Urea Synthesis via Coelectrolysis of CO<sub>2</sub> and Nitrate over Heterostructured Cu–Bi Catalysts

Electrocatalytic coupling of CO<sub>2</sub> and NO<sub>3</sub><sup>-</sup> to urea is a promising way to mitigate greenhouse gas emissions, reduce waste from industrial processes, and store renewable energy. However, the poor selectivity and activity limit its application due to the multistep process involving diverse reactants and reactions. Herein, we report the first work to design heterostructured Cu-Bi bimetallic catalysts for urea electrosynthesis. A high urea Faradaic efficiency (FE) of 23.5% with a production rate of 2180.3 μg h<sup>-1</sup> mg<sub>cat</sub><sup>-1</sup> was achieved in H-cells, which surpassed most reported electrocatalysts in the literature. Moreover, the catalyst had a remarkable recycling stability. Experiments and density functional theory calculations demonstrated that introduction of moderate Bi induced the formation of the Bi-Cu/O-Bi/Cu<sub>2</sub>O heterostructure with abundant phase boundaries, which are beneficial for NO<sub>3</sub><sup>-</sup>, CO<sub>2</sub>, and H<sub>2</sub>O activation and enhance C-N coupling and promote *HONCON intermediate formation. Moreover, favorable *HNCONH<sub>2</sub> protonation and urea desorption processes were also validated, further explaining the reason for high activity and selectivity toward urea.

Authors

Institutions

Publication Details

Journal
Journal of the American Chemical Society
Published
2024-09-05
DOI
https://doi.org/10.1021/jacs.4c08564
Citations
107
Primary Topic
Ammonia Synthesis and Nitrogen Reduction
Type
article
Field-Weighted Citation Impact
11.44

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Urea Synthesis via Coelectrolysis of CO2 and Nitrate over Heterostructured Cu–Bi Catalysts

Chunjun Chen, Shunhan Jia, Xinning Song, Xinchen Kang et al.
107 citations
Journal of the American Chemical Society
Ammonia Synthesis and Nitrogen Reduction
11.44
article

Urea Synthesis via Coelectrolysis of CO2 and Nitrate over Heterostructured Cu–Bi Catalysts

Chunjun Chen, Shunhan Jia, Xinning Song, Xinchen Kang, Libing Zhang, Xingxing Tan, Ruhan Wang, Tianhui Chen, Xiaofu Sun, Qinggong Zhu, Jun Ma, Limin Wu, Xiaodong Ma, Jiaqi Feng, Liang Xu, Buxing Han
article en
107 citations

Abstract

Electrocatalytic coupling of CO<sub>2</sub> and NO<sub>3</sub><sup>-</sup> to urea is a promising way to mitigate greenhouse gas emissions, reduce waste from industrial processes, and store renewable energy. However, the poor selectivity and activity limit its application due to the multistep process involving diverse reactants and reactions. Herein, we report the first work to design heterostructured Cu-Bi bimetallic catalysts for urea electrosynthesis. A high urea Faradaic efficiency (FE) of 23.5% with a production rate of 2180.3 μg h<sup>-1</sup> mg<sub>cat</sub><sup>-1</sup> was achieved in H-cells, which surpassed most reported electrocatalysts in the literature. Moreover, the catalyst had a remarkable recycling stability. Experiments and density functional theory calculations demonstrated that introduction of moderate Bi induced the formation of the Bi-Cu/O-Bi/Cu<sub>2</sub>O heterostructure with abundant phase boundaries, which are beneficial for NO<sub>3</sub><sup>-</sup>, CO<sub>2</sub>, and H<sub>2</sub>O activation and enhance C-N coupling and promote *HONCON intermediate formation. Moreover, favorable *HNCONH<sub>2</sub> protonation and urea desorption processes were also validated, further explaining the reason for high activity and selectivity toward urea.

Journal of the American Chemical SocietyVol. 146(37)
Chinese Academy of Sciences (CN), Nankai University (CN), Beijing National Laboratory for Molecular Sciences (CN), Institute of Chemistry (CN), University of Chinese Academy of Sciences (CN), East China Normal University (CN), Beijing University of Chemical Technology (CN)
National Natural Science Foundation of China, Chinese Academy of Sciences, Beijing Municipal Natural Science Foundation
Openalex Percentile: Top 1%
Ammonia Synthesis and Nitrogen Reduction
11.44
DOISourceOpenAlex
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.