Oxygen Vacancy-Driven Heterointerface Breaks the Linear-Scaling Relationship of Intermediates toward Electrocatalytic CO<sub>2</sub> Reduction
Bibliographic record
Abstract
Smart metal–metal oxide heterointerface construction holds promising potentials to endow an efficient electron redistribution for electrochemical CO 2 reduction reaction (CO 2 RR). However, inhibited by the intrinsic linear-scaling relationship, the binding energies of competitive intermediates will simultaneously change due to the shifts of electronic energy level, making it difficult to exclusively tailor the binding energies to target intermediates and the final CO 2 RR performance. Nonetheless, creating specific adsorption sites selective for target intermediates probably breaks the linear-scaling relationship. To verify it, Ag nanoclusters were anchored onto oxygen vacancy-rich CeO 2 nanorods (Ag/O V -CeO 2 ) for CO 2 RR, and it was found that the oxygen vacancy-driven heterointerface could effectively promote CO 2 RR to CO across the entire potential window, where a maximum CO Faraday efficiency (FE) of 96.3% at −0.9 V and an impressively high CO FE of over 62.3% were achieved at a low overpotential of 390 mV within a flow cell. The experimental and computational results collectively suggested that the oxygen vacancy-driven heterointerfacial charge spillover conferred an optimal electronic structure of Ag and introduced additional adsorption sites exclusively recognizable for *COOH, which, beyond the linear-scaling relationship, enhanced the binding energy to *COOH without hindering *CO desorption, thus resulting in the efficient CO 2 RR to CO.
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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".