Yang Yingke, Wang Jiawen, Shu Yunpeng, Ji Yujin, Dong Huilong, Li Youyong
School of Materials Engineering, Changshu Institute of Technology, Changshu, Jiangsu 215500, China.
Institute of Functional Nano & Soft Materials (FUNSOM), Soochow University, Suzhou, Jiangsu 215123, China.
Phys Chem Chem Phys. 2022 Apr 13;24(15):8591-8603. doi: 10.1039/d1cp05442b.
Density functional theory (DFT) based computational methods have shown great significance in developing high-performance electrocatalysts. In this perspective, we briefly summarized the state-of-the-art research progress of electrocatalysts for the nitrogen reduction reaction (NRR) and CO reduction reaction (CORR), which are important processes for the conversion of common molecules into value-added products. With the help of DFT calculations, various modulation strategies are employed to improve the catalytic activity and performance of NRR and CORR electrocatalysts. DFT calculations are performed to confirm the surface catalytic sites, evaluate the catalytic activity, reveal the possible reaction mechanisms, and design novel structures with high catalytic performance. By discussing the currently applied computational methods and conditions during the calculations, we outlined our concerns on the prospects and future challenges of DFT calculations in electrocatalysis studies.
基于密度泛函理论（DFT）的计算方法在开发高性能电催化剂方面已显示出重大意义。从这个角度出发，我们简要总结了用于氮还原反应（NRR）和CO还原反应（CORR）的电催化剂的最新研究进展，这两个反应是将普通分子转化为增值产品的重要过程。借助DFT计算，采用了各种调制策略来提高NRR和CORR电催化剂的催化活性和性能。进行DFT计算以确定表面催化位点、评估催化活性、揭示可能的反应机理，并设计具有高催化性能的新型结构。通过讨论当前计算过程中应用的计算方法和条件，我们概述了对DFT计算在电催化研究中的前景和未来挑战的关注。
