| Autor: |
Cui CX; School of Chemistry and Chemical Engineering, Institute of Computational Chemistry, Henan Institute of Science and Technology, Xinxiang, Henan 453003, P. R. China.; Institute of Intelligent Innovation, Henan Academy of Sciences, Zhengzhou, Henan 451162, P. R. China., Shen Y; Key Laboratory of Precision and Intelligent Chemistry, School of Chemistry and Materials Science, University of Science and Technology of China, Hefei, Anhui 230026, P. R. China., He JR; School of Chemistry and Chemical Engineering, Institute of Computational Chemistry, Henan Institute of Science and Technology, Xinxiang, Henan 453003, P. R. China., Fu Y; Hefei National Research Center for Physical Sciences at the Microscale, University of Science and Technology of China, Hefei, Anhui 230026, P. R. China.; CAS Key Laboratory of Urban Pollutant Conversion, Anhui Province Key Laboratory of Biomass Clean Energy, Department of Applied Chemistry, University of Science and Technology of China, Hefei, Anhui 230026, P. R. China.; Hefei National Laboratory, University of Science and Technology of China, Hefei, Anhui 230088, P. R. China., Hong X; Center of Chemistry for Frontier Technologies, Department of Chemistry, State Key Laboratory of Clean Energy Utilization, Zhejiang University, Hangzhou, Zhejiang 310027, P. R. China., Wang S; Key Laboratory of Precision and Intelligent Chemistry, School of Chemistry and Materials Science, University of Science and Technology of China, Hefei, Anhui 230026, P. R. China., Jiang J; Institute of Intelligent Innovation, Henan Academy of Sciences, Zhengzhou, Henan 451162, P. R. China.; Key Laboratory of Precision and Intelligent Chemistry, School of Chemistry and Materials Science, University of Science and Technology of China, Hefei, Anhui 230026, P. R. China.; Hefei National Laboratory, University of Science and Technology of China, Hefei, Anhui 230088, P. R. China., Luo Y; Hefei National Research Center for Physical Sciences at the Microscale, University of Science and Technology of China, Hefei, Anhui 230026, P. R. China.; Hefei National Laboratory, University of Science and Technology of China, Hefei, Anhui 230088, P. R. China. |
| Abstrakt: |
During chemical reactions, especially for electrocatalysis and electrosynthesis, the electric field is the most central driving force to regulate the reaction process. However, due to the difficulty of quantitatively measuring the electric field effects caused at the microscopic level, the regulation of electrocatalytic reactions by electric fields has not been well digitally understood yet. Herein, we took the infrared/Raman spectral signals of CO 2 molecules as descriptors to quantitatively predict the effects of different electric fields on the catalytic properties. Taking the metal-doped graphitic C 3 N 4 ( g -C 3 N 4 ) catalyst as an example, we theoretically investigated the adsorption mode and energy of CO 2 molecules adsorbed on 27 distinct metal single-atom catalysts under different directions and intensities of electric field. Through a machine learning approach, a spectroscopy-property model between infrared/Raman spectral descriptors and adsorption energy/charge transfer was established, which quantified the facilitation of electric field effects on the CO 2 catalytic conversion. Meanwhile, based on the attention mechanism, the catalytic insight of the relationship between spectra and adsorption modes was mined, and the inverse prediction of electric field strength from spectra was realized. This work opens a new quantitative pathway for monitoring and regulating electrocatalytic reactions using machine learning spectroscopy. |
