Manipulating Au−CeO<sub>2</sub> Interfacial Structure Toward Ultrahigh Mass Activity and Selectivity for CO<sub>2</sub> Reduction
Bibliographic record
Abstract
Abstract Deploying the application of Au‐based catalysts directly on CO2 reduction reactions (CO2RR) relies on the simultaneous improvement of mass activity (usually lower than 10 mA mg−1Au at −0.6 V) and selectivity. To achieve this target, we herein manipulate the interface of small‐size Au (3.5 nm) and CeO2 nanoparticles through adjusting the surface charge of Au and CeO2. The well‐regulated interfacial structure not only guarantees the utmost utilization of Au, but also enhances the CO2 adsorption. Consequently, the mass activity (CO) of the optimal AuCeO2/C catalyst reaches 139 mA mg−1Au with 97 % CO faradaic efficiency (FECO) at −0.6 V. Moreover, the strong interaction between Au and CeO2 endows the catalyst with excellent long‐term stability. This work affords a charge‐guided approach to construct the interfacial structure for CO2RR and beyond.
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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".