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

• Published in: Applied surface science Vol. 614; no. C; p. 156182
• Main Authors: Fernandez, Vincent, Morgan, David, Bargiela, Pascal, Fairley, Neal, Baltrusaitis, Jonas
• Format: Journal Article
• Language: English
• Published: Netherlands Elsevier B.V 30-03-2023; Elsevier
• Subjects: Catalysis; Cellulose; Chemical Sciences; Environment and Society; Environmental Sciences; PCA; Peak model; Spectra; XPS; XPS; Spectra; Peak model; Cellulose; PCA
• ISSN: 0169-4332; 1873-5584
• DOI: 10.1016/j.apsusc.2022.156182

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•X-rays were used to evolve C 1s peak shape of cellulose fibers.•A mathematical analysis of the least squares optimization is presented.•Nonlinear least squares applied to multiple spectra.•Linear least squares fitting of spectral forms was calculated directly from data.•OCO and C-OH bonds are replaced by OCO, CO and CO upon exposure.
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 CO, CO and OCO. Since cellulose 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.