Construction of Hierarchically Porous Nanoparticles@Metal–Organic Frameworks Composites by Inherent Defects for the Enhancement of Catalytic Efficiency
Bibliographic record
Abstract
Abstract Nanoparticles@metal–organic frameworks (MOFs) composites have attracted considerable attention in recent years due to the prominent selective catalytic activity. However, it is highly desirable to develop a simple and universal way to settle the trade‐off between the catalytic efficiency and selectivity. Herein, by employing the thermal instability of inherent defects, hierarchically porous Pt@UiO‐66‐NH 2 , Pt@UiO‐66, Pt@ZIF‐8, and Au@ZIF‐8 are successfully constructed after annealing at an appropriate temperature, respectively. The generated mesopores in the MOFs can be located around the external nanoparticle to retain the MOF shell for catalytic selectivity. Finally, when tested in olefin hydrogenation, Pt@UiO‐66‐NH 2 shows significantly improved catalytic rate and enhanced dynamic selectivity.
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How this classification was reachedexpand
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) | 0.000 | 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 teacher head, 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".