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CO_(2)光还原

Constructing dual electron transfer channels to accelerate CO_(2) photoreduction guided by machine learning and first‐principles calculation

光催化还原CO_(2)技术可以将CO_(2)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,CO_(2)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-C_(3)N_(4);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-C_(3)N_(4)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-C_(3)N_(4)异质结具有最佳的吉布斯自由能(|ΔG|),有利于光催化H_(2)O解离和CO_(2)还原.实验发现,在300 W Xe灯照射下,CO_(2)还原光催化活性高达43μmol g^(-1)h^(-1).与Bi-BiOBr和Bi-g-C_(3)N_(4)相比,BiOBr-Bi-g-C_(3)N_(4)催化CO_(2)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-C_(3)N_(4)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-C_(3)N_(4)向Bi转移;此外,g-C_(3)N_(4)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-C_(3)N_(4)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-C_(3)N_(4)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CO_(2)还原的多电子反应动力学路径.(3)g-C_(3)N_(4)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-C_(3)N_(4)光催化剂,其表现出较高的光催化CO_(2)还原性能.理论计算和实验结果表明,BiOBr和g-C_(3)N_(4)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CO_(2)还原的良好活性位点,能够同时提高BiOBr和g-C_(3)N_(4)的载流子转移性能.因此,具有不同寿命的电子可以参与到CO_(2)还原的过程中去.本文可为高性能CO_(2)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.

Designing dual electron transfer channels to achieve efficient carrier separation and understanding the corresponding mechanisms for CO_(2) 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 Bi-OBr-Bi-g-C_(3)N_(4) heterojunction has optimal free energy(|ΔG|)for H2O dissociation and CO_(2) 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 CO_(2) reduction,resulting in a 4.7 and 3.1 fold increase compared to pristine Bi-BiOBr and Bi-g-C_(3)N_(4) 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.