谷歌浏览器插件
订阅小程序
在清言上使用

Insights into Metal–Organic Framework-Derived Copper Clusters for CO2 Electroreduction

Journal of physical chemistry C/Journal of physical chemistry C(2022)

引用 3|浏览15
暂无评分
摘要
The unique material properties of metal-organic frameworks (MOFs) (e.g., high porosity, facile modularity, and isolated sites) have highlighted their potential as a next-generation electrocatalyst candidate. However, utilizing MOFs as electro-catalysts necessitates investigations into the changes to the MOF structure under electrochemical bias and subsequent identification and benchmarking of structure-function relationships. Herein, we demonstrate the synthesis of a Cu-based MOF (HKUST-1) film from an in situ nucleation and film growth procedure and the morphological and structural transformation of the said film under electrochemical bias. Additionally, we benchmark the resulting MOF-derived (MOF-d) Cu-based material for electrochemical CO2 reduction (CO2R) applications. Both ex situ and in situ characterization methods highlight substantial morphological and structural changes to the HKUST-1 film during electrochemical CO2R in CO2-saturated 0.1 M KHCO3 aqueous supporting electrolytes. We found that a MOF-d film containing Cu clusters was formed during the electrolysis under a cathodic bias. Potential-dependent CO2R electrolysis experiments show that the normalized current density for CO2R production of the MOF-d Cu film when normalized by the electrochemically active surface area (ECSA) converges to the ECSA-normalized current densities for previously reported nanostructured metallic Cu materials, which indicates that the MOF-d Cu films function as a high surface area, nanostructured Cu electrode for CO2R. These results demonstrate the utility of in situ spectroscopic techniques to examine the morphological and structural changes to the HKUST-1 film under electrochemical bias and provide insights into the electrochemical CO2R activity of the MOF-d Cu film by critically benchmarking its intrinsic reactivity against known materials using established activity descriptors.
更多
查看译文
AI 理解论文
溯源树
样例
生成溯源树,研究论文发展脉络
Chat Paper
正在生成论文摘要