Multi-scale modeling and experimental investigation of oxidation behavior in platinum nanoparticles

Understanding the impact of oxidation on the reactivity and performance of Pt nanoparticles (NPs) is crucial for developing durable and efficient catalysts. In this study, we investigate the oxidation process of a realistic Pt NP using a multistep approach combining computational methods (ReaxFF, MACE-MP-0, and DFT) with experimental techniques (XRD, TEM, and EXAFS). Our workflow aims to measure oxidation extent, compare different computational models, analyze electronic structure changes, and provide guidance for selecting appropriate computational models in catalytic atomistic studies. We perform hybrid MD-MC simulations using ReaxFF which reveal significant oxidation with oxygen penetrating deep into the core at high oxygen partial pressure, with the formation of detached small cluster oxide Pt₆O₈ species. We investigate the plausibility of these configurations and possible degradation mechanism by carrying out XRD, TEM, and EXAFS measurements on samples of various average particle sizes. Experimental measurements show partial agreement with our simulations in terms of coordination numbers, bond distances, oxygen fractional occupancy and onset/place-exchange potentials. Despite these agreements, we find poor matches between the binding energies calculated by ReaxFF and DFT, casting doubt on the predictive power of ReaxFF and the existence of Pt₆O₈ species. In contrast, the universal MACE-MP-0 model shows significant improvement in the prediction of energetics. Comparing these force fields with DFT calculations on oxidized and non-idealized systems is essential for understanding the limitations of such models in predicting catalytically relevant properties at high potentials and was previously unexplored in the literature. Our study provides a foundation for understanding the complex interplay between nanoparticle structure, oxidation state, and catalytic performance, aiming to guide the rational design of advanced catalytic materials through atomistic modeling.