Impregnating Subnanometer Metallic Nanocatalysts into Self-Pillared Zeolite Nanosheets
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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 molH2 molRh–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 H2 generation rate from AB hydrolysis over Rh0.8Ru0.2/SP-ZSM-5-100 reaches up to 1006 molH2 molmetal–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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it