High-Throughput Screening Strategy for Electrocatalysts for Selective Catalytic Oxidation of Formaldehyde to Formic Acid.
Ting-Ting ZhuYing ZhaoQing-Kai LiShuai-Shuai GaoChun-Lei ChiShuang-Ling TangXue-Bo ChenPublished in: The journal of physical chemistry letters (2024)
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.
Keyphrases
- transition metal
- room temperature
- machine learning
- high throughput
- metal organic framework
- hydrogen peroxide
- aqueous solution
- electron transfer
- visible light
- ionic liquid
- drug delivery
- reduced graphene oxide
- single cell
- artificial intelligence
- big data
- nitric oxide
- cancer therapy
- molecularly imprinted
- deep learning
- risk assessment
- neural network