The small pore Cu-CHA zeolite is attracting increasing attention as a versatile platform to design novel single-site catalysts for deNO_x applications and for the direct conversion of methane to methanol. Understanding at the atomic scale how the catalyst composition influences the Cu-species formed during thermal activation is a key step to unveil the relevant composition–activity relationships. Herein, we explore by in situ XAS the impact of Cu-CHA catalyst composition on temperature-dependent Cu-speciation and reducibility. Advanced multivariate analysis of in situ XANES in combination with DFT-assisted simulation of XANES spectra and multi-component EXAFS fits as well as in situ FTIR spectroscopy of adsorbed N₂ allow us to obtain unprecedented quantitative structural information on the complex dynamics during the speciation of Cu-sites inside the framework of the CHA zeolite.

X-ray absorption near-edge structure (XANES) spectra are the fingerprint of the local atomic and electronic structures around the absorbing atom. However, the quantitative analysis of these spectra is not straightforward. Even with the most recent advances in this area, for a given spectrum, it is not clear a priori which structural parameters can be refined and how uncertainties should be estimated. Here, we present an alternative concept for the analysis of XANES spectra, which is based on machine learning algorithms and establishes the relationship between intuitive descriptors of spectra, such as edge position, intensities, positions, and curvatures of minima and maxima on the one hand, and those related to the local atomic and electronic structure which are the coordination numbers, bond distances and angles and oxidation state on the other hand. This approach overcoms the problem of the systematic difference between theoretical and experimental spectra. Furthermore, the numerical relations can be expressed in analytical formulas providing a simple and fast tool to extract structural parameters based on the spectral shape. The methodology was successfully applied to experimental data for the multicomponent Fe:SiO₂system and reference iron compounds, demonstrating the high prediction quality for both the theoretical validation sets and experimental data.

Transition metal-nitrogen-doped carbons (TMNCs) are a promising class of catalysts for the CO₂ electrochemical reduction reaction. In particular, high CO₂-to-CO conversion activities and selectivities were demonstrated for Ni-based TMNCs. Nonetheless, open questions remain about the nature, stability, and evolution of the Ni active sites during the reaction. In this work, we address this issue by combining operando X-ray absorption spectroscopy with advanced data analysis. In particular, we show that the combination of unsupervised and supervised machine learning approaches is able to decipher the X-ray absorption near edge structure (XANES) of the TMNCs, disentangling the contributions of different metal sites coexisting in the working TMNC catalyst. Moreover, quantitative structural information about the local environment of active species, including their interaction with adsorbates, has been obtained, shedding light on the complex dynamic mechanism of the CO₂ electroreduction.