A Universal Descriptor for Complicated Interfacial Effects on Electrochemical Reduction Reactions

Abstract

Supported catalysts have exhibited excellent performance in various reactions. However, the rational design of supported catalysts with high activity and certain selectivity remains a great challenge because of the complicated interfacial effects. Using recently emerged two-dimensional materials supported dual-atom catalysts (DACs@2D) as a prototype, we propose a simple and universal descriptor based on inherent atomic properties (electronegativity, electron type, and number), which can well evaluate the complicated interfacial effects on the electrochemical reduction reactions (i.e., CO 2 , O 2 , and N 2 reduction reactions). Based on this descriptor, activity and selectivity trends in CO 2 reduction reaction are successfully elucidated, in good agreement with available experimental data. Moreover, several potential catalysts with superior activity and selectivity for target products are predicted, such as CuCr/g-C 3 N 4 for CH 4 and CuSn/N-BN for HCOOH. More importantly, this descriptor can also be extended to evaluate the activity of DACs@2D for O 2 and N 2 reduction reactions, with very small errors between the prediction and reported experimental/computational results. This work provides feasible principles for the rational design of advanced electrocatalysts and the construction of universal descriptors based on inherent atomic properties.

A Machine Learning Model To Predict CO2 Reduction Reactivity and Products Transferred from Metal-Zeolites

“…Furthermore, the ML models can be successfully transferred to predict reaction free energy change, CT values, and products on the MOFs, 2D materials, and molecular complexes. Therefore, the explored ML models with universal descriptors have potential ability to predict the energy and product in other reduction reactions (i.e., N 2 and O 2 ), providing the guidance for rational catalyst design with high activity and selectivity. , …”

“…Therefore, the explored ML models with universal descriptors have potential ability to predict the energy and product in other reduction reactions (i.e., N 2 and O 2 ), providing the guidance for rational catalyst design with high activity and selectivity. 36,76 The free energy change prediction involves the process of gaining or losing electrons from a particular reactant or intermediates as well as the chemical bond change along all reaction pathways. Thus, the number of atoms in various intermediates is an important index for telling the difference between different reaction steps in the complex reaction network.…”

“…), and electronic ( d band center, etc.) features. − The deep neural network approach has been used for CO 2 and N 2 adsorption capacity and CO 2 /N 2 selectivity adsorption prediction on porous carbon materials with textural properties (micropore and mesopore volume and BET surface area) as input features, indicating the significance of the hierarchical pore structures. , An automated ML tool for the CO adsorption energy prediction on Cu-based alloys was applied with atomic numbers, the Pauling electronegativity, and coordination numbers. , Extreme gradient boosting regression was successfully used to predict the Gibbs free energy change of CO adsorption in 1060 metal and nonmetal co-doped graphenes with 14 accessible electronic and structure features (coefficient of determination values, R 2 = 0.90) . Some atomic (atomic radius), electronic ( d electron count, electron affinity, etc.…”

Descriptor-Driven Computational Design of Bifunctional Double-Atom Hydrogen Evolution and Oxidation Reaction Electrocatalysts for Rechargeable Hydrogen Gas Batteries

“…Such a success prompted us to further explore low-cost catalysts for RHGBs in large-scale energy storage . In addition to alloy-based catalysts, single-atom catalysts (SACs) demonstrate excellent performance in various reactions because of their high catalytic activity and significantly reduced metal content with lower costs. , Compared with SACs, double-atom catalysts (DACs) possess similar characteristics, which are able to have better catalytic performance for some reactions involving a variety of intermediates, such as HER/HOR in alkaline conditions. − However, because of the diversity of elemental compositions, the discovery of DACs by traditional trial-and-error experimentation is time-consuming and resource-wasting. Therefore, it is very important to develop an efficient design principle for low-cost and high catalytic activity HER/HOR DACs to further promote their practical applications toward RHGBs.…”

eg Occupancy as a Predictive Descriptor for Spinel Oxide Nanozymes

“…Since the report on the POD-like activity of Fe 3 O 4 nanoparticles, spinel oxides (AB 2 O 4 ) have attracted great attention due to their well-defined but tunable crystal structures (containing both tetrahedral and octahedral sites at a ratio of 1:2), which is very beneficial for regulating enzyme-like activity − and discovering predictive descriptorsparameters that indicate enzyme-like activity . Although a variety of descriptors have been reported for other catalysts (especially electrocatalysts), − the descriptors for nanozymes have rarely been identified, and no descriptors for the enzyme-like activity of spinel oxides have been reported yet.…”