Controllable Preparation of N-modified Cu-MOFs for Enhanced Steering CO2 Electroreduction Toward CH4 Products

The catalyst materials 0N-Cu-MOF, 1N-Cu-MOF, and 2N-Cu-MOF were successfully synthesized usinga solvothermal method, and using different concentrations of nitrogen-modified Cu organic frameworks (xN-Cu-MOF).Characterizations using X-ray diffraction (XRD), scanning electron microscopy (SEM), fourier transform infrared (FT-IR) spectroscopy, X-ray photoelectron spectroscopy (XPS), and Brunauer-Emmett-Teller (BET) surface area analysis showed that 1N-Cu-MOF had the largest SSA and pore size among the three materials synthesized.1N-Cu-MOF exhibited the largest pore size and specific surface area among the three materials, which had a decisive effect on CO 2 reduction.In addition, stability and CO 2 reduction reaction (CO 2 RR) activity were evaluated by linear sweep voltmeter, cyclic voltmeter, electrochemical impedance spectroscopy, and time flow tests.Faradaic efficiency (FE) was determined by product analysis.Among the three catalyst materials, 1N-Cu-MOF showed the best catalytic performance at 50 mAcm -2 (maximum current density).The charge transfer resistance was 8.23 , the average current density was 19.9 mAcm -2 , and the FE of methane (CH 4 ) production showed a high efficiency of 70.45 % when tested for 12 h at an overpotential of -0.35 V (to-RHE).

Authors

Institutions

Publication Details

Journal
Korean Journal of Materials Research
Published
2025-08-27
DOI
https://doi.org/10.3740/mrsk.2025.35.8.352
Primary Topic
CO2 Reduction Techniques and Catalysts
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Controllable Preparation of N-modified Cu-MOFs for Enhanced Steering CO2 Electroreduction Toward CH4 Products

Yilei Sun, Kunjie Li, Won-Chun Oh, Haixia Wang et al.
4 links
Korean Journal of Materials Research
CO2 Reduction Techniques and Catalysts
article

Controllable Preparation of N-modified Cu-MOFs for Enhanced Steering CO2 Electroreduction Toward CH4 Products

Yilei Sun, Kunjie Li, Won-Chun Oh, Haixia Wang, Ze-Da Meng, Yao Liu
article en

Abstract

The catalyst materials 0N-Cu-MOF, 1N-Cu-MOF, and 2N-Cu-MOF were successfully synthesized usinga solvothermal method, and using different concentrations of nitrogen-modified Cu organic frameworks (xN-Cu-MOF).Characterizations using X-ray diffraction (XRD), scanning electron microscopy (SEM), fourier transform infrared (FT-IR) spectroscopy, X-ray photoelectron spectroscopy (XPS), and Brunauer-Emmett-Teller (BET) surface area analysis showed that 1N-Cu-MOF had the largest SSA and pore size among the three materials synthesized.1N-Cu-MOF exhibited the largest pore size and specific surface area among the three materials, which had a decisive effect on CO 2 reduction.In addition, stability and CO 2 reduction reaction (CO 2 RR) activity were evaluated by linear sweep voltmeter, cyclic voltmeter, electrochemical impedance spectroscopy, and time flow tests.Faradaic efficiency (FE) was determined by product analysis.Among the three catalyst materials, 1N-Cu-MOF showed the best catalytic performance at 50 mAcm -2 (maximum current density).The charge transfer resistance was 8.23 , the average current density was 19.9 mAcm -2 , and the FE of methane (CH 4 ) production showed a high efficiency of 70.45 % when tested for 12 h at an overpotential of -0.35 V (to-RHE).

Korean Journal of Materials ResearchVol. 35(8)
Aluminum Corporation of China (China) (CN), Hanseo University (KR), Suzhou University of Science and Technology (CN), Soochow University (CN)
Openalex Percentile: Top 84%
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.