February 29, 2024

Highly efficient CO2 photoreduction guided by machine learning and DFT calculation
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. 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 pathways is meaningful. Compared with the traditional single electron transfer , 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 model for multi-electron channels with optimized parameters is quite challenging.

Recently, a study titled “Constructing dual electron transfer channels to accelerate CO2 photoreduction guided by machine learning and first-principles calculation” was designed and 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 BiOBr-Bi-g-C3N4 quantum well structure. Five 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.

More information:
Lijing Wang et al, Constructing dual electron transfer channels to accelerate CO2 photoreduction guided by machine learning and first-principles calculation, Chinese Journal of Catalysis (2023). DOI: 10.1016/S1872-2067(23)64546-2

Highly efficient carbon dioxide photoreduction guided by machine learning and first-principles calculation (2023, December 11)
retrieved 11 December 2023
from https://phys.org/news/2023-12-highly-efficient-carbon-dioxide-photoreduction.html

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