Synergetic Effect of Ultrasmall Metal Clusters and Zeolites Promoting Hydrogen Generation
Bibliographic record
Abstract
Abstract Taking advantage of the synergetic effect of confined ultrasmall metal clusters and zeolite frameworks is an efficient strategy for improving the catalytic performance of metal nanocatalysts. Herein, it is demonstrated that the synergetic effect of ultrasmall ruthenium (Ru) clusters and intrinsic Brønsted acidity of zeolite frameworks can significantly promote the hydrogen generation of ammonia borane (AB) hydrolysis. Ultrasmall Ru clusters are embedded onto the silicoaluminophosphate SAPO‐34 ( CHA ) and various aluminosilicate zeolites ( MFI, * BEA , and FAU ) with tunable acidities by a facile incipient wetness impregnation method. Evidenced by high‐resolution scanning transmission electron microscopy, the sub‐nanometric Ru clusters are uniformly distributed throughout the zeolite crystals. The X‐ray absorption spectroscopy measurements reveal the existence of Ru‐H species between Ru clusters and adjacent Brønsted acid sites of zeolites, which could synergistically activate AB and water molecules, significantly enhancing the hydrogen evolution rate of AB hydrolysis. Notably, the Ru/SAPO‐34‐0.8Si (Si/Al = 0.8) and Ru/FAU (Si/Al = 30) catalysts with strong acidities afford high turnover frequency values up to 490 and 627 min −1 , respectively. These values are more than a 13‐fold enhancement than that of the commercial Ru/C catalyst, and among the top level over other heterogeneous catalysts tested under similar conditions.
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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".