Sn Dopants with Synergistic Oxygen Vacancies Boost CO<sub>2</sub> Electroreduction on CuO Nanosheets to CO at Low Overpotential
Bibliographic record
Abstract
Using the electrochemical CO 2 reduction reaction (CO 2 RR) with Cu-based electrocatalysts to achieve carbon-neutral cycles remains a significant challenge because of its low selectivity and poor stability. Modulating the surface electron distribution by defects engineering or doping can effectively improve CO 2 RR performance. Herein, we synthesize the electrocatalyst of V o -CuO(Sn) nanosheets containing oxygen vacancies and Sn dopants for application in CO 2 RR-to-CO. Density functional theory calculations confirm that the incorporation of oxygen vacancies and Sn atoms substantially reduces the energy barrier for *COOH and *CO intermediate formation, which results in the high efficiency, low overpotential, and superior stability of the CO 2 RR to CO conversion. This electrocatalyst possesses a high Faraday efficiency (FE) of 99.9% for CO at a low overpotential of 420 mV and a partial current density of up to 35.22 mA cm –2 at −1.03 V versus reversible hydrogen electrode (RHE). The FE CO of V o -CuO(Sn) could retain over 95% within a wide potential area from −0.48 to −0.93 V versus RHE. Moreover, we obtain long-term stability for more than 180 h with only a slight decay in its activity. Therefore, this work provides an effective route for designing environmentally friendly electrocatalysts to improve the selectivity and stability of the CO 2 RR to CO 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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".