(453d) Morphology Evolution of Bimetallic Nanoparticle Structures Under Environment-Driven Reconstruction

Bimetallic catalysts are known to frequently exhibit high, synergistic performance as compared to their metal constituents, and can undergo adsorbate-induced surface segregation and reconstruction upon exposure to reaction conditions. Meanwhile, the morphology change of metallic nanoparticles under such reconstruction continues to be a significant concern with great research interest in the field of heterogeneous catalysis. Although such phenomena have been extensively studied by both experimental and computational approaches, there is still a lack of consistent benchmarking among various modeling methods. Without such insights, the consistent interpretation of outputs from various computational tools remains questionable whether the morphology description of metallic nanoparticles under reaction environments is both consistent and accurate for each model, which further hinders the broader application of multiscale modeling. Thus, we performed a comprehensive study to demonstrate the induced morphological evolution of nanoparticle models under cyclic oxidizing/reducing reaction conditions through several multiscale modeling methods using density functional theory (DFT), mean-field models (Wulff construction, kubic harmonics), and molecular dynamics with machine learning interatomic potentials (MD+ML-IAP). While mean-field models require smaller DFT datasets, their accuracy can be questioned by potentially not capturing entire surface configurations. Meanwhile, much more DFT input is needed for MD+ML-IAP to give a thorough description of morphology evolution of nanoparticle reconstruction, but it extends DFT accuracy to more experimentally relevant length scales and temperatures. Taking Rh-Pd system as a case study, both mean-field and large-scale nanoparticle models indicate consistent significant evolution regarding the morphology of bimetallic nanoparticles upon O₂ environment exposure. Notably, the comparison of prediction accuracy and computational cost shed light on the effectiveness of mean-field models with benchmarked predicting capabilities for multiscale nanoparticle modeling studies. This work advances current knowledge of bimetallic nanoparticle catalyst structural evolution and accelerates the potential of tunable nanoparticle engineering as a novel catalyst design strategy.