News Release

Highly efficient CO2 photoreduction guided by machine learning and DFT calculation

Peer-Reviewed Publication

Dalian Institute of Chemical Physics, Chinese Academy Sciences

Figure Abstract


BiOBr-Bi-g-C3N4 heterojunction with double electron transfer channels was successfully constructed, which can localize the photoexcited carriers at the interlayers rather than randomly distributing, resulting in a 4.7- and 3.1-fold increase compared to Bi-BiOBr and Bi-g-C3N4 samples.

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Credit: Chinese Journal of Catalysis

Photocatalytic reduction of CO2 to high-value carbon-based fuels holds tremendous potential in addressing the growing energy crisis. However, the high C=O bond energy of CO2 molecules (750 kJ·mol-1) makes it challenging to activate and reduce CO2. Therefore, the construction of photocatalysts with novel electron transfer pathway is meaningful. Compared with the traditional single electron transfer channel, the development of multi-electron channels based on layered materials has obvious advantages in the improvement of carrier transport. Nevertheless, the rational design of a desirable photocatalytic model for multi-electron channels with optimized parameters is quite challenging.

Recently, a research titled “Constructing dual electron transfer channels to accelerate CO2 photoreduction guided by machine learning and first-principles calculation” was designed led by Prof. Jizhou Jiang from Wuhan Institute of Technology, China. This work combines first-principles calculating and machine learning to successfully predict and prepare a novel BiOBr-Bi-g-C3N4 sandwich structure with dual electron transport channels for photocatalytic CO2 reduction. There are three main reasons for the favorable activity by the novel structure: (1) the introduced g-C3N4 nanosheets demonstrate a similar energy level structure with BiOBr, which benefits for forming an electronic superposition state; (2) the excited carriers can be efficient separation and transferred owing to the special double electron transfer channels; (3) since the photo-generated carrier of BiOBr and g-C3N4 have different time decay behavior, a multi-timescale reaction mechanism for CO2 reduction can be constructed to optimize the reaction pathway. An enhanced photocatalytic performance of CO2 reduction (43 μmol g-1 h-1) is received by the well structure of BiOBr-Bi-g-C3N4 quantum. Five machine learning models were used to explore the linear law of the various influence factors on the efficiency of multi-electron channels. The mechanism of photocatalysis was investigated systematically. The results were published in Chinese Journal of Catalysis (


About the Journal

Chinese Journal of Catalysis is co-sponsored by Dalian Institute of Chemical Physics, Chinese Academy of Sciences and Chinese Chemical Society, and it is currently published by Elsevier group. This monthly journal publishes in English timely contributions of original and rigorously reviewed manuscripts covering all areas of catalysis. The journal publishes Reviews, Accounts, Communications, Articles, Highlights, Perspectives, and Viewpoints of highly scientific values that help understanding and defining of new concepts in both fundamental issues and practical applications of catalysis. Chinese Journal of Catalysis ranks among the top one journals in Applied Chemistry with a current SCI impact factor of 16.5. The Editors-in-Chief are Profs. Can Li and Tao Zhang.

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