Speaker
Description
High-entropy oxides (HEOs) are multicomponent materials containing five or more cations distributed within a single crystalline lattice and are gaining attention as promising catalysts due to their unique structural stability, redox properties, and synergistic multi-cation effects [1]. Although HEOs are promising for a wide range of applications, their rational design remains challenging due to the large compositional space and complex characterization. X-ray absorption spectroscopy (XAS) has proven to be a valuable tool to understanding high entropy materials: for instance, EXAFS can determine the random and uniform distribution of cations, a standard criterion for defining HEOs [2]. Additionally, this technique allows to observe changes in oxidation state to identify mechanisms for charge compensation during the structures formation [3]. Since HEOs are only now emerging as heterogeneous catalysts [4], there is a decided lack of fundamental knowledge regarding how the high-entropy form plays a role in catalysis and which cations are participating in the reaction.
In this work, HEOs are tailored for thermocatalytic CO₂ hydrogenation towards value-added products, such as carbon monoxide, methane [5], methanol [6], and larger hydrocarbons. In order to address the possible permutations for chemical composition, in collaboration with SwissCat+ Hub East, we have employed high-throughput synthesis and catalytic testing with machine learning to provide AI-assisted synthesis to optimize the performance for CO2 hydrogenation for different product selectivities. Furthermore, this work utilizes ex-situ XAS to elucidate structure-activity correlations by probing low-, medium-, and high-entropy oxides before and after reaction. Specifically, the K-edges of Cr, Fe, Cu, Ni, Co, and Zn were probed to track changes in the local environment of each cation and to elucidate their individual roles during the CO2 hydrogenation reaction.
