Binding Site Diversity Promotes CO<sub>2</sub> Electroreduction to Ethanol
Bibliographic record
Abstract
The electrochemical reduction of CO 2 has seen many record-setting advances in C 2 productivity in recent years. However, the selectivity for ethanol, a globally significant commodity chemical, is still low compared to the selectivity for products such as ethylene. Here we introduce diverse binding sites to a Cu catalyst, an approach that destabilizes the ethylene reaction intermediates and thereby promotes ethanol production. We develop a bimetallic Ag/Cu catalyst that implements the proposed design toward an improved ethanol catalyst. It achieves a record Faradaic efficiency of 41% toward ethanol at 250 mA/cm 2 and −0.67 V vs RHE, leading to a cathodic-side (half-cell) energy efficiency of 24.7%. The new catalysts exhibit an in situ Raman spectrum, in the region associated with CO stretching, that is much broader than that of pure Cu controls, a finding we account for via the diversity of binding configurations. This physical picture, involving multisite binding, accounts for the enhanced ethanol production for bimetallic catalysts, and presents a framework to design multimetallic catalysts to control reaction paths in CO 2 reductions toward desired products.
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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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".