High-Throughput Screening Strategy for Electrocatalysts for Selective Catalytic Oxidation of Formaldehyde to Formic Acid

作者：Tingting Zhu, Ying Zhao, Qing-Kai Li, Shuaishuai Gao, Chun-Lei Chi, Shuang-Ling Tang, Xuebo Chen · 发表于：The Journal of Physical Chemistry Letters · 年份：2024 · DOI：10.1021/acs.jpclett.4c01081 · 被引用次数：2 · 研究领域：Electrocatalysts for Energy Conversion、CO2 Reduction Techniques and Catalysts、Catalysis and Oxidation Reactions

Electrocatalytic oxidation of formaldehyde (FOR) is an effective way to prevent the damage caused by formaldehyde and produce high-value products. A screening strategy of a single-layer MnO 2 -supported transition metal catalyst for the selective oxidation of formaldehyde to formic acid was designed by high-throughput density functional calculation. N-MnO 2 @Cu and MnO 2 @Cu are predicted to be potential FOR electrocatalysts with potential-limiting steps (PDS) of 0.008 and −0.009 eV, respectively. Electronic structure analysis of single-atom catalysts (SACs) shows that single-layer MnO 2 can regulate the spin density of loaded transition metal and thus regulate the adsorption of HCHO ( E ad ), and E ad is volcanically distributed with the magnetic moment descriptor −| m M – m H |. In addition, the formula quantifies E ad and | m M – m H | to construct a volcano-type descriptor α describing the PDS [Δ G (*CHO)]. Other electronic and structural properties of SACs and α are used as input features for the GBR method to construct machine learning models predicting the PDS ( R 2 = 0.97). This study hopes to provide some insights into FOR electrocatalysts.