CuNi Nanoalloys with Tunable Composition and Oxygen Defects for the Enhancement of the Oxygen Evolution Reaction.

Colloidal synthesis is an excellent tool for the study of cooperative effects in nanoalloys. In this work, bimetallic CuNi nanoparticles of defined size and composition are fully characterized and tested for the oxygen evolution reaction. Copper addition to nickel leads to modifications in the structural and electronic properties, showing a higher concentration of surface oxygen defects and formation of active Ni3+ sites under reaction conditions. The ratio OV /OL between oxygen vacancies and lattice oxygen shows a clear correlation with the overpotential, being an excellent descriptor of the electrocatalytic activity. This is attributed to modifications in the crystalline structure, leading to lattice strain and grain size effects. Bimetallic Cu50 Ni50 NP showed the lowest overpotential (318 mV vs RHE), low Tafel slope (63.9 mV dec-1 ), and excellent stability. This work unravels the relative concentration between oxygen defects and lattice oxygen (OV /OL ) as an excellent descriptor of the catalytic activity of bimetallic precatalysts.

Ni Single Atom Catalysts for CO2 Activation.

We report on the activation of CO2 on Ni single-atom catalysts. These catalysts were synthesized using a solid solution approach by controlled substitution of 1-10 atom % of Mg2+ by Ni2+ inside the MgO structure. The Ni atoms are preferentially located on the surface of the MgO and, as predicted by hybrid-functional calculations, favor low-coordinated sites. The isolated Ni atoms are active for CO2 conversion through the reverse water-gas shift (rWGS) but are unable to conduct its further hydrogenation to CH4 (or MeOH), for which Ni clusters are needed. The CO formation rates correlate linearly with the concentration of Ni on the surface evidenced by XPS and microcalorimetry. The calculations show that the substitution of Mg atoms by Ni atoms on the surface of the oxide structure reduces the strength of the CO2 binding at low-coordinated sites and also promotes H2 dissociation. Astonishingly, the single-atom catalysts stayed stable over 100 h on stream, after which no clusters or particle formation could be detected. Upon catalysis, a surface carbonate adsorbate-layer was formed, of which the decompositions appear to be directly linked to the aggregation of Ni. This study on atomically dispersed Ni species brings new fundamental understanding of Ni active sites for reactions involving CO2 and clearly evidence the limits of single-atom catalysis for complex reactions.

Artificial-intelligence-driven discovery of catalyst genes with application to CO2 activation on semiconductor oxides.

Catalytic-materials design requires predictive modeling of the interaction between catalyst and reactants. This is challenging due to the complexity and diversity of structure-property relationships across the chemical space. Here, we report a strategy for a rational design of catalytic materials using the artificial intelligence approach (AI) subgroup discovery. We identify catalyst genes (features) that correlate with mechanisms that trigger, facilitate, or hinder the activation of carbon dioxide (CO2) towards a chemical conversion. The AI model is trained on first-principles data for a broad family of oxides. We demonstrate that surfaces of experimentally identified good catalysts consistently exhibit combinations of genes resulting in a strong elongation of a C-O bond. The same combinations of genes also minimize the OCO-angle, the previously proposed indicator of activation, albeit under the constraint that the Sabatier principle is satisfied. Based on these findings, we propose a set of new promising catalyst materials for CO2 conversion.

Single-atom alloy catalysts designed by first-principles calculations and artificial intelligence.

Single-atom-alloy catalysts (SAACs) have recently become a frontier in catalysis research. Simultaneous optimization of reactants' facile dissociation and a balanced strength of intermediates' binding make them highly efficient catalysts for several industrially important reactions. However, discovery of new SAACs is hindered by lack of fast yet reliable prediction of catalytic properties of the large number of candidates. We address this problem by applying a compressed-sensing data-analytics approach parameterized with density-functional inputs. Besides consistently predicting efficiency of the experimentally studied SAACs, we identify more than 200 yet unreported promising candidates. Some of these candidates are more stable and efficient than the reported ones. We have also introduced a novel approach to a qualitative analysis of complex symbolic regression models based on the data-mining method subgroup discovery. Our study demonstrates the importance of data analytics for avoiding bias in catalysis design, and provides a recipe for finding best SAACs for various applications.

Sonogenerated metal-hydrogen sponges for reactive hard templating.

We present sonogenerated magnesium-hydrogen sponges for effective reactive hard templating. Formation of differently organized nanomaterials is possible by variation of sonochemical parameters and solution composition: Fe2O3 nanorods or composite dendritic Fe2O3/Fe3O4 nanostructures.