Suppr超能文献

用于高效电化学还原二氧化碳的分级锡枝晶电极的合理设计

Rational Design of a Hierarchical Tin Dendrite Electrode for Efficient Electrochemical Reduction of CO2.

作者信息

Won Da Hye, Choi Chang Hyuck, Chung Jaehoon, Chung Min Wook, Kim Eun-Hee, Woo Seong Ihl

机构信息

Department of Chemical and Biomolecular Engineering, Korea Advanced Institute of Science and Technology, Daejeon 305-701 (Republic of Korea).

Graduate School of EEWS (BK21PLUS), Korea Advanced Institute of Science and Technology, Daejeon 305-701 (Republic of Korea).

出版信息

ChemSusChem. 2015 Sep 21;8(18):3092-8. doi: 10.1002/cssc.201500694. Epub 2015 Jul 27.

Abstract

Catalysis is a key technology for the synthesis of renewable fuels through electrochemical reduction of CO2 . However, successful CO2 reduction still suffers from the lack of affordable catalyst design and understanding the factors governing catalysis. Herein, we demonstrate that the CO2 conversion selectivity on Sn (or SnOx /Sn) electrodes is correlated to the native oxygen content at the subsurface. Electrochemical analyses show that the reduced Sn electrode with abundant oxygen species effectively stabilizes a CO2 (.-) intermediate rather than the clean Sn surface, and consequently results in enhanced formate production in the CO2 reduction. Based on this design strategy, a hierarchical Sn dendrite electrode with high oxygen content, consisting of a multi-branched conifer-like structure with an enlarged surface area, was synthesized. The electrode exhibits a superior formate production rate (228.6 μmol h(-1)  cm(-2) ) at -1.36 VRHE without any considerable catalytic degradation over 18 h of operation.

摘要

催化是通过二氧化碳的电化学还原合成可再生燃料的关键技术。然而,成功的二氧化碳还原仍然面临着缺乏经济实惠的催化剂设计以及对催化控制因素理解不足的问题。在此,我们证明了Sn(或SnOx/Sn)电极上的二氧化碳转化选择性与次表面的本征氧含量相关。电化学分析表明,具有丰富氧物种的还原态Sn电极有效地稳定了二氧化碳(·-)中间体,而不是清洁的Sn表面,因此在二氧化碳还原中提高了甲酸盐的产量。基于这种设计策略,合成了一种具有高氧含量的分级Sn枝晶电极,其由具有扩大表面积的多分支针叶状结构组成。该电极在-1.36 VRHE下表现出优异的甲酸盐生成速率(228.6 μmol h-1 cm-2),在18小时的运行过程中没有任何明显的催化降解。

文献AI研究员

20分钟写一篇综述,助力文献阅读效率提升50倍。

立即体验

用中文搜PubMed

大模型驱动的PubMed中文搜索引擎

马上搜索

文档翻译

学术文献翻译模型,支持多种主流文档格式。

立即体验