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Understanding the nature of active sites is central to controlling the activity of a given catalyst. This work combines operando characterization and computational techniques to examine the oxygen evolution reaction mechanism on RuO2 surfaces. Understanding the nature of active sites is central to controlling (electro)catalytic activity. Here we employed surface X-ray scattering coupled with density functional theory and surface-enhanced infrared absorption spectroscopy to examine the oxygen evolution reaction on RuO2 surfaces as a function of voltage. At 1.5 V-RHE, our results suggest that there is an -OO group on the coordinatively unsaturated ruthenium (Ru-CUS) site of the (100) surface (andmore » similarly for (110)), but adsorbed oxygen on the Ru-CUS site of (101). Density functional theory results indicate that the removal of -OO from the Ru-CUS site, which is stabilized by a hydrogen bond to a neighbouring -OH (-OO-H), could be the rate-determining step for (100) (similarly for (110)), where its reduced binding on (100) increased activity. A further reduction in binding energy on the Ru-CUS site of (101) resulted in a different rate-determining step (-O + H2O - (H+ + e(-)) -> -OO-H) and decreased activity. Our study provides molecular details on the active sites, and the influence of their local coordination environment on activity.« less

Materials discovery is increasingly being impelled by machine learning methods that rely on pre-existing datasets. Where datasets are lacking, unbiased data generation can be achieved with genetic algorithms. Here a machine learning model is trained on-the-fly as a computationally inexpensive energy predictor before analyzing how to augment convergence in genetic algorithm-based approaches by using the model as a surrogate. This leads to a machine learning accelerated genetic algorithm combining robust qualities of the genetic algorithm with rapid machine learning. The approach is used to search for stable, compositionally variant, geometrically similar nanoparticle alloys to illustrate its capability for accelerated materialsmore » discovery, e.g., nanoalloy catalysts. The machine learning accelerated approach, in this case, yields a 50-fold reduction in the number of required energy calculations compared to a traditional “brute force” genetic algorithm. This makes searching through the space of all homotops and compositions of a binary alloy particle in a given structure feasible, using density functional theory calculations.« less

RuO2 catalysts exhibit record activities towards the oxygen evolution reaction (OER), which is crucial to enable efficient and sustainable energy storage. Here we examine the RuO2 OER kinetics on rutile (110), (100), (101), and (111) orientations, finding (100) the most active. We assess the potential involvement of lattice oxygen in the OER mechanism with online 3 electrochemical mass spectrometry, which showed no evidence of oxygen exchange on these oriented facets in acidic or basic electrolytes. Similar results were obtained for polyoriented RuO2 films and particles, in contrast to previous work, suggesting lattice oxygen is not exchanged in catalyzing OER onmore » crystalline RuO2 surfaces. This hypothesis is supported by the correlation of activity with the number of active Ru-sites calculated by DFT, where more active facets bind oxygen more weakly. This new understanding of the active sites provides a design strategy to enhance the OER activity of RuO2 nanoparticles by facet engineering.« less

Surface structural transitions and active sites are identified using X-ray scattering and density functional theory.