Kinetic Monte Carlo-Based Reactor Model Including Catalyst Shape Changes

Artikel i vetenskaplig tidskrift, 2025

The dynamic character of heterogeneous catalyst particles makes direct comparisons between first-principles kinetics and experimental results obtained for technical catalysts challenging. First-principles kinetics is commonly based on a single model structure and constant reaction conditions, whereas experiments are performed over a particle distribution with shapes that respond to the reaction conditions. Here, we develop a framework for particle-shape adaptive kinetic Monte Carlo simulations in a reactor model (PAKS-R), which integrates first-principles-based kinetic Monte Carlo (kMC) simulations with a reactor model. The framework bridges the gap between the experimental situation by allowing for (i) particle size distributions, (ii) reaction conditions that change along the reactor, and (iii) dynamic shape changes of the NPs as a response to the coverages. The method is applied to ammonia synthesis over Ru NPs, reproducing the previous experimental reaction kinetics. The results show that the activity depends sensitively on the particle size and reaction conditions. The effect of dynamical shape changes is, on average, limited but strongly particle dependent. The PAKS-R approach is robust and general and can be used to explore the reaction kinetics of complex, technical catalysts for a range of reactions.