In situ grown oxygen-vacancy-rich copper oxide nanosheets on a copper foam electrode afford the selective oxidation of alcohols to value-added chemicals
Bibliographic record
Abstract
Abstract Selective oxidation of low-molecular-weight aliphatic alcohols like methanol and ethanol into carboxylates in acid/base hybrid electrolytic cells offers reduced process operating costs for the generation of fuels and value-added chemicals, which is environmentally and economically more desirable than their full oxidation to CO2. Herein, we report the in-situ fabrication of oxygen-vacancies-rich CuO nanosheets on a copper foam (CF) via a simple ultrasonication-assisted acid-etching method. The CuO/CF monolith electrode enables efficient and selective electrooxidation of ethanol and methanol into value-added acetate and formate with ~100% selectivity. First principles calculations reveal that oxygen vacancies in CuO nanosheets efficiently regulate the surface chemistry and electronic structure, provide abundant active sites, and enhance charge transfer that facilitates the adsorption of reactant molecules on the catalyst surface. The as-prepared CuO/CF monolith electrode shows excellent stability for alcohol oxidation at current densities >200 mA·cm2 for 24 h. Moreover, the abundant oxygen vacancies significantly enhance the intrinsic indicators of the catalyst in terms of specific activity and outstanding turnover frequencies of 5.8k s−1 and 6k s−1 for acetate and formate normalized by their respective faradaic efficiencies at an applied potential of 1.82 V vs. RHE.
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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".