Influence of the Metal Center in M–N–C Catalysts on the CO2 Reduction Reaction on Gas Diffusion Electrodes

催化作用 X射线光电子能谱 化学 电解质 法拉第效率 循环伏安法 电化学 金属 过渡金属 无机化学 线性扫描伏安法 分析化学(期刊) 电极 化学工程 物理化学 有机化学 工程类
作者
Stephen Paul,Yi-Lin Kao,Lingmei Ni,Rayko Ehnert,Iris Herrmann-Geppert,Roel van de Krol,Robert W. Stark,Wolfram Jaegermann,Ulrike I. Kramm,Peter Bogdanoff
出处
期刊:ACS Catalysis 卷期号:11 (9): 5850-5864 被引量:62
标识
DOI:10.1021/acscatal.0c05596
摘要

In this work, the influences of various transition metal ions as active sites in high purity metal- and nitrogen-doped carbon catalysts (in short M–N–C), where M: Mn3+, Fe3+, Co2+, Ni2+, Cu2+, Zn2+, or Sn4+ in the catalyst powders, were systematically investigated for the electrochemical reduction of CO2 in the aqueous electrolyte. The almost exclusive presence of isolated M–N4 centers as catalytic sites was determined by X-ray photoelectron spectroscopy (XPS). The catalysts were electrochemically investigated in a gas diffusion electrode arrangement in bypass mode coupled in-line to a mass spectrometer. This allowed for the nearly simultaneous detection of products and current densities in linear sweep voltammetry experiments, from which potential-dependent specific production rates and faradaic efficiencies could be derived. Postmortem XPS analyses were performed after various stages of operation on the Cu–N–C catalyst, which was the only catalyst to produce hydrocarbons (CH4 and C2H4) in significant amounts. The data provided insights into the potential-induced electronic changes of the Cu–N–C catalyst occurring under operating conditions. Our work further experimentally revealed the high affinity of M–N–C catalysts to convert CO2 to industrially relevant carbonaceous raw materials, while effectively suppressing the competing hydrogen evolution reaction. These results led to a better understanding of the role of the active sites, especially the central metal ion, in M–N–C and could contribute significantly to the improvement of selectivities and activities for the CO2RR in this catalyst class through tailor-made optimization strategies.

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
KINGMach发布了新的文献求助10
刚刚
刚刚
刚刚
刚刚
李冰完成签到,获得积分10
刚刚
刚刚
天天天蓝完成签到,获得积分10
1秒前
脂蛋白抗原应助小猫宝采纳,获得10
1秒前
深情安青应助小猫宝采纳,获得10
1秒前
叉烧完成签到 ,获得积分10
3秒前
KY Mr.WANG完成签到,获得积分0
3秒前
4秒前
迷你蛋黄应助清爽难敌采纳,获得80
6秒前
所所应助一裤子灰采纳,获得10
6秒前
6秒前
3242晶发布了新的文献求助10
6秒前
ymly25发布了新的文献求助10
6秒前
上官若男应助doudou采纳,获得10
7秒前
传奇3应助呆梨医生采纳,获得10
8秒前
hu发布了新的文献求助10
8秒前
小彩彩完成签到,获得积分10
8秒前
9秒前
一方通行完成签到 ,获得积分10
10秒前
刘志斌发布了新的文献求助10
11秒前
3242晶完成签到,获得积分10
12秒前
星月发布了新的文献求助10
15秒前
15秒前
ymly25完成签到,获得积分10
15秒前
BetterH完成签到 ,获得积分10
15秒前
Neko发布了新的文献求助10
15秒前
一只猫发布了新的文献求助10
16秒前
16秒前
深海鳕鱼子完成签到,获得积分10
16秒前
希望天下0贩的0应助Ann采纳,获得10
17秒前
17秒前
斯文败类应助日川冈坂采纳,获得10
17秒前
炉子发布了新的文献求助10
19秒前
丁昆完成签到,获得积分10
20秒前
Akim应助Hedy采纳,获得20
21秒前
21秒前
高分求助中
Continuum Thermodynamics and Material Modelling 3000
Production Logging: Theoretical and Interpretive Elements 2700
Mechanistic Modeling of Gas-Liquid Two-Phase Flow in Pipes 2500
Structural Load Modelling and Combination for Performance and Safety Evaluation 1000
Conference Record, IAS Annual Meeting 1977 710
電気学会論文誌D(産業応用部門誌), 141 巻, 11 号 510
Virulence Mechanisms of Plant-Pathogenic Bacteria 500
热门求助领域 (近24小时)
化学 材料科学 生物 医学 工程类 有机化学 生物化学 物理 纳米技术 计算机科学 内科学 化学工程 复合材料 基因 遗传学 物理化学 催化作用 量子力学 光电子学 冶金
热门帖子
关注 科研通微信公众号,转发送积分 3563859
求助须知:如何正确求助?哪些是违规求助? 3137060
关于积分的说明 9420785
捐赠科研通 2837499
什么是DOI,文献DOI怎么找? 1559874
邀请新用户注册赠送积分活动 729212
科研通“疑难数据库(出版商)”最低求助积分说明 717187