Electron‐Deficient Cu Sites on Cu<sub>3</sub>Ag<sub>1</sub> Catalyst Promoting CO<sub>2</sub> Electroreduction to Alcohols
Bibliographic record
Abstract
Abstract Copper‐based catalysts electrochemically convert CO 2 into multicarbon molecules. However, the selectivity toward alcohol products has remained relatively low, due to the lack of catalysts favoring the adsorption of key intermediates in the alcohol pathways. Herein, a Cu 3 Ag 1 electrocatalyst is developed using galvanic replacement of an electrodeposited Cu matrix. The Cu 3 Ag 1 electrocatalyst enables a 63% Faradaic efficiency for CO 2 ‐to‐alcohol production and an alcohol partial current density of −25 mA cm −2 at −0.95 V versus reversible hydrogen electrode, corresponding to a 126‐fold enhancement in selectivity and 25‐fold increase in activity compared to the bare electrodeposited Cu matrix. Density functional theory calculations reveal that the interphase electron transfer from Cu to Ag generates electron‐deficient Cu sites and favors the adsorption of CO 2 reduction intermediates in the alcohol pathway, such as CH 3 CHO* and CH 3 CH 2 O*. Thus, for this electron‐deficient catalyst, the C 2 H 5 OH pathway is more preferable than the ethylene (C 2 H 4 ) pathway, endowing the catalyst with an alcohol/ethylene ratio of 38:1. These findings suggest both experimental approaches and theoretical insights for exploring highly selective CO 2 ‐to‐alcohol conversion.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".