化学
选择性
还原(数学)
电催化剂
化学工程
纳米技术
催化作用
电极
电化学
生物化学
物理化学
几何学
数学
工程类
材料科学
作者
Yaqi Cheng,Qixun Li,Muhammad Iskandar B. Salaman,Chaolong Wei,Qilun Wang,Xuehu Ma,Bin Liu,Andrew Barnabas Wong
摘要
The performance of the electrocatalytic CO2 reduction reaction (CO2RR) is highly dependent on the microenvironment around the cathode. Despite efforts to optimize the microenvironment by modifying nanostructured catalysts or microporous gas diffusion electrodes, their inherent disorder presents a significant challenge to understanding how interfacial structure arrangement within the electrode governs the microenvironment for CO2RR. This knowledge gap limits fundamental understanding of CO2RR while also hindering efforts to enhance CO2RR selectivity and activity. In this work, we investigate this knowledge gap using a tunable system featuring superhydrophobic hierarchical Cu nanowire arrays with microgrooves (NAMs). Adjusting the NAM structure tunes multiple synergistic effects in the microenvironment, which include stabilization of the microwetting state, confinement of CO*, improvement to local CO2 concentration, and modulation of the local pH. Notably, using mass transport modeling, we quantify the role of the gas–liquid–solid interface in boosting local CO2 concentrations within several microns of the interface itself. Leveraging these effects, we elucidate how CO* and H* competitively occupy active sites, influencing reaction pathways toward multicarbon products based on tuning the microenvironment. Consequently, we provide new insights into why the optimized configuration significantly increased CO2RR activity by 690% (as normalized by electrochemical active surface area), C2+ product selectivity by 72%, and Faradaic efficiency by 36%, compared to CO2RR with hydrophobic Cu foil. Based on these insights, our findings unlock new opportunities to engineer the CO2RR microenvironment through the rational organization of hierarchical interface materials in gas diffusion electrodes toward improved CO2RR selectivity and activity.
科研通智能强力驱动
Strongly Powered by AbleSci AI