Synergistic modulation of valence state and oxygen vacancy induced by surface reconstruction of the CeO<sub>2</sub>/CuO catalyst toward enhanced electrochemical CO<sub>2</sub> reduction
Bibliographic record
Abstract
Abstract Electrochemical CO2 reduction reaction (CO2RR) offers a promising strategy for CO2 conversion into value‐added C2+ products and facilitates the storage of renewable resources under comparatively mild conditions, but still remains a challenge. Herein, we propose the strategy of surface reconstruction and interface integration engineering to construct tuneable Cu0–Cu+–Cu2+ sites and oxygen vacancy oxide derived from CeO2/CuO nanosheets (OD‐CeO2/CuO NSs) heterojunction catalysts and promote the activity and selectivity of CO2RR. The optimized OD‐CeO2/CuO electrocatalyst shows the maximum Faradic efficiencies for C2+ products in the H‐type cell, which reaches 69.8% at −1.25 V versus a reversible hydrogen electrode (RHE). Advanced characterization analysis and density functional theory (DFT) calculations further confirm the fact that the existence of oxygen vacancies and Cu0–Cu+–Cu2+ sites modified with CeO2 is conducive to CO2 adsorption and activation, enhances the hydrogenation of *CO to *CHO, and further promotes the dimerization of *CHO, thus promoting the selectivity of C2+ generation. This facile interface integration and surface reconstruction strategy provides an ideal strategy to guide the design of CO2RR electrocatalysts.
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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".