Open source and web-based platforms for metabolomics data analysis.
\r\n\tAnimal food additives are products used in animal nutrition for purposes of improving the quality of feed or to improve the animal’s performance and health. Other additives can be used to enhance digestibility or even flavour of feed materials. In addition, feed additives are known which improve the quality of compound feed production; consequently e.g. they improve the quality of the granulated mixed diet.
\r\n\r\n\tGenerally feed additives could be divided into five groups:
\r\n\t1.Technological additives which influence the technological aspects of the diet to improve its handling or hygiene characteristics.
\r\n\t2. Sensory additives which improve the palatability of a diet by stimulating appetite, usually through the effect these products have on the flavour or colour.
\r\n\t3. Nutritional additives, such additives are specific nutrient(s) required by the animal for optimal production.
\r\n\t4.Zootechnical additives which improve the nutrient status of the animal, not by providing specific nutrients, but by enabling more efficient use of the nutrients present in the diet, in other words, it increases the efficiency of production.
\r\n\t5. In poultry nutrition: Coccidiostats and Histomonostats which widely used to control intestinal health of poultry through direct effects on the parasitic organism concerned.
\r\n\tThe aim of the book is to present the impact of the most important feed additives on the animal production, to demonstrate their mode of action, to show their effect on intermediate metabolism and heath status of livestock and to suggest how to use the different feed additives in animal nutrition to produce high quality and safety animal origin foodstuffs for human consumer.
",isbn:"978-1-83969-404-2",printIsbn:"978-1-83969-403-5",pdfIsbn:"978-1-83969-405-9",doi:null,price:0,priceEur:0,priceUsd:0,slug:null,numberOfPages:0,isOpenForSubmission:!1,hash:"8ffe43a82ac48b309abc3632bbf3efd0",bookSignature:"Prof. László Babinszky",publishedDate:null,coverURL:"https://cdn.intechopen.com/books/images_new/10496.jpg",keywords:"Technological Feed Additives, Feed Industry, Quality of Compound Feed, Non-Antibiotic Growth Promoter, Product Quality, Additive Enzymes, Digestibility of Nutrients, NSP Enzymes, Farm Animals, Livestock, Immunity, Microbiome",numberOfDownloads:null,numberOfWosCitations:0,numberOfCrossrefCitations:null,numberOfDimensionsCitations:null,numberOfTotalCitations:null,isAvailableForWebshopOrdering:!0,dateEndFirstStepPublish:"November 24th 2020",dateEndSecondStepPublish:"December 22nd 2020",dateEndThirdStepPublish:"February 20th 2021",dateEndFourthStepPublish:"May 11th 2021",dateEndFifthStepPublish:"July 10th 2021",remainingDaysToSecondStep:"2 months",secondStepPassed:!0,currentStepOfPublishingProcess:4,editedByType:null,kuFlag:!1,biosketch:"Professor Emeritus from the University of Debrecen, Hungary who authored 297 publications (papers, book chapters) and edited 3 books. Member of various committees and chairman of the World Conference of Innovative Animal Nutrition and Feeding (WIANF).",coeditorOneBiosketch:null,coeditorTwoBiosketch:null,coeditorThreeBiosketch:null,coeditorFourBiosketch:null,coeditorFiveBiosketch:null,editors:[{id:"53998",title:"Prof.",name:"László",middleName:null,surname:"Babinszky",slug:"laszlo-babinszky",fullName:"László Babinszky",profilePictureURL:"https://mts.intechopen.com/storage/users/53998/images/system/53998.jpg",biography:"László Babinszky is Professor Emeritus of animal nutrition at the University of Debrecen, Hungary. From 1984 to 1985 he worked at the Agricultural University in Wageningen and in the Institute for Livestock Feeding and Nutrition in Lelystad (the Netherlands). He also worked at the Agricultural University of Vienna in the Institute for Animal Breeding and Nutrition (Austria) and in the Oscar Kellner Research Institute in Rostock (Germany). From 1988 to 1992, he worked in the Department of Animal Nutrition (Agricultural University in Wageningen). In 1992 he obtained a PhD degree in animal nutrition from the University of Wageningen.He has authored 297 publications (papers, book chapters). He edited 3 books and 14 international conference proceedings. His total number of citation is 407. \r\nHe is member of various committees e.g.: American Society of Animal Science (ASAS, USA); the editorial board of the Acta Agriculturae Scandinavica, Section A- Animal Science (Norway); KRMIVA, Journal of Animal Nutrition (Croatia), Austin Food Sciences (NJ, USA), E-Cronicon Nutrition (UK), SciTz Nutrition and Food Science (DE, USA), Journal of Medical Chemistry and Toxicology (NJ, USA), Current Research in Food Technology and Nutritional Sciences (USA). From 2015 he has been appointed chairman of World Conference of Innovative Animal Nutrition and Feeding (WIANF).\r\nHis main research areas are related to pig and poultry nutrition: elimination of harmful effects of heat stress by nutrition tools, energy- amino acid metabolism in livestock, relationship between animal nutrition and quality of animal food products (meat).",institutionString:"University of Debrecen",position:null,outsideEditionCount:0,totalCites:0,totalAuthoredChapters:"1",totalChapterViews:"0",totalEditedBooks:"0",institution:{name:"University of Debrecen",institutionURL:null,country:{name:"Hungary"}}}],coeditorOne:null,coeditorTwo:null,coeditorThree:null,coeditorFour:null,coeditorFive:null,topics:[{id:"25",title:"Veterinary Medicine and Science",slug:"veterinary-medicine-and-science"}],chapters:null,productType:{id:"1",title:"Edited Volume",chapterContentType:"chapter",authoredCaption:"Edited by"},personalPublishingAssistant:{id:"185543",firstName:"Maja",lastName:"Bozicevic",middleName:null,title:"Ms.",imageUrl:"https://mts.intechopen.com/storage/users/185543/images/4748_n.jpeg",email:"maja.b@intechopen.com",biography:"As an Author Service Manager my responsibilities include monitoring and facilitating all publishing activities for authors and editors. From chapter submission and review, to approval and revision, copyediting and design, until final publication, I work closely with authors and editors to ensure a simple and easy publishing process. I maintain constant and effective communication with authors, editors and reviewers, which allows for a level of personal support that enables contributors to fully commit and concentrate on the chapters they are writing, editing, or reviewing. I assist authors in the preparation of their full chapter submissions and track important deadlines and ensure they are met. I help to coordinate internal processes such as linguistic review, and monitor the technical aspects of the process. As an ASM I am also involved in the acquisition of editors. Whether that be identifying an exceptional author and proposing an editorship collaboration, or contacting researchers who would like the opportunity to work with IntechOpen, I establish and help manage author and editor acquisition and contact."}},relatedBooks:[{type:"book",id:"7144",title:"Veterinary Anatomy and Physiology",subtitle:null,isOpenForSubmission:!1,hash:"75cdacb570e0e6d15a5f6e69640d87c9",slug:"veterinary-anatomy-and-physiology",bookSignature:"Catrin Sian Rutland and Valentina Kubale",coverURL:"https://cdn.intechopen.com/books/images_new/7144.jpg",editedByType:"Edited by",editors:[{id:"202192",title:"Dr.",name:"Catrin",surname:"Rutland",slug:"catrin-rutland",fullName:"Catrin Rutland"}],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:"Theophanides",surname:"Theophile",slug:"theophanides-theophile",fullName:"Theophanides Theophile"}],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"}],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"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"72",title:"Ionic Liquids",subtitle:"Theory, Properties, New Approaches",isOpenForSubmission:!1,hash:"d94ffa3cfa10505e3b1d676d46fcd3f5",slug:"ionic-liquids-theory-properties-new-approaches",bookSignature:"Alexander Kokorin",coverURL:"https://cdn.intechopen.com/books/images_new/72.jpg",editedByType:"Edited by",editors:[{id:"19816",title:"Prof.",name:"Alexander",surname:"Kokorin",slug:"alexander-kokorin",fullName:"Alexander Kokorin"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"1373",title:"Ionic Liquids",subtitle:"Applications and Perspectives",isOpenForSubmission:!1,hash:"5e9ae5ae9167cde4b344e499a792c41c",slug:"ionic-liquids-applications-and-perspectives",bookSignature:"Alexander Kokorin",coverURL:"https://cdn.intechopen.com/books/images_new/1373.jpg",editedByType:"Edited by",editors:[{id:"19816",title:"Prof.",name:"Alexander",surname:"Kokorin",slug:"alexander-kokorin",fullName:"Alexander Kokorin"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"57",title:"Physics and Applications of Graphene",subtitle:"Experiments",isOpenForSubmission:!1,hash:"0e6622a71cf4f02f45bfdd5691e1189a",slug:"physics-and-applications-of-graphene-experiments",bookSignature:"Sergey Mikhailov",coverURL:"https://cdn.intechopen.com/books/images_new/57.jpg",editedByType:"Edited by",editors:[{id:"16042",title:"Dr.",name:"Sergey",surname:"Mikhailov",slug:"sergey-mikhailov",fullName:"Sergey Mikhailov"}],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. Venkateswarlu",coverURL:"https://cdn.intechopen.com/books/images_new/371.jpg",editedByType:"Edited by",editors:[{id:"58592",title:"Dr.",name:"Arun",surname:"Shanker",slug:"arun-shanker",fullName:"Arun Shanker"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"878",title:"Phytochemicals",subtitle:"A Global Perspective of Their Role in Nutrition and Health",isOpenForSubmission:!1,hash:"ec77671f63975ef2d16192897deb6835",slug:"phytochemicals-a-global-perspective-of-their-role-in-nutrition-and-health",bookSignature:"Venketeshwer Rao",coverURL:"https://cdn.intechopen.com/books/images_new/878.jpg",editedByType:"Edited by",editors:[{id:"82663",title:"Dr.",name:"Venketeshwer",surname:"Rao",slug:"venketeshwer-rao",fullName:"Venketeshwer Rao"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"4816",title:"Face Recognition",subtitle:null,isOpenForSubmission:!1,hash:"146063b5359146b7718ea86bad47c8eb",slug:"face_recognition",bookSignature:"Kresimir Delac and Mislav Grgic",coverURL:"https://cdn.intechopen.com/books/images_new/4816.jpg",editedByType:"Edited by",editors:[{id:"528",title:"Dr.",name:"Kresimir",surname:"Delac",slug:"kresimir-delac",fullName:"Kresimir Delac"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}}]},chapter:{item:{type:"chapter",id:"52527",title:"Processing and Visualization of Metabolomics Data Using R",doi:"10.5772/65405",slug:"processing-and-visualization-of-metabolomics-data-using-r",body:'\nMetabolomics is a rapidly growing discipline focusing on the global study of small molecule metabolites in biological systems. Through the characterization of metabolite dynamics, interactions, and responses to genetic or environmental perturbations, metabolomics can provide a comprehensive picture of both baseline physiology and global biochemical responses to genetic, abiotic, and biotic factors [1].
\nAs the diversity in abundance and chemical properties of metabolites varies greatly in organisms, a range of analytical techniques must be utilized to survey the entire metabolome. A number of methods have been developed for the extraction, detection, identification, and quantification of the metabolome [2]. Mass spectrometry coupled with gas chromatography (GC-MS) or liquid chromatography (LC-MS) are the most common analytical platforms, although capillary electrophoresis mass spectrometry (CE-MS) and nuclear magnetic resonance (NMR) are also widely used in metabolomics research [3–6].
\nSince metabolomics experiments typically produce large amounts of data, sophisticated bioinformatic tools are needed for efficient and high-throughput data processing to remove systematic bias and to explore biologically significant findings. Both multivariate statistical analysis and data visualization play a critical role in extracting relevant information and interpreting the results of metabolomics experiments.
\nThe data generated in a metabolomics experiment generally can be represented as a matrix of intensity values containing N observations (samples) of K variables (peaks, bins, etc.). Additional information, such as experimental group, genotype, time point, gender, etc., is also required for some statistical procedures. For multivariate analysis, very few mathematical constraints are placed on the values contained in the data matrix. Therefore, a common set of statistical tools can be used to analyze metabolomics data of almost any type. However, as discussed below, multiple preprocessing steps are often necessary to yield interpretable results [7, 8].
\nThe focus of this chapter is on describing methods for processing and visualizing metabolomics data obtained by liquid chromatography mass spectrometry (LCMS). LCMS is the most widely used method in metabolomics research due to its dynamic range, coverage, ease of sample preparation, and high information content [3–5]. We present a standard workflow for handling LCMS data, from raw data processing to downstream statistical analysis using open source tools available within the R software environment.
\nR is a software environment for statistical computing, data analysis, and graphics, which has become an essential tool in all areas of bioinformatics research. A major advantage of R over commercial software is that it is open source and free to all users. The base distribution of R and a large number of user contributed packages are available under the terms of the Free Software Foundation’s GNU General Public License in source code form. There are versions of R for Unix, Windows, and Macintosh at the official CRAN website (http://cran.r-project.org/).
\nIn addition to being a popular language for performing high level statistics, R has a wide array of graphical tools that make it an ideal environment for exploratory data analysis and generating publication quality figures. All work is done using the command line-based text functions with user-defined scripts. Although R can be challenging for new users, it is quite flexible once the basic commands, functions, and data structures have been learned. A detailed description of every function with examples can be obtained by typing help followed by the name of the function, i.e., help(plot). In addition, there are ample online resources to help users learn the basics of R as well as solve a wide range of common data analysis problems [9–11].
\nR has a powerful set of functions for creating graphics, from fairly simple graphs using base graphics commands to highly sophisticated graphs using the one of several advanced graphics packages [12]. The focus of this chapter is on using the ggplot2 package, a high-level platform for creating graphics that is especially powerful for working with high-dimensional data [13]. The basic idea in ggplot2 is to build graphs by adding successive layers that include visual representations as well as statistical summaries of the data [14]. A layer is defined as an R data frame or matrix, a specification mapping columns of that frame into aesthetic properties, a statistical approach to summarize the rows of that frame, a geometric object to visually represent that summary, and an optional position adjustment to move overlapping geometric objects. This approach allows great flexibility in producing highly customizable graphs by combining layers that describe single or multiple data objects. We will demonstrate these ideas throughout this chapter.
\nA number of free software tools are available for processing, visualization, and statistical analysis of metabolomics data. Some of the more popular platforms are presented in Table 1.
\nXCMS is a powerful R-based software for LCMS data processing. As with any R-based package, it is command line driven and requires some background knowledge of the R programming language. XCMS uses nonlinear retention time correction, matched filtration, peak detection, and peak matching to extract relevant information from raw LCMS data [15]. Peak detection parameters can be optimized to process the raw data in an appropriate and efficient manner. As shown in the following sections, XCMS can be combined with base R functions and additional R packages to provide a complete solution to LCMS data processing needs. Statistical analysis and data visualization can all be incorporated into the scripts to quickly process the large amounts of data from start to finish.
\nXCMS online is a web-based version of XCMS that provides many of the advantages of the traditional R package without the use of a command line-based environment [16]. It allows limited control over processing parameters and gives interactive graphs of univariate and multivariate analyses [17].
\nMetaboAnalyst is a popular web-based resource that provides an easy to use, comprehensive interface for metabolomics data analysis [18]. It includes a variety of data preprocessing and statistical tools for univariate and multivariate analysis and generates high resolution, interactive graphics. Depending on the type of data being analyzed, it can also be used for biomarker analysis, enrichment analysis, pathway analysis, and more [19].
\nHaystack is a web server-based processing tool that uses mass bins to filter and extract information from raw LCMS data [20]. Haystack also provides graphical tools to visualize raw and processed data and incorporates some exploratory statistical analysis tools. Because extracted features are based on mass bins, missing values due to redundant or missing peaks are absent from the processed data. Processed data files are downloadable in .csv format, which can be imported into analysis software of the user’s preference.
\nPlatform | \nDescription | \nAdvantages | \nDisadvantages | \nReference | \n
---|---|---|---|---|
XCMS | \nR-based platform for raw LCMS data processing and visualization | \n-Adjustable parameters -Streamlined workflow | \n-Requires knowledge of R language -Command line based | \n[15] | \n
XCMS online | \nWeb-based graphical user interface version of XCMS | \n-Cloud storage and sharing -Relatively easy to use | \n-Not as customizable as the R version | \n[16, 17] | \n
MetaboAnalyst | \nOnline statistical analysis | \n-Easy to use -Wide variety of statistical tests available -Interactive plots | \n-Relies on pre-processed data -Limited options for customizing graphics | \n[18, 19] | \n
Haystack | \nRaw data processing and visualization using mass bins | \n-Unbiased -No zero values -Not dependent on quality of chromatography | \n-Graphics not customizable -Does not take into account peak retention time | \n[20] | \n
MZmine 2 | \nRaw data processing and visualization | \n-Java based -User friendly -Project batching | \n-Limited options for customizing graphics -Numerous options can be overwhelming | \n[21] | \n
MET-IDEA | \nRaw data processing for GCMS and LCMS data | \n-Works well with very large data sets -Optional manual integration | \n-Aimed more for GCMS data -Low-quality graphics | \n[22] | \n
Open source and web-based platforms for metabolomics data analysis.
MZmine 2 is a Java-based platform that allows for flexible MS data processing through a user-friendly graphical interface and customizable parameters for data processing and visualization [21].
\nMetabolomics Ion-Based Data Extraction Algorithm (MET-IDEA) is a large-scale metabolomics data processing program generally used for GCMS data but can also be used for LCMS data. It performs peak alignment, annotation, and integration of hyphenated mass spectrometry data and allows visualization of integrated peaks along with their accompanying mass spectra [22].
\nRobust computational tools are essential to analyze and interpret metabolomics data. The first step in data processing, especially in untargeted metabolomics, is to convert the raw data into a numerical format that can be used for downstream statistical analysis. For LCMS data, this involves multiple steps, including filtering, feature detection, alignment, and normalization [23, 24]. Filtering methods aim to remove effects like measurement or baseline noise. Feature detection is used to identify measured ions from the raw signal. Alignment methods cluster measurements from across different samples and normalization removes unwanted systematic variation between samples.
\nOnce the data have been converted to a numerical matrix, statistical tools are used to reveal patterns in the data, determine class membership, and identify relevant biological features. Univariate methods are often used as a first step to obtain a rough ranking of potentially important features before applying more sophisticated statistical techniques [25]. Familiar examples include fold change differences, t-tests, and volcano plots.
\nMultivariate methods treat multiple, often correlated, variables simultaneously, and attempt to model relationships between variables and observations [25–27]. Well known examples include principal component analysis (PCA) and partial least squares (PLS). PCA is an unsupervised method meaning that only the data matrix itself is used to model the data. Since class membership is not considered, PCA provides an unbiased summary of the data structure. For exploratory studies where differences between experimental groups may be unknown or unpredictable, it is appropriate to apply PCA as a first step to reveal patterns in the data and relationships between groups.
\nA shortcoming of PCA is that it can only reveal group differences when within-group variation is sufficiently less than between-group variation [26]. To overcome this problem, supervised forms of discriminant analysis such as PLS that rely on class membership are also routinely applied in metabolomics studies [28]. The primary goal of these methods is to identify class differences from a multivariate data set and to identify biologically important features that account for these differences. Often, the results of a PCA are used to formulate a hypothesis that PLS or other supervised methods can test or verify in more detail.
\nIn the following sections, we will provide an overview of the data processing steps used in a typical metabolomics experiment. We do not aim to provide a thorough description of the statistical methods but rather introduce the basic concepts behind these methods and demonstrate how to perform them in the R computing environment.
\nTo illustrate the concepts and methods presented in this chapter, we produced a data set from two tomato varieties harvested at two developmental stages using untargeted LCMS. The varieties used were “Manapal” and its nearly isogenic counterpart, the high pigment-2 dark green (hp-2dg) mutant. Hp-2dg plants have a mutation in the tomato homolog of the DEETIOLATED-1 gene involved in light-mediated signal transduction and plant photomorphogenesis [29]. These plants display a number of interesting traits, including shorter stature, slower growth rates, darker foliage, and elevated levels of certain metabolites such as flavonoids and carotenoids [30–32]. Since many of these phytochemicals are important for human health, there is great interest in understanding the molecular mechanisms that underlie the altered phenotype of the hp-2dg mutation.
\nFully expanded fruits were harvested at green and red stages, lyophilized, and stored at −80°C until analysis. Samples from 10 individual fruits were extracted in 80% methanol and analyzed using an Agilent 1100 HPLC/MSD-VI Ion Trap mass spectrometer with an electrospray ionization (ESI) source. Chromatograms were saved in netCDF format using the instrument software.
\nThe data were analyzed using R as described in the following sections. A summary of the data processing workflow is presented in Figure 1. The full source code for all procedures is available online at https://github.com/ualr-Rgroup/metabolomics-in-r.
Summary of metabolomic data processing workflow.
While web-based tools such as MetaboAnalyst and XCMS online are inarguably convenient, learning data analysis procedures in R gives researchers much greater flexibility not only in processing and analyzing their data but also in creating high-quality custom graphics. Here, we demonstrate how to perform raw data processing in R using the XCMS package. XCMS is a powerful and flexible software package that has gained widespread use for untargeted metabolomic studies [15]. It is available through Bioconductor and can be installed in R using the following commands:
\nXCMS requires data in an open access nonproprietary format such as Network Common Data Form (NetCDF) or mzXML. File conversion often can be done within the operating software of the instrument. A popular online tool, ProteoWizard (http://proteowizard.sourceforge.net), is also available that can be used to convert raw LCMS data into an open data format. The converted data files are placed in a subdirectory named “cdf” within the R working directory. From here the data can be imported, processed, and visualized.
\nAn LCMS data file is a series of successively recorded mass spectra over a range of m/z values. The total intensity of all ions at each time point is known as the total ion chromatogram (TIC). The xcmsRaw function is used to read data files into R’s memory environment. The plotTIC function can be used to produce a TIC or base peak chromatogram (BPC). This function can also be used to obtain the numerical data for custom plotting. The following example demonstrates how to do this in R:
\nThis script loads the xcms package, defines the raw data files, and creates an xcmsRaw object (xr1) from file number 6. This object stores information in the raw data that can be extracted and combined into a data frame. We add factor columns for sample and group that will be used to make a custom plot with ggplot2.
\nThis process is repeated for additional raw data files. The individual data frames are combined into a single data frame with the rbind function.
\nOnce the data have been combined into a single data frame, a multipanel plot can be produced using the facet_wrap function in ggplot2. The result is shown in Figure 2. Variation in peak profiles can be readily observed between the different groups.
\nXCMS uses several algorithms to process LCMS data. The first step is to filter and detect ion peaks using the xcmsSet method. The peak detection algorithm is based on cutting the data into slices of predefined mass widths and then finding peaks in the chromatographic time domain by applying a Gaussian model peak matched filter. Although the default arguments for the xcmsSet method may provide acceptable results in some cases, it is recommended that the parameters used for peak selection be optimized for this step. Without optimization, common problems that may arise include oversampling, i.e., assigning the same metabolite to multiple peaks, and missing values, i.e., failure to detect certain peaks in some samples that can interfere with downstream statistical analysis [8].
\nRepresentative total ion chromatograms (TICs) of green fruits (A, C) and red fruits (B, D) of the hp-2dg (A, B) and Manapal (C, D) tomato varieties.
The next step after filtration and peak identification is matching peaks across samples. Peaks representing the same analyte across samples must be placed into groups. This is accomplished with the group function, which returns a new xcmsSet object. After matching peaks into groups, XCMS can use those groups to identify and correct correlated drifts in retention time using the retcor function. The aligned peaks can then be used for a second pass of peak grouping which will be more accurate than the first.
\nAfter the second pass of peak grouping, there will still be missing peaks from some of the samples. This can occur because peaks were missed during peak identification or because an analyte was not present or below the detection limit in a sample. Missing values can be problematic for statistical methods that require a fully defined data matrix. Those missing data points can be filled in by re-reading the raw data files and integrating them in the regions of missing peaks using the fillPeaks function.
\nXCMS can generate a report showing fold change differences in analyte intensities and their statistical significance using the diffreport function. However, we recommend obtaining the raw peak integration results using the groupval function. This function returns a numerical matrix in which each row represents a peak defined by its mass and retention time and each column represents a different sample. In an example used here, 308 peaks were identified with 1.4% missing values. This matrix provides the starting point for downstream statistical analysis.
\nOnce the raw data have been processed in XCMS, it is often useful to obtain descriptive statistics for each variable. This can be accomplished in R by creating a function to calculate the mean, median, standard deviation, standard error, and coefficient of variation for each variable. The apply function executes these operations on each row and returns the results into a data frame.
\nThis function creates a new data object containing descriptive statistics for each variable that can be used to rank variables according to mean or median intensity or to assess the degree of dispersion for each variable.
\nBefore higher order statistical methods can be applied, it is often necessary to “clean up” the data to improve the analysis. Typical steps include (1) imputation of missing values, (2) transformation, (3) scaling, and (4) normalization.
\nA common phenomenon in metabolomics measurements is the occurrence of missing values, i.e., empty cells where a respective metabolite peak has not been assigned any numerical value. As many multivariate methods require a fully defined matrix or become computationally inefficient for incomplete data, estimation of missing values is an important step in the preparation of the data [8].
\nEven after using the fillPeaks function in XCMS, there are still typically a large number of missing values. There are several strategies for dealing with these, including removing variables with missing values that exceed a certain threshold. However, these are often interesting features that are important in discriminating experimental groups. An alternative and widely used approach is imputation, where missing values are replaced with a small value with the assumption that the feature in question is below the limit of detection in those samples where XCMS fails to detect a peak.
\nThe following function can be used to find the minimum nonzero value in a set of numbers and then divide that value by 2. We can then use the apply function to replace the missing values in each row of the matrix using this function.
\nSince metabolomics studies are generally concerned with relative changes in metabolite levels, a log or other suitable transformation is normally applied before performing higher order statistical analysis. A log transformation helps to remove heteroscedasticity from the data and correct for a skewed data distribution [7]. This operation is easily performed in R using the log function. The default option is to compute the natural logarithm. However, the general form log (x, base) computes logarithms with any desired based. The base 2 log transformation is commonly used in metabolomics studies. Note that the log function will return NA for any zero values in the data matrix.
\nThe purpose of data normalization is to reduce systematic variation and to separate biological variation from nonbiological variation introduced by the experimental process. This is often necessary to improve the results of higher order statistical analysis [7, 23, 25].
\nNormalization can be sample wise or feature wise or both. Sample wise normalization makes the samples more comparable to each other. Common approaches include normalization to constant sum, to a reference sample or feature, or sample specific normalization such as dry weight or tissue volume.
\nFeature wise normalization involves centering the data around the mean combined with various types of scaling. Centering focuses the data on the amount of variation instead of the mean intensity. Scaling involves dividing each variable by a factor that approximates the amount of data dispersion. The most common scaling approach is known as unit scaling or autoscaling where each variable is mean centered and then divided by the standard deviation. After autoscaling all variables become equally important and are analyzed on the basis of correlations instead of covariances. A disadvantage of autoscaling is that it tends to inflate the importance of small variables which are more likely to contain measurement errors [7]. Other scaling operations include Pareto scaling, which uses the square root of the standard deviation as the scaling factor, vast scaling, which uses the standard deviation and the coefficient of variation as scaling factors, and range scaling, where the range is used as the scaling factor [7].
\nThe scale function in R automatically performs centering and autoscaling. Other scaling procedures can also be carried out using the custom functions. For example, the following function can be used for Pareto scaling which is recommended for metabolomics data.
\nPrincipal component analysis (PCA) is the foundation for many multivariate techniques that seek to describe a set of observations based on a large number of variables [25, 26]. The core idea of PCA is to reduce the dimensionality of the data, i.e., the number of variables while retaining as much of the variation as possible. Using PCA, it is possible to extract and display the systematic variation in the data and identify related groups, trends, and outliers.
\nPCA returns two important types of information: a scores matrix and a loadings matrix. The scores matrix contains the coordinates of the samples (i.e., observations) for each principal component and provides a summary of the observations in a lower dimensional space. The first principal component describes the largest variation in the data matrix, the second component describes the second largest, and so on. All PCA components are mutually orthogonal, meaning they are uncorrelated. Generally, most of the variation is captured in the first two or three principal components. Therefore, a scatter plot of the first two score vectors usually provides a good summary of all the samples and can reveal if there are differences between the groups as well as outliers.
\nAnalogous to the scores matrix, the loadings matrix describes the relationships among the measured variables for each principal component. A scatterplot of the first two loading vectors can reveal the influence (weights) of individual variables in the model. An important aspect of PCA is that directions in the scores plot correspond to directions in the loadings plot, and so a comparison of these two plots can be used to identify, which variables (loadings) are most important for separating the different samples (scores) [28].
\nWhen there are more than two experimental classes, it is generally appropriate to use multivariate methods such as PCA to find patterns in the data [27]. The primary goal of these methods is to determine if the classes can be predicted from the variables (class assignment) and to identify which variables are important in predicting class membership.
\nThere are several ways to perform PCA in R. Here, we will demonstrate the procedure using the prcomp function, which comes with the built-in R stats package. This method uses singular value decomposition (SVD) to calculate eigenvalues, which is the standard approach in PCA. The following syntax is used.
\nwhere “data” is a dataframe or matrix containing the data, retx is a logical value that indicates whether the scores will be returned, center is a logical value indicating whether the variables should be mean centered, and scale is a logical value indicating whether the variables should be scaled to unit variance.
\nAfter missing values have been replaced, the data are log transformed and Pareto scaled. Note that the Pareto scale function must first be defined as above.
\nNow, we perform PCA. The center and scale options are set to FALSE since these operations have already been performed with the paretoscale function.
\nThe t function is used here to transpose the matrix so that each row represents an observation (sample) and each column represents a variable (peak). This is necessary if we want the scores matrix to correspond to samples and the loadings matrix to correspond to variables.
\nThe prcomp function returns several outputs that can be accessed by the summary command.
\nThis returns a list that contains the standard deviations (eigenvalues) and proportion of the total variance for each principal component, the scores matrix, and the loadings matrix. We can extract each of these outputs into a new data frame and save the results to file for later use.
\nThe results of a PCA can be easily visualized using the base graphics functions in R. However, it is often desirable to produce a high-quality figure with custom formatting using ggplot2. To do this, we first import the scores matrix from the PCA. Since the first two principal components capture most of the variance, we will subset the data to include only those values.
\nIn order to map individual samples to their respective groups, we need to add a new column to the data frame indicating the group to which each sample belongs. To do this, we first create a vector of group names and then add the vector to the data frame with the cbind function. We can then generate the scores plot with ggplot2.
\nThis script defines the data and adds layers for data points, text labels, and confidence ellipses. The resulting plot is shown in Figure 3.
\nThe scores plot shows that the two genotypes are well separated along the first PC axis while the developmental stage (green versus red) is separated along the second PC axis. There is more variation among the hp-2dg green samples than among the other groups.
\nPCA scores plot.
We can create a loadings plot using a similar approach. Since there are a large number of variables, we would also like to know which ones have the largest influence on the PCA. Variables with high loadings (positive and negative) are more likely to be important for discriminating groups that are separated in the scores plot.
\nOne of the major advantages of R is that it has many powerful and flexible functions for subsetting data. One approach might be to identify the maximum and minimum loadings using the range function and then subset the data based on a percentage of these values. Alternatively, we can make a plot of PC1 versus PC2 loadings and visually inspect the data for high and low values. The subset function can be used to select rows that meet certain criteria. In this example, 0.09 and −0.09 were selected for threshold values.
\nFor plotting in ggplot2, it is generally recommended to add factor columns to the data frame for the purpose of mapping aesthetics to variables. A factor is a categorical value in R with predefined levels. We can use the ifelse function to specify the factor level in the new columns much in the same way as the subset function was used to create a new data frame. Loadings above and below the threshold values are marked for subsetting in this way.
\nWith the added factors, we can make the plot in ggplot2 and indicate significant loadings with different colors. The grid package provides several options for adding text annotations. The resulting plot is shown in Figure 4.
\nPCA loadings plot.
Since the scores matrix and the loadings matrix share similar geometric properties, the direction of the loadings indicate those variables that have the greatest influence on class separation. Based on these criteria, 64 potentially significant peaks were identified out of the original 308 (Figure 4). Separation along the PC1 axis identified features that show high variation by genotype while separation along the PC2 axis identified features that show high variation by developmental stage.
\nIt should be emphasized that since PCA is an exploratory method, the interpretation of PCA results for the purpose of inferring biological importance must be done with caution. Potentially interesting features must be further analyzed to assess their biological significance. This can be done using boxplots, heatmaps, or other suitable graphical displays and rechecking the raw data by generating extracted ion chromatograms.
\nThe first step in this process is to subset the original data to include only those variables of interest, i.e., the colored symbols in Figure 4. This can be done in R out using the extremely useful merge function.
\nThis command creates a new data frame containing the peak intensity values for the 64 variables with high PCA loadings that can be further analyzed as described below.
\nPartial least squares-discriminant analysis (PLS-DA) is a supervised method that uses multiple linear regression to find the direction of maximum covariance between a data set and class labels [28]. Supervised techniques can be very helpful for highlighting sample/group differences when PCA results are masked by high levels of spectral noise, strong batch effects, or high within group variation [26]. PLS-DA sharpens the separation between groups of observations by rotating PCA components such that a maximum separation among classes is obtained and identifies variables that carry most of the class separating information. However, contrary to PCA, supervised methods like PLS-DA aggressively overfit models to the data, almost always yielding scores in which classes are separated [26, 27]. As a result, these methods can generate excellent class separation even with random data. For this reason results of these types of tests should be critically checked and properly cross-validated.
\nThe pls, plsdepot, and muma packages can all be used for partial least squares analysis in R [33, 34]. We will demonstrate how to perform an extension of the PLS method known as OPLS-DA using the muma package below.
\nHeatmaps are an effective tool for displaying feature variation among groups of samples [35]. The basic concept of a heatmap is to represent relationships among variables as a color image. Rows and columns typically are reordered so that variables and/or samples with similar profiles are closer to one another, making these profiles more visible. Each value in the data matrix is displayed as a color, making it possible to view the patterns graphically.
\nHeatmaps use an agglomerative hierarchical clustering algorithm to order and display the data as a dendrogram. Two important factors to consider when constructing a heatmap are the type of distance measure and the agglomeration method used. For details on the various methods available see [35].
\nThe heatmap.2 function in the gplots package can be used to construct a heatmap that is easily customizable and include options for both the distance and agglomeration methods, as well as scaling options for rows or columns. Unless the actual numerical values in the data matrix have an explicit meaning, row scaling is usually advisable [35].
\nA heatmap showing the scaled data from the 64 loadings extracted by PCA is shown in Figure 5. Four well-defined clusters are evident that correlate well with the four different experimental groups. These variables form a starting point for further experiments and analyses.
Heatmap of significant features obtained from PCA loadings.
Boxplots are another good way to visualize and compare features among different samples. A boxplot graphically depicts a vector through its five-number summary. The edges of the box represent the lower and upper quartiles while the whiskers represent the minimum and maximum values. The median is displayed as a line within the box. Outliers are represented as symbols outside of the whiskers.
\nA simple boxplot can be generated from any numeric vector using the boxplot function in R. However, a more customizable boxplot can be created using the ggplot2 package. Figure 6 shows boxplots for four significant features from the PCA results. The data first were log transformed and Pareto scaled to show relative differences.
\nBoxplot of four significant peaks identified from PCA loadings.
The 495.2/2285 peak, which had a high positive PC1 loading, was significantly higher in the Manapal strain, whereas the 529.8/992 peak, which had a high negative PC1 loading, was significantly higher in the hp-2dg strain. The 1136.4/2038 peak, which had the highest positive loading for PC2, was significantly higher in green fruits of both varieties. Interestingly, the 805.2/2198 peak, which had a high negative loadings for both PC1 and PC2, was only significant in red fruits of the hp-2dg strain.
\nWhile statistical procedures provide important clues about potentially significant variables, a critical but often overlooked step in analyzing metabolomics data is reinspecting the raw data to assess the validity of these results. Not only can this provide confirmation of meaningful features but it can also reveal false positives caused by scaling artifacts or spurious peak assignment, which is common in XCMS-processed data.
\nLCMS ion peaks can be visualized through extracted ion chromatograms (EICs). An EIC is essentially a “slice” of the raw data that covers a specific m/z and time range. XCMS automatically generates EICs for peaks that show high significance, but these are low quality “snapshot” images. However, the plotEIC function in XCMS can be used to extract the numerical data for any EIC of interest. The following commands describe how to obtain EIC data for all samples in a data set and generate an EIC plot for grouped samples.
\nWe first create a list of xcmsRaw objects from the raw data files with the lapply function.
\nNext, we set the upper and lower limits for m/z and time. For this example, we will look at the 805.2/2198 peak since PCA and boxplots indicate that this peak was highly significant in red fruits of the hp-2dg strain.
\nWe can use the lapply function again to create an EIC for all samples. The data are then merged into a data frame for custom plotting in ggplot2.
\nExtracted ion chromatograms for the m/z range 804.5-805.5 in hp-2dg green fruits (A), hp-2dg red fruits (B), Manapal green fruits (C) and Manapal red fruits (D). Each panel represents 10 samples.
The results are shown in Figure 7. The grouped EIC data clearly show that this feature is completely absent in green fruits and is very low in red fruits of Manapal. In contrast, there are several large peaks over this mass range in red fruits of hp-2dg, indicating this feature is a class-specific biomarker.
\n\nA metabolomics experiment often involves a comparison of two groups, e.g., a treatment group versus a control. It is customary in such cases to use univariate methods to obtain a summary of the data and identify potentially important variables before applying multivariate methods [27]. A common tool to identify discriminatory features is to construct a volcano plot. This type of plot displays the fold change differences and the statistical significance for each variable. The log of the fold change is plotted on the x-axis so that changes in both directions (up and down) appear equidistant from the center. The y-axis displays the negative log of the p-value from a two-sample t-test. Data points that are far from the origin, i.e., near the top of the plot and to the far left or right, are considered important variables with potentially high biological relevance.
\nThe steps required to construct a volcano plot can be carried out using several base R functions. The fold change is typically calculated as the ratio of the two means. We can use the apply function to determine the means for each variable.
\nWe then divide the means of each variable to obtain the ratio and take the logarithm so that changes in both directions appear equidistant from the center.
\nThe t.test function in the R stats package returns the p-value for an unpaired t-test of two independent samples. The default option is Welch’s t-test, which assumes unequal variance. Note that data preprocessing steps, such as sum normalization and log transformation, are usually applied to make the samples more comparable and to reduce heteroscedasticity.
\nIt is recommended to use a multiple testing correction when performing t-test on multiple variables. There p.adjust function in R provides several options for this, including the family wise error rate (FWER), also known as the Bonferroni correction, and the false discovery rate (FDR), also known as the Benjamini-Hochberg correction. The false discovery rate is a less stringent condition than the family-wise error rate, so this method is preferred when one is interested in having more true positives.
\nWe take the negative log10 values so that variables with low adjusted p-values (i.e., high significance) appear near the top of the plot.
\nFinally, the data are merged into a single data frame that can be plotted in ggplot2.
\nA volcano plot comparison of the two tomato genotypes in green and red fruits is shown in Figure 8. Significant variables are shown as colored symbols. We selected rather conservative cutoff values of 2 and −2 for the log2 fold change (fold change >4) and 2 for the –log FDR adjusted p-value (p < 0.01) to highlight those features that showed the largest differences. In green fruits, this led to the identification of 38 metabolite peaks that were significantly higher in Manapal and 20 peaks that were higher in hp-2dg, while in red fruits, 40 metabolite peaks were higher in Manapal while 24 were higher in hp-2dg. As with the PCA loadings, these variables can be explored further with heatmaps, boxplots, EICs, etc.
Volcano plot analysis of Manapal versus hp-2dg in green (A) and red (B) fruits.
The R package muma (Metabolomic Univariate and Multivariate Analysis) has a more sophisticated procedure for testing significance and returning p-values for a volcano plot [34]. Briefly, Shapiro Wilk’s test for normality is performed to assess whether each variable has a normal distribution and to decide whether to perform a parametric test (Welch’s t-test) or a nonparametric test (Wilcoxon-Mann Whitney test). The analysis returns fold change differences and a merged set of p-values from both tests and also applies a multiple testing correction that the user can specify. Finally, a volcano plot is generated highlighting significant variables based on the corrected p-values.
\nAn extension of the PLS technique known as orthogonal projection to latent structure is another very useful tool for analyzing metabolomics data. Like PLS this is a supervised method that pairs a data matrix X with a corresponding matrix Y containing sample information. The basic concept in OPLS is to separate the systematic variation in X into two parts, one that is correlated to Y and one that is not correlated (orthogonal) with Y [28]. Only the Y-predictive variation is used to model the data. When working with discrete variables such as class labels the method is called OPLS discriminant analysis. The main advantages of OPLS-DA over PCA are better class discrimination and more robust identification of important features. The OPLS-DA algorithm normally is applied when there are only two classes comprising Y.
\nS-plots from OPLS-DA modeling of green and red fruits in Manapal and hp-2dg varieties.
The muma package can be used to perform OPLS-DA in R. A numerical class vector must be added to represent the Y matrix, i.e., the control group is given a value of 1 and the experimental group is given a value of 2. The data frame is saved in the working directory, and the analysis is carried out with two simple functions.
\nThis method automatically creates a new folder in the working directory that contains the OPLS-DA results in both numerical and graphical formats. The numerical data can be merged into a new data frame for custom plotting in ggplot2.
\nThe loadings from an OPLS-DA model are displayed by means of an “S-plot” where the modeled covariance p[1] is plotted on the x-axis and the correlation profile p(corr)[1] is plotted on the y-axis. Variables with higher p[1] values in both positive and negative directions have a larger impact on the variance between the groups, whereas variables with higher p(corr)[1] values have more reliability. Therefore, data points that fall in the upper right and lower left quadrants have a high impact on the model and represent possible class-specific biomarkers.
\nFigure 9 shows S-plots from an OPLS-DA model of the two tomato varieties in green and red fruits. Variables with |p[1]| > 0.004 are highlighted, and the top 10 for each class are listed in tabular form on the graph. In general, there was good agreement between the OPLS-DA and volcano plot results for identifying significant variables.
\nOne of the major challenges in LCMS-based metabolomics is metabolite annotation, i.e., identifying biological molecules from mass spectral data. Although metabolic profiling approaches that do not assign observed features to known metabolites can provide a powerful means of classifying and directly comparing samples, metabolite identification remains a crucial step for obtaining mechanistic insights into cellular processes. However, the complexity of the metabolome combined with the fact that many metabolites have not been structurally identified means that untargeted metabolomic studies typically yield a large number of unknown peaks.
\nThe accurate identification of metabolites usually requires the ability to match candidate spectra with standard compounds run under the same conditions. Ideally, an orthogonal descriptor such as retention index is used for further validation. However, the lack of readily available standards remains a major obstacle in this regard, particularly in plant phytochemical studies [36]. Consequently, a number of strategies are being brought forward to assist in the chemical identification of unknown metabolites, including the development comprehensive mass spectral libraries [37–39], searchable databases [40–42], and information networks that integrate genomic, transcriptomic, and metabolomic data [43–45]. The construction, maintenance, and integration of these resources are crucial to the advancement of the field of metabolomics.
\nUntargeted metabolomics has become an increasingly powerful tool to investigate biological problems in agriculture, medicine, and a number of other fields. Therefore, efficient processing methods must be developed and refined to enable robust interpretation of metabolomics data. Method development and new software tools have helped address these challenges over the last decade. However, since improvements are still required at the various stages of data processing, establishing and refining new methods will continue to be important in the future of metabolomics research.
\nIn this chapter, we have presented an overview of several common methods used for processing and analyzing LCMS-based metabolomics data and how to carry out these methods in the R programming environment. Although a variety of open source and web-based tools are available to support metabolomics data analysis, the ability to tailor the data processing workflow to one’s own needs and generate custom graphics in R offers major advantages.
\nAs the data sets used in all scientific disciplines get ever larger and more complex, it is becoming critical for scientists to be knowledgeable about how to use high-level languages such as R, which allow for easy and intuitive data manipulation. Along with powerful statistical capabilities, graphical tools make R an ideal environment for exploratory data analysis and provide exceptional flexibility for preparing high-quality publication-ready figures. Nevertheless, many technical and methodological issues must still be addressed to create analytical platforms that readily answer biological questions efficiently.
\nThe authors like to thank Arthur Colvis for help with the LCMS experiments.
\nSince the first ‘modern’ 2D material, monolayer graphene, was mechanically exfoliated in 2004 [1], the family of 2D materials has been extensively flourishing, covering insulators, semiconductors, semimetals, metals, and superconductors (Figure 1). In addition to semimetal graphene, other actively researched 2D materials include wide-bandgap insulator hexagonal boron nitride (hBN) [2], direct bandgap semiconductor phosphorene [3], Xenes (e.g., Monolayers of silicon (silicene), germanium (germanene) and tin (stanene)) [4], and transition metal dichalcogenides (TMDs) with the chemical formula MX2 (M: transition metal; X: chalcogen) [5]. Compared with bulk materials, 2D materials exhibit some unparallel characteristics: removal of van der Waals interactions, an increase in the ratio of surface area-to-volume, and confinement of electrons in a plane. The change in properties, caused by a reduction in the dimensionality of 2D materials, makes them becoming the promising candidates for next-generation electronics and optoelectronics [6, 7, 8].
The gallery of 2D materials.
Whereas these materials are marvelous per se, the more astounding discovery is that these 2D crystals can be combined freely to create layered compounds, paving a way for design of new functional materials and nano-devices [9, 10]. Such designer materials are called van der Waals heterostructures (vdWHs) since the atomically thin layers are not attached through a chemical reaction but rather held together via a weak van der Waals interaction. By stacking together any number of atomically thin layers, the concept provides a huge potential to tailor the unique 2D electronic states with atomic scale precision, opening the door to broaden the versatility of 2D materials and devices. Such stacked vdWHs are quite distinctive from the traditional 3D semiconductor heterostructures, as each layer acts simultaneously as the bulk material and the interface, reducing the amount of charge displacement within each layer. These vdWHs have already gained an insight into the discovery of considerably engaging physical phenomena. For instance, by combining semiconducting monolayers with graphene, one can fabricate optically active heterostructures used for photovoltaic and light-emitting devices [11, 12, 13].
Because of the charge confinement and reduced dielectric screening, the optical properties of semiconducting 2D materials are dominated by excitonic effects [14, 15, 16, 17, 18, 19, 20]. When a material goes from bulk to 2D, there is less material to screen the electric field, giving rise to an increase in Coulomb interaction and more strongly-bound electron–hole pairs (excitons). In addition, since the excitons are confined in a plane that is thinner than their Bohr radius in most 2D semiconductors, quantum confinement enhances the exciton binding energy, altering the wavelength of light they absorb and emit. These two distinctively physical phenomena naturally make the excitons bound even at room temperature with a binding energy of hundreds of meV [21]. As a consequence, such materials’ two-dimensionality makes the excitons easily tunable, with a variety of external stimuli or internal stacking layers, enabling them potential candidates for various applications in optics and optoelectronics.
In this chapter, we provide a topical summary towards recent frontier research progress related to excitons in atomically thin 2D materials and vdWHs. To begin with, we clarify the different types of excitons in 2D materials, including bright and dark excitons, trions, biexcitons, and interlayer excitons. Moreover, we analyze the electronic structures and excitonic effects for two typical 2D materials (i.e., TMDs and phosphorene), as well as the excited-state dynamics in vdWHs. Furthermore, we address how external stimuli, such applied electric fields, strain, magnetic fields, and light, modulate the excitonic behavior and emission in 2D materials. Afterward, we introduce several representative optoelectronic and photonic applications based on excitonic effects of 2D materials. Finally, we give our personal insights into the challenges and outlooks in this field.
When the dimension of crystals converts from 3D to 2D, the electronic Coulomb screening is dramatically reduced out of quantum confinement. As a consequence, dielectric constant ϵ can fall to ϵ = 1 from ϵ ≫ 1 in conventional bulk materials [22, 23]. Generally, the binding energies of the strongly bound excitons can reach up to 30% of the quasiparticle (QP) band gap because of the tremendous decrease in dielectric constant, rising to the magnitude of 0.1–1 eV [21, 24]. The large binding energies, which lead to a strong absorption of excitons linking to light, can not only contribute to a substantial modification in the optical spectrum both below and above the QP band gap, but also ensure a long lifetime of excitons in room temperature. Since the large binding energies of excitons in 2D monolayer hBN was initially predicted theoretically in 2006 [25], the research relating to excitons of 2D materials boomed, ranging from monolayer 2D semiconductors and insulators to vdWHs.
Excitons are hydrogen-like bound states of a negatively charged electron and a positively charged hole which are attracted to each other by the electrostatic Coulomb force [26]. It is an electrically neutral quasiparticle that exists mostly in semiconductors, as well as some insulators and liquids, derived from the photo-excitation. Excitons are the main mechanism for light emission and recombination because of their large oscillator strength and enhanced light-matter interaction [27]. When it comes to low-dimension crystals, the types of excitons experience a boom. Weak dielectric screening and strong geometrical confinement mutually contribute to an extremely strong Coulomb interaction, bringing in engaging many-particle phenomena: bright and dark excitons, trions, biexcitons, and interlayer excitons.
Excitons can be bright or dark subject to the spin orientation of the individual carriers: the electron and the hole, as shown in Figure 2(b). If the electron and hole have opposite spins, the two particles can easily recombine through the emission of a photon. These electron–hole pairs are called bright excitons. Whereas if they have the same spins, the electron and hole cannot easily recombine via direct emission of a photon due to the lack of required spin momentum conservation. These electron–hole pairs are called dark excitons. This darkness makes dark excitons becoming promising qubits because dark excitons cannot emit light and are thus unable to relax to a lower energy level. As a consequence, dark excitons have relatively long radiative lifetimes, lasting for over a microsecond, a period that is a thousand times longer than bright excitons and long enough to function as a qubit. By harnessing the recombination time to create ‘fast’ or ‘slow’ light, the highly stable, non-radiative nature of dark excitons paves a way for optically controlled control information processing. For instance, according to inducing light emission from dark excitons in monolayer WSe2, it is possible to selectively control spin and valley, making dark excitons possible to encode and transport information on a chip [28, 29].
Different exciton types in atomically thin nanomaterials and related heterostructures. (a) The schematic for the energy level. (b) Excitons are coulomb-bound electron hole pairs (ovals in the picture): Bright excitons consist of electrons and holes with antiparallel spins, while dark excitons consist of electrons and holes with parallel spins. (c) Trions emerge when an additional electron (hole) joins the exciton. (d) Biexcitons are created from two free excitons with different total momenta. (e) Interlayer excitons appear when electrons and holes are located in different layers.
Because of the significant Coulomb interactions in 2D materials, exciton can capture an additional charge to form charged exciton known as trion, a localized excitation consisting of three charged quasiparticles (Figure 2(c)). Compared to exciton, a neutral electron–hole pair, trion can be negative or positive depending on its charged state: a negative trion (negative e–e-h) is a complex of two electrons and one hole and a positive trion (negative e–e-h) is a complex of two holes and one electron. Trion states were predicted theoretically [30] and then observed experimentally in various 2D materials, by means of temperature-dependent photoluminescence (PL) [31] and nonlinear optical spectroscopy [32], and scanning tunneling spectroscopy [33]. Trions play a significant role in in manipulating electron spins and the valley degree of freedom for the reasons below. First, the trion binding energies are surprisingly large, reaching to about 15–45 meV in monolayer TMDs [34, 35, 36] and 100 meV in monolayer phosphorene on SiO2/Si substrate [37]. In addition, trions possess an extended population relaxation time up to tens of picoseconds [38, 39]. Finally, trions have an impact on both transport and optical properties and can be easily detected and tuned experimentally [40]. As a consequence, the electrical manipulation and detection of trion, as well as its enhanced stability, make it promising for trion-based optoelectronics.
Biexcitons, also known as exciton molecules, are created from two free excitons. Biexciton configurations can be distinguished from unbound or bound biexciton cases (Figure 2(d)). The bound biexciton is considered as a single particle since Coulomb interaction is dominant in this complex; while the unbound biexciton is regarded as two-exciton isolated from each other because of the predominance of the repulsive Coulomb interaction [41, 42]. Similar to trions stably existing in 2D materials, biexcitons can also exist in room temperature. Among 2D materials, biexcitons were firstly observed in monolayer TMDs [43, 44], followed by predicting their binding energies of biexcitons via computational simulation [45, 46].
In addition to above-mentioned intralayer excitons, interlayer excitons, where the involved electrons and holes are located in different layers, can also form in bilayer or few-layer 2D materials especially in vdWHs because of the strong Coulomb interaction (Figure 2(e)). After optically exciting a coherent intralayer exciton, the hole can tunnel to the other layer forming an incoherent exciton with the assistance of emission and absorption of phonons. Generally, these interlayer excitons occupy the energetically lower excitonic state than the excitons confined within one layer owing to an offset in the alignment of the monolayer band structures [47, 48]. Similar to excitons in one layer, interlayer excitons can also be either bright or dark depending on spin and momentum of the states involved [49, 50].
Among 2D semiconductors and insulators, TMDs and phosphorene have drawn tremendous attention owing to their intrinsic band gaps and strong excitonic emissions, making them potential candidates for high-performance optoelectronic applications in the visible to near-infrared regime [51]. The electronic and optical properties of 2D materials rely on their electronic band structure, which demonstrates the movement of electrons in the material and results from the periodicity of its crystal structure. When the dimension of a material degrades from bulk to 2D, the periodicity will disappear in the direction perpendicular to the plane, changing the band structure dramatically. This means by changing the number of layers in the 2D material, one can tune the band structures (e.g., a MoS2 will become emissive when reducing to monolayer), as well as tailor the binding energies of excitons (e.g., a monolayer 2D material will absorb/emit higher energy light than a bilayer).
All TMDs have a hexagonal structure, with each monolayer consisting of the metal layer sandwiched between two chalcogenide layers (X-M-X). The two most common crystal structures are the semiconducting 2H-phase with trigonal symmetry (e.g., MoS2, WS2, MoSe2, WSe2, as shown in Figure 3(a)) and the metallic 1 T phase (e.g., WTe2). For the semiconducting 2H-phase TMDs, they are well-known to possess an indirect band gap in bulk crystals; however, when mechanically exfoliated to a monolayer, these crystals experience a crossover from indirect to direct bandgap since the lack of interlayer interaction (Figure 3(b)). In addition, a decreasing layer numbers in TMDs attributes to larger absorption energy and strong photoluminescence (PL) emission in the visible spectrum, accompanying with enhanced excitonic effects, because of the reduced electronic Coulomb screening (Figure 4(a)) [53].
Atomic structures and electronic structures of TMDs and phosphorene: Side view (left) and top view (right) of the atomic structures of the monolayer semiconducting 2H-phase TMDs (a) and of the monolayer phosphorene (c); band structures of bulk and monolayer MoS2 (b) and phosphorene (d) [52]. Note that the bandgap shows a widening in phosphorene and both a widening and a crossover from indirect to direct bandgap in MoS2. Reproduced with permission [52]. Copyright 2019 Ossila ltd.
The effects of layer number on the PL spectra and peak energy of TMDs and phosphorene. (a, b) normalized PL spectra of 2H-WS2, 2H-WSe2 and phosphorene flakes consisting of 1–5 layers. Each PL spectra is normalized to its peak intensity and system background [37, 53]. (c) Evolution of PL peak energy with layer number of 2H-WS2, 2H-WSe2, and phosphorene from (a, b), showing an increase in peak energy as the layer number reduces. (a) Reproduced with permission [53]. Copyright 2012 American Chemical Society. (b) Reproduced with permission [37]. Copyright 2015 Springer Nature Publishing AG.
More importantly, TMDs are time-reversal symmetry but spatial inversion asymmetric. Since the strong spin–orbit coupling, the time-reversal symmetry dictates the spin splitting to have opposite spins at the K and K′ valleys of the Brillouin zones, making the excitons in TMDs are called valley excitons, which is different from the transition at the Γ valley in other 2D semiconductors such as phosphorene. As shown in Figure 5, the spin splitting is pretty strong in the valence band, in which spin splitting values are calculated theoretically up to 0.15 eV in 2H-MoS2 monolayer and 0.46 eV in 2H-WSe2 monolayer [56]. On the other hand, the broken inversion symmetry of TMD systems gives rise to a valley-dependent optical selection rule. This unique characteristic arouses the potential to control valley polarization and electronic valley. In this sense, a valley refers to the region in an electronic band structure where excitons are localized; valley polarization refers to the ratio of valley populations; and electronic valley refers a degree of freedom that is akin to charge and spin. As a consequence, optical transitions such as excitons in opposite valleys are able to be excited selectively using light with disparate chirality, paving the way to enable valleytronic devices based on photon polarizations [54, 55].
Lattice structure, valley polarization, and exciton-polaritons in 2D TMDs. (a) The honeycomb lattice structure of monolayer TMDs, with broken inversion symmetry and the high-symmetry points in the first Brillouin zone. (b) Electronic bands around the K and K’ points, which are spin-split by the spin–orbit interactions. The spin (up and down arrows) and valley (K and K’) degrees of freedom are locked together. (c) Exciton–polariton states in a 2D semiconductor embedded inside a photonic microcavity. (a, b) reproduced with permission [54]. Copyright 2016 Springer Nature Publishing AG. (c) Reproduced with permission [55]. Copyright 2019 John Wiley & Sons, Inc.
As shown in Figure 3(c), phosphorene possesses a puckered orthorhombic lattice structure with P atoms distributed on two parallel planes and each P atom is covalently bonded to three adjacent atoms, resulting in strong in-plane anisotropy. Unlike TMDs that exhibit an indirect-to-direct bandgap transition when scaled down from bilayer to monolayer, phosphorene retains a direct band gap all the time, as shown in Figure 3(d) [57, 58]. As the layer number decrease from 5 to 1, bandgap energy of phosphorene rises remarkably because of the weaker coupling of the conduction band and the valence band caused by reduced interactions in thinner layers, showing a layer-dependent direct bandgap energies (Figure 4(c)). In contrast to TMDs whose PL emission occurs in the visible spectrum, the light emission of phosphorene mainly covers the near-infrared spectral regime (Figure 4(a, b)). Moreover, its structural anisotropy also strongly affects the excitonic effects and in phosphorene. The results from first-principles simulations demonstrate that excitonic effects can only be observed when the incident light is polarized along the armchair direction of the crystal [59].
To have an impact on excitonic effects and relevant applications, the binding energy of these quasiparticles must be clarified. As schematically illustrated in Figure 2(a), the exciton binding energy is the energy difference between the electronic bandgap (Eg) and optical bandgap (Eopt). When higher-order excitonic quasiparticles form, more energy, i.e., the binding energy of trion or biexciton, is needed. Thus, the binding energies of exciton, trion and biexciton can be expressed as
Linear relationship between quasiparticle bandgap (Eg) and exciton binding energy (EbE). Reproduced with permission [21]. Copyright 2017 American Physical Society.
Composed of stacks of atomically thin 2D materials, the properties of vdWHs are determined not only by the constituent monolayers but also by the layer interactions. In particular, the excited-state dynamics is unique, such as the formation of interlayer excitons [47], ultrafast charge transfer between the layers [71, 72], the existence of long-lived spin and valley polarization in resident carriers [73, 74], and moiré-trapped valley excitons in moire superlattices in vdWHs [75, 76, 77, 78]. In terms of 2D vdWHs, the semiconducting vdWHs composed of stacked TMDC layers are the most widely studied due to their prominent exciton states and accessibility to the valley degree of freedom. More interestingly, the introduction of moiré superlattices (Figure 7(a)), a periodic pattern formed by stacking two monolayer 2D materials with lattice mismatch or rotational misalignment, enables to modulate the electronic band structure and the optical properties of vdWHs [79].
Excitonic effects in vdWHs. (a) Sketch of MoS2/MoSe2 heterobilayer (left) and its moiré superlattice (right) [10]. (b) Schematic of a pump-probe configuration (left), and time-resolved differential reflection of a MoS2/MoSe2 heterobilayer (blue) and of MoS2 monolayer (purple) (right) [71]. (c) Comparison between spin-valley lifetime (circles) and hole population lifetime (triangles) under different carrier concentration in MoS2/MoSe2 heterostructure (left), and schematic illustration of the interlayer electron–hole recombination process in electron-doped and hole-doped heterostructures [74]. (d) Moiré superlattice modulates the electronic and optical properties in WSe2/MoSe2 heterostructure: Three different local atomic alignments and their corresponding schematic (top), the moiré potential of the interlayer exciton transition (left lower), and spatial map of the optical selection rules for K-valley excitons (right lower) [76]. (a) Reproduced with permission [10]. Copyright 2016 American Association for the Advancement of Science. (b) Reproduced with permission [71]. Copyright 2014, American Chemical Society. (c) Reproduced with permission [74]. Copyright 2018 American Association for the Advancement of Science. (d) Reproduced with permission [76]. Copyright 2019 Springer Nature Publishing AG.
After demonstrating the appearance of interlayer excitons in PL spectra, the research on exciton dynamics in vdWHs flourishes. The discovery of intralayer excitons in 2D materials can be traced back to 2015, when long-lived interlayer excitons were demonstrated in monolayer MoSe2/Wse2 heterostructures, where a pronounced additional resonance was observed at an energy below the intralayer excitons [80]. Compared with the intralayer excitons in the weak excitation regime, the PL intensity of this low-energy peak is rather prominent, which attributes to the presence of interlayer excitons as their spectral position is highly occupied. Furthermore, measuring the binding energy of interlayer excitons directly is also demonstrated in WSe2/WS2 heterobilayers, where a novel 1 s–2p resonance are measured by phase-locked mid-infrared pulses [81]. For other excited-state dynamics, such as ultrafast kinetics, long lifetimes, and moiré excitons, some research indicate they have something to do with interlayer excitons [71, 72, 73, 74, 75, 76, 77, 78].
Empirically, charge transfer between layers of vertically stacking vdWHs is supposed to be much slow. However, transient absorption measurements, which are implemented by resonantly injecting excitons using ultrafast laser pulse, show a sub-picosecond charge separation in vdWHs: the holes injected in MoS2 takes 200 fs transferring to MoSe2 and even only 50 fs transferring to WS2, as shown in Figure 7(b) [73, 74]. It is noteworthy that this process is reversible, i.e., holes transfer to MoSe2 on the same ultrafast time scale when excitons are selectively injected in MoS2 using excitation resonant with the higher-energy exciton feature in MoS2. In addition, another interesting phenomenon is that when mismatching the bilayer vdWHs with different twist angle, the charge transfer signal keeps a constant period within 40 fs, while the recombination lifetime of these indirect excitons varies with the twist angle without any clear trend [82].
In contrast to the ultrafast charge transfer dynamics in vdWHs (<1 ps), spin and valley relaxation dynamics take place on considerably longer timescale [73, 74]. For the two distinctive relaxation processes in vdWHs (i.e., the population decay of optically excited excitons, and the exciton spin–valley lifetime which determines the information storage time in the spin– valley degree of freedom), they both are significantly longer than the monolayer case. For instance, by tuning the carrier concentration, holes’ spin–valley lifetime and population lifetime possess a doping-dependent pattern in a WSe2/WS2 heterostructure [74]: in charge-neutral and electron-doped heterostructures (i.e., neutral and positive carrier concentrations), the spin–valley lifetime is closed to the population lifetime; nevertheless, in hole-doping heterostructures (i.e., negative carrier concentration), the spin–valley lifetime becomes orders of magnitude longer than the population lifetime (Figure 7(c)). The remarkable dynamics of doping-dependent lifetime attributes to the distinctive interlayer electron–hole recombination process in the heterostructure, as shown in Figure 7(c). In electron-doped or charge-neutral heterostructures, all holes in WSe2 are pump-generated excess holes; hence, when the hole population decays to zero out of interlayer electron–hole recombination, no holes can remain, let alone valley-polarized holes. The valley lifetime is thus limited by the lifetime of the total hole excess. On the contrary, in hole-doped case, the original hole density is much higher than the photo-generated density, give an equal probability for the recombination of excess electrons in WS2 with holes from both valleys of WSe2.
Since 2019, important breakthroughs about excitons in vdWHs has been obtained, especially three independent research simultaneously reporting the observation of moiré excitons in TMDs vdWHs, which lays a firm foundation to the engineering artificial excitonic crystals using vdWHs for nanophotonics and quantum information applications [75, 76, 77]. For example, in MoSe2/WSe2 heterobilayers with a small twist angle of ~1°, there are three points at which the local atomic registration preserves the threefold rotational symmetry Ĉ3 in the moiré supercell. The local energy extrema in the three high-symmetry points not only localizes the excitons but also provides an array of identical quantum-dot potentials (Figure 7(d)) [75]. The research on moiré excitons in TMDs vdWHs has been promoted after experimentally confirming the hybridization of excitonic bands that can result in a resonant enhancement of moiré superlattice effects.
To have an impact on industrial applications especially photovoltaics, the binding energies of excitons in 2D semiconductors and insulators must be delicately designed and tuned. More importantly, these common control measures, from electrical to optical methods, function more potently in 2D materials than in 3D materials.
Since the electric field can hardly modulate the dielectric constant in monolayer 2D materials [83], early electrical tuning for excitonic behavior is mostly based on carrier density-dependent many-body Coulomb interactions, namely charged excitons or trions [84, 85]. By increasing electron doping density using different gate voltage (−100 to +80 V) in monolayer MoS2 field-effect transistors, Mak et al. firstly reported the observation of tightly bound negative trions by means of absorption and photoluminescence spectroscopy [84]. These negative trions hold a large trion binding energy up to ~20 meV, and can be optically created with valley and spin polarized holes. At the same time, Ross et al. also observed positive and negative trions along with neutral excitons in monolayer MoSe2 field-effect transistors via photoluminescence [85]. The exciton charging effects showed a reversible electrostatic tunability, as shown in Figure 8(a–c). More interestingly, the positive and negative trions exhibited a nearly identical binding energy (~30 meV), implying the same effective mass for electrons and holes. Another work demonstrated continuous tuning of the exciton binding energy in monolayer WS2 field-effect transistors, finding the ground and excited excitonic states as a function of gate voltage [87].
Electrical tuning of excitons. (a–c) Electrical control in monolayer 2D materials [85]: (a) MoSe2 PL is plotted as a function of back-gate voltage, showing a transition from positive Trion to negative Trion as gate voltage increases. (b) Illustration of the gate-dependent transitions and quasiparticles. (c) the relationship between Trion and exciton peak intensity and gate voltage at dashed arrows in (a). Solid lines are fits based on the mass action model. (d–g) Electrical control in vdWHs [86]: (d) optoelectronic transport device consisting of hBN/MoSe2/hBN heterostructure. (e) SEM image of a gate-defined monolayer MoSe2 quantum dot. (f) Typically measured current across the device as a function of local gate voltage Vg at different silicon backgate voltage VBG. (g) Recombination emission signals of excitons and trions as a function of emission wavelength at different Vg values. (a–c) Reproduced with permission [85]. Copyright 2013 Springer Nature Publishing AG. (d–g) Reproduced with permission [86]. Copyright 2018 Springer Nature Publishing AG.
The above-mentioned works are related to monolayer 2D materials, while when it comes to heterostructures, the electrical tuning functions more efficiently [86, 88]. Employing a van der Waals heterostructure consisting of hBN/MoSe2/hBN (Figure 8(d, e)), Wang el al. obtained homogeneous 2D electron gases by controlling disorder in TMDs, which allows for excellent electrical control of both charge and excitonic degrees of freedom [86]. Measuring the optoelectronic transport in the gate-defined heterostructure, they demonstrated gate-defined and tunable confinement of charged exciton, i.e., confinement happens when local gate voltages ΔVg is zero or negative while being absent when ΔVg local gate voltages are positive (Figure 8(f)). To further demonstrate controlled localization of charged excitons, they excited the device with a laser source at λ = 660 nm, observing both the exciton and trion recombination in PL spectra (Figure 8(g)). The ratio between trion and exciton recombination emission declines as ΔVg becomes more negative, because of local depletion of trions as the device transits from the accumulation regime (ΔVg > 0) to confinement (ΔVg = 0) and depletion regimes (ΔVg < 0), respectively.
TMDs have drawn more attention with respect to magnetic tuning than other 2D materials, since they preserve time-reversal symmetry with excitons formed at K and K′ points at the boundary of the Brillouin zone, which restricts valley polarization. However, when imposing magnetic fields, time-reversal symmetry can be broken, which splits the degeneracy between the nominally time-reversed pairs of exciton optical transitions at K and K′ valley: this is the valley Zeeman effect, as shown in Figure 9(a, b) [89, 91, 92, 93, 94]. Based on the Zeeman effect, magnetic manipulation is effectively used on valley pseudospin [91], valley splitting and polarization [92], and valley angular momentums [89]. For high-order excitonic quasiparticles, valley Zeeman effect also exhibit significant effects on trions [94] and biexcitons [90] under applied magnetic fields.
Magnetic tuning of excitons. (a, b) valley Zeeman effect [89]. (a)valley Zeeman effect in a finite out-of-plane B, the degeneracy between the ±K valleys is attributed to three factors: The spin-Zeeman effect (ΔEs), the intercellular orbital magnetic moment (ΔEinter), and the intracellular contribution from the d ± id orbitals of the valence band (ΔEintra). The signs of these contributions are opposite in the two valleys. (b) Normalized polarization-resolved PL spectra of the neutral exciton peak as a function of the out-of-plane magnetic field (B), indicating a B-dependent splitting phenomenon via valley Zeeman effect. (c, d) electrical control by surrounding dielectric environment [90]: (c) the surrounding dielectric environments are changing by encapsulating hBN, polymer, or nothing on WSe2 monolayer on silica substrate, where the average dielectric constant is defined as k = (εt + εb)/2 (εt and εb are the relative dielectric constants of the bottom substrate and the top encapsulation overlayer, respectively). (d) Exciton root-mean-square (rms) radius rX and exciton binding energy as a function of k (points and lines are the results from experiments and screened Keldysh model, respectively), where me, mr, and r0 are the exciton mass, the reduced mass of the exciton, and the characteristic screening length, respectively. (a, b) reproduced with permission [89]. Copyright 2013 Springer Nature Publishing AG. (c, d) reproduced with permission [90]. Copyright 2016 American Physical Society.
In addition, magnetic fields, which change the surrounding dielectric environment, can also have an impact on the size and binding energy of excitons. By encapsulating the flakes with different materials on a monolayer WSe2, Stier et al. changed the average dielectric constant, k = (εt + εb)/2, ranging from 1.55 to 3.0 (Figure 9(c)) [95]. The average energy of the field-split exciton transitions was measured in pulsed magnetic fields to 65 T, exhibiting an increasing trend with field which reveals the diamagnetic shift can infer both exciton binding energy and radius. They demonstrated increased environmental screening will enlarge exciton size but reduce exciton binding energy in 2D semiconductors, which shows a quantitatively agreement with theoretical models (Figure 9(d)).
To control excitonic effects by breaking time-reversal symmetry in TMDs, imposing an intense circularly polarized light can also achieve the aim based on optical Stark effect, a phenomenon that photon-dressed states (Floquet states) can hybridize with the equilibrium states resulting in energy repulsion between the two states [96, 97], as shown in Figure 10(a, b). The interaction between Floquet and equilibrium states can not only bring in a wider energy level separation, but also enhance the magnitude of the energy repulsion if they are energetically close. Based on the optical Stark effect triggered off by circularly polarized light, two independent works demonstrated that the exciton level in K and K′ valleys can be selectively tuned by as much as 18 meV in WS2 monolayer and 10 meV in WSe2 monolayer, respectively.
Optical tuning of excitons. (a, b) optical stark effect [96]. (a) Illustration of optical stark effect for two-level system. Ground state |a〉 and excited state |b〉 can hybridize with Floquet states |a + ћω〉 and |b + ћω〉, bringing in shifted energy levels. (b) the valley selectivity of the optical stark effect, showing an effect only at K valley by σ − polarization pump pulses. (c–e) valley polaritons via optical pumping [98, 99]. (c) Schematic of the valley polariton phenomena. The lower polariton branch (LBP) and the upper polariton branch (UPB) are the solid curves. The valley-polarization phenomena, caused by the broken inversion symmetry, is inserted in the top. (d) Polariton emission with angle-dependent helicity. Angle-resolved helicity was measured for three detuned cavities Δ at the σ+ excitation, where only the positive detuned cavities shows increasing helicity as a function of angle. (e) Exciton-polaritons with a temperature-dependent emission polarization. Emission polarization for bare exciton, and upper polariton (UP) and lower polariton (LP) branches change with temperature. (a, b) reproduced with permission [96]. Copyright 2014 Springer Nature Publishing AG. (c, d) reproduced with permission [98]. Copyright 2017 Springer Nature Publishing AG. (e) Reproduced with permission [99]. Copyright 2017 Springer Nature Publishing AG.
Besides, optical control and manipulation have been shown effective towards valley polaritons, a half-light half-matter quasiparticles arising from hybridization of an exciton mode and a cavity mode. Owing to the large exciton binding energy and oscillator strength in TMDs, spin–valley coupling can persist at room temperature when excitons are coherently coupled to cavity photons, leading to a stable exciton-polariton formation [98, 99, 100, 101]. Exciton polaritons are interacting bosons with very light mass, and can be independently combined in the intracavity and extracavity field. A schematic of the valley-polariton phenomena is shown in Figure 10(c), where the microcavity structure consists of silver mirrors with a silicon dioxide cavity layer embedded with the WS2 monolayer. The valley-polarized exciton–polaritons are optical pumped using two pumps to excite the exciton reservoir and the lower polariton branch, showing an angle-dependent helicity because of the excitonic component of the polariton states [98]. In addition, another work based on similar method demonstrates that exciton-polaritons possess a temperature-dependent emission polarization, exhibiting stronger valley polarization at room temperature compared with bare excitons [99], as shown in Figure 10(d).
2D materials possess excellent mechanical flexibility, making them stable under high compressive, tensile, and bending strain [102]. Applying mechanical strain on 2D materials, their band gaps will reduce, increase, or transit from direct to indirect, thus resulting in a strain-dependent exciton binding energy [103, 104, 105, 106, 107, 108]. Based on density functional theory, Su et al. investigated the natural physical properties of TMD monolayers and hBN- TMD heterostructures, finding that they have distinctive bandgap and exciton binding energy under compressive strain (Figure 11(a)) [103]. MS2 monolayers exhibit direct-to-indirect transition, while hBN-TMD heterostructures keep direct band-gap characters because of the strong charge transfer between hBN and TMD monolayers. With increasing compressive strain, the exciton binding energies of TMD monolayers gradually reduce, but the binding energies of hBN- TMD heterostructures experience a dramatically growth before decreasing (Figure 11(b)).
Mechanical tuning of excitons. (a, b) Exciton binding energies under compressive and tensile strain [103]. (a) Schematics of hBN-TMDs heterostructures nanodevices with tensile and compressive strain. (b) Exciton binding energies of TMD monolayer and hBN-TMD heterostructures as functions of strain. (c) Funnel effect of Excitons under indentation. When an indenter creates an inhomogeneous strain profile that modulates the gap, excitons (in green) in MoS2 concentrate on isotropically the center, while excitons in phosphorene disperse, especially along the armchair direction [109]. (a, b) reproduced with permission [103]. Copyright 2019 Springer Nature Publishing AG. (c) Reproduced with permission [109]. Copyright 2016 American Physical Society.
Another mechanical tuning method is by implying heterogeneous strain on 2D materials, which would result in a spatially varying bandgap with tunable exciton binding energy distribution, namely funnel effect [110, 111, 112]. In a TMDs monolayer, excitons will move towards high tensile strain region, resulting in a funnel-like band energy profile. In contrast, excitons in phosphorene are pushed away from high tensile strain region, exhibits inverse funnel effect of excitons, which is moreover highly anisotropic with more excitons flowing along the armchair direction (Figure 11(c)) [109]. Funnel effect is a rare method for control exciton movement, paving a way for creating a continuously varying bandgap profile in an initially homogeneous, atomically thin 2D materials.
2D materials possess strong light-matter coupling and direct band gaps from visible to infrared spectral regimes with strong excitonic resonances and large optical oscillation strength. Recent observation of valley polarization, exciton–polaritons, optically pumped lasing, exciton–polaritons, and single-photon emission highlights the potential for 2D materials for applications in novel optoelectronic devices. Combined with external stimuli, like electrical and magnetic fields, optical pumps, and strain, exciton effects in 2D materials show a highly tunability and flexibility in electroluminescent devices, photovoltaic solar cells, and photodetectors.
Excitonic electroluminescence (EL) emission in 2D materials is key to fully exploiting the EL devices [54, 113]. Based on different carrier injection and transport mechanisms, light-emitting devices have distinctive structures, depending on their mechanism of exciton generation: bipolar carrier injection in p-n heterojunction [114, 115], quantum well heterostructures [116], unipolar injection [117], impact excitation [118], thermal excitation [119], and interlayer excitons [48] (Figure 12).
EL device structures and emission mechanisms [54]. (a) Vertical and lateral p-n junctions. (b) Quantum well heterojunction structure. (c) Metal–insulator–semiconductor (MIS) and semiconductor–insulator–semiconductor (SIS) structure. (d) Lateral unipolar device where emission is induced by impact excitation. (e) Locally suspended thermal emission device. (f) Hetero-bilayer device exhibiting interlayer exciton emission. Reproduced with permission [54]. Copyright 2016 Springer Nature Publishing AG.
As exciton emission induced by bipolar carrier injection, p-n heterojunction is the simplest device to achieving EL. Depending on the contacting way the two monolayers connect, it can be vertical or lateral. Typical p-n junction is MS2/MSe2 heterostructure, since their counterparts lack the caliber to function effectively [120, 121]. For instance, MoS2 and WS2, in which sulfur vacancies act as electron donors, are often naturally n-type; while WSe2 and MoSe2 are typically ambipolar but often unconsciously p-doped by adsorbed moisture [122, 123].
Quantum well (QW) heterostructures consist of semiconductor layer sandwiched between insulator layers and metal electrodes. EL in QW heterostructures can be observed by bipolar recombination of injected electrons and holes in the semiconductor layer when applied a bias in the metal electrodes. Since the long lifetime of carriers and enhanced exciton formation in the semiconductor layer (typical one is TMDs), the emission efficiency of multiple QW devices is much higher than that of single QW devices, and can be improved by preparing alternating layers of TMDs [116, 124].
Unipolar injection happens in a metal–insulator–semiconductor (MIS) or a semiconductor–insulator–semiconductor (SIS) heterostructures when a positive bias applied to the metal and semiconductor layers. The common insulator layer is hBN since its ability to transport holes but block electrons. If the bias is increased above a threshold, EL will be observed at extremely low current densities below 1 nA μm−2, attributed to the unipolar tunneling across the hBN layer, which transfers holes from one metal/semiconductor layer (e.g., graphene and TMDs) to another electron-rich semiconductor layer (e.g., TMDs) [125].
The remaining three emission mechanism is relatively simple. For impact excitation devices, excitons are generated by impact excitation of excitons in the high field regime rather than bipolar recombination. For thermal emission devices, a semiconductor monolayer or few layers are partly suspended on a substrate, and thermal excitation and emission are evoked by locally heating the high current density regime. For bilayer emission devices, emission occurs due to the recombination of electrons and holes residing in the adjacent layers.
2D materials possess large exciton binding energy with the bandgap ranging from visible to near-infrared part of the spectrum, making them attractive as candidates for photovoltaic solar cells [126, 127]. Light absorption in the active layers of a photovoltaic cell significantly determines device efficiency. To improve light absorption of 2D semiconductor photovoltaics in the ultrathin limit, light trapping designs are need, such as use of plasmonic metal particles, shells, or resonators to amplify photocurrent and photoluminescence. For large area photovoltaic applications, a common strategy is thin film interference, in which a highly reflective metal (e.g., Au or Ag) is used as a part of an “open cavity” to enhance absorption due to multipass light interactions within the semiconductor (Figure 13(a)) [128]. If the semiconductor layer is a monolayer absorber, an atomically thin absorber with λ/4 in thickness can be sandwiched between conductor layer and reflector layer, enabling destructive interference at the interface and thus resulting in significant absorption enhancement (Figure 13(b)) [129]. Another strategy to enhance light trapping is by the use of nanostructured resonators, which are coupled to or etched in thin film absorbers (Figure 13(c, d)) [130, 131].
Possible light trapping configurations for enhancing sunlight absorption for Photovoltaics [126]. (a) Salisbury screen-like configuration where a spacer with ∼λ/4 thickness sandwichs between a low loss metal reflector and a monolayer absorber. (b) Multilayer vdWH absorber directly placed on a smooth reflective metal reflector. (c) TMD monolayer coupled with resonators/antennas. (d) Multilayer vdWH absorber etched by nanometer scale antennas/resonators. Reproduced with permission [126]. Copyright 2017 American Chemical Society.
Compared with free-standing monolayer with merely 10% absorption [132], the above-mentioned strategy exhibits outstanding strength for TMDC devices. For example, TMD-reflector coupled photovoltaics can have high broadband absorption of 90% and quantum efficiency of 70% [133, 134]. Accompanied with reflector, resonator, or antennas, 2D semiconductor photovoltaics trapping nearly 100% of the incident light may be achieved for nanoscale thick active layers. However, improving light absorption in sub-nanoscale thick monolayers faces more challenging, because not only the low absorption of monolayer (~10%) but also the limited technique to fabricate nanoscopic resonators or antennas [135].
Photodetection is a process converting light signals to electric signals, consisting of three physical mechanisms: light harvesting, exciton separation, and charge carrier transport to respective electrodes. According to the operation modes, photodetectors can be divided into two categories: photoconduction (i.e., photoconductor) and photocurrent (i.e., photodiode) [136, 137]. The former one refers to the overall conductivity change out of photoexcited carriers, and the latter one involves a junction which converts photoexcited carriers into current. Generally, photoconduction-based devices possess higher quantum efficiency than photocurrent-based devices, since transporting carriers can circulate many times before recombination in photoconductors. However, the response in photocurrent-based devices is faster than that in photoconduction-based devices, because of the short carrier lifetime that transporting carriers (electrons and holes) are both involved in the photocurrent generation and recombine with their counterpart after reaching to their own electrodes.
Two common 2D materials used for photodetectors are graphene [138, 139, 140] and TMDs (Figure 14) [55, 143, 144]. Based on photothermal with weak photovoltaic effect, graphene photodetectors usually show higher dark currents and smaller responsivity, but much wider operational bandwidths. In contrast, TMDs photodetectors operating on photovoltaic effect, exhibiting lower dark currents and higher responsivity.
Typical 2D photodetectors. (a) Schematic of a hybrid graphene photoconductor [141]. (b) Schematic of a single-bilayer graphene interface junction, in which photocurrent generation is dominant by photothermoelectric effect [142]. (c) Schematic of monolayer MoS2 lateral photoconductor [143]. (d) Schematic of vertical p–n photodiode formed by monolayer MoS2 and WSe2, in which a photocurrent hot spot is produced at the heterojunction [120]. (a) Reproduced with permission [141]. Copyright 2017 Springer Nature Publishing AG. (b) Reproduced with permission [142]. Copyright 2009 American Chemical Society. (c) Reproduced with permission [143]. Copyright 2013 Springer Nature Publishing AG. (d) Reproduced with permission [120]. Copyright 2014 Springer Nature Publishing AG.
For graphene-based photoconductors, typical devices are hybrid, adding a light absorption material, such as quantum dots [145], perovskites [146], silicon [147], carbon nanotubes [148], and TMDs [141], as active layer to improve the responsivity. For graphene-based photodiodes, this earliest reported one is metal–graphene–metal photodiodes, in which photocurrent was generated by local illumination of the metal/graphene interfaces of a back-gated graphene field-effect transistor. The resulting current can be attributed to either photovoltaic effect [149] or photo-thermoelectric effect [142]. To additionally improve the performance, common structures are graphene–semiconductor heterojunction photodiodes, in which planar junctions of graphene and group-IV elements or other compound semiconductors act as Schottky diodes [150, 151].
For TMD photodetectors, devices can have in-plane or out-of-plane structures, based on the semiconductor layers stacking laterally or vertically. In-plane devices take advantage of better control of the material’s properties via electrostatic gating [143]. But out-of-plane devices can bear a much higher bias field (up to ~1 V nm−1), enabling a reduced excitonic binding energy in multilayer structures for more efficient exciton dissociations [152]. TMDs-based photoconductors are usually enhanced by illuminating the semiconductor–metal contacts [40] and in short-channel devices [153], and their conductance can be changed by doping and trapping of photogenerated carriers by impurity states [154, 155]. On the other hand, TMDs-based photodiodes exhibit higher tunability based on the photocurrent mode, consisting of an in-plane or out-of-plane junction where a built-in electric field is created [120, 156, 157]. In this situation, electrostatic gates can further tune the device doping levels, owing to the very small interlayer separation (<1 nm) which produces extremely high built-in electric fields (~1 V nm−1).
In summary, 2D materials exhibit excitonic effects due to spatial confinement and reduced screening at the 2D limit, resulting in fascinating many-particle phenomena, such as excitons, trions, biexcitons, and interlayer excitons. Enhanced binding energies owing to the strong Coulomb interaction make these quasiparticles easy to characterize and control. In addition, the sensitivity of these quasiparticles to a variety of external stimuli allows the possibility of modulating the inherent optical, electrical, and optoelectronic properties of 2D materials, making them potential candidates for novel optoelectronic applications.
In addition to well-studied 2D materials, such as graphene, phosphorene, and TMDs, the family of 2D crystals is continuously growing, making excitonic effects versatile in different 2D systems. In particular, assembling vdWHs, which now can be mechanically assembled or grown by ample methods, can open up a new route for exploring unique exciton physics and applications. For example, an in-plane moiré superlattice, formed by vertically stacking two monolayer semiconductors mismatching or rotationally misaligning, can modulate the electronic band structure and thus lead to electronic phenomena, such as fractal quantum Hall effect, unconventional superconductivity, and tunable Mott insulators.
This work is supported by the start-up funds at Stevens Institute of Technology.
The authors declare no conflict of interest.
IntechOpen - where academia and industry create content with global impact
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