CO2 reduction to higher value products is a promising way to produce fuels and key chemical building blocks while reducing CO2 emissions. The reaction at atmospheric pressure mainly yields CH4 via methanation and CO via the reverse water-gas shift (RWGS) reaction. Describing catalyst features that control the selectivity of these two pathways is important to determine the formation of specific products. At the same time, identification of morphological changes occurring to catalysts under reaction conditions can be crucial to tune their catalytic performance. In this contribution we investigate the dependency of selectivity for CO2 reduction on the size of Ru nanoparticles (NPs) and on support. We find that even at rather low temperatures (210 °C), oxidative pretreatment induces redispersion of Ru NPs supported on CeO2 and leads to a complete switch in the performance of this material from a well-known selective methanation catalyst to an active and selective RWGS catalyst. By utilizing in situ X-ray absorption spectroscopy, we demonstrate that the low-temperature redispersion process occurs via decomposition of the metal oxide phase with size-dependent kinetics, producing stable single-site RuO x /CeO2 species strongly bound to the CeO2 support that are remarkably selective for CO production. These results show that reaction selectivity can be heavily dependent on catalyst structure and that structural changes of the catalyst can occur even at low temperatures and can go unseen in materials with less defined structures.

In the high-temperature environments needed to perform catalytic processes, supported precious metal catalysts severely lose their activity over time. Even brief exposure to high temperatures can lead to significant losses in activity, which forces manufacturers to use large amounts of noble metals to ensure effective catalyst function for a required lifetime. Generally, loss of catalytic activity is attributed to nanoparticle sintering, or processes by which larger particles grow at the expense of smaller ones. Here, by independently controlling particle size and particle loading using colloidal nanocrystals, we reveal the opposite process as a novel deactivation mechanism: nanoparticles rapidly lose activity by high-temperature nanoparticle decomposition into inactive single atoms. This deactivation route is remarkably fast, leading to severe loss of activity in as little as ten minutes. Importantly, this deactivation pathway is strongly dependent on particle density and concentration of support defect sites. A quantitative statistical model explains how for certain reactions, higher particle densities can lead to more stable catalysts.Increased catalyst stability, especially in automotive emissions control applications, is of paramount importance in order to decrease the loading of rare and precious noble metals 1,2 .

Properties of mono- and bimetallic metal nanoparticles (NPs) may depend strongly on their compositional, structural (or geometrical) attributes, and their atomic dynamics, all of which can be efficiently described by a partial radial distribution function (PRDF) of metal atoms. For NPs that are several nanometers in size, finite size effects may play a role in determining crystalline order, interatomic distances, and particle shape. Bimetallic NPs may also have different compositional distributions than bulk materials. These factors all render the determination of PRDFs challenging. Here extended X-ray absorption fine structure (EXAFS) spectroscopy, molecular dynamics simulations, and supervised machine learning (artificial neural-network) method are combined to extract PRDFs directly from experimental data. By applying this method to several systems of Pt and PdAu NPs, we demonstrate the finite size effects on the nearest neighbor distributions, bond dynamics, and alloying motifs in mono- and bimetallic particles and establish the generality of this approach.