Park Byoung Joon, Wang Ying, Lee Yechan, Noh Kyung-Jong, Cho Ara, Jang Myeong Gon, Huang Rui, Lee Kug-Seung, Han Jeong Woo
Department of Chemical Engineering, Pohang University of Science and Technology (POSTECH), Pohang, Gyeongbuk, 37673, Republic of Korea.
Beamline Division, Pohang Accelerator Laboratory (PAL), Pohang, Gyeongbuk, 37673, Republic of Korea.
Small. 2021 Oct;17(42):e2103705. doi: 10.1002/smll.202103705. Epub 2021 Sep 23.
To identify high-efficiency metal-nitrogen-doped (M-N-C) electrocatalysts for the electrochemical CO -to-CO reduction reaction (CO RR), a method that uses density functional theory calculation is presented to evaluate their selectivity, activity, and structural stability. Twenty-three M-N -C catalysts are evaluated, and three of them (M = Fe, Co, or Ni) are identified as promising candidates. They are synthesized and tested as proof-of-concept catalysts for CO -to-CO conversion. Different key descriptors, including the maximum reaction energy, differences of the *H and *CO binding energy (ΔG -ΔG ), and *CO desorption energy (ΔG ), are used to clarify the reaction mechanism. These computational descriptors effectively predict the experimental observations in the entire range of electrochemical potential. The findings provide a guideline for rational design of heterogeneous CO RR electrocatalysts.
为了识别用于电化学CO还原反应(CO RR)的高效金属氮掺杂(M-N-C)电催化剂,提出了一种使用密度泛函理论计算的方法来评估其选择性、活性和结构稳定性。对23种M-N-C催化剂进行了评估,其中三种(M = Fe、Co或Ni)被确定为有前景的候选物。它们被合成并作为CO转化为CO的概念验证催化剂进行测试。使用不同的关键描述符,包括最大反应能量、H和CO结合能的差异(ΔG -ΔG )以及*CO脱附能(ΔG ),来阐明反应机理。这些计算描述符有效地预测了整个电化学电位范围内的实验观察结果。这些发现为合理设计非均相CO RR电催化剂提供了指导。