Constructing dual electron transfer channels to accelerate CO2 photoreduction guided by machine learning and first-principles calculation

doi:10.1016/S1872-2067(23)64546-2

Abstract

Abstract:

Designing dual electron transfer channels to achieve efficient carrier separation and understanding the corresponding mechanisms for CO₂ photoreduction is of great significance. However, it is still challenging to find desirable model to achieve optimal photocatalytic performance. Herein, first-principles calculations and machine learning were combined to predict an optimized microstructure with dual electron transfer channels. The results indicate that the construction of BiOBr-Bi-g-C₃N₄ heterojunction has optimal free energy (|ΔG|) for H₂O dissociation and CO₂ reduction. Besides, the double electron transfer channels and excellent Bi active site can localize the photoexcited carriers at the interlayers rather than randomly distributing. These localized carriers generate intriguing superposition states at a particular timescale that optimize the multi-electronic reaction kinetics pathway of CO₂ reduction, resulting in a 4.7 and 3.1 fold increase compared to pristine Bi-BiOBr and Bi-g-C₃N₄ with single electron transfer pathway. Machine learning was further used to optimize the experimental parameters, and the photocatalytic mechanism was verified by combining first-principles calculation with comprehensive experimental characterizations. This work provides experimental and theoretical references for the accurate prediction, rational design and ingenious fabrication of high-performance photocatalytic materials.

Key words: Dual electron transfer channel, Photocatalytic CO₂ reduction, Machine learning, First-principles calculations

Lijing Wang, Tianyi Yang, Bo Feng, Xiangyu Xu, Yuying Shen, Zihan Li, Arramel , Jizhou Jiang. Constructing dual electron transfer channels to accelerate CO₂ photoreduction guided by machine learning and first-principles calculation[J]. Chinese Journal of Catalysis, 2023, 54: 265-277.

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URL: https://www.cjcatal.com/EN/10.1016/S1872-2067(23)64546-2

https://www.cjcatal.com/EN/Y2023/V54/I11/265

Figures/Tables 6

Fig. 1. (a) Atomic configuration of BiOBr-Bi-g-C3N4. (b-d) The work functions of g-C3N4, Bi, and BiOBr. The potential energy curves for H2O dissociation (e) and CO2 reduction (f) of BiOBr, Bi, g-C3N4, Bi-BiOBr, Bi-g-C3N4, BiOBr-g-C3N4 and BiOBr-Bi-g-C3N4.

Fig. 2. The preparation process of BiOBr-Bi-g-C3N4 sample.

Fig. 3. (a) The preliminary exploration process via machine learning. (b,c) The regression curve of training data and (blue dot line) testing data (red dot line) for XGB and KNN model. (d) The importance of each influencing factor. (e) Heat map of Pearson's correlation coefficient matrix of different factors.

Fig. 4. (a,b) The TEM images of g-C3N4 and Bi-BiOBr nanesheet. (c) Fast Fourier transform spectrum of Bi nanoparticles. (d) TEM image of BiOBr-Bi-g-C3N4. (e,f) TEM and HR-TEM images of BiOBr-Bi-g-C3N4 samples. AFM images of BiOBr (g) and g-C3N4 (h). (i) XRD spectra of BiOBr, g-C3N4, and BiOBr-Bi-g-C3N4 samples. (j) N 1s XPS spectra of g-C3N4, Bi-g-C3N4 and BiOBr-Bi-g-C3N4 samples. Bi 4f (k) and Br 3d (l) XPS spectra of BiOBr, Bi-BiOBr and BiOBr-Bi-g-C3N4 samples.

Fig. 5. (a) The CO/H2/CH4 evolution rates of BiOBr, g-C3N4 and BiOBr-g-C3N4 within 30 h. (b) The CER and kinetic constants of Bi-BiOBr, Bi-g-C3N4 and BiOBr-Bi-g-C3N4. (c) The CO/H2/CH4 evolution rates and CO selectivity of Bi-BiOBr, Bi-g-C3N4 and BiOBr-Bi-g-C3N4 within 30 h. (d) The cyclic stability of BiOBr-Bi-g-C3N4, (e) the CER of BiOBr-Bi-g-C3N4 with different TC amounts. (f) The TC degradation efficiency of BiOBr-Bi-g-C3N4 with different TC amounts. UV-vis spectra (g), nitrogen adsorption-desorption curves (h), transient photocurrent curves (i), electrochemical impedance spectra, (j) surface photovoltaic spectroscopy (k) and PL curves (l) of BiOBr, g-C3N4, Bi-BiOBr, Bi-g-C3N4, BiOBr-g-C3N4 and BiOBr-Bi-g-C3N4 samples containing 5 × 10-4 mol L-1 TA.

Fig. 6. (a) The charge density difference for BiOBr-Bi-g-C3N4. (b) The density of states of BiOBr and g-C3N4. (c) The possible reaction pathways for the CO2 reduction of BiOBr-Bi-g-C3N4. (d-i) Time-resolved TA kinetic curves of g-C3N4, BiOBr, BiOBr-g-C3N4, Bi-g-C3N4, Bi-BiOBr and BiOBr-Bi-g-C3N4 samples. (j-k) Schematic illustration of proposed charge trapping model of Bi-BiOBr, Bi-g-C3N4, and BiOBr-Bi-g-C3N4.

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Constructing dual electron transfer channels to accelerate CO₂ photoreduction guided by machine learning and first-principles calculation

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