Practical guide for x-ray photoelectron spectroscopy: Applications to the study of catalysts

Davies, Philip R.; Morgan, David

doi:10.1116/1.5140747

Cited by 25 publications

(10 citation statements)

References 135 publications

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“…However, a considerable amount of incorrect surface data and accompanying analysis have recently entered the scientific literature 13–15 . In response to this, a series of guides on XPS was recently written in the Journal of Vacuum Science and Technology 16–25 . The Insight Notes in Surface and Interface Analysis are also a response to this problem 26 …”

Section: Introductionmentioning

confidence: 99%

“…[13][14][15] In response to this, a series of guides on XPS was recently written in the Journal of Vacuum Science and Technology. [16][17][18][19][20][21][22][23][24][25] The Insight Notes in Surface and Interface Analysis are also a response to this problem. 26 With increasing advances in vacuum components, software, and instrumentation, XPS has become a precise and powerful technique that is now available in multiple modes.…”

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confidence: 99%

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Surface analysis insight note. Principal component analysis (PCA) of an X‐ray photoelectron spectroscopy image. The importance of preprocessing

Moeini,

Avval,

Gallagher

et al. 2023

Surface & Interface Analysis

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This Insight Note follows two previous Insight Notes on XPS image analysis that dealt with the importance of analyzing the raw data and the use of summary statistics. As a next step in the exploratory data analysis (EDA) of XPS images, we now show principal component analysis (PCA) of an XPS image. PCA is appropriate when the spectra in a data set are correlated to some degree and the noise in the spectra is unimportant. In these cases, PCA can significantly reduce the dimensionality and complexity of data sets. Preprocessing is an important part of many PCAs. Its usefulness is illustrated with a small, mock data set, where the potential pitfalls of not preprocessing are shown. PCAs of an XPS image data that was not preprocessed and preprocessed by mean centering are illustrated. Scree plots, which are used to determine the number of abstract factors (principal components, PCs) that describe a data set, are shown. The spectra in our XPS image are quite noisy, which is consistent with the moderate, but still significant, amount of variance that is captured by the first two PCs in our PCA. With both preprocessing methods, the loadings on PC1 and PC2 are remarkably smooth. The loadings on the next six PCs also appear to contain some chemical information. Scores images generated using both no preprocessing and preprocessing by mean centering reveal many of the same general features in the data set that were found in our two previous Insight Notes.

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Section: Introductionmentioning

confidence: 99%

mentioning

confidence: 99%

Surface analysis insight note. Principal component analysis (PCA) of an X‐ray photoelectron spectroscopy image. The importance of preprocessing

Moeini,

Avval,

Gallagher

et al. 2023

Surface & Interface Analysis

View full text Add to dashboard Cite

show abstract

“…In response to this problem, the XPS community has been writing ISO and ASTM standards, as well as tutorial articles, for years. A recent series of guides on XPS covers many aspects of the technique 3,4,10–17 …”

Section: Introductionmentioning

confidence: 99%

A detailed view of the Gaussian–Lorentzian sum and product functions and their comparison with the Voigt function

Major

Fernandez

Fairley

et al. 2021

Surface & Interface Analysis

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The Gaussian–Lorentzian sum (GLS) and product (GLP) functions remain important in X‐ray photoelectron spectroscopy (XPS) peak fitting. Here, we present a detailed view of these functions, comparing them with each other and with the Voigt function (the “LA(m)” function). First, we show the GLS, GLP, and LA(m) functions as a function of their mixing parameters, m, which reveals differences between them. We then illustrate the use of these functions to fit a series of spectra acquired at different pass energies (resolutions). Next, we show the underlying Gaussian and Lorentzian components of a series of GLS and GLP functions as a function of m, which confirms that the GLS is a simple linear combination of Gaussian and Lorentzian functions. However, one of the two functions used to make the GLP can be very wide, that is, at its extremes, one of these functions has infinite width. We then discuss a plot of the areas of the GLS, GLP, and LA(m) functions as a function of m, which reveals the expected, linear increase in area of the GLS, but nonlinear changes in the areas of the other two functions. Finally, to better understand them, we fit these functions to each other. These results indicate that the GLS and GLP better match the LA(m) function at lower and higher values of the mixing parameter, respectively.

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“…[7] XPS can also provide information about film/surface structure and morphology. [8] XPS is widely used in many areas of science and technology, including in the analysis of thin films, [9,10] catalysts, [11] semiconducting materials, [12] nanoparticles, [13] and polymers. [14] Indeed, XPS is the most widely used and important method for chemically analyzing surfaces.…”

Section: Introductionmentioning

confidence: 99%

Box plots: A simple graphical tool for visualizing overfitting in peak fitting as demonstrated with X-ray photoelectron spectroscopy data

Moeini

Haack

Fairley

et al. 2021

Journal of Electron Spectroscopy and Related Phenomena

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Peak fitting is frequently performed in X-ray photoelectron spectroscopy (XPS). However, recent reports suggest that the current quality of this peak fitting is often inadequate in the scientific literature. Various statistical methods may be advantageously applied to an XPS peak fit to help determine the quality and validity of a fit. In this paper we describe a new statistical tool, which we believe will be helpful for determining the quality of protocols for fitting XPS data. This tool, box plots of random starting conditions, helps identify multiple local minima in a fit space. That is, ideally, different, reasonable starting conditions for a fit should lead to the same result, i.e., ideally, there should be a single global minimum for a fitting protocol. To determine whether a fit space contains multiple local minima, a series of reasonable, random starting conditions are chosen for the fit. If the boxes in the box plot of the peak areas of these fits are narrow, the different possibilities converge to a single global minimum. Conversely, if the boxes are wide, multiple local minima are present. Our approach is similar to the mathematical concept of 'disproof by contradiction'. It is demonstrated herein in four-and tencomponent fits to a moderately complex C 1s narrow scan. The resulting box plots compare favorably to traditional Monte Carlo analyses and uniqueness plots, although each of these statistical tools performs a different function/probes the fit space differently.

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Practical guide for x-ray photoelectron spectroscopy: Applications to the study of catalysts

Cited by 25 publications

References 135 publications

Surface analysis insight note. Principal component analysis (PCA) of an X‐ray photoelectron spectroscopy image. The importance of preprocessing

Surface analysis insight note. Principal component analysis (PCA) of an X‐ray photoelectron spectroscopy image. The importance of preprocessing

A detailed view of the Gaussian–Lorentzian sum and product functions and their comparison with the Voigt function

Box plots: A simple graphical tool for visualizing overfitting in peak fitting as demonstrated with X-ray photoelectron spectroscopy data

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