Highly‐Exposed Single‐Interlayered Cu Edges Enable High‐Rate CO<sub>2</sub>‐to‐CH<sub>4</sub> Electrosynthesis
Bibliographic record
Abstract
Abstract The electrochemical CO2 reduction to CH4 is a promising approach for producing highly specific combustion fuel but has relatively poor selectivity and activity at high‐current‐density electrolysis. In this work, ultrathin CuGaO2 nanosheets with highly exposed single‐interlayered Cu edges are synthesized via an induced anisotropic growth strategy. Density functional theory calculations indicate that the exposed single‐interlayered Cu(I) edges on the (001) surface of CuGaO2 present a high‐density of single‐atomic Cu sites, which feature excellent CO2 electroreduction catalytic activity toward CH4. The CuGaO2 nanosheet catalysts exhibit efficient and stable CO2‐to‐CH4 electroreduction with Faradaic efficiency (FECH4) of 71.7% at a high current density of –1 A cm−2, corresponding to a superior CH4 partial current density of 717 ± 33 mA cm−2. This work suggests an attractive design strategy for tuning both the crystal facets and Cu–Cu distance to promote the CH4 electrosynthesis at high‐current‐density CO2 reduction.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".