How Much Structural Information Could Be Extracted from XANES Spectra for Palladium Hydride and Carbide Nanoparticles

Abstract

X-ray absorption near-edge structure (XANES) spectroscopy is a powerful characterization technique that is sensitive to both three-dimensional (3D) geometry and the electronic state of the selected element. In this work, we have suggested a set of structural descriptors that can be used to characterize the state of palladium nanoparticles in hydrogenation reactions and explored the possibility of their extraction from Pd K-edge XANES spectra. A theoretical spectral database was calculated for palladium atoms in the bulk and at the (111) surface with variable Pd–Pd interatomic distances. Carbon and hydrogen atoms randomly occupied octahedral interstitial sites for different H/Pd and C/Pd ratios. The presence of hydrogen and hydrocarbon molecules adsorbed at the surface was also considered. The obtained spectral database was subjected to the principal component analysis (PCA) to estimate the number of strongly contributing components and the multivariate curve resolution (MCR) approach to deconvolve the whole set of data into the XANES spectra of “pure” species and their concentrations. The latter were also used as descriptors of spectra, and machine learning (ML) algorithms were then trained to predict them based on the descriptors of structure and vice versa. We have shown that some of the structural parameters, namely, the concentration of surface-adsorbed molecules, have minor effects on the spectra and cannot be predicted. For interatomic distances, their averaged value can be extracted with good prediction quality based on only one MCR concentration, while independent prediction of the distances in the bulk or at the surface gives unsatisfactory results. Finally, we constructed a new set of structural descriptors that have direct relevance to the MCR components.

The structure of Pd-functionalized UiO-67 during CO2 hydrogenation

“…The second component (dotted blue line) is characterized by a shift of the second peak towards smaller energies, due to the increased Pd-Pd distances, characteristic for palladium hydride. 45,50,54 Furthermore, its concentration decreases when after activation in H 2 the sample is flushed with He (Fig. 6 and Fig.…”

“…To get further insights into the local structure of Pd sites, we analyzed XANES spectra, which are known to be sensitive to the presence of light atoms, such as H or C, that are not visible in Pd K-edge EXAFS. [51][52][53][54] Principle component analysis (PCA) analysis revealed the presence of three independent species formed under reaction conditions (Fig. S4 and S5, ESI †).…”

Machine-learning strategies for the accurate and efficient analysis of x-ray spectroscopy

“…Additional unsupervised approaches, e.g. dimensionality reduction [including principle component analysis (PCA)], [181] t-distributed stochastic neighbor embeddings (t-SNE), [129] uniform manifold approximation and projection (UMAP) operations, [166] multivariate curve resolution, [182][183][184] and autoencoding [129,131] have all been applied to x-ray spectroscopy with the objective of finding simplified representations of x-ray spectra which can then be connected directly to the structural/electronic properties of the molecules and materials under study.…”

Deep Reinforcement Learning Environment Approach Based on Nanocatalyst XAS Diagnostics Graphic Formalization

“…Consequently, in situ XAS is often used to characterize the catalyst's structural evolution and the local environment of the active sites during both the heterogeneous form, where the catalyst is present in a different phase to that of the reactants, usually as a solid catalyst in a gas or liquid reaction mixture, and the homogenous version, which, in turn, involves a catalyst in the same phase as the substrate, most commonly a transition metal complex in a liquid solution [40]. The great advantage of XAS over other techniques such as X-ray diffraction (XRD) is that XAS is an element-specific technique [28,29], which can be used in operando and in situ regimes [38,43,44], and it is sensitive to both the surface and bulk [39,44,45] of the studied catalyst.…”