Subnanometer Bimetallic Platinum–Zinc Clusters in Zeolites for Propane Dehydrogenation
Bibliographic record
Abstract
Abstract Propane dehydrogenation (PDH) has great potential to meet the increasing global demand for propylene, but the widely used Pt‐based catalysts usually suffer from short‐term stability and unsatisfactory propylene selectivity. Herein, we develop a ligand‐protected direct hydrogen reduction method for encapsulating subnanometer bimetallic Pt–Zn clusters inside silicalite‐1 (S‐1) zeolite. The introduction of Zn species significantly improved the stability of the Pt clusters and gave a superhigh propylene selectivity of 99.3 % with a weight hourly space velocity (WHSV) of 3.6–54 h −1 and specific activity of propylene formation of 65.5 mol g Pt −1 h −1 (WHSV=108 h −1 ) at 550 °C. Moreover, no obvious deactivation was observed over PtZn4@S‐1‐H catalyst even after 13000 min on stream (WHSV=3.6 h −1 ), affording an extremely low deactivation constant of 0.001 h −1 , which is 200 times lower than that of the PtZn4/Al 2 O 3 counterpart under the same conditions. We also show that the introduction of Cs + ions into the zeolite can improve the regeneration stability of catalysts, and the catalytic activity kept unchanged after four continuous cycles.
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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) | 0.000 | 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 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".