Atomic imaging of nucleation of trimethylaluminum on clean and H2O functionalized Ge(100) surfaces

Atomistic Insights into H2O2 Direct Synthesis of Ni-Pt Nanoparticle Catalysts under Water Solvents by Reactive Molecular Dynamics Simulations

In computational catalysis, density-functional theory (DFT) calculations are usually utilized, although they suffer from high computational costs. Thus, it would be challenging to explicitly predict the catalytic properties of nanoparticles (NPs) at the nanoscale under solvents. Using molecular dynamics (MD) simulations with a reactive force field (ReaxFF), we investigated the catalytic performance of Ni-Pt NPs for the direct synthesis of hydrogen peroxide (H₂O₂), in which water solvents were explicitly considered along with the effects of the sizes (1.5, 2.0, 3.0, and 3.5 nm) and compositions (Ni₉₀Pt₁₀, Ni₈₀Pt₂₀, and Ni₅₀Pt₅₀) of the NPs. Among the Ni-Pt NPs, 3.0 nm NPs show the highest activity and selectivity for the direct synthesis of H₂O₂, revealing that the catalytic performance is not well correlated with the surface areas of NPs. The superior catalytic performance results from the high H₂ dissociation and low O₂ dissociation properties, which are correlated with the numbers of NiNiPt-fcc and NiNi-bridge sites on the surface of Ni-Pt NPs, respectively. The ReaxFF-MD simulations propose the optimum composition (Ni₈₀Pt₂₀) of 3.0 nm Ni-Pt NPs, which is also explained by the numbers of NiNiPt-fcc and NiNi-bridge sites. Furthermore, from the ReaxFF-MD simulations, the direct synthesis of H₂O₂ for the Ni-Pt NPs can be achieved not only with the Langmuir-Hinshelwood mechanism, which has been conventionally considered, but also with the water-induced mechanism, which is unlikely to occur on pure Pd and Pd-based alloy catalysts; these results are supported by DFT calculations. These results reveal that the ReaxFF-MD method provides significant information for predicting the catalytic properties of NPs, which could be difficult to provide with DFT calculations; thus, it can be a useful framework for the design of nanocatalysts through complementation with a DFT method.