Enhancement Mechanism of CO <sub>2</sub> RR toward Ethanol on Cu-CoPc/CNT Electrocatalysts: Dual-Site Synergetic C–C Coupling

The electrochemical reduction of CO2 to high-value C2 products, especially ethanol, presents a feasible approach for alleviating greenhouse gas emissions and tackling climate-related challenges. Nevertheless, the rational design of efficient electrocatalysts is still restricted by insufficient activity, suboptimal selectivity, and a limited understanding of the underlying mechanisms. In this study, a hierarchically structured Cu-CoPc/CNT catalyst was fabricated by electrochemical deposition and drop-casting techniques. Comprehensive characterization (BET/SEM/XRD/EDS/XPS) confirmed its electronic configuration and surface properties. Electrochemical evaluations (CV/LSV/EIS/GC/1H NMR) demonstrated an ethanol Faradaic efficiency of 42.3% at a partial current density of 10.65 mA·cm–2, with exceptional ethanol selectivity of 98.6% among C2 products at −1.15 V vs RHE. In conjunction with density functional theory (DFT) calculations, mechanistic investigations have demonstrated that an adequate supply of those intermediates such *COOH and the facilitation of the C–C coupling are the primary factors contributing to the performance improvement of the CO2RR toward ethanol through the synergistic effect between CoPc/CNT and Cu. This research proposes a novel strategy for designing efficient and stable CO2-to-ethanol catalysts, thereby deepening the fundamental understanding of the ethanol formation mechanisms.

Authors

Institutions

Publication Details

Journal
Energy & Fuels
Published
2026-04-01
DOI
https://doi.org/10.1021/acs.energyfuels.6c00147
Primary Topic
CO2 Reduction Techniques and Catalysts
Type
article
Field-Weighted Citation Impact
0.00

Funders

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

Enhancement Mechanism of CO 2 RR toward Ethanol on Cu-CoPc/CNT Electrocatalysts: Dual-Site Synergetic C–C Coupling

B.H. Liu, Yanyan Feng, Da Jin, Yarong Du et al.
Energy & Fuels
CO2 Reduction Techniques and Catalysts
article

Enhancement Mechanism of CO 2 RR toward Ethanol on Cu-CoPc/CNT Electrocatalysts: Dual-Site Synergetic C–C Coupling

B.H. Liu, Yanyan Feng, Da Jin, Yarong Du, Gujie Yao, Tianxiang Guo, Xilai Wang, Xiaoqing Wang
article en

Abstract

The electrochemical reduction of CO2 to high-value C2 products, especially ethanol, presents a feasible approach for alleviating greenhouse gas emissions and tackling climate-related challenges. Nevertheless, the rational design of efficient electrocatalysts is still restricted by insufficient activity, suboptimal selectivity, and a limited understanding of the underlying mechanisms. In this study, a hierarchically structured Cu-CoPc/CNT catalyst was fabricated by electrochemical deposition and drop-casting techniques. Comprehensive characterization (BET/SEM/XRD/EDS/XPS) confirmed its electronic configuration and surface properties. Electrochemical evaluations (CV/LSV/EIS/GC/1H NMR) demonstrated an ethanol Faradaic efficiency of 42.3% at a partial current density of 10.65 mA·cm–2, with exceptional ethanol selectivity of 98.6% among C2 products at −1.15 V vs RHE. In conjunction with density functional theory (DFT) calculations, mechanistic investigations have demonstrated that an adequate supply of those intermediates such *COOH and the facilitation of the C–C coupling are the primary factors contributing to the performance improvement of the CO2RR toward ethanol through the synergistic effect between CoPc/CNT and Cu. This research proposes a novel strategy for designing efficient and stable CO2-to-ethanol catalysts, thereby deepening the fundamental understanding of the ethanol formation mechanisms.

Energy & FuelsVol. 40(15)
North China Electric Power University (CN), CAS Key Laboratory of Urban Pollutant Conversion (CN)
National Natural Science Foundation of China, Natural Science Foundation of Hebei Province, Beijing Municipal Natural Science Foundation
Climate action
Openalex Percentile: Top 32%
CO2 Reduction Techniques and Catalysts
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.