Practical guide on chemometrics/informatics in x-ray photoelectron spectroscopy (XPS). II. Example applications of multiple methods to the degradation of cellulose and tartaric acid

被引:12
作者
Avval, Tahereh G. [1 ]
Haack, Hyrum [1 ]
Gallagher, Neal [2 ]
Morgan, David [3 ,4 ]
Bargiela, Pascal [5 ]
Fairley, Neal [6 ]
Fernandez, Vincent [7 ]
Linford, Matthew R. [1 ]
机构
[1] Brigham Young Univ, Dept Chem & Biochem, C100 BNSN, Provo, UT 84602 USA
[2] Eigenvector Res Inc, Manson, WA 98831 USA
[3] Cardiff Univ, Cardiff Catalysis Inst, Max Planck Cardiff Ctr Fundamentals Heterogeneous, Sch Chem, Main Bldg,Pk Pl, Cardiff CF10 3AT, Wales
[4] RCaH, HarwellXPS EPSRC Natl Facil Photoelectron Spect, Didcot OX11 0FA, Oxon, England
[5] Inst Res Catalysis & Environm Lyon IRCELYON, 2 Ave Albert Einstein, F-69626 Villeurbanne, France
[6] Casa Software Ltd, Bay House, Teignmouth TQ14 8NE, England
[7] Nantes Univ, Inst Materiaux Nantes Jean Rouxel, CNRS, IMN, F-44000 Nantes, France
来源
JOURNAL OF VACUUM SCIENCE & TECHNOLOGY A | 2022年 / 40卷 / 06期
关键词
TOF-SIMS SPECTRA; SURFACE CHARACTERIZATION; CURVE RESOLUTION; XPS; POLYMERS; IMAGES; DIFFERENTIATION; DEPTH; MCR;
D O I
10.1116/6.0001969
中图分类号
TB3 [工程材料学];
学科分类号
0805 ; 080502 ;
摘要
Chemometrics/informatics, and data analysis in general, are increasingly important in x-ray photoelectron spectroscopy (XPS) because of the large amount of information (spectra/data) that is often collected in degradation, depth profiling, operando, and imaging studies. In this guide, we present chemometrics/informatics analyses of XPS data using a summary statistic (pattern recognition entropy), principal component analysis, multivariate curve resolution (MCR), and cluster analysis. These analyses were performed on C 1s, O 1s, and concatenated (combined) C 1s and O 1s narrow scans obtained by repeatedly analyzing samples of cellulose and tartaric acid, which led to their degradation. We discuss the following steps, principles, and methods in these analyses: gathering/using all of the information about samples, performing an initial evaluation of the raw data, including plotting it, knowing which chemometrics/informatics analyses to choose, data preprocessing, knowing where to start the chemometrics/informatics analysis, including the initial identification of outliers and unexpected features in data sets, returning to the original data after an informatics analysis to confirm findings, determining the number of abstract factors to keep in a model, MCR, including peak fitting MCR factors, more complicated MCR factors, and the presence of intermediates revealed through MCR, and cluster analysis. Some of the findings of this work are as follows. The various chemometrics/informatics methods showed a break/abrupt change in the cellulose data set (and in some cases an outlier). For the first time, MCR components were peak fit. Peak fitting of MCR components revealed the presence of intermediates in the decomposition of tartaric acid. Cluster analysis grouped the data in the order in which they were collected, leading to a series of average spectra that represent the changes in the spectra. This paper is a companion to a guide that focuses on the more theoretical aspects of the themes touched on here. Published under an exclusive license by the AVS.
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页数:24
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共 67 条
  • [1] Electron irradiation of poly(3-hexylthiophene) films
    Ahn, H
    Oblas, DW
    Whitten, JE
    [J]. MACROMOLECULES, 2004, 37 (09) : 3381 - 3387
  • [2] [Anonymous], 2022, J VAC SCI TECHNOL A, V40, DOI [10.1116/6.0001969, DOI 10.1116/6.0001969]
  • [3] Identification of chemical components in XPS spectra and images using multivariate statistical analysis methods
    Artyushkova, K
    Fulghum, JE
    [J]. JOURNAL OF ELECTRON SPECTROSCOPY AND RELATED PHENOMENA, 2001, 121 (1-3) : 33 - 55
  • [4] Quantification of PVC-PMMA polymer blend compositions by XPS in the presence of x-ray degradation effects
    Artyushkova, K
    Fulghum, JE
    [J]. SURFACE AND INTERFACE ANALYSIS, 2001, 31 (05) : 352 - +
  • [5] Avval T.G., 2021, J CHEM INF MODEL, V61, P4173, DOI [10.1021/acs.jcim.1c00244, DOI 10.1021/ACS.JCIM.1C00244]
  • [6] Practical guide on chemometrics/informatics in x-ray photoelectron spectroscopy (XPS). I. Introduction to methods useful for large or complex datasets
    Avval, Tahereh G.
    Gallagher, Neal
    Morgan, David
    Bargiela, Pascal
    Fairley, Neal
    Fernandez, Vincent
    Linford, Matthew R.
    [J]. JOURNAL OF VACUUM SCIENCE & TECHNOLOGY A, 2022, 40 (06):
  • [7] Polyethylene terephthalate by near-ambient pressure XPS
    Avval, Tahereh G.
    Hodges, Grant T.
    Wheeler, Joshua
    Ess, Daniel H.
    Bahr, Stephan
    Dietrich, Paul
    Meyer, Michael
    Thissen, Andreas
    Linford, Matthew R.
    [J]. SURFACE SCIENCE SPECTRA, 2020, 27 (01):
  • [8] Baer D. R., 2003, Surface Science Spectra, V10, P47, DOI 10.1116/11.20040199
  • [9] Introduction to topical collection: Reproducibility challenges and solutions with a focus on guides to XPS analysis
    Baer, Donald R.
    McGuire, Gary E.
    Artyushkova, Kateryna
    Easton, Christopher D.
    Engelhard, Mark H.
    Shard, Alexander G.
    [J]. JOURNAL OF VACUUM SCIENCE & TECHNOLOGY A, 2021, 39 (02):
  • [10] Guide to making XPS measurements on nanoparticles
    Baer, Donald R.
    [J]. JOURNAL OF VACUUM SCIENCE & TECHNOLOGY A, 2020, 38 (03):