0 Datasets
0 Files
Get instant academic access to this publication’s datasets.
Yes. After verification, you can browse and download datasets at no cost. Some premium assets may require author approval.
Files are stored on encrypted storage. Access is restricted to verified users and all downloads are logged.
Yes, message the author after sign-up to request supplementary files or replication code.
Join 50,000+ researchers worldwide. Get instant access to peer-reviewed datasets, advanced analytics, and global collaboration tools.
✓ Immediate verification • ✓ Free institutional access • ✓ Global collaborationJoin our academic network to download verified datasets and collaborate with researchers worldwide.
Get Free AccessThe advancement of cost-effective and selective electrocatalyst towards CO2 to CO conversion is crucial for renewable energy conversion and storage, thus to achieve carbon-neutral cycle in a sustainable manner. In this communication, we report that Cu2Sb decorated Cu nanowire arrays on Cu foil act as a highly active and selective electrocatalyst for CO2 to CO conversion. In CO2-saturated 0.1 M KHCO3, it achieves a high Faraday efficiency (FE) of 86.5% for CO, at −0.90 V vs. reversible hydrogen electrode (RHE). The H2/CO ratio is tunable from 0.08:1 to 5.9:1 by adjusting the potential. It is worth noting that HCOO− product was totally suppressed on such catalyst, compared with Sb counterpart. The improving selectivity for CO could be attributed to the bimetallic effect and nanowire arrays structure.
Shiyong Mou, Yonghao Li, Luchao Yue, Jie Liang, Yonglan Luo, Qian Liu, Tingshuai Li, Siyu Lu, Abdullah Mohamed Asiri, Xiaoli Xiong, Dongwei Ma, Xuping Sun (2021). Cu2Sb decorated Cu nanowire arrays for selective electrocatalytic CO2 to CO conversion. , 14(8), DOI: https://doi.org/10.1007/s12274-021-3295-1.
Datasets shared by verified academics with rich metadata and previews.
Authors choose access levels; downloads are logged for transparency.
Students and faculty get instant access after verification.
Type
Article
Year
2021
Authors
12
Datasets
0
Total Files
0
Language
en
DOI
https://doi.org/10.1007/s12274-021-3295-1
Access datasets from 50,000+ researchers worldwide with institutional verification.
Get Free Access