Paper AS-MoM5
Statistical Analysis and Peak Fitting of X-ray Photoelectron Spectroscopy Data. Good Practices and Procedures for Working up this Information.

Monday, October 22, 2018, 9:40 am, Room 204

Determining appropriate methods of peak fitting X-ray photoelectron spectroscopy (XPS) data remains an active area of research. In this talk we discuss some of our recent efforts to better understand and interpret XPS narrow and survey scans. These include a description and comparison of the Gaussian-Lorentzian sum (GLS) and product (GLP) functions and their use in XPS peak fitting. This discussion will include a description of the shapes of the GLS and GLP as a function of the mixing parameter in their definitions and a comparison of these functions to the Voigt function. We will also discuss uniqueness plots as used to identify fit parameter correlation, width functions for identifying changes in peak envelopes, and principal components analysis, multivariate curve resolution, and pattern recognition entropy for studying and comparing series/groups of spectra. Finally, we also hope to briefly mention the importance of other statistical tools for better understanding XPS peak fitting. These include using chi squared to compare fits, showing the residuals to identify poor fits and/or questionable parts of fits, showing the sum of the fit components as a comparison to the original spectra, and employing the Abbe criterion for identifying correlated residuals.