Computation‐Guided Regulation of Thiophene‐Based Covalent Organic Frameworks for Boosting Photocatalytic Hydrogen Evolution†

化学 光催化 Boosting(机器学习) 噻吩 共价键 计算 组合化学 有机化学 光化学 催化作用 人工智能 算法 计算机科学
作者
Xiao Luo,Yuxiang Chen,Jia‐Tong Lin,Jie Luo,Ri‐Qin Xia,Na Yin,Yih-Jiun Lin,Haiyan Duan,Shi‐Bin Ren,Qiang Gao,Guo‐Hong Ning,Dan Li
出处
期刊:Chinese Journal of Chemistry [Wiley]
标识
DOI:10.1002/cjoc.202401245
摘要

Comprehensive Summary Compared to the conventional trial‐and‐error approach, computational prediction is becoming an increasingly prominent approach in the discovery of covalent organic frameworks (COFs) with specific applications, yet it has been rarely demonstrated. Herein, we employed density functional theory (DFT) to pre‐screen the electronic and optical properties of thiophene‐based donor‐acceptor (D‐A) pairs simplified from their corresponding COF structures. Theoretical calculation illustrates the BMTB‐BTTC with the highest number of thiophene units is expected to exhibit the best photocatalytic performance for hydrogen production. According to calculation prediction, four COFs have been prepared and their photocatalytic activities have been experimentally validated. Interestingly, the corresponding BMTB‐BTTC‐COF shows the highest photocatalytic hydrogen production rate of 12.37 mmol·g –1 ·h –1 among the four COFs. Combining the calculation and experimental results, it has been proven that the photocatalytic activity can be fine‐tuned by modulating the number of thiophene units. Our study provides a new strategy for the rational design and regulation of D‐A COFs to enhance photocatalytic activity through computational prediction.
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