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 CO2 reduction reaction (CO2RR) 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 CO2RR performance. Herein, we synthesize the electrocatalyst of Vo-CuO(Sn) nanosheets containing oxygen vacancies and Sn dopants for application in CO2RR-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 CO2RR 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 FECO of Vo-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 CO2RR to CO conversion.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 |
| Science and technology studies | 0.001 | 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 teacher head, 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".