Impregnating Subnanometer Metallic Nanocatalysts into Self-Pillared Zeolite Nanosheets
Bibliographic record
Abstract
Impregnation is the most commonly used approach to prepare supported metal catalysts in industry. However, this method suffers from the formation of large metal particles with uneven dispersion, poor thermal stability, and thus unsatisfied catalytic performance. Here, we demonstrate that the self-pillared MFI zeolite (silicalite-1 and ZSM-5) nanosheets with larger surface area and abundant Si–OH groups are ideal supports to immobilize ultrasmall monometallic (e.g., Rh and Ru) and various bimetallic clusters via simple incipient wetness impregnation method. The loaded subnanometric metal clusters are uniformly dispersed within sinusoidal five-membered rings of MFI and remain stable at high temperatures. The Rh/SP-S-1 is highly efficient in ammonia borane (AB) hydrolysis, showing a TOF value of 430 mol H2 mol Rh –1 min –1 at 298 K, which is more than 6-fold improvement over that of nanosized zeolite-supported Rh catalyst and even comparable with that of zeolite-supported Rh single-atom catalyst. Because of the synergistic effect between bimetallic Rh–Ru clusters and zeolite acidity, the H 2 generation rate from AB hydrolysis over Rh 0.8 Ru 0.2 /SP-ZSM-5-100 reaches up to 1006 mol H2 mol metal –1 min –1 at 298 K, and also shows record activities in cascade hydrogenation of various nitroarenes by coupling with the hydrolysis of AB. This work demonstrates that zeolite nanosheets are excellent supports to anchor diverse ultrasmall metallic species via the simple impregnation method, and the obtained nanocatalysts can be applied in various industrially important catalytic reactions.
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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".