X-ray Spectroscopy at the SuperXAS and Debye Beamlines: from in situ to Operando.

Understanding structure-performance relationships are essential for the rational design of new functional materials or in the further optimization of (catalytic) processes. Due to the high penetration depth of the radiation used, synchrotron-based hard X-ray techniques (with energy > 4.5 keV) allow the study of materials under realistic conditions (in situ and operando) and thus play an important role in uncovering structure-performance relationships. X-ray absorption and emission spectroscopies (XAS and XES) give insight into the electronic structure (oxidation state, spin state) and local geometric structure (type and number of nearest neighbor atoms, bond distances, disorder) up to ~5 Å around the element of interest. In this mini review, we will give an overview of the in situ and operando capabilities of the SuperXAS beamline, a facility for hard X-ray spectroscopy, through recent examples from studies of heterogeneous catalysts, electrochemical systems, and photoinduced processes. The possibilities for time-resolved experiments in the time range from ns to seconds and longer are illustrated. The extension of X-ray spectroscopy at the new Debye beamline combined with operando X-ray scattering and diffraction and further developments of time-resolved XES at SuperXAS will open new possibilities after the Swiss Light Source upgrade mid 2025.

Machine Learning for Quantitative Structural Information from Infrared Spectra: The Case of Palladium Hydride.

Infrared spectroscopy (IR) is a widely used technique enabling to identify specific functional groups in the molecule of interest based on their characteristic vibrational modes or the presence of a specific adsorption site based on the characteristic vibrational mode of an adsorbed probe molecule. The interpretation of an IR spectrum is generally carried out within a fingerprint paradigm by comparing the observed spectral features with the features of known references or theoretical calculations. This work demonstrates a method for extracting quantitative structural information beyond this approach by application of machine learning (ML) algorithms. Taking palladium hydride formation as an example, Pd-H pressure-composition isotherms are reconstructed using IR data collected in situ in diffuse reflectance using CO molecule as a probe. To the best of the knowledge, this is the first example of the determination of continuous structural descriptors (such as interatomic distance and stoichiometric coefficient) from the fine structure of vibrational spectra, which opens new possibilities of using IR spectra for structural analysis.

Restructuring dynamics of surface species in bimetallic nanoparticles probed by modulation excitation spectroscopy.

Restructuring of metal components on bimetallic nanoparticle surfaces in response to the changes in reactive environment is a ubiquitous phenomenon whose potential for the design of tunable catalysts is underexplored. The main challenge is the lack of knowledge of the structure, composition, and evolution of species on the nanoparticle surfaces during reaction. We apply a modulation excitation approach to the X-ray absorption spectroscopy of the 30 atomic % Pd in Au supported nanocatalysts via the gas (H2 and O2) concentration modulation. For interpreting restructuring kinetics, we correlate the phase-sensitive detection with the time-domain analysis aided by a denoising algorithm. Here we show that the surface and near-surface species such as Pd oxides and atomically dispersed Pd restructured periodically, featuring different time delays. We propose a model that Pd oxide formation is preceded by the build-up of Pd regions caused by oxygen-driven segregation of Pd atoms towards the surface. During the H2 pulse, rapid reduction and dissolution of Pd follows an induction period which we attribute to H2 dissociation. Periodic perturbations of nanocatalysts by gases can, therefore, enable variations in the stoichiometry of the surface and near-surface oxides and dynamically tune the degree of oxidation/reduction of metals at/near the catalyst surface.

Cobalt-free layered perovskites RBaCuFeO5+δ (R = 4f lanthanide) as electrocatalysts for the oxygen evolution reaction.

Co-based perovskite oxides are intensively studied as promising catalysts for electrochemical water splitting in an alkaline environment. However, the increasing Co demand by the battery industry is pushing the search for Co-free alternatives. Here we report a systematic study of the Co-free layered perovskite family RBaCuFeO5+δ (R = 4f lanthanide), where we uncover the existence of clear correlations between electrochemical properties and several physicochemical descriptors. Using a combination of advanced neutron and X-ray synchrotron techniques with ab initio DFT calculations we demonstrate and rationalize the positive impact of a large R ionic radius in their oxygen evolution reaction (OER) activity. We also reveal that, in these materials, Fe3+ is the transition metal cation the most prone to donate electrons. We also show that similar R3+/Ba2+ ionic radii favor the incorporation and mobility of oxygen in the layered perovskite structure and increase the number of available O diffusion paths, which have an additional, positive impact on both, the electric conductivity and the OER process. An unexpected result is the observation of a clear surface reconstruction exclusively in oxygen-rich samples (δ > 0), a fact that could be related to their superior OER activity. The encouraging intrinsic OER values obtained for the most active electrocatalyst (LaBaCuFeO5.49), together with the possibility of industrially producing this material in nanocrystalline form should inspire the design of other Co-free oxide catalysts with optimal properties for electrochemical water splitting.