Laser-Synthesized Ligand-Free Cu Nanocatalysts in Electrochemical CO2 Reduction to Methane

Electrochemical CO2 reduction (eCO2R) represents a pivotal strategy for mitigating global carbon emissions while simultaneously converting renewable energy into storable chemical fuels. Copper-based catalysts have been extensively explored in this field due to their unique capability to catalyze multi-carbon products. However, the intrinsic complexity of eCO2R pathways on Cu surfaces often leads to mixed product distributions, posing a significant challenge for achieving high selectivity toward a single desired hydrocarbon. Herein, we report a breakthrough in methane selectivity using laser-synthesized, ligand-free Cu nanomaterials. Unlike conventional Cu catalysts that produce diverse products, these ligand-free nanoparticles exhibit unprecedented selectivity for methane (CH4) with a Faradaic efficiency (FE) exceeding 70% at superior overpotentials. The absence of surface ligands, a direct consequence of the ultrafast laser ablation synthesis, ensures abundant exposed active sites with tailored electronic and geometric configurations. We attribute the exceptional methane selectivity to the synergistic effects of active sites-rich surfaces and optimized *CO intermediate binding energetics, which favor the protonation pathway toward CH4 rather than C-C coupling. This work not only resolves the long-standing selectivity dilemma in Cu-catalyzed eCO2R but also establishes laser-synthesized ligand-free nanomaterials as a versatile platform for designing high-performance electrocatalysts.

Publication Details

Journal
ArXiv.org
Published
2025-04-18
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

Laser-Synthesized Ligand-Free Cu Nanocatalysts in Electrochemical CO2 Reduction to Methane

ArXiv.org
CO2 Reduction Techniques and Catalysts
article

Laser-Synthesized Ligand-Free Cu Nanocatalysts in Electrochemical CO2 Reduction to Methane

article en

Abstract

Electrochemical CO2 reduction (eCO2R) represents a pivotal strategy for mitigating global carbon emissions while simultaneously converting renewable energy into storable chemical fuels. Copper-based catalysts have been extensively explored in this field due to their unique capability to catalyze multi-carbon products. However, the intrinsic complexity of eCO2R pathways on Cu surfaces often leads to mixed product distributions, posing a significant challenge for achieving high selectivity toward a single desired hydrocarbon. Herein, we report a breakthrough in methane selectivity using laser-synthesized, ligand-free Cu nanomaterials. Unlike conventional Cu catalysts that produce diverse products, these ligand-free nanoparticles exhibit unprecedented selectivity for methane (CH4) with a Faradaic efficiency (FE) exceeding 70% at superior overpotentials. The absence of surface ligands, a direct consequence of the ultrafast laser ablation synthesis, ensures abundant exposed active sites with tailored electronic and geometric configurations. We attribute the exceptional methane selectivity to the synergistic effects of active sites-rich surfaces and optimized *CO intermediate binding energetics, which favor the protonation pathway toward CH4 rather than C-C coupling. This work not only resolves the long-standing selectivity dilemma in Cu-catalyzed eCO2R but also establishes laser-synthesized ligand-free nanomaterials as a versatile platform for designing high-performance electrocatalysts.

ArXiv.org
Openalex Percentile: Top 65%
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.