Boosting the electroreduction of CO2 to liquid products via nanostructure engineering of Cu2O catalysts

化学 催化作用 纳米结构 Boosting(机器学习) 纳米技术 电化学 化学工程 多相催化 有机化学 电极 物理化学 材料科学 机器学习 计算机科学 工程类
作者
Fangqi Yang,Tonglin Yang,Jing Li,Pengfei Li,Quan Zhang,Huihui Lin,Luyan Wu
出处
期刊:Journal of Catalysis [Elsevier BV]
卷期号:432: 115458-115458 被引量:3
标识
DOI:10.1016/j.jcat.2024.115458
摘要

The electrochemical reduction of CO2 presents a promising pathway for storing intermittent renewable energy in the form of chemical bonds, thereby mitigating CO2 emissions and enabling the production of sustainable fuels. In this work, we demonstrate nanoscale engineering of oxygen vacancy and morphology simultaneously on Cu2O catalysts for electrochemical reduction of CO2 to liquid products (formate and ethanol). By comparing the performance of cube- and tetrakaidecahedron-like Cu2O catalysts, we have demonstrated that the flower-like Cu2O catalyst, enclosed with rich oxygen vacancy defects, exhibited superior performance in the reduction of CO2 to liquid products. Moreover, the synergetic role of Cu+ also contributed to the enhanced activity by promoting CO2 adsorption and facilitating C–C coupling. As a result, the peak Faradaic efficiency (FE) for liquid products of 95.5 % was obtained, associated with a high ethanol FE of 52.6 % and formation rate of 23.8 μmol h−1 cm−2 within a H-cell. Furthermore, within a flow cell configuration, we have observed a significant improvement in the generation of formate, maintaining FE values above 70 % even under high current densities of up to 400 mA cm−2. In-situ Raman spectroscopic measurements allow us to identify and track key intermediates involved in the CO2 reduction to formate and ethanol. This detailed understanding of the reaction pathways adds to our fundamental knowledge and provides valuable insights for the development of morphology-controlled electrocatalysts targeting efficient conversion of CO2 into liquid products.
最长约 10秒,即可获得该文献文件

科研通智能强力驱动
Strongly Powered by AbleSci AI
科研通是完全免费的文献互助平台,具备全网最快的应助速度,最高的求助完成率。 对每一个文献求助,科研通都将尽心尽力,给求助人一个满意的交代。
实时播报
11号楼203完成签到,获得积分10
1秒前
llchen完成签到,获得积分0
2秒前
平常的苡完成签到,获得积分10
3秒前
隐形曼青应助科研通管家采纳,获得10
4秒前
SYLH应助科研通管家采纳,获得10
4秒前
4秒前
彭于晏应助科研通管家采纳,获得10
4秒前
丘比特应助科研通管家采纳,获得10
5秒前
慕青应助科研通管家采纳,获得10
5秒前
小蘑菇应助科研通管家采纳,获得10
5秒前
丘比特应助科研通管家采纳,获得10
5秒前
大模型应助科研通管家采纳,获得10
5秒前
SYLH应助科研通管家采纳,获得20
5秒前
领导范儿应助勤劳柚子采纳,获得30
5秒前
冰魂应助bibgyueli采纳,获得10
5秒前
香蕉觅云应助谢书繁采纳,获得10
6秒前
精明的善斓完成签到,获得积分10
6秒前
wangwangwang完成签到,获得积分10
8秒前
9秒前
香蕉觅云应助log采纳,获得10
10秒前
慕青应助魔幻傲霜采纳,获得10
11秒前
多多发布了新的文献求助10
14秒前
renpp822发布了新的文献求助10
15秒前
闪闪寒云完成签到 ,获得积分10
17秒前
XL发布了新的文献求助30
17秒前
zhentg完成签到,获得积分10
20秒前
20秒前
Ricardo完成签到,获得积分10
20秒前
hsiao_yang完成签到 ,获得积分10
20秒前
EthanLu发布了新的文献求助30
22秒前
22秒前
23秒前
胡志飞发布了新的文献求助10
23秒前
冯哥侃大山完成签到 ,获得积分10
25秒前
26秒前
26秒前
今后应助一只小黑胖采纳,获得10
26秒前
sangsang发布了新的文献求助10
27秒前
土豆酱发布了新的文献求助10
28秒前
yuki发布了新的文献求助10
28秒前
高分求助中
Thinking Small and Large 500
Algorithmic Mathematics in Machine Learning 500
Getting Published in SSCI Journals: 200+ Questions and Answers for Absolute Beginners 300
Deciphering Earth's History: the Practice of Stratigraphy 200
New Syntheses with Carbon Monoxide 200
Quanterion Automated Databook NPRD-2023 200
Interpretability and Explainability in AI Using Python 200
热门求助领域 (近24小时)
化学 材料科学 医学 生物 工程类 有机化学 物理 生物化学 纳米技术 计算机科学 化学工程 内科学 复合材料 物理化学 电极 遗传学 量子力学 基因 冶金 催化作用
热门帖子
关注 科研通微信公众号,转发送积分 3835028
求助须知:如何正确求助?哪些是违规求助? 3377526
关于积分的说明 10498888
捐赠科研通 3097008
什么是DOI,文献DOI怎么找? 1705417
邀请新用户注册赠送积分活动 820558
科研通“疑难数据库(出版商)”最低求助积分说明 772123