4.7 Article

Combining PCA and nonlinear fitting of peak models to re-evaluate C 1s XPS spectrum of cellulose

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APPLIED SURFACE SCIENCE
卷 614, 期 -, 页码 -

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ELSEVIER
DOI: 10.1016/j.apsusc.2022.156182

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XPS; PCA; Cellulose; Peak model; Spectra

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Cellulose undergoes new chemistry in response to XPS, but recent studies have shown that this can be detrimental to some materials. This study analyzes cellulose spectra obtained during a degradation study, revealing that cellulose degrades through the creation of carbon chemistry involving C-O, C--O and O-C--O. The findings are relevant to any material analyzed by XPS dependent on hydrogen bonds. The analysis techniques are based on an informed vectorial approach, utilizing mathematical methods such as Principal Component Analysis and linear analysis.
Cellulose is an example of a material that responds to XPS by the creation of new chemistry not present in the as-received sample. While improvements in instrumentation may be seen in general as beneficial to surface science, recent studies have shown that the consequences for some materials are detrimental. In this work, these problems are illustrated through an analysis of cellulose spectra obtained during a degradation study. C 1s spectra are decomposed into two well-formed component curves that are open to chemical interpretation. In particular, a component-curve representative of pure cellulose is obtained as well as a second component curve that implies cellulose is degraded through the creation of carbon chemistry involving C-O, C--O and O-C--O. Since cel-lulose is a crystalline material, formed through the alignment of molecules under the influence of hydrogen bonds, the analysis and findings presented in this paper are relevant to any material analyzed by XPS whose properties are dependent on hydrogen bonds. The analysis techniques are based on an informed vectorial approach, which extracts directly from data spectral shapes that are used to monitor sample degradation via linear least squares optimization. Related mathematics of Principal Component Analysis and linear analysis are presented.

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