Jeff Terry

In actinide chemistry, the formal number of open-shell 5f electrons, n_open, is a well-defined quantity with an integer value. The effective 5f occupation, n_f, additionally takes donation and back-donation into account, generally has a non-integer value, and has varying numerical definitions. The present study explores the important distinction between n_f and n_open in actinide chemistry with the example of PuO₂, by using electronic structure methods with a relativistic Hamiltonian in combination with experimental Pu M₅-edge high-energy-resolution X-ray absorption and emission spectroscopic data. The total donation to the metal in PuO₂ is between 3.1 and 2.4 electrons, depending on the type of calculation, most of which is to the Pu 6d and 5f shells. The donation into 5f is sensitive to the approximations in the electronic structure model but likely amounts to 1.6/0.8 electrons when the diffuse regions of the 5f shell are included/excluded. Valence band resonant inelastic X-ray scattering experiments demonstrate that Pu 5f electron density is present in the valence band; thus, there is a clear experimental signature of covalent bonding in PuO₂. Pu M₅-edge and M₃-edge high-energy-resolution X-ray absorption near-edge structures for Pu³⁺ and Pu⁴⁺ in an aqueous solution are compared to PuO₂, showing that Pu in PuO₂ has an n_f closer to Pu⁴⁺(aq).

We have developed an artificial intelligence tool, XES Neo, for fitting x-ray emission spectroscopy (XES) data using a genetic algorithm. The Neo package has been applied to extended x-ray absorption fine structure [Terry et al., Appl. Surf. Sci. 547, 149059 (2021)] as well as Nanoindentation data [Burleigh et al., Appl. Surf. Sci. 612, 155734 (2023)] and is in development for x-ray photoelectron spectroscopy data. This package has been expanded to the fitting of XES data by incorporating basic background removal methods (baseline and linear) optimized simultaneously with peak-fitting using the active background approach, as well as the peak shapes Voigt, and an asymmetrical Voigt, known as the Double Lorentzian. The fit parameters are optimized using a robust metaheuristic method, which starts with a population of temporary solutions known as the chromosomes. This population is then evaluated and assigned a fitness score, from which the best solution is then found. Future generations are created through crossover of the best sets of parameters along with some random parameters. Mutation is then done on the new generation using random perturbations to the chromosomal parameters. The population is then evaluated again, and the process continues. The analyzed data presented here are available in the corresponding XESOasis discussion forum (https://xesoasis.org/ai_posted).

In actinide chemistry, the formal number of open‐shell 5f electrons, n(open), is a well‐defined quantity with an integer value. The effective 5f occupation, n(f), additionally takes donation and back‐donation into account, generally has a non‐integer value, and varying numerical definitions. The present study explores the important distinction between n(f) and n(open) in actinide chemistry with the example of PuO2, by using electronic structure methods with a relativistic Hamiltonian in combination with experimental Pu M5‐edge high energy resolution X‐ray absorption and emission spectroscopic data. The total donation to the metal in PuO2 is between 3.1 and 2.4 electrons, depending on the type of calculation, most of which is to the Pu 6d and 5f shells. The donation into 5f is sensitive to the approximations in the electronic structure model but likely amounts to 1.6/0.8 electrons when the diffuse regions of the 5f shell are included/excluded. Valence band resonant inelastic X‐ray scattering (VB‐RIXS) experiments demonstrate that Pu 5f electron density is present in the valence band; thus, there is a clear experimental signature of covalent bonding in PuO2. Pu M5‐edge and M3‐edge high energy resolution X‐ray absorption near edge structure (HR‐XANES) for Pu3+ and Pu4+ in aqueous solution are compared to PuO2 showing that Pu in PuO2 has n(f) closer to Pu4+(aq).

Traditionally, the smoothing of X-ray photoelectron spectroscopy (XPS) data has been avoided. Data smoothing should not be viewed as a means of “fixing” poorly collected or extremely noisy XPS data. However, it is not always possible or desirable to acquire high-quality data. Here, we suggest that, under appropriate circumstances, and with good algorithms, it may be appropriate to denoise XPS spectra. In particular, we propose the Fourier denoising of XPS data. Fourier analysis is commonly used in science and engineering. Fourier analysis of spectra is advantageous because it naturally divides the data into signal- and noise-dominated coefficients in reciprocal space. Accordingly, Fourier analysis has long been used to reduce or remove the high frequencies (noise) from data, leaving the lower frequencies (signal and baseline trends). We provide an overview of the Fourier transform. The Gauss-Hermite Fourier filter employed in this study shows improvements over the commonly used boxcar and Savitzky-Golay smooths, and also over other apodization approaches used in Fourier analysis, e.g., boxcar apodization. The Gauss-Hermite filter avoids sharp cutoffs that can introduce significant artifacts into data. The Fourier denoising/reconstruction of two XPS narrow scans, an Auger signal, and a valence band signal is demonstrated. Filtered spectra are compared to corresponding “true” spectra, that are obtained by signal averaging. Spectra denoised through the Gauss-Hermite filter can be more accurate approximations to the “true” spectra than the original noisy spectra.

In an interview in 1982, which was 1 year after he shared the Nobel Prize, Kai Siegbahn was asked about his opinion regarding the name of the technique he had developed. Siegbahn had named it “electron spectroscopy for chemical analysis” (ESCA), but the community was choosing to call it “X‐ray photoelectron spectroscopy” (XPS). Now, more than 40 years later, 20 XPS experts have given their opinions on Siegbahn's response and the name of the technique. Some of these participants have been doing XPS for many years and have provided a historical perspective on this issue. While there is no call in these comments for the community to return to “ESCA”—“XPS” is regarded as a more than an adequate name, and insisting on a name change at this point in time would probably only create confusion. However, some of the participants of this study still consider “ESCA” to be an acceptable way to refer to the technique, especially when it is used in a chemical context.

Photoelectron spectroscopy allows for the investigation of the electronic structure and chemical bonding of actinide elements and their compounds, providing insights into oxidation states, chemical environments, and electronic configurations. This knowledge can aid in comprehending reactivity, stability, and other properties of actinide materials, which is essential for ensuring safe handling, storage, and disposal in nuclear applications. We have reviewed a number of results in actinide core-level photoemission studies, with a particular focus on x-ray photoemission spectroscopy (XPS) techniques. Actinides, due to their inherent radioactivity, have not been as well studied with XPS as have other segments of the periodic table. Given the inherent safety concerns, equipment requirements, and short isotopic lifetimes associated with actinide research, we outline the strategies and precautions necessary for conducting successful and safe XPS experiments on these elements. Core-level photoemission can be a powerful proven tool for investigating the electronic structure, chemical bonding behaviors, and physical properties of actinides, providing valuable insights into an incredibly complex behavior of these systems. We highlight key findings from recent studies that demonstrate the potential of core-level photoemission in uncovering the unique properties of actinides and their compounds. Finally, we identify current knowledge gaps and future research directions that could enhance our understanding of actinide chemistry and physics.

Material characterization techniques are widely used to characterize the physical and chemical properties of materials at the nanoscale and, thus, play central roles in material scientific discoveries. However, the large and complex datasets generated by these techniques often require significant human effort to interpret and extract meaningful physicochemical insights. Artificial intelligence (AI) techniques such as machine learning (ML) have the potential to improve the efficiency and accuracy of surface analysis by automating data analysis and interpretation. In this perspective paper, we review the current role of AI in surface analysis and discuss its future potential to accelerate discoveries in surface science, materials science, and interface science. We highlight several applications where AI has already been used to analyze surface analysis data, including the identification of crystal structures from XRD data, analysis of XPS spectra for surface composition, and the interpretation of TEM and SEM images for particle morphology and size. We also discuss the challenges and opportunities associated with the integration of AI into surface analysis workflows. These include the need for large and diverse datasets for training ML models, the importance of feature selection and representation, and the potential for ML to enable new insights and discoveries by identifying patterns and relationships in complex datasets. Most importantly, AI analyzed data must not just find the best mathematical description of the data, but it must find the most physical and chemically meaningful results. In addition, the need for reproducibility in scientific research has become increasingly important in recent years. The advancement of AI, including both conventional and the increasing popular deep learning, is showing promise in addressing those challenges by enabling the execution and verification of scientific progress. By training models on large experimental datasets and providing automated analysis and data interpretation, AI can help to ensure that scientific results are reproducible and reliable. Although integration of knowledge and AI models must be considered for the transparency and interpretability of models, the incorporation of AI into the data collection and processing workflow will significantly enhance the efficiency and accuracy of various surface analysis techniques and deepen our understanding at an accelerated pace.

This study was motivated by earlier observations. It is a systematic examination of the adequacy of reporting of information (metadata) necessary to understand x-ray photoelectron spectroscopy (XPS) data collection and data analysis in the scientific literature. The information for this study was obtained from papers published in three high-quality journals over a six-month period in 2019 and throughout 2021. Each paper was evaluated to determine whether the authors had reported (percentages of the papers properly providing the information are given in parentheses) the spectrometer (66%), fitting software (15%), x-ray source (40%), pass energy (10%), spot size (5%), synthetic peak shapes in fits (10%), backgrounds in fits (10%), whether the XPS data are shown in the main body of the paper or in the supporting information (or both), and whether fitted or unfitted spectra were shown (80% of published spectra are fit). The Shirley background is the most widely used background in XPS peak fitting. The Al Kα source is the most widely used x-ray source for XPS data collection. casaxps is the most widely used fitting program for XPS data analysis. There is good agreement between the results gathered during the two years of our survey. There are some hints the situation may be improving. This study also provides a list of the information/parameters that should be reported when XPS is performed.