Mikkel Jørgensen

Single-atom alloys, which are prepared by embedding isolated metal sites in host metals, are promising systems for improved catalyst selectivity. For technical applications, catalysts based on nanoparticles are preferred, thanks to a large surface area. Herein, we investigate acetylene to ethylene hydrogenation using kinetic Monte Carlo simulations based on density functional theory, and compare the performance of Pd/Cu nanoparticles with Pd(111) and Pd/Cu(111). We find that embedding Pd in Cu systems strongly enhances the selectivity, and that the reaction mechanism is fundamentally different for nanoparticles and extended surfaces. The reaction mechanism on nanoparticles is complex and involves elementary steps that proceed preferentially over different sites. Edge and corner sites on nanoparticles are predicted to lower the selectivity, and we infer that a rational design strategy in selective acetylene hydrogenation is to maximize the number of (111) sites in relation to edge sites for Pd/Cu nanoparticles.

Kinetic Monte Carlo (kMC) is an essential tool in heterogeneous catalysis enabling the understanding of dominant reaction mechanisms and kinetic bottlenecks. Here we present MonteCoffee, which is a general-purpose object-oriented and programmable kMC application written in python. We outline the implementation and provide examples on how to perform simulations of reactions on surfaces and nanoparticles and how to simulate sorption isotherms in zeolites. By permitting flexible and fast code development, MonteCoffee is a valuable alternative to previous kMC implementations.

Heterogeneous catalysis is an enabling technology that utilises transition metal nanoparticles (NPs) supported on oxides to promote chemical reactions. Structural mismatch at the NP–support interface generates lattice strain that could affect catalytic properties. However, detailed knowledge about strain in supported NPs remains elusive. We experimentally measure the strain at interfaces, surfaces and defects in Pt NPs supported on alumina and ceria with atomic resolution using high-precision scanning transmission electron microscopy. The largest strains are observed at the interfaces and are predominantly compressive. Atomic models of Pt NPs with experimentally measured strain distributions are used for first-principles kinetic Monte Carlo simulations of the CO oxidation reaction. The presence of only a fraction of strained surface atoms is found to affect the turnover frequency. These results provide a quantitative understanding of the relationship between strain and catalytic function and demonstrate that strain engineering can potentially be used for catalyst design.

Heterogeneous catalysts are often designed as metal nanoparticles supported on oxide surfaces. Here, the relation between particle morphology and reaction kinetics is investigated by scaling relation kinetic Monte Carlo simulations using CO oxidation over Pt nanoparticles as a model reaction. We find that different particle morphologies result in vastly different catalytic activities. The activity is strongly affected by kinetic couplings between sites, and a wide site distribution generally enhances the activity. The present study highlights the role of site‐assemblies as a concept that, in addition to isolated active sites, can be used to understand catalytic reactions over nanoparticles.

Heterogeneous catalysts are often designed as metal nanoparticles supported on oxide surfaces. Here, the relation between particle morphology and reaction kinetics is investigated by scaling relation kinetic Monte Carlo simulations using CO oxidation over Pt nanoparticles as a model reaction. We find that different particle morphologies result in vastly different catalytic activities. The activity is strongly affected by kinetic couplings between sites, and a wide site distribution generally enhances the activity. The present study highlights the role of site‐assemblies as a concept that, in addition to isolated active sites, can be used to understand catalytic reactions over nanoparticles.