We report on the activation of CO 2 on Ni single-atom catalysts. These catalysts were synthesized using a solid solution approach by controlled substitution of 1–10 atom % of Mg 2+ by Ni 2+ 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 CO 2 conversion through the reverse water–gas shift (rWGS) but are unable to conduct its further hydrogenation to CH 4 (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 CO 2 binding at low-coordinated sites and also promotes H 2 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 CO 2 and clearly evidence the limits of single-atom catalysis for complex reactions.

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.

The adsorption of silver atoms, dimers, tetramers, and octamers on the perfect anatase (100) surface has been studied theoretically at density functional theory level. Two complementary approaches based on linear combinations of atom-centered Gaussian-type basis functions and plane waves were applied. The most preferable adsorption positions were found to be hollow sites between two-coordinated oxygen atoms (O(2c)). Adsorption on the Ti–O planes is energetically less favorable. The binding mechanism was interpreted in terms of the interaction between cluster orbitals and orbitals of O(2c) atoms. The charge transfer leads to a deformation of the cluster shape compared with the stable gas-phase structures. In the Ti–O plane sites, an additional binding mechanism exists through the overlap between the silver cluster’s HOMO and unoccupied orbitals of five-coordinated titanium atoms. The same bonding mechanisms have been found previously for the adsorption of Ag particles on the rutile (110) surface1. The growth of silver clusters on the anatase (100) surface via aggregation is an exothermic process and leads to the formation of three-dimensional structures.

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.

A theoretical study of nitric oxide (NO) conversion on the anatase (100) surface covered with silver clusters has been performed. Two complementary approaches based on density functional theory (DFT) have been applied, in which the electron density was expanded in plane waves and in atom-centered Gaussian-type orbitals, respectively. It was observed that the NO interaction with the surface occurs mainly via the N atom. Adsorption of NO on silver clusters or at the border between silver and the TiO2 surface is more exothermic than at the uncovered anatase surface. Therefore, all stages of NO degradation proceed mainly on these active sites. Further adsorption of NO molecules leads to the formation of dimer species with previously adsorbed ones. Only acyclic cis-isomers of ONNO are formed according to the calculated energies. An analysis of electron density shows that the LUMO of adsorbed (NO)2 becomes partially occupied so that the adsorbed nitric oxide dimers are negatively charged. As a result of this charge transfer, the (NO)2 species are decomposed by breaking one or two N–O bonds, followed by the formation and desorption of N2 or N2O. In the case of decomposition at silver-surface boundaries, the main gas-phase product is N2O, whereas on the silver cluster both N2 and N2O are formed. After the (NO)2 decomposition, oxygen atoms remain on the surface and can further react with NO molecules from the gas phase, leading to the formation of rather tightly bound nitrogen dioxide molecules.