This study was undertaken to understand the extent and nature of problems in x-ray photoelectron spectroscopy (XPS) data reported in the literature. It first presents an assessment of the XPS data in three high-quality journals over a six-month period. This analysis of 409 publications showing XPS spectra provides insight into how XPS is being used, identifies the common mistakes or errors in XPS analysis, and reveals which elements are most commonly analyzed. More than 65% of the 409 papers showed fitting of XP spectra. An ad hoc group (herein identified as “the committee”) of experienced XPS analysts reviewed these spectra and found that peak fitting was a common source of significant errors. The papers were ranked based on the perceived seriousness of the errors, which ranged from minor to major. Major errors, which, in the opinion of the ad hoc committee, can render the interpretation of the data meaningless, occurred when fitting protocols ignored underlying physics and chemistry or contained major errors in the analysis. Consistent with other materials analysis data, ca. 30% of the XPS data or analysis was identified as having major errors. Out of the publications with fitted spectra, ca. 40% had major errors. The most common elements analyzed by XPS in the papers sampled and researched at an online database, include carbon, oxygen, nitrogen, sulfur, and titanium. A scrutiny of the papers showing carbon and oxygen XPS spectra revealed the classes of materials being studied and the extent of problems in these analyses. As might be expected, C 1s and O 1s analyses are most often performed on sp2-type materials and inorganic oxides, respectively. These findings have helped focus a series of XPS guides and tutorials that deal with common analysis issues. The extent of problematic data is larger than the authors had expected. Quantification of the problem, examination of some of the common problem areas, and the development of targeted guides and tutorials may provide both the motivation and resources that enable the community to improve the overall quality and reliability of XPS analysis reported in the literature.

The existence of asymmetry in X-ray photoelectron spectroscopy (XPS) photoemission lines is widely accepted, but line shapes designed to accommodate asymmetry are generally lacking in theoretical justification. In this work, we present a new line shape for describing asymmetry in XPS signals that is based on two facts. First, the most widely known line shape for fitting asymmetric XPS signals that has a theoretical basis, referred to as the Doniach-Sunjic (DS) line shape, suffers from a mathematical inconvenience, which is that for asymmetric shapes the area beneath the curve (above the x-axis) is infinite. Second, it is common practice in XPS to remove the inelastically scattered background response of a peak in question with the Shirley algorithm. The new line shape described herein attempts to retain the theoretical virtues of the DS line shape, while allowing the use of a Shirley background, with the consequence that the resulting line shape has a finite area. To illustrate the use of this Doniach-Sunjic-Shirley (DSS) line shape, a set of spectra obtained from varying amounts of graphene oxide (GO) and reduced GO on a patterned, heterogeneous surface are fit and discussed.

Unsupervised exploratory data analysis (EDA) is often the first step in understanding complex data sets. While summary statistics are among the most efficient and convenient tools for exploring and describing sets of data, they are often overlooked in EDA. In this paper, we show multiple case studies that compare the performance, including clustering, of a series of summary statistics in EDA. The summary statistics considered here are pattern recognition entropy (PRE), the mean, standard deviation (STD), 1-norm, range, sum of squares (SSQ), and X 4 , which are compared with principal component analysis (PCA), multivariate curve resolution (MCR), and/or cluster analysis. PRE and the other summary statistics are direct methods for analyzing datathey are not factor-based approaches. To quantify the performance of summary statistics, we use the concept of the "critical pair," which is employed in chromatography. The data analyzed here come from different analytical methods. Hyperspectral images, including one of a biological material, are also analyzed. In general, PRE outperforms the other summary statistics, especially in image analysis, although a suite of summary statistics is useful in exploring complex data sets. While PRE results were generally comparable to those from PCA and MCR, PRE is easier to apply. For example, there is no need to determine the number of factors that describe a data set. Finally, we introduce the concept of divided spectrum-PRE (DS-PRE) as a new EDA method. DS-PRE increases the discrimination power of PRE. We also show that DS-PRE can be used to provide the inputs for the k-nearest neighbor (kNN) algorithm. We recommend PRE and DS-PRE as rapid new tools for unsupervised EDA.

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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