Please use this identifier to cite or link to this item: http://hdl.handle.net/1942/47752
Title: In situ-tuning lamellar bismuth-samarium catalysts for efficient CO2-to-formate electrosynthesis
Authors: Yu , Yadan
Li, Wenyu
Qiao, Huiting
Liu , Yuyu
SAFARI, Momo 
Huang , Haitao
Qiao, Jinli
Issue Date: 2025
Publisher: PERGAMON-ELSEVIER SCIENCE LTD
Source: Electrochimica acta, 543 (Art N° 147632)
Abstract: CO2 electroreduction enables the sustainable conversion of greenhouse gases into value-added chemicals, thereby reducing global warming. In this study, a bismuth-samarium (Bi-Sm) bimetallic catalyst (Bi4Sm1) was prepared, exhibiting excellent performance for the CO2-to-formate electrosynthesis facilitated by situ-generated nanosheets. The incorporation of Sm into Bi catalyst enriched the active sites on the catalyst surface, achieving excellent stability (>150 h) and high formate selectivity (Faradaic efficiency, FEformate = 93.74 %) with a current density of 13.5 mA cm(-2) at -1.0 V vs. reversible hydrogen electrode (V-RHE) in a H-cell reactor containing 0.5 M KHCO3 electrolyte. Moreover, in a flow cell containing 1 M KOH electrolyte, the catalyst achieved a current density exceeding 162 mA cm(-2) at -0.7 V-RHE, with a slight decrease in FEformate (92.55 %). This catalyst outperformed most reported catalysts, highlighting its large-scale potential applications.
Notes: Qiao, JL (corresponding author), Donghua Univ, Coll Environm Sci & Engn, State Key Lab Adv Fiber Mat, Shanghai 201620, Peoples R China.; Liu, YY (corresponding author), Shanghai Univ, Inst Sustainable Energy, Coll Sci, Shanghai 200444, Peoples R China.; Qiao, JL (corresponding author), Shanghai Inst Pollut Control & Ecol Secur, Shanghai 200092, Peoples R China.
liuyuyu@shu.edu.cn; qiaojl@dhu.edu.cn
Keywords: Electrochemical reduction;Catalyst;formate;CO2;Bi4Sm1
Document URI: http://hdl.handle.net/1942/47752
ISSN: 0013-4686
e-ISSN: 1873-3859
DOI: 10.1016/j.electacta.2025.147632
ISI #: 001607974500003
Rights: 2025 Elsevier Ltd. All rights are reserved, including those for text and data mining, AI training, and similar technologies.
Category: A1
Type: Journal Contribution
Appears in Collections:Research publications

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