Peak fitting of x-ray photoelectron spectroscopy (XPS) data is the primary method for identifying and quantifying the chemical states of the atoms near the surface of a sample. Peak fitting is typically based on the minimization of a figure-of-merit, such as the residual standard deviation (RSD). Here, we show that optimal XPS peak fitting is obtained when the peak shape (the synthetic mathematical function that represents the chemical states of the material) best matches the physics and chemistry of the underlying data. However, because this ideal peak shape is often unknown, constraints on the components of a fit are usually necessary to obtain good fits to data. These constraints may include fixing the relative full width at half maxima (peak widths), area ratios, and/or the relative positions of fit components. As shown in multiple examples, while unconstrained, less-than-optimal peak shapes may produce lower RSDs, they often lead to incorrect results. Thus, the “suboptimal” results (somewhat higher RSDs) that are obtained when constraints are applied to less-than-perfect peak shapes are often preferable because they prevent a fit from yielding unphysical or unchemical results. XPS peak fitting is best performed when all the information available about a sample is used, including its expected chemical and physical composition, information from other XPS narrow and survey scans from the same material, and information from other analytical techniques.
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Guide to XPS data analysis: Applying appropriate constraints to synthetic peaks in XPS peak fitting
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December 2022
Research Article|
September 22 2022
Guide to XPS data analysis: Applying appropriate constraints to synthetic peaks in XPS peak fitting
George H. Major
;
George H. Major
(Conceptualization, Formal analysis, Investigation, Methodology, Writing – original draft, Writing – review & editing)
1
Department of Chemistry and Biochemistry, Brigham Young University
, Provo, Utah 84602
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Vincent Fernandez;
Vincent Fernandez
(Conceptualization, Data curation, Investigation, Methodology)
2
Institut des Matériaux Jean Rouxel—Sciences, Université of Nantes
, Nantes F-44000, France
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Neal Fairley;
Neal Fairley
(Conceptualization, Data curation, Investigation, Methodology, Software, Visualization, Writing – original draft, Writing – review & editing)
3
Casa Software Ltd.
, Bay House, TQ14 8NE Teignmouth, United Kingdom
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Emily F. Smith
;
Emily F. Smith
(Data curation)
4
School of Chemistry, University of Nottingham
, NG7 2RD Nottingham, United Kingdom
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Matthew R. Linford
Matthew R. Linford
a)
(Conceptualization, Formal analysis, Investigation, Methodology, Project administration, Resources, Writing – original draft, Writing – review & editing)
1
Department of Chemistry and Biochemistry, Brigham Young University
, Provo, Utah 84602a)Author to whom correspondence should be addressed: mrlinford@chem.byu.edu
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a)Author to whom correspondence should be addressed: mrlinford@chem.byu.edu
Note: This paper is part of the Special Topic Collection: Reproducibility Challenges and Solutions II with a Focus on Surface and Interface Analysis.
J. Vac. Sci. Technol. A 40, 063201 (2022)
Article history
Received:
May 19 2022
Accepted:
August 09 2022
Citation
George H. Major, Vincent Fernandez, Neal Fairley, Emily F. Smith, Matthew R. Linford; Guide to XPS data analysis: Applying appropriate constraints to synthetic peaks in XPS peak fitting. J. Vac. Sci. Technol. A 1 December 2022; 40 (6): 063201. https://doi.org/10.1116/6.0001975
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