Electrochemical Carbon Dioxide Reduction to Ethylene: From Mechanistic Understanding to Catalyst Surface Engineering
Bibliographic record
Abstract
Abstract Electrochemical carbon dioxide reduction reaction (CO2RR) provides a promising way to convert CO2 to chemicals. The multicarbon (C2+) products, especially ethylene, are of great interest due to their versatile industrial applications. However, selectively reducing CO2 to ethylene is still challenging as the additional energy required for the C–C coupling step results in large overpotential and many competing products. Nonetheless, mechanistic understanding of the key steps and preferred reaction pathways/conditions, as well as rational design of novel catalysts for ethylene production have been regarded as promising approaches to achieving the highly efficient and selective CO2RR. In this review, we first illustrate the key steps for CO2RR to ethylene (e.g., CO2 adsorption/activation, formation of *CO intermediate, C–C coupling step), offering mechanistic understanding of CO2RR conversion to ethylene. Then the alternative reaction pathways and conditions for the formation of ethylene and competitive products (C1 and other C2+ products) are investigated, guiding the further design and development of preferred conditions for ethylene generation. Engineering strategies of Cu-based catalysts for CO2RR-ethylene are further summarized, and the correlations of reaction mechanism/pathways, engineering strategies and selectivity are elaborated. Finally, major challenges and perspectives in the research area of CO2RR are proposed for future development and practical applications.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".