Catalytic reduction of NO with NH3 on a Pt(100) surface: Monte Carlo simulations

Transition-Metal Borides (MBenes) as New High-Efficiency Catalysts for Nitric Oxide Electroreduction to Ammonia by a High-Throughput Approach

Here, the authors performed density functional theory calculations to study the catalytic performance of the nitric oxide reduction reaction (NORR) via a series of transition metal borides (MBenes). This work screened the M₂ B₂ type MBenes from the IVB to V transition metals from the periodic table and systematically probed the catalytic activity and selectivity for the NORR process. It has been reported that Fe₂ B₂ , Mn₂ B₂ , and Rh₂ B₂ can be high-performance catalysts for converting NO to NH₃ with smaller limiting potentials than other MBenes, and Nb₂ B₂ and Hf₂ B₂ have low limiting potentials of -0.11 V and -0.17 V for the NO production of NH₃ . The binding energy of ΔG*N can be a good descriptor of catalytic performance and is determined by the volcano plot of the rate-determining step. The reaction mechanisms for NO reduction to NH₃ , N₂ , and N₂ O have been studied in detail, atomic *N can interact with another *N or one *NO molecule to form N₂ and N₂ O via two successive hydrogenations. In this regard, *NO hydrogenation to *NOH has a lower formation energy than *HNO, and the MBenes have high selectivity for promoting the NORR and suppressing the hydrogen evolution reaction competition process.

Kinetic modelling of heterogeneous catalytic systems

The importance of heterogeneous catalysis in modern life is evidenced by the fact that numerous products and technologies routinely used nowadays involve catalysts in their synthesis or function. The discovery of catalytic materials is, however, a non-trivial procedure, requiring tedious trial-and-error experimentation. First-principles-based kinetic modelling methods have recently emerged as a promising way to understand catalytic function and aid in materials discovery. In particular, kinetic Monte Carlo (KMC) simulation is increasingly becoming more popular, as it can integrate several sources of complexity encountered in catalytic systems, and has already been used to successfully unravel the underlying physics of several systems of interest. After a short discussion of the different scales involved in catalysis, we summarize the theory behind KMC simulation, and present the latest KMC computational implementations in the field. Early achievements that transformed the way we think about catalysts are subsequently reviewed in connection to latest studies of realistic systems, in an attempt to highlight how the field has evolved over the last few decades. Present challenges and future directions and opportunities in computational catalysis are finally discussed.

Comparative study of methane adsorption on graphite

To study methane adsorption on graphite in a wide range of coverages and temperatures, we compare experimental results with Monte Carlo simulations (MCSs) of the grand canonical ensemble (GCE) and mean-field approximation (MFA) of the lattice gas model (LGM). MCSs were performed by employing two models for the substrate description; we utilized Steele's 10-4-3 analytical potential, and as a second approach, we represented the graphite surface as composed of several graphene layers (at the atomic level). We obtained adsorption isotherms and density profiles that confirm a layer-by-layer mechanism at low temperatures; the later results in the analytical model having a denser condensed phase than the atomistic one. LGM calculations show a close-packed lattice configuration and also allow us to describe the adsorption mechanism changes with temperature. The isosteric heat of adsorption that was found was approximately 13 kJ/mol. We can also conclude that, in spite of the greater computational cost, the atomistic model could be employed for surfaces that are not necessarily homogeneous and beyond the low-pressure range that are not covered by the simple, fast description given by the analytical model.