters:15,numberOfOpenTopics:5,numberOfUpcomingTopics:0,issn:null,doi:"10.5772/intechopen.100361",isOpenForSubmission:!0}],testimonialsList:[{id:"6",text:"It is great to work with the IntechOpen to produce a worthwhile collection of research that also becomes a great educational resource and guide for future research endeavors.",author:{id:"259298",name:"Edward",surname:"Narayan",institutionString:null,profilePictureURL:"https://mts.intechopen.com/storage/users/259298/images/system/259298.jpeg",slug:"edward-narayan",institution:{id:"3",name:"University of Queensland",country:{id:null,name:"Australia"}}}},{id:"13",text:"The collaboration with and support of the technical staff of IntechOpen is fantastic. The whole process of submitting an article and editing of the submitted article goes extremely smooth and fast, the number of reads and downloads of chapters is high, and the contributions are also frequently cited.",author:{id:"55578",name:"Antonio",surname:"Jurado-Navas",institutionString:null,profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bRisIQAS/Profile_Picture_1626166543950",slug:"antonio-jurado-navas",institution:{id:"720",name:"University of Malaga",country:{id:null,name:"Spain"}}}}]},series:{item:{id:"14",title:"Artificial Intelligence",doi:"10.5772/intechopen.79920",issn:"2633-1403",scope:"Artificial Intelligence (AI) is a rapidly developing multidisciplinary research area that aims to solve increasingly complex problems. In today's highly integrated world, AI promises to become a robust and powerful means for obtaining solutions to previously unsolvable problems. This Series is intended for researchers and students alike interested in this fascinating field and its many applications.",coverUrl:"https://cdn.intechopen.com/series/covers/14.jpg",latestPublicationDate:"June 11th, 2022",hasOnlineFirst:!0,numberOfPublishedBooks:9,editor:{id:"218714",title:"Prof.",name:"Andries",middleName:null,surname:"Engelbrecht",slug:"andries-engelbrecht",fullName:"Andries Engelbrecht",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bRNR8QAO/Profile_Picture_1622640468300",biography:"Andries Engelbrecht received the Masters and PhD degrees in Computer Science from the University of Stellenbosch, South Africa, in 1994 and 1999 respectively. He is currently appointed as the Voigt Chair in Data Science in the Department of Industrial Engineering, with a joint appointment as Professor in the Computer Science Division, Stellenbosch University. Prior to his appointment at Stellenbosch University, he has been at the University of Pretoria, Department of Computer Science (1998-2018), where he was appointed as South Africa Research Chair in Artifical Intelligence (2007-2018), the head of the Department of Computer Science (2008-2017), and Director of the Institute for Big Data and Data Science (2017-2018). In addition to a number of research articles, he has written two books, Computational Intelligence: An Introduction and Fundamentals of Computational Swarm Intelligence.",institutionString:null,institution:{name:"Stellenbosch University",institutionURL:null,country:{name:"South Africa"}}},editorTwo:null,editorThree:null},subseries:{paginationCount:6,paginationItems:[{id:"22",title:"Applied Intelligence",coverUrl:"https://cdn.intechopen.com/series_topics/covers/22.jpg",isOpenForSubmission:!0,editor:{id:"27170",title:"Prof.",name:"Carlos",middleName:"M.",surname:"Travieso-Gonzalez",slug:"carlos-travieso-gonzalez",fullName:"Carlos Travieso-Gonzalez",profilePictureURL:"https://mts.intechopen.com/storage/users/27170/images/system/27170.jpeg",biography:"Carlos M. Travieso-González received his MSc degree in Telecommunication Engineering at Polytechnic University of Catalonia (UPC), Spain in 1997, and his Ph.D. degree in 2002 at the University of Las Palmas de Gran Canaria (ULPGC-Spain). He is a full professor of signal processing and pattern recognition and is head of the Signals and Communications Department at ULPGC, teaching from 2001 on subjects on signal processing and learning theory. His research lines are biometrics, biomedical signals and images, data mining, classification system, signal and image processing, machine learning, and environmental intelligence. He has researched in 52 international and Spanish research projects, some of them as head researcher. He is co-author of 4 books, co-editor of 27 proceedings books, guest editor for 8 JCR-ISI international journals, and up to 24 book chapters. He has over 450 papers published in international journals and conferences (81 of them indexed on JCR – ISI - Web of Science). He has published seven patents in the Spanish Patent and Trademark Office. He has been a supervisor on 8 Ph.D. theses (11 more are under supervision), and 130 master theses. He is the founder of The IEEE IWOBI conference series and the president of its Steering Committee, as well as the founder of both the InnoEducaTIC and APPIS conference series. He is an evaluator of project proposals for the European Union (H2020), Medical Research Council (MRC, UK), Spanish Government (ANECA, Spain), Research National Agency (ANR, France), DAAD (Germany), Argentinian Government, and the Colombian Institutions. He has been a reviewer in different indexed international journals (<70) and conferences (<250) since 2001. He has been a member of the IASTED Technical Committee on Image Processing from 2007 and a member of the IASTED Technical Committee on Artificial Intelligence and Expert Systems from 2011. \n\nHe has held the general chair position for the following: ACM-APPIS (2020, 2021), IEEE-IWOBI (2019, 2020 and 2020), A PPIS (2018, 2019), IEEE-IWOBI (2014, 2015, 2017, 2018), InnoEducaTIC (2014, 2017), IEEE-INES (2013), NoLISP (2011), JRBP (2012), and IEEE-ICCST (2005)\n\nHe is an associate editor of the Computational Intelligence and Neuroscience Journal (Hindawi – Q2 JCR-ISI). He was vice dean from 2004 to 2010 in the Higher Technical School of Telecommunication Engineers at ULPGC and the vice dean of Graduate and Postgraduate Studies from March 2013 to November 2017. He won the “Catedra Telefonica” Awards in Modality of Knowledge Transfer, 2017, 2018, and 2019 editions, and awards in Modality of COVID Research in 2020.\n\nPublic References:\nResearcher ID http://www.researcherid.com/rid/N-5967-2014\nORCID https://orcid.org/0000-0002-4621-2768 \nScopus Author ID https://www.scopus.com/authid/detail.uri?authorId=6602376272\nScholar Google https://scholar.google.es/citations?user=G1ks9nIAAAAJ&hl=en \nResearchGate https://www.researchgate.net/profile/Carlos_Travieso",institutionString:null,institution:{name:"University of Las Palmas de Gran Canaria",institutionURL:null,country:{name:"Spain"}}},editorTwo:null,editorThree:null},{id:"23",title:"Computational Neuroscience",coverUrl:"https://cdn.intechopen.com/series_topics/covers/23.jpg",isOpenForSubmission:!0,editor:{id:"14004",title:"Dr.",name:"Magnus",middleName:null,surname:"Johnsson",slug:"magnus-johnsson",fullName:"Magnus Johnsson",profilePictureURL:"https://mts.intechopen.com/storage/users/14004/images/system/14004.png",biography:"Dr Magnus Johnsson is a cross-disciplinary scientist, lecturer, scientific editor and AI/machine learning consultant from Sweden. \n\nHe is currently at Malmö University in Sweden, but also held positions at Lund University in Sweden and at Moscow Engineering Physics Institute. \nHe holds editorial positions at several international scientific journals and has served as a scientific editor for books and special journal issues. \nHis research interests are wide and include, but are not limited to, autonomous systems, computer modeling, artificial neural networks, artificial intelligence, cognitive neuroscience, cognitive robotics, cognitive architectures, cognitive aids and the philosophy of mind. \n\nDr. Johnsson has experience from working in the industry and he has a keen interest in the application of neural networks and artificial intelligence to fields like industry, finance, and medicine. \n\nWeb page: www.magnusjohnsson.se",institutionString:null,institution:{name:"Malmö University",institutionURL:null,country:{name:"Sweden"}}},editorTwo:null,editorThree:null},{id:"24",title:"Computer Vision",coverUrl:"https://cdn.intechopen.com/series_topics/covers/24.jpg",isOpenForSubmission:!0,editor:{id:"294154",title:"Prof.",name:"George",middleName:null,surname:"Papakostas",slug:"george-papakostas",fullName:"George Papakostas",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002hYaGbQAK/Profile_Picture_1624519712088",biography:"George A. 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He has (co)authored more than 150 publications in indexed journals, international conferences and book chapters, 1 book (in Greek), 3 edited books, and 5 journal special issues. His publications have more than 2100 citations with h-index 27 (GoogleScholar). His research interests include computer/machine vision, machine learning, pattern recognition, computational intelligence. \nDr. Papakostas served as a reviewer in numerous journals, as a program\ncommittee member in international conferences and he is a member of the IAENG, MIR Labs, EUCogIII, INSTICC and the Technical Chamber of Greece (TEE).",institutionString:null,institution:{name:"International Hellenic University",institutionURL:null,country:{name:"Greece"}}},editorTwo:null,editorThree:null},{id:"25",title:"Evolutionary Computation",coverUrl:"https://cdn.intechopen.com/series_topics/covers/25.jpg",isOpenForSubmission:!0,editor:{id:"136112",title:"Dr.",name:"Sebastian",middleName:null,surname:"Ventura Soto",slug:"sebastian-ventura-soto",fullName:"Sebastian Ventura Soto",profilePictureURL:"https://mts.intechopen.com/storage/users/136112/images/system/136112.png",biography:"Sebastian Ventura is a Spanish researcher, a full professor with the Department of Computer Science and Numerical Analysis, University of Córdoba. Dr Ventura also holds the positions of Affiliated Professor at Virginia Commonwealth University (Richmond, USA) and Distinguished Adjunct Professor at King Abdulaziz University (Jeddah, Saudi Arabia). Additionally, he is deputy director of the Andalusian Research Institute in Data Science and Computational Intelligence (DaSCI) and heads the Knowledge Discovery and Intelligent Systems Research Laboratory. He has published more than ten books and over 300 articles in journals and scientific conferences. Currently, his work has received over 18,000 citations according to Google Scholar, including more than 2200 citations in 2020. In the last five years, he has published more than 60 papers in international journals indexed in the JCR (around 70% of them belonging to first quartile journals) and he has edited some Springer books “Supervised Descriptive Pattern Mining” (2018), “Multiple Instance Learning - Foundations and Algorithms” (2016), and “Pattern Mining with Evolutionary Algorithms” (2016). He has also been involved in more than 20 research projects supported by the Spanish and Andalusian governments and the European Union. He currently belongs to the editorial board of PeerJ Computer Science, Information Fusion and Engineering Applications of Artificial Intelligence journals, being also associate editor of Applied Computational Intelligence and Soft Computing and IEEE Transactions on Cybernetics. Finally, he is editor-in-chief of Progress in Artificial Intelligence. 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He is currently a principal researcher in data analytics and optimisation at TECNALIA (Spain), a visiting fellow at the Basque Center for Applied Mathematics (BCAM) and a part-time lecturer at the University of the Basque Country (UPV/EHU). His research interests gravitate on the use of descriptive, prescriptive and predictive algorithms for data mining and optimization in a diverse range of application fields such as Energy, Transport, Telecommunications, Health and Industry, among others. In these fields he has published more than 240 articles, co-supervised 8 Ph.D. theses, edited 6 books, coauthored 7 patents and participated/led more than 40 research projects. 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