Tuning Adsorbate‐Mediated Strong Metal‐Support Interaction by Oxygen Vacancy: A Case Study in Ru/TiO<sub>2</sub>
Bibliographic record
Abstract
Abstract The adsorbate‐mediated strong metal‐support interaction (A‐SMSI) offers a reversible means of altering the selectivity of supported metal catalysts, thereby providing a powerful tool for facile modulation of catalytic performance. However, the fundamental understanding of A‐SMSI remains inadequate and methods for tuning A‐SMSI are still in their nascent stages, impeding its stabilization under reaction conditions. Here, we report that the initial concentration of oxygen vacancy in oxide supports plays a key role in tuning the A‐SMSI between Ru nanoparticles and defected titania (TiO2‐x). Based on this new understanding, we demonstrate the in situ formation of A‐SMSI under reaction conditions, obviating the typically required CO2‐rich pretreatment. The as‐formed A‐SMSI layer exhibits remarkable stability at various temperatures, enabling excellent activity, selectivity and long‐term stability in catalyzing the reverse water gas‐shift reaction. This study deepens the understanding of the A‐SMSI and the ability to stabilize A‐SMSI under reaction conditions represents a key step for practical catalytic applications.
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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 | 0.001 | 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".