Colloidal synthesis of Pt–In bimetallic nanoparticles for propane dehydrogenation
Bibliographic record
Abstract
Recently, Pt-based bimetallic nanoparticles have drawn much attention because of their great catalytic performance and wide applications in diverse fields. In this work, we report that bimetallic Pt–In nanoparticles with uniform size distribution and controllable composition can be synthesized through a one-step, facile colloidal approach. Various characterization tools such as XRD, TEM, XPS, and synchrotron techniques have been used to characterize the as-obtained nanoparticles. It is demonstrated that the Pt and In elements are homogeneously distributed in the whole nanoparticle. The bimetallic Pt–In nanoparticles have shown great catalytic performance, including high activity, high selectivity, and high stability, for the propane dehydrogenation reaction to produce propene, one of the most important chemicals. The excellent catalytic performance makes Pt–In bimetallic nanoparticles promising catalysts in future industrial application.
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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 | 0.001 | 0.003 |
| 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 | 0.001 | 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 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".