Tracking the structure of catalysts under <i>operando</i> conditions is a challenge due to the paucity of experimental techniques that can provide atomic-level information for active metal species. As a result, the search of activity descriptors – attributes of structure, dynamics and electronic properties of working nanoscale devices that are responsible for their functionality – relies almost exclusively on theoretical prediction and human expertise. X-ray absorption fine structure spectroscopy (XAFS) stands out as an element-specific method that is very sensitive to the local geometric, dynamic and electronic properties of the metal atoms and their surroundings and, is, therefore, able to track local structure modifications in operando conditions. Here we report on the use of X-ray absorption near edge structure (XANES) spectroscopy and supervised machine learning (SML) for investigating the information content “hidden” in the spectra. Using an autoencoder-based approach, we zoom in on the latent space for obtaining the information on the number of key descriptors that affect the X-ray absorption spectrum. Using a multilayer perceptron, we determine the values of key descriptors in monometallic and bimetallic nanocatalysts and size-selective clusters. We demonstrate that these methods are superior with respect to commonly used extended X-ray absorption fine structure (EXAFS) analysis of local structural properties in many cases, notably when EXAFS data quality is limited by low catalyst loadings and harsh reaction conditions in operando experiments. In both cases, we train the artificial neural network on theoretical XANES simulations and use it to “invert” the experimental spectrum and obtain the corresponding structural properties. An application of these methods to the determination of catalytic descriptors in <i>operando</i> conditions, such as studies of reactivity of dilute Pd-in-Au catalysts will be demonstrated.

Search for active site ensembles in dilute nanoparticle catalysts

ACS Spring 2022

The official archive and repository for all ACS Meetings & Expositions abstracts and select posters or presentations.