Sintering Rate and Mechanism of Supported Pt Nanoparticles by Multiscale Simulation

Thermal stability is the key issue in the industrial application of supported metal nanocatalysts. A combination method of density functional theory calculations, machine learning, and molecular dynamics simulation is adopted to study the sintering behavior of supported platinum (Pt) nanoparticles on graphene or TiO2 nanosheet, and analyze sintering mechanisms under different temperatures, particle sizes, and metal support interactions (MSIs). The results show that the agglomeration of supported nanoparticles is mainly based on the mechanism of small particle migration and growth. Small-sized particles with high surface energy determine the sintering rate. In addition, the increase of temperature is conducive to the agglomeration of particles, especially for systems with strong MSI. Based on the analysis of the sintering process, a sintering kinetic model of supported Pt nanoparticles related to particle size, temperature, and MSI is established, which provides theoretical guidance for the design of supported metal catalysts with high thermal stability.

Automated summary

The study investigates the sintering behavior and mechanisms of supported Pt nanoparticles under different conditions using a combination of density functional theory calculations, machine learning, and molecular dynamics simulations. The results indicate that small-sized nanoparticles with high surface energy govern the sintering rate, and an increase in temperature promotes particle agglomeration.