Electrocatalytic CO<sub>2</sub> Reduction to Ethylene: From Advanced Catalyst Design to Industrial Applications
Bibliographic record
Abstract
Abstract The value‐added chemicals, monoxide, methane, ethylene, ethanol, ethane, and so on, can be efficiently generated through the electrochemical CO 2 reduction reaction (eCO 2 RR) when equipped with suitable catalysts. Among them, ethylene is particularly important as a chemical feedstock for petrochemical manufacture. However, despite its high Faradaic efficiency achievable at relatively low current densities, the substantial enhancement of ethylene selectivity and stability at industrial current densities poses a formidable challenge. To facilitate the industrial implementation of eCO 2 RR for ethylene production, it is imperative to identify key strategies and potential solutions through comprehending the recent advancements, remaining challenges, and future directions. Herein, the latest and innovative catalyst design strategies of eCO 2 RR to ethylene are summarized and discussed, starting with the properties of catalysts such as morphology, crystalline, oxidation state, defect, composition, and surface engineering. The review subsequently outlines the related important state‐of‐the‐art technologies that are essential in driving forward eCO 2 RR to ethylene into practical applications, such as CO 2 capture, product separation, and downstream reactions. Finally, a greenhouse model that integrates CO 2 capture, conversion, storage, and utilization is proposed to present an ideal perspective direction of eCO 2 RR to ethylene.
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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 | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| 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".