A Surface Reconstruction Route to High Productivity and Selectivity in CO<sub>2</sub> Electroreduction toward C<sub>2+</sub> Hydrocarbons
Bibliographic record
Abstract
Abstract Electrochemical carbon dioxide reduction (CO 2 ) is a promising technology to use renewable electricity to convert CO 2 into valuable carbon‐based products. For commercial‐scale applications, however, the productivity and selectivity toward multi‐carbon products must be enhanced. A facile surface reconstruction approach that enables tuning of CO 2 ‐reduction selectivity toward C 2+ products on a copper‐chloride (CuCl)‐derived catalyst is reported here. Using a novel wet‐oxidation process, both the oxidation state and morphology of Cu surface are controlled, providing uniformity of the electrode morphology and abundant surface active sites. The Cu surface is partially oxidized to form an initial Cu (I) chloride layer which is subsequently converted to a Cu (I) oxide surface. High C 2+ selectivity on these catalysts are demonstrated in an H‐cell configuration, in which 73% Faradaic efficiency (FE) for C 2+ products is reached with 56% FE for ethylene (C 2 H 4 ) and overall current density of 17 mA cm ‐2 . Thereafter, the method into a flow‐cell configuration is translated, which allows operation in a highly alkaline medium for complete suppression of CH 4 production. A record C 2+ FE of ≈84% and a half‐cell power conversion efficiency of 50% at a partial current density of 336 mA cm ‐2 using the reconstructed Cu catalyst are reported.
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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) | 0.001 | 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".