Enhanced Interface with Strong Charge Delocalization toward Ultralow Overpotential CO<sub>2</sub> Electroreduction
Bibliographic record
Abstract
The construction of an interface has been demonstrated as one of the most insightful strategies for designing efficient catalysts toward electrochemical CO2 reduction (CO2RR). However, the weak interfacial interaction and inherent instability inevitably hinder a further performance enhancement in CO2RR attributable to the interface effect. Herein, 2 nm Ag nanoclusters (Ag NCs) are embedded onto CeO2 nanospheres (CeO2 NSs) with highly interconnected porosity (Ag NCs@CeO2 NSs) to exclusively study the pure interface effect toward CO2RR. The enhanced Ag–CeO2 pure interface endows Ag NCs@CeO2 NSs with a remarkably larger current density, significantly higher Faraday efficiency (FE), and energy efficiency as compared to Ag NCs, CeO2 NSs, and the one with Ag NCs dispersed on CeO2 nanoparticles. More importantly, an impressively high CO FE of over 70.0% is achieved at an ultralow overpotential (η) of 146 mV. The free energy and differential charge calculations, coupled with X‐ray photoelectron spectroscopy results jointly imply that the effective initiation of CO2RR to CO at a lower η over Ag NCs@CeO2 NSs derives from the enhanced interface‐induced charge delocalization, which enhances the electron transfer ability toward *COOH intermediate, thus overcoming the energy barrier demanded for the rate‐determining step.
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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".