X-ray photoelectron spectroscopy (XPS) is a powerful technique for surface analysis, but such analysis can be hindered by uncertainty in modelling spectra. Often, many spectral models have a similar goodness of fit, and distinguishing between them can be impossible without additional information. A further challenge is found in interpreting spectra from samples consisting of multiple chemical compounds. We show here how correlation analysis can be used to interpret large XPS datasets. Correlations in atomic concentrations and binding energies of core lines can be interpreted within a framework of an underlying chemical model and this can yield additional information compared with analysis of each spectrum individually. We give examples of the usage of this analysis on some simple systems, and discuss the potential and limitations of the technique.
By combining experiment and theory, this study unveils a direct link between the electronic states in metal hydrides and their enthalpy of formation, advancing our understanding and potential applications in energy materials, catalysis, and gas storage.
Materials discovery lays the foundation for many technological advancements. The prediction and discovery of new materials are not simple tasks. Here, we outline some basic principles of solid-state chemistry, which might help to advance both, and discuss pitfalls and challenges in materials discovery. Using the recent work of Szymanski [Nature 624, 86 (2023)], which reported the autonomous discovery of 43 novel materials, as an example, we discuss problems that can arise in unsupervised materials discovery and hope that by addressing these, autonomous materials discovery can be brought closer to reality. We discuss all 43 synthetic products and point out four common shortfalls in the analysis. These errors unfortunately lead to the conclusion that no new materials have been discovered in that work. We conclude that there are two important points of improvement that require future work from the community, as follows. (i) Automated Rietveld analysis of powder x-ray diffraction data is not yet reliable. Future improvement of such, and the development of a reliable artificial-intelligence-based tool for Rietveld fitting, would be very helpful, not only for autonomous materials discovery but also for the community in general. (ii) We find that disorder in materials is often neglected in predictions. The predicted compounds investigated herein have all their elemental components located on distinct crystallographic positions but in reality, elements can share crystallographic sites, resulting in higher-symmetry space groups and—very often—known alloys or solid solutions. This error might be related to the difficulty of modeling disorder in a computationally economical way and needs to be addressed both by computational and experimental material scientists. We find that two thirds of the claimed successful materials in Szymanski are likely to be known compositionally disordered versions of the predicted ordered compounds. We highlight important issues in materials discovery, computational chemistry, and autonomous interpretation of x-ray diffraction. We discuss concepts of materials discovery from an experimentalist point of view, which we hope will be helpful for the community to further advance this important new aspect of our field. Published by the American Physical Society 2024
Materials discovery lays the foundation for many technological advancements. Predicting and discovering new materials are not simple tasks. We here outline some basic principles of solid-state chemistry, which might help to advance both, and discuss pitfalls and challenges in materials discovery. Using the recent work of Szymanski et al., which reported the autonomous discovery of 43 novel materials, as an example, we discuss problems that can arise in unsupervised materials discovery, and hope that by addressing these, autonomous materials discovery can be brought closer to reality. We discuss all 43 synthetic products and point out four common shortfalls in the analysis. These errors unfortunately lead to the conclusion that no new materials have been discovered in that work. We conclude that there are two important points of improvement that require future work from the community: (i) automated Rietveld analysis of powder x-ray diffraction data is not yet reliable. Future improvement of such, and the development of a reliable artificial intelligence-based tool for Rietveld fitting, would be very helpful, not only to autonomous materials discovery, but also the community in general. (ii) We find that disorder in materials is often neglected in predictions. The predicted compounds investigated herein have all their elemental components located on distinct crystallographic positions, but in reality, elements can share crystallographic sites, resulting in higher symmetry space groups and - very often - known alloys or solid solutions. This error might be related to the difficulty of modeling disorder in a computationally economical way, and needs to be addressed both by computational and experimental material scientists. We find that two-thirds of the claimed successful materials in Szymanski et al are likely to be known, compositionally disordered versions of the predicted, ordered compounds. We highlight important issues in materials discovery, computational chemistry, and autonomous interpretation of x-ray diffraction. We discuss concepts of materials discovery from an experimentalist point of view, which we hope will be helpful for the community to further advance this important new aspect of our field.
Hydrogen as a fuel plays a crucial role in driving the transition to net zero greenhouse gas emissions. To realise its potential, obtaining a means of efficient storage is paramount. One solution is using metal hydrides, owing to their good thermodynamical absorption properties and effective hydrogen storage. Although metal hydrides appear simple compared to many other energy materials, understanding the electronic structure and chemical environment of hydrogen within them remains a key challenge. This work presents a new analytical pathway to explore these aspects in technologically relevant systems using Hard X-ray Photoelectron Spectroscopy (HAXPES) on thin films of two prototypical metal dihydrides: YH$_{2-\delta}$ and TiH$_{2-\delta}$. By taking advantage of the tunability of synchrotron radiation, a non-destructive depth profile of the chemical states is obtained using core level spectra. Combining experimental valence band spectra collected at varying photon energies with theoretical insights from density functional theory (DFT) calculations, a description of the bonding nature and the role of d versus sp contributions to states near the Fermi energy are provided. Moreover, a reliable determination of the enthalpy of formation is proposed by using experimental values of the energy position of metal s band features close to the Fermi energy in the HAXPES valence band spectra.
X-ray photoelectron spectroscopy (XPS) is a powerful technique for surface analysis, but its application can be hindered by uncertainty in modelling spectra. Often, many spectral models have a similar goodness of fit, and distinguishing between them can be impossible without additional information. A further challenge is found in interpreting spectra from samples consisting of multiple chemical compounds. We show here how correlation analysis can be used to interpret large XPS datasets. Correlations in atomic concentrations and binding energies of core lines can be interpreted within a framework of an underlying chemical model and this can yield additional information compared with analysis of each spectrum individually. We give examples of the usage of this analysis on some simple systems, and discuss the potential and limitations of the technique.
Sefik Suzer opened discussion of the introductory lecture by Wendy Flavell: What are your thoughts about cryo-XPS? Is environmental XPS similar to cryo-XPS? A lot of biologists ask when cryo-XPS will be widely available. In your opinion, when do you think that the instrument makers will conside
Hard x-ray photoelectron spectroscopy (HAXPES) is establishing itself as an essential technique for the characterisation of materials. The number of specialised photoelectron spectroscopy techniques making use of hard x-rays is steadily increasing and ever more complex experimental designs enable truly transformative insights into the chemical, electronic, magnetic, and structural nature of materials. This paper begins with a short historic perspective of HAXPES and spans from developments in the early days of photoelectron spectroscopy to provide an understanding of the origin and initial development of the technique to state-of-the-art instrumentation and experimental capabilities. The main motivation for and focus of this paper is to provide a picture of the technique in 2020, including a detailed overview of available experimental systems worldwide and insights into a range of specific measurement modi and approaches. We also aim to provide a glimpse into the future of the technique including possible developments and opportunities.
Close integration of metal nanoparticles (NPs) into a metal-organic framework (MOF) can be leveraged to achieve tailored functionality of the resulting composite structure. Here, we demonstrate a "ship-in-a-bottle" approach to produce ≈4.0 nm bismuth (Bi) NPs within a thiol-rich zirconium-based MOF of Zr-DMBD (DMBD = 2,5-dimercapto-1,4-benzenedicarboxylate). We found that the incorporation of Bi NPs into the Zr-DMBD framework relies on the free-standing thiol groups. These thiols have two roles - (i) aid in binding precursor Bi
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