Photovoltaic parameters of perovskite solar cell devices using dichalcogenide transition metals (TMDs) as ETLs.
\\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:"4655",leadTitle:null,fullTitle:"Applications of Digital Signal Processing through Practical Approach",title:"Applications of Digital Signal Processing through Practical Approach",subtitle:null,reviewType:"peer-reviewed",abstract:"This book is recommended to readers who can ponder on the collection of chapters authored/co-authored by various researchers as well as to researchers around the world covering the field of digital signal processing. This book highlights current research in the digital signal processing area such as communication engineering, image processing and power conversion system. The entire work available in the book mainly focusses on researchers who can do quality research in the area of digital signal processing and related fields. Each chapter is an independent research, which will definitely motivate young researchers to further study the subject. These six chapters divided into three sections will be an eye-opener for all those engaged in systematic research in these fields.",isbn:null,printIsbn:"978-953-51-2190-9",pdfIsbn:"978-953-51-5764-9",doi:"10.5772/59529",price:119,priceEur:129,priceUsd:155,slug:"applications-of-digital-signal-processing-through-practical-approach",numberOfPages:198,isOpenForSubmission:!1,isInWos:1,isInBkci:!1,hash:"b20308efd28e8a487949997c8d673fb8",bookSignature:"Sudhakar Radhakrishnan",publishedDate:"October 28th 2015",coverURL:"https://cdn.intechopen.com/books/images_new/4655.jpg",numberOfDownloads:13344,numberOfWosCitations:5,numberOfCrossrefCitations:5,numberOfCrossrefCitationsByBook:0,numberOfDimensionsCitations:6,numberOfDimensionsCitationsByBook:0,hasAltmetrics:1,numberOfTotalCitations:16,isAvailableForWebshopOrdering:!0,dateEndFirstStepPublish:"October 23rd 2014",dateEndSecondStepPublish:"November 13th 2014",dateEndThirdStepPublish:"February 17th 2015",dateEndFourthStepPublish:"May 18th 2015",dateEndFifthStepPublish:"June 17th 2015",currentStepOfPublishingProcess:5,indexedIn:"1,2,3,4,5,6,7",editedByType:"Edited by",kuFlag:!1,featuredMarkup:null,editors:[{id:"26327",title:"Dr.",name:"Sudhakar",middleName:null,surname:"Radhakrishnan",slug:"sudhakar-radhakrishnan",fullName:"Sudhakar Radhakrishnan",profilePictureURL:"https://mts.intechopen.com/storage/users/26327/images/system/26327.png",biography:"Dr. R. Sudhakar is a professor and head of the Department of Electronics and Communication Engineering, Dr. Mahalingam College of Engineering and Technology, Pollachi, India. He is also an associate editor for IEEE Access, from which he received the Outstanding Associate Editor Award in 2019. He is a reviewer of sixteen international journals, including IEEE Transactions on Systems, Man, and Cybernetics: Systems, International Arab Journal of Information Technology, and International Journal of Computer and Electrical Engineering, among others. He has published 110 papers in international, and national journals and conference proceedings. His areas of research include digital image processing, image analysis, wavelet transforms, and digital signal processing.",institutionString:"Dr. Mahalingam College of Engineering and Technology",position:null,outsideEditionCount:0,totalCites:0,totalAuthoredChapters:"2",totalChapterViews:"0",totalEditedBooks:"5",institution:null}],equalEditorOne:null,equalEditorTwo:null,equalEditorThree:null,coeditorOne:null,coeditorTwo:null,coeditorThree:null,coeditorFour:null,coeditorFive:null,topics:[{id:"561",title:"Signal Processing",slug:"computer-science-and-engineering-signal-processing"}],chapters:[{id:"49358",title:"Optical Signal Processing for High-Order Quadrature- Amplitude Modulation Formats",doi:"10.5772/61681",slug:"optical-signal-processing-for-high-order-quadrature-amplitude-modulation-formats",totalDownloads:1972,totalCrossrefCites:0,totalDimensionsCites:0,hasAltmetrics:0,abstract:"In this book chapter, optical signal processing technology, including optical wavelength conversion, wavelength exchange and wavelength multicasting, for phase-noise-sensitive high-order quadrature-amplitude modulation (QAM) signals will be discussed. Due to the susceptibility of high-order QAM signals against phase noise, it is imperative to avoid the phase noise in the optical signal processing subsystems. To design high-performance optical signal processing subsystems, both linear and nonlinear phase noise and distortions are the main concerns in the system design. We will first investigate the effective monitoring approach to optimize the performance of wavelength conversion for avoiding undesired nonlinear phase noise and distortions, and then propose coherent pumping scheme to eliminate the linear phase noise from local pumps in order to realize pump-phase-noise-free wavelength conversion, wavelength exchange and multicasting for high-order QAM signals. All of the discussions are based on experimental investigation.",signatures:"Guo-Wei Lu",downloadPdfUrl:"/chapter/pdf-download/49358",previewPdfUrl:"/chapter/pdf-preview/49358",authors:[{id:"174507",title:"Associate Prof.",name:"Guo-Wei",surname:"Lu",slug:"guo-wei-lu",fullName:"Guo-Wei Lu"}],corrections:null},{id:"49240",title:"High-Base Optical Signal Proccessing",doi:"10.5772/61504",slug:"high-base-optical-signal-proccessing",totalDownloads:1810,totalCrossrefCites:1,totalDimensionsCites:2,hasAltmetrics:1,abstract:"Optical signal processing is a promising technique to enable fast data information processing in the optical domain. Traditional optical signal processing functions pay more attention to binary modulation formats (i.e., binary numbers) with single-bit information contained in one symbol. The ever-growing data traffic has propelled great success in high-speed optical signal transmission by using advanced multilevel modulation formats (i.e., high-base numbers), which encode multiple-bit information in one symbol with resultant enhanced transmission capacity and efficient spectrum usage. A valuable challenge would be to perform various optical signal processing functions for multilevel modulation formats, i.e., high-base optical signal processing. In this chapter, we review recent research works on high-base optical signal processing for multilevel modulation formats by exploiting degenerate and nondegenerate four-wave mixing in highly nonlinear fibers or silicon photonic devices. Grooming high-base optical signal processing functions including high-base wavelength conversion, high-base data exchange, high-base optical computing, and high-base optical coding/decoding are demonstrated. High-base optical signal processing may facilitate advanced data management and superior network performance.",signatures:"Jian Wang and Alan E. Willner",downloadPdfUrl:"/chapter/pdf-download/49240",previewPdfUrl:"/chapter/pdf-preview/49240",authors:[{id:"174233",title:"Prof.",name:"Jian",surname:"Wang",slug:"jian-wang",fullName:"Jian Wang"},{id:"174665",title:"Prof.",name:"Alan",surname:"Willner",slug:"alan-willner",fullName:"Alan Willner"}],corrections:null},{id:"48732",title:"Multitones’ Performance for Ultra Wideband Software Defined Radar",doi:"10.5772/60804",slug:"multitones-performance-for-ultra-wideband-software-defined-radar",totalDownloads:2118,totalCrossrefCites:3,totalDimensionsCites:3,hasAltmetrics:0,abstract:"This chapter proposes and tests an approach for an unbiased study of radar waveforms’ performances. Through an empirical performance analysis, the performances of Chirp and Multitones are compared with both simulations and measurements. An ultra wideband software defined radar prototype was designed and the prototype has performances comparable to the state of the art in software defined radar. The study looks at peak-to-mean-envelope power ratio, spectrum efficiency, and pulse compression as independent waveform criteria. The experimental results are consistent with the simulations. The study shows that a minimum of 10 bits resolution for the AD/DA converters is required to obtain near-optimum performances.",signatures:"Julien Le Kernec and Olivier Romain",downloadPdfUrl:"/chapter/pdf-download/48732",previewPdfUrl:"/chapter/pdf-preview/48732",authors:[{id:"174716",title:"Prof.",name:"Olivier",surname:"Romain",slug:"olivier-romain",fullName:"Olivier Romain"},{id:"174717",title:"Dr.",name:"Julien",surname:"Le Kernec",slug:"julien-le-kernec",fullName:"Julien Le Kernec"}],corrections:null},{id:"49264",title:"Application of DSP Concept for Ultrasound Doppler Image Processing System",doi:"10.5772/61164",slug:"application-of-dsp-concept-for-ultrasound-doppler-image-processing-system",totalDownloads:2153,totalCrossrefCites:0,totalDimensionsCites:0,hasAltmetrics:0,abstract:"Blood-flow measurements using Doppler ultrasound system are popular in ultrasonic diagnoses. But the blood-flow measurement inside the heart is difficult. There are many reasons behind it. The deep range and fast blood-flow are difficult to measure because of limitation of acoustic velocity. Moreover, strong heart valve signals mix into the blood-flow signal. Against such difficulties, the statistics mathematical model was applied to analyze many clinical data sets. The system identification method based on the mathematical model could realize a new blood-flow measurement system that has ultrasound Doppler information as input and electrocardiogram as output.",signatures:"Baba Tatsuro",downloadPdfUrl:"/chapter/pdf-download/49264",previewPdfUrl:"/chapter/pdf-preview/49264",authors:[{id:"65121",title:"Dr.",name:"Baba",surname:"Tatsuro",slug:"baba-tatsuro",fullName:"Baba Tatsuro"}],corrections:null},{id:"49098",title:"Lossy-to-Lossless Compression of Biomedical Images Based on Image Decomposition",doi:"10.5772/60650",slug:"lossy-to-lossless-compression-of-biomedical-images-based-on-image-decomposition",totalDownloads:2078,totalCrossrefCites:0,totalDimensionsCites:0,hasAltmetrics:0,abstract:"The use of medical imaging has increased in the last years, especially with magnetic resonance imaging (MRI) and computed tomography (CT). Microarray imaging and images that can be extracted from RNA interference (RNAi) experiments also play an important role for large-scale gene sequence and gene expression analysis, allowing the study of gene function, regulation, and interaction across a large number of genes and even across an entire genome. These types of medical image modalities produce huge amounts of data that, for several reasons, need to be stored or transmitted at the highest possible fidelity between various hospitals, medical organizations, or research units.",signatures:"Luís M. O. Matos, António J. R. Neves and Armando J. Pinho",downloadPdfUrl:"/chapter/pdf-download/49098",previewPdfUrl:"/chapter/pdf-preview/49098",authors:[{id:"1177",title:"Prof.",name:"Antonio",surname:"Neves",slug:"antonio-neves",fullName:"Antonio Neves"}],corrections:null},{id:"48835",title:"Application of DSP in Power Conversion Systems — A Practical Approach for Multiphase Drives",doi:"10.5772/60450",slug:"application-of-dsp-in-power-conversion-systems-a-practical-approach-for-multiphase-drives",totalDownloads:3216,totalCrossrefCites:1,totalDimensionsCites:1,hasAltmetrics:0,abstract:"Digital Signal Processing is not a recent research field, but has become a powerful technology to solve engineering problems in the last few decades due to the introduction by Texas Instruments in 1982 of the Digital Signal Processor. Fast digital signal processors have quickly become a cornerstone of high-performance electrical drives, where power electronic conversion systems have heavy online computation burdens and must be controlled using complex control algorithms. In this sense, multiphase drives represent a particularly interesting case of study, where the computational cost highly increases with each extra phase. This technology has been recognized in recent times as an attractive electrical drive due to its usefulness in traction, more-electric aircraft applications and wind power generation systems. However, the complexity of the required control algorithms and signal processing techniques notably increases in relation with conventional three-phase drives. This chapter makes a revision of the necessities of a high-performance multiphase drive from the digital signal processing perspective. 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When considering the ethical and legal constraints, the need to use animal models maintains its importance from past to present. The success of the research depends on the suitability of the chosen model. For example, while single-celled organisms such as yeast are a convenient model for the study of basic eukaryotic cell functions such as cell cycle regulation, vesicular transport, protein folding, and DNA repair, simple invertebrates such as Caenorhabditis elegans or Drosophila are regarded as good models in studies examining the coordinated functions of genes related to signal transmission or protein-protein interactions. Simple vertebrates (zebrafish, frogs, etc.) or mammals (rat, mouse, rabbit, guinea pig, etc.) are used to establish human disease models or to explain their effects at the organism level. In addition, the use of experimental animals is quite common in experimental medicine research, especially in pharmaceutical developments and molecular pathway studies.
\r\n\r\n\tThis book aims to discuss current developments such as the definition of model animals, the purposes of using model animals and the diseases in which they are used, the selection of appropriate models and subjects, and the technological methods used in the experimental model.
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So far, he has authored 65 journal papers, 5 book chapters, and 75 other publications. He worked as an assistant professor in the Department of Biochemistry from 2004 to 2009. He earned the title of Associate Professor of Biochemistry in the field of Health Sciences in 2009. Dr. Karapehlivan was appointed Professor in 2014.",coeditorOneBiosketch:"Dr. Gelen has been engaged in various academic activities at Kafkas University since 2016. There he completed 5 projects and has 3 ongoing projects. He has 60 articles published in scientific journals and 20 poster presentations in scientific congresses. His research interests include physiology, endocrine system, cancer, diabetes, cardiovascular system diseases, and isolated organ bath system studies.",coeditorTwoBiosketch:"Dr. Kükürt has 27 published research articles in academic journals, 11 book chapters, and 37 papers. He took part in 10 academic projects. He served as a reviewer for many articles. 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He worked as an assistant professor in the Department of Biochemistry from 2004 to 2009. He earned the title of Associate Professor of Biochemistry in the field of Health Sciences in 2009. He was appointed Professor in 2014. He is currently working as a Professor in the Department of Biochemistry at Kafkas University Faculty of Medicine.",institutionString:"Kafkas University",position:null,outsideEditionCount:0,totalCites:0,totalAuthoredChapters:"2",totalChapterViews:"0",totalEditedBooks:"0",institution:{name:"Kafkas University",institutionURL:null,country:{name:"Turkey"}}}],coeditorOne:{id:"178366",title:"Associate Prof.",name:"Volkan",middleName:null,surname:"Gelen",slug:"volkan-gelen",fullName:"Volkan Gelen",profilePictureURL:"https://mts.intechopen.com/storage/users/178366/images/system/178366.jpg",biography:"Volkan Gelen is a Physiology specialist who received his veterinary degree from Kafkas University in 2011. 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He is currently working at the Department of Biochemistry, Kafkas University. He has 27 published research articles in academic journals, 11 book chapters, and 37 papers. He took part in 10 academic projects. He served as a reviewer for many articles. He still serves as a member of the review board in many academic journals. 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Two landmark papers (Evans & Kaufman, 1981; Martin, 1981) described the isolation from the blastocyst of a cell line, that grew rapidly, was maintained by passaging, had a normal karyotype unlike embryonal carcinoma cells and could be induced to differentiate into a wide variety of cell types by injecting them into the mouse or by culturing them in the absence of feeder cells
This chapter will explain what a protein interaction network is and why it is used for looking at pluripotency. It will cover methods used to build protein-interaction networks and the methods of validations for these protein interactions. The chapter will also present an integrated dataset that merges the current understanding into one protein interaction network. Base on this integrated network, we will discuss what constitutes key factors in pluripotency, how these key factors are connected in the network and the protein machineries that they recruit to set up the pluripotent state. Finally, the chapter will look at the future challenges in the completion and utilization of the protein interaction network for the manipulation of pluripotency.
A protein interaction network comprises proteins as nodes and protein-protein interactions as undirected links between the nodes. Drawing networks allows researchers to manage and interpret large datasets. Interpretation of the dataset is done by adopting concepts from other fields such graph theory to describe network properties. Such interpretations can explain how the structure of the network is serving its biological function. For example, in the field of graph theory, several parameters can be computed for a network. These parameters describe the architecture of the network so that it can be compared to other networks. This can provide some insights into the behaviour of the network particularly if it is compared to a network that is similar and already better understood. The most fundamental parameter of a network is the number of links a node has. This is referred to as ‘the degree of a node’ and is a variable that is designated by “
Networks and their corresponding graphs. (A) Random network shows a normal distribution and (B) Scale-free network shows a power-law distribution when their number or fraction of nodes with different number of links are plotted.
In scale-free networks, most of the nodes will have very few links and only a few nodes will have many links (Figure 1B). Systems that are approximately scale-free include many biological networks like the yeast proteome, the prokaryote and eukaryote transcription network, all metabolic networks, and even the internet (Albert, 2005; Barabasi, 2009). Networks of this class show robustness against failure of single components. Besides degree distribution, some other network properties include the average number of neighbours, the average or characteristic path length, the network diameter and the clustering coefficient (Barabasi & Oltvai, 2004). Classifying networks by its degree distribution is one way graph theory can be used to associate universal laws or organizational principals to networks. Understanding network principals makes it easier to predict protein functions; to generate testable hypothesis; and to simulate manipulations of protein components to see if it gives desired consequences.
Different methods can be used to build a protein interaction network. The simplest method is to build the network based on available information about protein interactions from the literature. A second method is to include interactions based on extrapolations of protein interactions in other organisms to the orthologs in the organism of interest. However, both of these methods are limited to known interactions. In addition, the second method may result in the inclusion of false interactions due to wrongly mapped orthologs or lost of conservation of interactions.
To discover protein-protein interactions without
A major concern with the use of affinity purification-mass spectrometry or yeast 2-hybrid approaches is the presence of false positives. In the yeast 2-hyrbid system, biologically irrelevant interactions can happen between two proteins inside the yeast nucleus to give a false-positive signal. In affinity purification-mass spectrometry, false positives are caused by background proteins that are not completely removed during affinity purification. Although mock purifications are included in experiments to allow identification of background proteins, there is a limitation in the mass spectrometer to capture all the peptides in a sample for identification. As such, sampling of the peptide population is not saturated. This causes estimations of relative abundance of proteins to be inaccurate and hence discrimination of noise from signal based on relative abundance of proteins in the actual versus the mock purifications also becomes inaccurate.
In view of these shortcomings of the approaches, validation of datasets becomes very important. The most direct method of validating a protein-protein interaction is by reciprocal co-precipitation. This can be done by expressing the two proteins in a cell culture system. However, some of these interactions are indirect and occur via a third protein, which if not present in the cell, would yield negative results in co-precipitation analysis. Even after direct association has been verified, it is important to further examine the functional significance of the interaction. Not all physical interactions have functional significance. For example, both Oct1 and Oct3/4 can interact with Sox2, but only the Oct3/4-Sox2 complex activates
Although the earliest protein-protein interaction network in embryonic stem cells was based on Nanog as that first bait protein, datasets of later work were mostly built upon Oct3/4 (Liang, et al., 2008; Pardo, et al., 2010; van den Berg, et al., 2010; Wang, et al., 2006). Other proteins that have been used as baits are Sall4, Tcfcp2l1, Dax1, Esrrb, Rex1, Nac1 and Zfp281 (van den Berg, et al., 2010). These proteins were used because they were found to interact with Oct3/4. To gain a more complete view of the pluripotency protein interaction network, datasets from the four published protein interaction networks of the embryonic stem cell was integrated as one (Figure 2). Integration of these datasets gives a network comprising 239 proteins. Of these, 131 proteins were directly associated to Oct3/4. As expected, the distribution of the nodes according to their degree of links follows a power law distribution curve (Figure 2). Theoretically, this would suggest that pluripotency is mediated by a highly robust mechanism that is insensitive to the loss of many of its individual components.
However, at this stage more work is required before such conclusion can become fully accepted. This is because the protein interaction network is currently incomplete. At this stage, the network structure can be strongly skewed by the methods used to generate the network (Futschik, et al., 2007). The observation that essential proteins like Oct3/4, tend to be more highly connected than nonessential proteins could be a true property or a consequence of their having been more thoroughly studied, or a combination of the two (Hakes, et al., 2008). As data accumulates, the power of system biology to catalogue and integrate data will become more meaningful (Pieroni, et al., 2008).
The pluripotency-associated protein interaction network of mouse embryonic stem cells. The network is derived from a combined dataset from four publications (
From the literature, Oct3/4 is already known to be a key factor in pluripotency. In mice, loss of Oct3/4 results in embryos that fail to form a pluripotent inner cell mass (Nichols, et al., 1998). The inner cell mass in these embryos takes on a trophoblast lineage and subsequently fails to proliferate. In adult cells, provision of Oct3/4 together with various cocktails of transcription factors induces pluripotency (Nakagawa, et al., 2008; Takahashi & Yamanaka, 2006). While the other components of these cocktails can change, the inclusion of Oct3/4 is indispensible. The level of Oct3/4 is also important in the subsequent maintenance of pluripotency. While decrease of Oct3/4 to half its physiological level leads to conversion of embryonic stem cells to trophectoderm, an increase of Oct3/4 by less than two fold of its physiological dosage leads to conversion of embryonic stem cells into primitive endoderm and mesoderm (Niwa, et al., 2000). Finally, as the embryo develops, the level of Oct3/4 decreases in the cells that differentiate; but in germ cells where pluripotency is kept, Oct3/4 expression is maintained (Scholer, et al., 1990; Scholer, et al., 1989). Taken together, this is evidence for the role of Oct3/4 in inducing and in maintaining pluripotency. While the key role of Oct3/4 in pluripotency is obvious and does not need construction of the protein interaction network to point this out. The emergence of other protein hubs (nodes with high number of links) can suggest new key factors. Following Oct3/4, are two other proteins, Esrrb and Tcfcp2l1 that have 82 and 87 links respectively. The importance of Esrrb in pluripotency is corroborated by the observation that this protein can help in the induction of pluripotency in fibroblast (Feng, et al., 2009). Although there are no similar observations for Tcfcp2l1, its hub position in the network would suggest that this protein might be another important coordinator of pluripotency.
Recently, the use of RNA interference has offered a means to functionally screen the genome. This would be a complimentary approach to the protein interaction network to find key factors of pluripotency. To find genes that are needed for maintenance of pluripotency, individual genes are knock-down by RNA interference. Combining the datasets from several studies (Ivanova, et al., 2006; Zhang, et al., 2006), including two which were genome-wide screens (Ding, et al., 2009; Hu, et al., 2009), led to the identification of a total of 166 proteins. In concurrence with the identification of Esrrb as a hub protein in the protein interaction network, the same protein was found to be one of the 166 proteins that were important for the maintenance of pluripotency (Table 1). However, a total of only 15 genes, inclusive of Esrrb, from the list of 166 are in the protein interaction network. This suggests that there are other key components found via RNA interference that are not yet discovered by protein-protein interaction. On the reverse, there are 224 proteins in the protein interaction network that are not found by RNA interference. These proteins could be involved in the induction of pluripotency but not in maintenance of it. Alternatively, these proteins may not have been identified via RNA interference because there can be redundancy of function, which is one mechanism for the robustness of the network.
For human embryonic stem cell, no protein interaction network based on yeast 2-hybrid or affinity p4urification-mass spectrometry approaches have been generated. However, determinants of human embryonic stem cell pluripotency have been identified by a genome-wide RNA interference screen (Chia, et al., 2010). The screen identified a total of 566 genes and a protein interaction network base on these has been reported. Information regarding possible interactions between any of the 566 genes was mapped based on the online database STRING, which stores known interaction and includes transfers from orthologous
Pluripotency-associated genes found via RNA interference assay of mouse embryonic stem cells. Proteins that are also found in the protein interaction network are shaded grey.
The pluripotency-associated protein interaction network of human embryonic stem cell. Genes for pluripotency are discovered from RNA interference assay and interactions are based on the STRING database that also considers interactions from orthologs. Bigger and brighter colored nodes represents proteins with more links. The nodes with the top connectivity are colored blue. POU5F1 is colored purple for easy identification. The network has a power law distribution for their degree of links. Network parameters are indicated in the box.
interactions. Among the 566 genes, a total of 279 genes have some form of protein-protein interaction within the group and this network is shown in Figure 3.
The human network also shows a power law distribution (Figure 3). The hubs in the network are POLR2E with 26 links, MY06 with 19 links and EP300 and CDC42 both with 18 links. Notably human OCT4 is not one of the hubs. Again this is most probably an artifact of the incomplete network due to the lack of publications on OCT4 interactions. Although human OCT4 did not show up as a key factor, it is known to be important in pluripotency of human embryonic stem cell. Hence this emphasizes the need for more work in the construction of the network before reliable deductions can be made.
The mouse embryonic stem cell protein interaction network for the transcription factors constructed based on the experimental datasets of four separate publications (
Proteins such as Oct3/4 and Esrrb are transcription factors and they appear to be key factors in pluripotency. On the genome, these transcription factors show clustering at embryonic stem cell-specific genes, supporting the notion that their collaborations forms codes for ensuring selective transcriptional activation (Chen, et al., 2008; Kim, et al., 2008). It remains to be confirmed if these clusterings require direct protein-protein interactions or simply are clustering at the same location. Protein-protein interaction between these transcription factors could provide structural changes required for regulation of gene expression for pluripotency. It was suggested that collaborations involving more transcription factors would activate embryonic stem cell-specific genes. While transcription factors with little interactions would activate more general genes.
From the integrated dataset, proteins with the GO annotation “transcription factor” constitute a total of 78. Figure 4 shows a protein-interaction network of these transcription factors. Certainly, there are transcriptions factors that are important to pluripotency that do not cluster into the highly interactive zone because the network is incomplete. For example, Sox2 is important for regulating pluripotency genes but does not have many mapped collaborators probably because the Sox2-interactome has yet to be investigated by any lab. The current network therefore serves as a guide for future research directions.
The nucleosome remodeling deacetylase (NuRD) complex and its interactions with transcription factors in the pluripotency protein interaction network.
The nucleosome remodeling histone deacetylase (NuRD) complex (Ahringer, 2000) was the most prominent complex identified in the embryonic stem cell protein-interaction network (Liang, et al., 2008; Pardo, et al., 2010; van den Berg, et al., 2010). All the components of this complex are in the network and each of the components interacts with one or more of the five transcription factors that was studied in greater detail (van den Berg, et al., 2010), namely Nanog, Esrrb, Oct4, Tcfcp2l1, and Sall4 which are themselves already tightly associated with one another (Figure 5).
This suggests that the transcription factors co-recruit the same machinery, NuRD for histone deacetylation as a gene repression mechanism to regulate pluripotency. Indeed case studies have shown that NuRD has specific developmental roles rather than being required for general cellular functions (Ahringer, 2000; Ch\'ng & Kenyon, 1999; Mannervik & Levine, 1999). Besides NuRD, other complexes have been reported in the study by Pardo et al. (Pardo, et al., 2010). Most of these are involved in chromosome remodeling. Confirmation of these findings would surely expand our knowledge of the extent to which each of these complexes contributes to pluripotency. This is because there is also converse evidence that chromosomal remodeling factors like the Polycomb Group and Polycomb Repressive Complex are not required for maintenance of pluripotency in embryonic stem cells (Azuara, et al., 2006; Boyer, et al., 2006; de Napoles, et al., 2004; Lee, et al., 2006; Montgomery, et al., 2005; Niwa, 2007; O\'Carroll, et al., 2001). It is believed that the chromatin of the embryonic stem cell is “loose” so as to allow free accessibility to transcription factors, but at the same time repressors are there to serve to prevent spontaneous differentiation of the embryonic stem cells. Having the different chromatin modifiers inserted into the protein interaction network may help to clarify their role in pluripotency. Besides the chromatin modifiers, the basic transcriptional machinery was also found to be recruited to the protein interaction network by Esrrb (van den Berg, et al., 2010). However this mechanism appears not to be utilized by the other transcription factors in the network. It remains to be seen if this mechanism is directly related to the regulation of pluripotency.
Ironically, pluripotency is best demonstrated by its loss. A population of cells is pluripotent if it can differentiate into many cell types; but once that happens, pluripotency is lost. In the embryonic stem cell, molecules for pluripotency work to balance two opposing features: the readiness to differentiate and the prevention of differentiation. To understand the molecular mechanism of pluripotency, we need to keep in mind this concept of pluripotency. In simulations, pluripotency should demonstrate these two opposing forces. In the current protein interaction network both of these features of pluripotency are not distinguished. Furthermore, it is necessary to consider the multifunctionality of proteins. In this case, looking at proteins for the assignment of “jobs” may be more confusing than helpful. Alternatively, assignment of processes may be more meaningful if this was done to the edges of the network rather than the nodes. This approach of analysis can be illustrated by the following example. The interaction (edge) between Oct3/4 and Cdx2 serves the purpose of “gene repression”; and the interaction (edge) between Oct3/4 and Sox2 serves the purpose of “gene activation”. Hence instead of annotating both functions to the Oct3/4 node, the annotations can be on the edges.
A protein interaction network by virtue of the protocols employed is a single snapshot of the protein-protein interactions of a population of cells at any given time. To understand how embryonic stem cells have the ability to differentiate into different cell types, further information will have to be integrated. The final protein interaction network should include information on protein subcellular location and protein concentration. All this information in the network will change as a function of time as the cell undergoes cell cycling and when the cell undergoes fate changes. A study on the system-level changes across the three mechanistic layers: epigenetic, transcriptional and translational during fate change in mouse embryonic stem cells show that changes in nuclear protein levels are not accompanied by concordant changes in the corresponding mRNA levels, suggesting that translational and post-translational mechanisms, rather than transcriptional regulation play important roles, during lost of pluripotency. (Lu, et al., 2009). For full understanding and successful simulation, information from the protein interaction network, the gene regulatory network and microRNA networks of ES cell should be fed back into one another. Integration of protein-protein interaction networks with transcriptional profiling networks has been done in yeast and has led to the discovery of new network features which are described as party hubs and date hubs (Vidal, et al., 2011). Party hubs are nodes that are coexpressed with its protein partners and date hubs are nodes that are not always transcribed at the same time and place as its partners. Biologically, party and date hubs may represent two kinds of protein-protein interactions. Transient protein-protein interactions that occur between transcription factors or between transcription factors and other protein complexes are date hubs. Static protein-protein interactions that occur between protein subunits of a stable protein complex are party hubs. The first type of interaction usually encodes instructions or messages while the second type of interaction functions mainly to execute the processes as a module. Identifying these interactions allow us to further understand how cell fate decisions are made and how these decisions are executed.
In view of the large number of proteins that have been associated with pluripotency. It is possible that there are also alternate means of achieving pluripotency. After all, pluripotency is a cellular state rather than a cellular composition. Proteins like Ronin (Dejosez, et al., 2008; Zwaka, 2008), which show strong associations with pluripotency, may operate via a separate network.
As data accumulation continues towards the point where the boundaries of the pluripotency-associated protein interaction network are felt, extra efforts will be needed towards looking for interactions amongst low concentration proteins and towards validation of this network. With a more complete embryonic stem cell protein interaction network, new hypothesis can be formulated. As more system biology data is generated from other fields, it will also become possible to compare between non-pluripotent and pluripotent networks. The embryonic stem cell protein interaction network, when ready, will serve as a point of comparison with other stem cells, with differentiated cells and with cancer cells. Such comparisons can potentially bring out unique features in each of these cellular conditions. Finally, in view of the differences between human and mice, the same work will have to be repeated with human embryonic stem cells. Knowledge gained from the challenges in mouse embryonic stem cell research ensues much faster progress with the human embryonic stem cell project.
Overall, we see great promise in getting answers and insights from a mature protein interaction network. Currently a total of 239 proteins form the mouse embryonic stem cell protein interaction network. More work is required in the construction of this network and this must be closely accompanied with attempts to annotate the purpose and nature of the interaction as discussed above. Another 151 proteins discovered to have a role in pluripotency by genome-wide RNA interference screening are yet to be connected to the protein interaction network. Multiple validations to confirm the involvement of these proteins in pluripotency are also necessary. In the network, the transcription factors show collaboration amongst themselves. A core group of transcription factors show recruitment of the same machinery, i.e. the NuRD. Some studies suggest that other chromatin modification machineries are also recruited. The role of these machineries remains to be investigated. When the network is reasonably saturated, system biology analysis should give greater insight into network properties. Inclusion of information on dynamic properties of the protein interaction network would also facilitate predictive capabilities
This work is supported by the Agency for Science, Technology and Research, Singapore.
Perovskite solar cells (PSCs) have received a great deal of attention in the past few decades due to their impressively high power conversion efficiency (PCE) [1]. To date, PCE as high as 25.6% has been successfully recorded. This performance has already been compared with the single-crystalline silicon solar cells system. With the advancement in the perovskite properties control, including the crystallinity properties, grain size, and stability properties, further improvement in the PCE is expected to be achieved soon. The continuous growth in the preparation of the high-performance charge selective layer in the perovskite solar cells further contributes to the rapid progress in the PCE improvement of the PSC [2].
Along with the transparent conducting electrode (TCE) and the top metal contact, a PSC device is composed of an electron-transport layer (ETL), an organometal-halide perovskite active layer, and a hole-transport layer (HTL). In these solar cells, the perovskite and its photoelectrical properties are the keys to the overall photovoltaic process. Its unique high-optical absorption constant drives massive photon absorption and exciton generation in the device. Despite this key fact, the carrier transport and interfacial charge transfer dynamics play another crucial factor for the generation of the overall PSC performance. These two parameters depend on the nature of the surface and the crystallinity properties of the charge-selective layers [3].
One of the serious problems in perovskite solar cell devices is the loss of charge carriers during the transport process in the carrier layer. This is because, the carrier layer has low crystallization, high grain boundary resistance as well as experiences loss of carrier charge during extraction to the outer electrode. The main factor of carrier charge lost during extraction to the outer electrode is due to the high interface resistance between the electrode and the carrier layer. Therefore, it is expected that when a carrier layer that has high crystallinity, very low thickness, and good coupling conditions with external electrodes is used, then the performance of the device will increase.
The electron transport layer (ETL), for example, TiO2, and other semiconducting oxides, such as SnO2, ZnO, have been widely applied in the perovskite solar cells fabrication. Despite the excellent performance demonstrated by them, this ETL suffers from large-density surface defects related to oxygen vacancy, particularly in the TiO2 system. The defect from such vacancy causes immense trap-limited (Shockley-Read-Hull) transport in the extraction of the photogenerated carrier to the external electrode. This in many cases degrades the photovoltaic performance of the PSC up to a certain degree, reducing the power conversion efficiency of the device. Even though there exist several methods in the passivation of such defects, such as acid passivation, etc., the improvement is minute. In addition, this method may add additional resistance to the photocarrier transport reducing the power conversion efficiency. Along with these crucial factors, the crystallinity properties of the ETL add an additional issue to the photocarrier transport dynamic in the device. As normal in the high-performance PSC fabrication, mesoporous TiO2 or SnO2 was used as ETL along with a compact layer of TiO2 or SnO2 (See Figure 1), [4]. As the figure reveals, the mesoporous layer is composed of a large number of interconnected small grain particles that produce grain boundary resistance due to lattice mismatch among the connected particles. This resistance should be massive due to their large-scale existence on the layer. This certainly complicates the transport of photogenerated electrons to the electrode layer, such as high internal resistance or radiationless recombination [5, 6]. Therefore, the selection of the right material for the carrier layer is important in determining the performance of a device. Such resistance boundary further augments the presence of mesoporous-compact layer interface resistance in the ETL system of the PSC. From this picture, we can estimate the loss would be suffered by the device during the photovoltaic process. This means that if such ETL is replaced with the single-crystalline ETL system, the performance of the perovskite solar cells can be improved.
Mesoporous TiO2 ETL. (A and C) Top and side view of mesoporous TiO2 layer on compact layer TiO2. (B and D) Top and side of mesoporous TiO2 layer. (Reprinted from [
Recently, materials of two-dimensional (2D) dichalcogenide transition metals (TMDs), such as MoS2, WS2, TiS2, CdTe, and others, have been used as carrier layers in perovskite solar cells due to their high charge carrier mobility, unique optoelectrical properties, large exciton binding energy, very fast interface charge transfer properties as well as excellent physical and chemical stability properties [7]. Their optoelectronic properties were also found to correspond to the number of layers, dopants, and strains (straining). The phenomenon of the massive charge transfer process in these van der Waals crystals driven by the collective motion of excitonic surfaces enables a high interfacial charge extraction and reduces charge recombination for an effective photovoltaic process [8]. One of the uniqueness of the TMDs layer is that it has an atomic-scale thickness (very thin) and has high crystallinity. With its planar (2D) structure, it will produce a strong coupling when grown on the electrode surface. Therefore, it has great potential for a carrier layer in perovskite solar cells.
Transition metal dichalcogenide (TMD) has the chemical formula of MX2 where M is the transition metal from groups 4 to 10 in the periodic table system, and X is a chalcogen atom such as sulfur (S), selenium (Se), or tellurium (Te). Figure 2 shows the typical structure of TMD. The structure has two layers of chalcogen that clamp a transition metal layer making this material have its uniqueness in electronic, optoelectronic properties, and chemical stability [10]. The electronic and optical properties of TMDs materials change significantly depending on the number of layers. For example, the MoS2 band gap increases from 1.29 eV (multilayered MoS2) to 1.59 eV (monolayer MoS2), and also this bandgap changes from an indirect bandgap to a direct bandgap as the number of layers decreases [11].
Typical structure of transition metal dichalcogenide materials. (A) Typical layer stacking structure in bulk transition metal dichalcogenide structure. T and X represent the transition metal and chalcogen elements, respectively. (B) Top and side view of single-layer of TMD with 2H-phase. (C) Side view of single-layer TMD with 1T-phase. (Reprinted from [
As is well known, most of these 2D TMD materials have ambipolar properties that enable the materials to transport both electrons and holes [12]. In other words, this allows 2D TMDs material to be used as ETL or HTL in n-i-p or p-i-n perovskite solar cells. However, most perovskite solar cell applications use these 2D TMD materials as HTL. Only MoS2 and TiS2 have been used as ETLs and have successfully produced efficiencies as high as 13.14% and 18.79% [7, 13]. Table 1 shows several PSC device structures utilizing TMD as ETL. Recently, there was a first simulation study on the photoelectric properties of WS2 as an ETL in perovskite solar cells reported with efficiencies as high as 25.70% [23]. By having high electron mobility as well as energy levels appropriate to the perovskite layer, the WS2 atomic layer is expected to function as an ETL capable of producing high-performance perovskite solar cell devices.
Material | Device structure | PCE (%) | Ref. | |||
---|---|---|---|---|---|---|
TiS2 | FTO/TiS2/MAPbI3/spiro-OMeTAD/Au | 23.38 | 1.05 | 0.71 | 17.37 | [14] |
TiS2 | ITO/TiS2/ FAxMA1-xBrxClyI1-x-y/spiro-OMeTAD/Ag | 24.68 | 1.00 | 0.75 | 18.79 | [7] |
MoS2 | FTO/MoS2/MAPbI3/spiro-OMeTAD/Au | 21.70 | 0.89 | 0.63 | 13.14 | [15] |
MoS2 | ITO/MoS2/Csx(MAyFA1-y)1-xPb(IzBr1-z)3/spiro-OMeTAD/Au | 16.24 | 0.56 | 0.37 | 3.36 | [16] |
MoS2/TiO2 | ITO/TiO2/MoS2/MAPbI3/spiro-OMeTAD/Au | 13.36 | 0.65 | 0.51 | 4.43 | [17] |
MoS2/SnO2 | ITO/SnO2/MoS2/FAxMA1-xBrxClyI1-x-y/spiro-OMeTAD/Ag | 24.57 | 1.11 | 0.79 | 21.73 | [18] |
MoS2 | Graphene/MoS2/MAPbI3/PTAA/Au | 20.92 | 0.91 | 0.76 | 14.42 | [19] |
MoS2 | ITO/MoS2/MAPbI3/PCBM/Al | 12.50 | 0.85 | 0.57 | 6.01 | [20] |
SnS2 | ITO/SnS2/MAPbI3/Spiro-OMeTAD/Au | 23.70 | 0.95 | 0.61 | 13.63 | [21] |
SnS2 | ITO/SnS2/MAPbI3/Spiro-OMeTAD/Au | 21.70 | 1.011 | 0.60 | 13.20 | [22] |
Photovoltaic parameters of perovskite solar cell devices using dichalcogenide transition metals (TMDs) as ETLs.
TiS2 is one of the TMDC family that has been intensively studied recently due to its semi-metallic properties with low-bandgap value, i.e., 0.2 eV. With high electrical conductivity, i.e., 1 x 104 S m−1, this material is potential as an electrode in many applications including lithium-ion batteries and solar cells. Despite its excellent electrical properties, the use of TiS2 as independent electrode material in the application is limitedly demonstrated. It is mainly stacked with other materials such as MoS2 [24] or TiO2 to improve the properties in applications. For the case of MoS2 stacked with TiS2, the TiS2 can form Schottky contact with MoS2 with barrier height [24] between these two atomic layers can be varied by the doping type and concentration either in the MoS2 or TiS2 side (Figure 3). This certainly provides a wider opportunity to modify the electrical properties of the system for desired performance in application. In the typical process, n-type-doped TiS2–MoS2 (ML) contacts exhibit a barrier height relatively larger, i.e., 1.0 eV below doping level degeneracy. Nevertheless, these n-type-doped contacts still have the potential as the switch in high-power as well as tunnel Schottky barrier MOSFETs. In contrary to the n-type doped system, the p-type-doped TiS2–MoS2 (ML) exhibits a zero barrier height at a particular doping concentration, i.e., 5 × 1018 cm−3. Under this condition, the depletion region width is zero and the band becomes flat, revealing that the contact is ohmic and the barrier height is small. These results reveal the unique unusual interfacial properties arising from this ultimate thin contact that promise a special function in the application. This phenomenon could be the driving factor for an efficient photocarrier extraction in the perovskite solar cells using ETL modified with MoS2 or TiS2 atomic layer.
PLDOS of TiS2–MoS2 (ML) FET-like junctions doped with different doping concentrations and the variation of band structure at interface B. a–d The doping concentrations are: N = 5 × 1019 cm−3, N = 1 × 1019 cm−3, N = 5 × 1018 cm−3, and P = 5 × 1018 cm−3. The thickness of TiS2 is four layers. On the right side, the plot shows the variation of band structure under different doping concentrations. The scale bar is from 0.0 to 90.0 (1/eV). Interface A is the interface between TiS2–MoS2. (Reprinted from [
For example, in the perovskite solar cells system with SnO2 ETL (Figure 4), there is an increase in the energy band alignment between the ETL and perovskite layer when the 2D TiS2 is attached to the surface of SnO2 [18]. The conduction band level of ETL (SnO2) reduced from 4.68 to 4.63 eV in the presence of 2D TiS2. This has narrowed the offset energy between the ETL and perovskite (conduction band level at 4.36 eV). As the result, the photogenerated carrier extraction becomes enhanced, improving the photocurrent and the power conversion efficiency. As shown in Figure 4C–4F, the power conversion efficiency increases from 19.65% to 21.73% when the SnO2 ETL is modified with the 2D TiS2 atomic layer. The nature of interfacial photocarrier dynamic improvement in the presence of the 2D TiS2 atomic layer can be seen from the increase of the
(A) Cross-sectional SEM image of the PSC. (B) The energy level diagram. (C) Representative J-V curves of the PSCs with SnO2 or SnO2 /2D TiS2 as ETLs. (D) EQE curve and integrated current density of the PSC with SnO2 /2D TiS2 as the ETL. (E) Histogram of the PCE of PSCs with SnO2 and SnO2 /2D TiS2 as ETLs analyzed from 25 cells. (F) Steady-state efficiency of the PSCs with SnO2 and SnO2/2D TiS2 as ETLs measured under constant voltages of 0.86 V and 0.92 V, respectively. (Reprinted from [
Figure 5 explains in detail how the photocarrier dynamic in the device was impressively modified in the presence of a 2D TiS2 atomic layer on the surface of SnO2 ETL. As presented, the photocurrent is enhanced impressively. This is the result of enhanced interfacial charge transfer as indicated by the transient and steady-state photoluminescence analysis result, which is also supported by the electrochemical impedance spectroscopy result, showing decrease in the interfacial charge transfer resistance in the device.
Comparison of SnO2 and SnO2/2D TiS2 as ETLs in PSCs: (A)
We also in our recent result have coupled the TiS2 atomic layer on top of the TiO2 surface to compensate for surface defect due to the oxygen vacancy, enhancing the interfacial charge transfer and transport dynamic when applied as ETL in perovskite solar cells [25]. The perovskite solar cells’ performance improves from 18.02 to 18.73% (Figure 6). Electrochemical impedance analysis revealed that there is an improvement as high as 13% in interfacial charge transfer in the ETL with 2D TiS2 and 43% improvement in the charge recombination resistance (Figure 7A). The latter is verified by the increase in the photocurrent (Figure 7B) and the decrease in the leakage current of the device when 2D TiS2 passivates the TiO2 surface (Figure 7C). We can relate this process to the reduction in the trap density in the device as shown by the value of
Photovoltaic performance of the 2D TiS2-TiO2 NG and TiO2 NG-based PSC. (A) Schematic structure of 2D TiS2-TiO2 NG-based PSC. (B)
Photoelectrical properties of the PSC device. (A) Electrochemical impedance spectra and equivalent circuit of the device. (B) Photogenerated current of the PSC device (
MoS2 atomic layer is the most studied TMD system because of its excellent optical and electrical properties [26, 27, 28] and has been used widely in perovskite solar cells as a hole-transport layer (HTL) and an electron-transport layer (ETL) [11, 15, 26] in the form of colloidal or flakes thin film [15, 28, 29, 30]. Table 1 lists down several perovskite solar cells using MoS2 as ETL with a particular device configuration. For example, Singh, Giri, et al. [13] have obtained power conversion efficiency as high as 13.2% from PSC devices using MoS2 material as ETL. In this study, they synthesized the MoS2 film directly on FTO substrate using microwave irradiation-assisted reduction method. It is found that the efficiency obtained by MoS2 material is close to the efficiency value obtained from TiO2 and SnO2 material making MoS2 material comparable to other ETL materials. Abd Malek et al. [16] have also developed different structures of MoS2 ETL on the ITO substrate. Instead of colloidal or flake structured film, an ultrathin layer of MoS2 prepared from ultrasonic spray pyrolysis was fabricated to obtain its functionalities as ultrathin ETL in the PSC device. The result showed that the PCE device performance depended on the condition during the preparation of the MoS2 atomic layer, particularly the substrate temperature. It is demonstrated that substrate temperature of 200°C is suitable for growing high-quality MoS2 atomic layer on ITO surface, thus, optimizing the power conversion efficiency of the PSC (Figure 8). This MoS2 thin-film-based device as ETL has shown high-stability properties where its efficiency can be maintained as much as 90.24% of the original efficiency after 80 s exposure continuously under simulated solar light illumination (AM1.5).
The photovoltaic parameter for MoS2 as ETL in PSC. (A) Schematic structure of the PSC device. (B) The
In addition to being used singly in the ETL, TMD materials can also be combined with other organic or inorganic electron transport materials to form electron transport materials. For example, Ahmed et al. [31] have added a MoS2 layer on top of the TiO2 layer to be used as ETL in perovskite solar cells. The use of MoS2/TiO2 as ETL has successfully increased the efficiency of the device by 16% higher than the device that only uses TiO2 as ETL. Similarly, Huang et al. [18] have successfully produced an n-i-p type plane device using SnO2 and 2D TiS2 as ETL. High efficiency was recorded by this group, which was as high as 21.73% with a relatively small hysteresis value. The increase in efficiency in this device is due to the matching of the ETL energy level and the appropriate perovskite layer as well as the lack of electron trap density in the ETL.
Tungsten disulfide (WS2) share common basic properties of TMD with other systems, such as high-mobility properties, unique optoelectronic properties, large exciton-binding energy, and good physical and chemical stability as well as ambipolar properties [11]. In addition, WS2 has an energy level that is suitable for the perovskite layer of three types of cations (Figure 9) and can be easily synthesized by the ultrasonic spray pyrolysis method. WS2 also has high stability as well as having fast interface charge transfer properties [32]. Among the available 2D TMD, the energy band structure of WS2 is a much better match with the common perovskite of MAPbI3 (Figure 10). Furthermore, it also has a relatively larger bandgap if compared with the other system in this class of materials, promising facile excitonic separation during the photovoltaic process and producing better power conversion efficiency.
Energy levels of dichalcogenide transition metal materials (TMDs) as ETLs and MAPbI3 as perovskite layers in perovskite solar cells.
Energy level diagram for n-i-p perovskite solar cells using WS2 ETL.
Recently, we have realized the PSC device utilizing the WS2 layer as ETL and evaluated how the number of layers of WS2 influences the carrier dynamic in the device [5]. We prepared the WS2 atomic layer via ultrasonic spray pyrolysis. Figure 11 shows a schematic diagram of the 2D atomic layer preparation. A modified commercially available ultrasonic spray system (Daiso, Japan) was used. A homemade solution container was placed on the top of the ultrasonic membrane of the system (Figure 11). Ultrasmall solution precursor mist can be produced from the process and fall on the ITO substrate surface that is positioned approximately 5 cm below the membrane. The temperature of the substrate was set at 350°C.
Schematic diagram of ultrasonic spray pyrolysis for the preparation of TMD ETL.
The typical morphology of the WS2 atomic layer on the ITO substrate is shown in Figure 12A. The WS2 nanosheet’s morphology resembles a circular structure that is produced from the precursors’ mist that emerged from the ultrasonic spray membrane. Confocal Raman imaging further indicated the existence of a very thin layer of structure from the circular structure as shown in Figure 12B. Raman analysis then confirmed the phase crystallinity of the WS2 (Figure 12C). As the figure reveals, there are two sharp peaks obtained from the Raman spectrum that is centered at 348.9 cm−1and 412.3 cm−1, which are associated with the in-plane (E2g) and the out-of-plane (A1g) vibration modes of the lattice (see inset in Figure 12C) [33, 34, 35, 36, 37, 38, 39]. According to the value of the separation between these two peaks, the thickness of the atomic layer is estimated to be in the range of 10 L. The X-ray diffraction analysis further confirmed the phase crystallinity of the WS2 layer (Figure 12D) [40, 41, 42]. The high-resolution transmission electron microscopy (HRTEM) and selected area electron diffraction (SAED) analysis results (Figure 12F and G) show that the sample is single crystalline. However, the presence of SAED composed of a triple spot is related to the stacking of the WS2 atomic layer during the transfer to the lacey grid for HRTEM analysis. The XPS analysis then further confirmed the Raman and XRD analysis results on the phase crystallinity of the sample of which it belongs to WS2 (Figure 12H–I).
The morphology, phase crystallinity, chemical state properties of WS2 nanosheet. (A) FESEM image of WS2 nanosheet on the ITO substrate. (B-C) Raman imaging and spectrum of WS2 were obtained using 532 nm laser excitation. The inset in (C) shows the corresponding main vibration mode of Raman. (D) XRD spectrum for WS2 nanosheet showing 2H phase. (E-F) Low and high-resolution TEM image of WS2 nanosheet. (G) SAED pattern of WS2 nanosheet showing at least three stacking WS2 nanosheets. (H-I) High-resolution scan of XPS at W and S binding energy of WS2 nanosheet. (Reprinted from [
PSCs device was fabricated using the WS2 atomic layer as ETL and investigated how the thickness of the WS2 ETL influenced the photovoltaic process. The structure of the PSC device is ITO/WS2 nanosheets/Perovskite/Spiro-OMeTAD/Au. Perovskite used was triple cations system of Cs0.05[MA0.13FA0.87]0.95Pb (I0.87Br0.13)3 [43].
It was found that the thickness, represented by the number of layers, of the WS2 atomic layer ETL, strongly influences the power conversion efficiency of the PSC device (Figure 13). The results show that the PCE performance improves with the increase of thickness from 4 L to the optimum thickness of 7 L (WS30 sample in the figure). The optimized WS2 ETL thickness can produce a PSC device with PCE as high as 18.21% with
The photovoltaic performance of PSC using different thicknesses of WS2 ETL. (A)
To understand the extent effect of the WS2 atomic layer as ETL in the PSC device, the device performance was compared with the reference PSC utilizing well-known SnO2 ETL. In the typical process, the performance of SnO2-based PSC shows lower performance than the WS2 atomic layer–based device (Figure 14). Steady-state and transient photoluminescence analysis revealed that the interfacial charge transfer from the perovskite to ETL is high in the WS2 atomic layer [45], the result of optimized coupling due to ultra-flat surface morphology offered by the WS2 atomic layer. This phenomenon is further confirmed by the electrochemical impedance spectroscopy analysis result where it is obtained that the interface charge transfer resistance is lower in the WS2-based PSC device than the SnO2-based device. Thus, it can be remarked that the WS2 atomic layer enables highly active interfacial charge transfer for a high-performance PSC device.
The comparison of the photovoltaic parameter between WS2 (7 L, WS30 sample) and SnO2-based PSC device. (A)
2D atom thick TMD promises facile charge extraction and transport in the perovskite solar cells due to its ultimate thin and single-crystalline nature. The optimization of the 2D TMD layer to obtain a large dimension on the substrate surface is necessary to further promote a highly dynamic photogenerated carrier in the perovskite solar cells device. These materials may become a potential platform for high-performance perovskite solar cells.
We acknowledged the financial support from the Universiti Kebangsaan Malaysia for supporting this project under GUP-2019-071 and DIP-2021-025.”
The authors declare no conflict of interest.
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Drawing from a telephone questionnaire survey involving a sample of 300 (150 male and 150 female) owners of Italian micro-enterprises, located in Marche Region, findings suggest that entrepreneurs mostly dealt with the crisis through a defensive approach (restructuring and resizing strategies). Compared to men, female entrepreneurs had a lower propensity towards offensive strategies (innovation, development and growth). Differences in the approach towards the crisis were also identified with respect to company’s age, industry and impact of the crisis. These findings contribute to the debate on gender-based differences in behaviors, attitudes and preferences. Moreover, they can help to identify economic policy measures useful to help men and women entrepreneurs to address the crisis. Policy-makers who want to support female entrepreneurship should consider these aspects, in order to identify tools and policies that can help female firms to resist the crisis more effectively in the short-term and to seize new market opportunities in the recovery phase. The sample is restricted to sole proprietors and to a limited geographical context. So further analysis should involve companies of different sizes and located in different geographical contexts, both in Italy and abroad.",book:{id:"6165",slug:"entrepreneurship-development-tendencies-and-empirical-approach",title:"Entrepreneurship",fullTitle:"Entrepreneurship - Development Tendencies and Empirical Approach"},signatures:"Andrea Buratti, Francesca Maria Cesaroni and Annalisa Sentuti",authors:[{id:"207476",title:"Associate Prof.",name:"Francesca Maria",middleName:null,surname:"Cesaroni",slug:"francesca-maria-cesaroni",fullName:"Francesca Maria Cesaroni"},{id:"207528",title:"Dr.",name:"Annalisa",middleName:null,surname:"Sentuti",slug:"annalisa-sentuti",fullName:"Annalisa Sentuti"},{id:"207529",title:"Dr.",name:"Andrea",middleName:null,surname:"Buratti",slug:"andrea-buratti",fullName:"Andrea Buratti"}]},{id:"31837",doi:"10.5772/35756",title:"Entrepreneurship Education and Pupils' Attitudes Towards Entrepreneurs",slug:"entrepreneurship-education-and-pupils-attitudes-towards-entrepreneurs",totalDownloads:2628,totalCrossrefCites:4,totalDimensionsCites:11,abstract:null,book:{id:"2261",slug:"entrepreneurship-born-made-and-educated",title:"Entrepreneurship",fullTitle:"Entrepreneurship - Born, Made and Educated"},signatures:"Vegard Johansen, Tuva Schanke and Tommy Høyvarde Clausen",authors:[{id:"105750",title:"Dr.",name:"Vegard",middleName:null,surname:"Johansen",slug:"vegard-johansen",fullName:"Vegard Johansen"},{id:"141010",title:"Ms.",name:"Tuva",middleName:null,surname:"Schanke",slug:"tuva-schanke",fullName:"Tuva Schanke"},{id:"141012",title:"Dr.",name:"Tommy Høyvarde",middleName:null,surname:"Clausen",slug:"tommy-hoyvarde-clausen",fullName:"Tommy Høyvarde Clausen"}]}],mostDownloadedChaptersLast30Days:[{id:"73293",title:"Innovation Processes in Aquaculture: Comparing Companies in Norway and Chile",slug:"innovation-processes-in-aquaculture-comparing-companies-in-norway-and-chile",totalDownloads:664,totalCrossrefCites:0,totalDimensionsCites:0,abstract:"In the last 20 years, aquaculture in general and harvested Atlantic salmon in particular has experienced very high growth rates compared to other food products, and at the same time, salmon production has evolved from semi-manual production techniques to the utilization of high-tech capital-intensive production equipment. This development has seriously challenged the environmental considerations and escalated fish health measures to combat existing and evolving problems. As an answer to these challenges and because of relatively high profit margins, aquaculture of harvested Atlantic salmon has also had a speedy innovation path. This chapter will give a theoretical background and an empirical analysis based on data collection at three companies, two in Norway and one in Chile. The focus is on how innovations take place in different stages of the production process, and how these are built into the production and managerial system. The results show, as expected, links between company operations and the actual innovations, but these links do not have the same structure in Norway and Chile. Factors like human and financial resources, technology, and company organization seem to explain most of the differences between how innovations take place in the companies.",book:{id:"9550",slug:"entrepreneurship-contemporary-issues",title:"Entrepreneurship",fullTitle:"Entrepreneurship - Contemporary Issues"},signatures:"Knut Ingar Westeren",authors:[{id:"322340",title:"Prof.",name:"Knut Ingar",middleName:null,surname:"Westeren",slug:"knut-ingar-westeren",fullName:"Knut Ingar Westeren"}]},{id:"73449",title:"Collaborative Entrepreneurship for Continuous Innovation: A Strategic Alliance Perspective",slug:"collaborative-entrepreneurship-for-continuous-innovation-a-strategic-alliance-perspective",totalDownloads:617,totalCrossrefCites:0,totalDimensionsCites:0,abstract:"Strategic alliances act as a platform to implement collaborative entrepreneurship while exposing a range of challenges. By capitalizing on entrepreneurial opportunities for continuous innovation, alliance partners can promote the productive utilization of resource-pooling systems and facilitate innovation processes for value co-creation. Simultaneously, the heterogeneity of partners in terms of different motivations and interests interferes with the advancement of collaborative entrepreneurship for resource exchange and orchestration. The objective of this paper is thus to explore how to deal with the potential coordination issues that can make an alliance vulnerable and its returns diminished through a preliminary integrative approach to the interface between collaborative entrepreneurship and strategic alliances. From this approach, three elements that can contribute to leverage values of collaborative entrepreneurship for continuous innovation are identified: social capital, entrepreneurial orientation, and interorganizational learning. Based on the discussion about the functions of each element in the context of alliance partners’ dynamic interactions, a model of analysis on collaborative entrepreneurship for continuous innovation is proposed. Hence, this chapter contributes to a better understanding of how firms can enact collaborative entrepreneurship productively to gain greater benefit from the alliance configuration for collaborative advantage.",book:{id:"9550",slug:"entrepreneurship-contemporary-issues",title:"Entrepreneurship",fullTitle:"Entrepreneurship - Contemporary Issues"},signatures:"Ribin Seo",authors:[{id:"321949",title:"Dr.",name:"Ribin",middleName:null,surname:"Seo",slug:"ribin-seo",fullName:"Ribin Seo"}]},{id:"57068",title:"Business Intelligence: An Innovative Technological Way to Influence Corporate Entrepreneurship",slug:"business-intelligence-an-innovative-technological-way-to-influence-corporate-entrepreneurship",totalDownloads:1851,totalCrossrefCites:7,totalDimensionsCites:7,abstract:"Adaptation to dynamism and complex environments in today’s Knowledge Society is key for firms to survive and improve their positions. This paper applies business intelligence (BI) to the firm to shape its organizational design and improve its performance. The paper also relates business intelligence to organizational performance management through organizational learning (OL), knowledge management (KM) and the technological competencies of the company’s employees and managers. Theoretical study of the main current research serves as the basis for the development of several propositions to fill the gaps in knowledge of business intelligence. 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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). 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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,editorialBoard:[{id:"13633",title:"Prof.",name:"Abdelhamid",middleName:null,surname:"Mellouk",slug:"abdelhamid-mellouk",fullName:"Abdelhamid Mellouk",profilePictureURL:"https://mts.intechopen.com/storage/users/13633/images/1567_n.jpg",institutionString:null,institution:{name:"Paris 12 Val de Marne 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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 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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,editorialBoard:[{id:"1177",title:"Prof.",name:"Antonio",middleName:"J. 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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. 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