基于机器学习和第一性原理计算构建双电子转移通道加速CO2光还原

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

催化学报 ›› 2023, Vol. 54: 265-277.DOI: 10.1016/S1872-2067(23)64546-2

基于机器学习和第一性原理计算构建双电子转移通道加速CO₂光还原

王立晶^a^,¹, 杨天一^a^,¹, 冯博^c, 许祥雨^a, 申玉莹^a, 李孜涵^a, Arramel^d, 江吉周^b^,^*()

^a商丘师范大学化学化工学院,河南省新能源电池材料工程中心, 河南省先进电池材料研发工程中心, 河南商丘476000, 中国
^b武汉工程大学环境生态与生物工程学院, 湖北新型催化材料工程研究中心, 教育部磷资源开发利用工程研究中心, 教育部绿色化工过程重点实验室, 湖北武汉430205, 中国
^c白城师范学院化学学院, 吉林白城137000, 中国
^dNano Center Indonesia, Jalan Raya PUSPIPTEK, Indonesia

收稿日期:2023-09-20 接受日期:2023-10-20 出版日期:2023-11-18 发布日期:2023-11-15
通讯作者: *电子信箱: 027wit@163.com (J. Jiang).
作者简介:¹共同第一作者.
基金资助:
国家自然科学基金(62004143);河南省科技项目(232102240073);湖北省重点研发计划(2022BAA084)

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

Lijing Wang^a^,¹, Tianyi Yang^a^,¹, Bo Feng^c, Xiangyu Xu^a, Yuying Shen^a, Zihan Li^a, Arramel ^d, Jizhou Jiang^b^,^*()

^aHenan Engineering Center of New Energy Battery Materials, Henan D&A Engineering Center of Advanced Battery Materials, College of Chemistry and Chemical Engineering, Shangqiu Normal University, Shangqiu 476000, Henan, China
^bSchool of Environmental Ecology and Biological Engineering, Key Laboratory of Green Chemical Engineering Process of Ministry of Education, Engineering Research Center of Phosphorus Resources Development and Utilization of Ministry of Education, Novel Catalytic Materials of Hubei Engineering Research Center, Wuhan Institute of Technology, Wuhan 430205, Hubei, China
^cCollege of Chemistry, Baicheng Normal University, Baicheng 137000, Jilin, China
^dNano Center Indonesia, Jalan Raya PUSPIPTEK, South Tangerang, Banten 15314, Indonesia

Received:2023-09-20 Accepted:2023-10-20 Online:2023-11-18 Published:2023-11-15
Contact: *E-mail: 027wit@163.com (J. Jiang).
About author:¹Contributed equally to this work.
Supported by:
National Natural Science Foundation of China(62004143);Science and Technology project of Henan Province(232102240073);Key R&D Program of Hubei Province(2022BAA084)

摘要/Abstract

摘要：

光催化还原CO₂技术可以将CO₂转化为高附加值化学品, 在解决日益严重的环境污染和能源危机方面具有巨大潜力. 然而, CO₂分子较高的C=O键键能(750 kJ mol^-1)为其活化和还原带来了挑战. 因此, 构建具有新型电子转移路径的光催化剂具有重要意义. 与传统的单电子传输通道相比, 层状材料的多电子传输通道在改善载流子传输能力方面具有明显的优势. 然而, 设计具有合适参数的多电子通道光催化剂模型仍是重要挑战.

本文首先采用理论计算预测了具有双电子转移通道、参数匹配的三元异质结BiOBr-Bi-g-C₃N₄; 然后, 通过机器学习探讨了各种实验参数对双电子传输通道的光催化活性影响的线性规律, 优化了实验参数, 制备了光催化活性较高的BiOBr-Bi-g-C₃N₄催化剂; 最后, 结合第一性原理计算和实验表征结果揭示了其光催化机理. 理论计算结果表明, BiOBr-Bi-g-C₃N₄异质结具有最佳的吉布斯自由能(|ΔG|), 有利于光催化H₂O解离和CO₂还原. 实验发现, 在300 W Xe灯照射下, CO₂还原光催化活性高达43 μmol g^-1 h^-1. 与Bi-BiOBr和Bi-g-C₃N₄相比, BiOBr-Bi-g-C₃N₄催化CO₂还原的速率分别提高了约4.7倍和3.1倍. 分析新型结构催化剂之所以具有良好的活性, 主要有以下三个原因: (1) 三者之间匹配的功函数使得BiOBr和g-C₃N₄纳米片可以与Bi形成肖特基异质结, 在光照下, 电子从BiOBr和g-C₃N₄向Bi转移; 此外, g-C₃N₄纳米片与BiOBr具有相似的层间结构和匹配的能级结构, 有利于形成Bi-BiOBr和Bi-g-C₃N₄双电子传输通道, 从而实现载流子的有效分离和转移. (2) 丰富的Bi活性位点可以抑制光生载流子的随机分布, 使其限域在BiOBr与g-C₃N₄层间; 这些载流子在特定的时间尺度上产生了独特的叠加态, 优化了CO₂还原的多电子反应动力学路径. (3) g-C₃N₄的引入提高了Bi-BiOBr的太阳光利用率和比表面积.

综上所述, 本文成功地预测、设计和制备了一种具有双电子传输通道的新型三元异质结BiOBr-Bi-g-C₃N₄光催化剂, 其表现出较高的光催化CO₂还原性能. 理论计算和实验结果表明, BiOBr和g-C₃N₄相似的层间结构和匹配的能级结构使得具有不同弛豫时间的电子能够形成相干态; 而Bi作为CO₂还原的良好活性位点, 能够同时提高BiOBr和g-C₃N₄的载流子转移性能. 因此, 具有不同寿命的电子可以参与到CO₂还原的过程中去. 本文可为高性能CO₂还原光催化剂的精准预测和合理设计提供实验和理论参考.

关键词: 双电子转移通道, 光催化还原CO₂, 机器学习, 第一性原理计算

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

王立晶, 杨天一, 冯博, 许祥雨, 申玉莹, 李孜涵, Arramel, 江吉周. 基于机器学习和第一性原理计算构建双电子转移通道加速CO₂光还原[J]. 催化学报, 2023, 54: 265-277.

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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图/表 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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基于机器学习和第一性原理计算构建双电子转移通道加速CO₂光还原

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

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