吸附
选择性
密度泛函理论
金属间化合物
合金
材料科学
半径
还原(数学)
兴奋剂
化学物理
催化作用
缩放比例
计算化学
无机化学
物理化学
化学
计算机科学
数学
冶金
几何学
光电子学
计算机安全
生物化学
作者
Zihao Jiao,Mengmeng Song,Wenhao Jing,Ya Liu,Liejin Guo
标识
DOI:10.1021/acs.jpclett.3c01358
摘要
Understanding the synergistic effect of Cu-based alloys on the adsorption behavior and selectivity of the CO2 reduction reaction is a crucial step toward directional catalyst design. To this end, density functional theory calculations are employed to investigate Cu-based alloys with diverse doping elements and contents. The results show that the scaling relation still holds in the alloy system, and the strategies to improve the selectivity are put forward based on the adsorption strength of *C and *OCHO intermediates. Further, a model combining the adsorption theory and machine learning algorithm is proposed to capture the relationship between the adsorption energy and the geometric environment. It explains that the difference in d-band centers between the doped metals and Cu affects the variation trend of the adsorption strength and reveals that the intermetallic synergistic effect can be quantified by the bonding distance and d orbital radius on both the adsorbate and metal side.
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