Copper-Based Catalysts for Electrochemical Carbon Dioxide Reduction to Multicarbon Products
Bibliographic record
Abstract
Abstract Electrochemical conversion of carbon dioxide into fuel and chemicals with added value represents an appealing approach to reduce the greenhouse effect and realize a carbon-neutral cycle, which has great potential in mitigating global warming and effectively storing renewable energy. The electrochemical CO 2 reduction reaction (CO 2 RR) usually involves multiproton coupling and multielectron transfer in aqueous electrolytes to form multicarbon products (C 2+ products), but it competes with the hydrogen evolution reaction (HER), which results in intrinsically sluggish kinetics and a complex reaction mechanism and places higher requirements on the design of catalysts. In this review, the advantages of electrochemical CO 2 reduction are briefly introduced, and then, different categories of Cu-based catalysts, including monometallic Cu catalysts, bimetallic catalysts, metal-organic frameworks (MOFs) along with MOF-derived catalysts and other catalysts, are summarized in terms of their synthesis method and conversion of CO 2 to C 2+ products in aqueous solution. The catalytic mechanisms of these catalysts are subsequently discussed for rational design of more efficient catalysts. In response to the mechanisms, several material strategies to enhance the catalytic behaviors are proposed, including surface facet engineering, interface engineering, utilization of strong metal-support interactions and surface modification. Based on the above strategies, challenges and prospects are proposed for the future development of CO 2 RR catalysts for industrial applications. Graphical Abstract
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".