Identification of a Pt<sub>3</sub>Co Surface Intermetallic Alloy in Pt–Co Propane Dehydrogenation Catalysts
Bibliographic record
Abstract
Bimetallic Pt–Co nanoparticles (NPs) were prepared and characterized by scanning transmission electron microscopy, in situ X-ray absorption spectroscopy, in situ synchrotron X-ray diffraction, and catalytic conversion for propane dehydrogenation with and without added H 2 . In addition, the surface extended X-ray absorption fine structure (EXAFS) obtained by fitting the difference spectrum between the fully reduced and room-temperature-oxidized catalysts suggest that the surface structure remains Pt 3 Co, although the core changes from Pt to Pt 3 Co and to PtCo. At low Co loading, the bimetallic nanoparticles form a Pt 3 Co intermetallic surface alloy with Pt-rich core. With increasing Co loading, a full alloy forms where both the surface and NP compositions are Pt 3 Co. A further increase in Co loading leads to a Co-rich NP core, likely PtCo, with a surface of Pt 3 Co. Although Pt–Co intermetallic alloys form two different phases and several morphologies, the surface structures are similar in all catalysts. Although both monometallic Pt and Co are active for alkane dehydrogenation, all bimetallic Pt–Co catalysts are significantly more olefin selective than either single metal. The turnover rates of the bimetallic catalysts indicate that Pt is the active atom with little contribution from Co atoms. The high olefin selectivity is suggested to be due to Co acting as a less active structural promoter to break up large Pt ensembles in bimetallic NPs.
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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".