Controlled Nanopore Sizes in Supraparticle Supports for Enhanced Propane Dehydrogenation with GaPt SCALMS Catalysts
Bibliographic record
Abstract
High Resolution Image Download MS PowerPoint Slide The efficient immobilization of GaPt liquid metal alloy droplets onto tailored supports improves catalytic performance by preventing coalescence and subsequent loss of active surface area. Herein, we use tailored supraparticle (SP) supports with controlled nanopores to systematically study the influence of pore sizes on the catalytic stability of GaPt supported catalytically active liquid metal solution (SCALMS) in propane dehydrogenation (PDH). Initially, GaPt droplets were prepared via an atom-efficient and scalable ultrasonication method with recycling loops to yield droplets <300 nm. Subsequently, these droplets were immobilized onto SiO 2 -based SPs with controlled pore sizes ranging from 45 to 320 nm. Catalytic evaluations in PDH revealed that GaPt immobilized on SPs with larger pores demonstrated superior stability over 15 h time-on-stream evidenced by reduced deactivation rates from 0.046 to 0.026 h –1 . Nanocomputed tomography and identical location SEM confirmed the successful immobilization of GaPt droplets within the interstitial sites formed by the primary particles constituting the SPs. These remained unchanged before and after the catalytic reaction, demonstrating efficient coalescence prevention. Our findings underscore the importance of support pore size engineering for improving the stability of GaPt SCALMS catalysts and highlight, particularly, the high potential of using SPs in this context.
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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".