(696d) Design of Active Sites in Bimetallic NO Decomposition Catalysis

The ability to connect catalytic activity to materials properties has played a pivotal role in the rational design of heterogeneous catalysis. Real catalytic nanoparticles, especially compositionally flexible bimetallic alloys, are complex nanostructures spanning diverse structures and compositions, and exposing a distribution of active sites. To bridge the gap between the structural complexity of nanocatalysts under operation and idealized crystal plane models, various structure-activity relationships have emerged for bimetallic surface facets and nanoparticles. Using features of active sites as inputs, both physics-based and data-driven relationships can evaluate catalytic descriptors with an accuracy of 0.1 to 0.2 eV compared to DFT. It is still a challenge, however, to translate these descriptors into active site-resolved reaction kinetics across the distribution of active sites ubiquitous on bimetallic nanoparticles. Herein, we present a novel physics-based method for predicting the activity of catalytic sites on-the-fly with atomic resolution and near DFT accuracy. Using a series of linear scaling relationships, we show that the local stability (BE_M) of an active site can be used as a direct descriptor of catalytic activity. Our approach renders site-specific Sabatier-type volcano plots that offer a powerful tool to assess nanostructural as well as alloying effects in catalysis. This paves the way towards atomic-level design of heterogeneous catalysts for thermal and electrochemical applications. In this work, we showcase our approach on the design of optimal active Pt-based alloy sites for NO decomposition–an important reaction process for, e.g., the purification of vehicle exhaust gas. Using the new approach with site stability as unifying descriptor for activity and durability, it is possible to prescribe specific sizes, morphologies, and compositions of optimal catalytic nanoparticles, guiding towards designing bimetallic catalysts with optimal turnovers.