Practical guide on chemometrics/informatics in x-ray photoelectron spectroscopy (XPS). II. Example applications of multiple methods to the degradation of cellulose and tartaric acid

Self Cite

With advances in instrumentation and image processing, imaging X-ray photoelectron spectroscopy (XPS) is becoming increasingly important in surface analysis. In this Insight Note, we suggest the following three steps for the initial evaluation of XPS images: (i) Plot all the data. Possible plots here include an overlay plot, a plot of the average and standard deviation of the spectra, a waterfall plot, and a contour plot.(ii) Examine slices through the data cube at different binding energies, identifying those that show significant contrast. (iii) Examine the spectra, including their average and standard deviation, in defined regions of slices through the data that show contrast. These steps allow XPS images to be probed without the use of more sophisticated exploratory data analysis (EDA) techniques. Indeed, these steps may set the stage for additional EDA analysis of XPS images. Furthermore, they provide an initial understanding of an XPS image that stays close to the original data. Our three-step protocol was applied to an XPS image of a region of a silicon wafer with different oxide thicknesses. This image primarily contained two types of pixels/regions: one of silicon with a thin oxide layer and the other of silicon with a thicker oxide layer. We emphasize the importance of contour plots of all the data and examining the average and standard deviation of regions of contrast in slices through the data cube.

Section: Resultsmentioning

confidence: 86%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Surface analysis insight note: Initial, statistical evaluation of X‐ray photoelectron spectroscopy images

Moeini

Linford

2023

Self Cite

“…We begin by discussing preprocessing, which is the initial statistical/mathematical treatment of the data. Some form of data preprocessing is often performed prior to PCA 43–48 . The three most common forms of preprocessing data in PCA are discussed here: no preprocessing, mean centering, and autoscaling.…”

Section: Introductionmentioning

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

Self Cite

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.

“…Due to their increasing size and complexity, there is increased awareness that chemometrics/informatics analyses will be needed for some XPS data sets. Pioneers in this area have included Artyushkova et al, 17,18 Leclerc and Pireaux, 19,20 and Béchu et al 21 Some of us recently wrote two guides on using chemometrics/informatics to analyze XPS data, 22,23 although neither of these guides showed the work‐up of an XPS hyperspectral image. Some of us have also been early proponents of the use of summary statistics in chemometrics/informatics, namely, pattern recognition entropy (PRE), 24–26 which is based on Shannon's entropy 27 .…”

Section: Introductionmentioning

confidence: 99%

Surface analysis insight note: Analysis of X‐ray photoelectron spectroscopy images with summary statistics

Moeini

Linford

2023

Self Cite

Developments in X‐ray photoelectron spectroscopy (XPS) instrumentation and the need for spatial information in surface characterization have led to advances in XPS imaging and related image processing techniques. In this Insight Note, we demonstrate the use of summary statistics as simple, but effective, tools for understanding XPS hyperspectral images (data cubes) prior to more advanced image processing. An XPS image obtained from a silicon surface patterned with different thicknesses of oxide was analyzed with three summary statistics: a tool in the MATLAB programming environment, the mean, and pattern recognition entropy (PRE). The MATLAB tool largely separates the spectra into two groups. The mean does a somewhat better job differentiating between the spectra, and PRE is even more effective. The results of the MATLAB summary statistic are confirmed by plotting the average and standard deviation spectra of different regions of the image it produces. The results of the mean and PRE summary statistics are confirmed by evenly segmenting the results and examining the average and standard deviation spectra of these segments. Fitting these average spectra demonstrates the greater effectiveness of the PRE summary statistic in segmenting the spectra into chemically distinct groups.