Quantifying Competitive Degradation Processes in Supported Nanocatalyst Systems.

Abstract

The stability of supported metal nanoparticles determines the activity and lifetime of heterogeneous catalysts. Catalysts can destabilize through several thermodynamic and kinetic pathways, and the competition between these mechanisms complicates efforts to quantify and predict the overall evolution of supported nanoparticles in reactive environments. Pairing in situ transmission electron microscopy with unsupervised machine learning, we quantify the destabilization of hundreds of supported Au nanoparticles in real-time to develop a model describing the observed particle evolution as a competition between evaporation and surface diffusion. Data mining of particle evolution statistics allows us to determine physically reasonable values for the model parameters, quantify the particle size at which the Gibbs-Thomson pressure accelerates the evaporation process, and explore how individual particle interactions deviate from the mean-field model. This approach can be applied to a wide range of supported nanoparticle systems, allowing quantitative insight into the mechanisms that control their evolution in reactive environments.