Oscillatory Behavior in the CO-oxidation over Bulk Ruthenium Dioxide — the Effect of the CO/O2 Ratio

Accelerating the prediction of inorganic surfaces with machine learning interatomic potentials

The surface properties of solid-state materials often dictate their functionality, especially for applications where nanoscale effects become important. The relevant surface(s) and their properties are determined, in large part, by the material's synthesis or operating conditions. These conditions dictate thermodynamic driving forces and kinetic rates responsible for yielding the observed surface structure and morphology. Computational surface science methods have long been applied to connect thermochemical conditions to surface phase stability, particularly in the heterogeneous catalysis and thin film growth communities. This review provides a brief introduction to first-principles approaches to compute surface phase diagrams before introducing emerging data-driven approaches. The remainder of the review focuses on the application of machine learning, predominantly in the form of learned interatomic potentials, to study complex surfaces. As machine learning algorithms and large datasets on which to train them become more commonplace in materials science, computational methods are poised to become even more predictive and powerful for modeling the complexities of inorganic surfaces at the nanoscale.

SQERT-T: alleviating kinetic Monte Carlo (KMC)-stiffness in transient KMC simulations

Lattice based kinetic Monte Carlo (KMC) is often used for simulating the dynamics of systems at a supramolecular scale, based on molecular scale transitions. A common challenge in KMC simulations is rapid 'back-and-forth' reactions, which dominate the events executed during simulations and inhibit the ability for simulations to reach longer time scales. Such processes are fast frivolous processes (FFPs) and are one manifestation of a phenomenon referred to as KMC-stiffness. Here, an algorithm for staggered quasi-equilibrium rank-based throttling geared towards transient kinetics (SQERT-T) is presented. Within the SQERT-T methodology, a pace-restrictor reaction and an FFP floor are utilized along with throttling of the process transition rate constants to accelerate the KMC simulations while still retaining sufficient time resolution for sampling of the data. KMC simulations were performed for CO oxidation over RuO₂(1 1 0) and over RuO₂(1 1 1), and the results were compared to experimental data obtained using RuO₂ powders. The experiments and simulations were for transient conditions: the system was subjected to a temperature program which included temperatures in the range of 363 to 453 K. The timescales that were achieved during the KMC simulations in this study would not have been accessible without KMC acceleration, and were enabled by the use of SQERT-T.

CO oxidation on Ru-Pt bimetallic nanoclusters supported on TiO2(101): The effect of charge polarization

Pt-based catalyst is widely used in CO oxidation, while its catalytic activity is often undermined because of the CO poisoning effect. Here, using density functional theory, we propose the use of a Ru-Pt bimetallic cluster supported on TiO₂ for CO oxidation, to achieve both high activity and low CO poisoning effect. Excellent catalytic activity is obtained in a Ru₁Pt₇/TiO₂(101) system, which is ascribed to strong electric fields induced by charge polarization between one Ru atom and its neighboring Pt atoms. Because of its lower electronegativity, the Ru atom donates electrons to neighboring Pt. This induces strong electric fields around the top-layered Ru, substantially promoting the adsorption of O₂/CO + O₂ and eliminating the CO poisoning effect. In addition, the charge polarization also drives the d-band center of the Ru₁Pt₇ cluster to up-shift to the Fermi level. For surface O₂ activation/CO oxidation, the strong electric field and d-band center close to the Fermi level can promote the adsorption of O₂ and CO as well as reduce the reaction barrier of the rate-determining step. Meanwhile, since O₂ easily dissociates on Ru₁Pt₇/TiO₂(101) resulting in unwanted oxidation of Ru and Pt, a CO-rich condition is necessary to protect the catalyst at high temperature.

Temperature-induced transformation of electrochemically formed hydrous RuO2 layers over Ru(0001) model electrodes

Hydrous RuO2 reveals excellent performance both as a supercapacitor and as a heterogeneous oxidation catalyst. Molecular understanding of these processes needs, however, a model system with preferably low structural and morphological complexity. This goal is partly accomplished here by using single crystalline Ru(0001) as a template on which hydrous RuO2 is electrochemically formed. The hydrous RuO2 layers on Ru(0001) and their temperature induced transformation under ultra high vacuum (UHV) conditions are comprehensively characterized by scanning electron microscopy and X-ray photoemission spectroscopy. The hydrous RuO2 layer grows with an intricate morphology governed by the presence of step bunching regions of the Ru(0001) surface. Upon annealing to 200 °C in UHV the hydrous RuO2 layer transforms mostly into flat metallic Ru islands and occasionally into (100) and (111) oriented RuO2 particles aligned along the high symmetry direction of Ru(0001).

Heterogeneous catalysis

A heterogeneous catalyst is a functional material that continually creates active sites with its reactants under reaction conditions. These sites change the rates of chemical reactions of the reactants localized on them without changing the thermodynamic equilibrium between the materials.