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Avval, T. G., Haack, H., Gallagher, N., Morgan, D., Bargiela, P., Fairley, N., Fernandez, V. & Linford, M. R. (2022) 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. Journal of Vacuum Science & Technology A, 40 063205.
Added by: Richard Baschera (2022-12-14 13:25:51) Last edited by: Richard Baschera (2022-12-14 13:28:05) |
| Type de référence: Article DOI: 10.1116/6.0001969 Numéro d'identification (ISBN etc.): 0734-2101 Clé BibTeX: Avval2022 Voir tous les détails bibliographiques  |
Catégories: IMN, INTERNATIONAL Créateurs: Avval, Bargiela, Fairley, Fernandez, Gallagher, Haack, Linford, Morgan Collection: Journal of Vacuum Science & Technology A |
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| Résumé |
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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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