Stability studies of <scp>PtSn</scp> structured catalysts supported on thin layers of <scp> MAl <sub>2</sub> O <sub>4</sub> </scp> ( <scp>M: Mg, or Zn</scp> ) for paraffins dehydrogenation reactions
Bibliographic record
Abstract
Abstract In order to determine the catalytic stability, PtSn structured catalysts supported on thin films of MgAl 2 O 4 or ZnAl 2 O 4 deposited on α‐Al 2 O 3 spheres were studied through five reaction‐regeneration cycles in the n ‐butane dehydrogenation. Bimetallic catalysts show good catalytic stability along the five reaction‐regeneration cycles. Among the different synthesized catalysts, the PtSn/Sp‐Zn‐CN catalyst showed the best catalytic behaviour, showing very good values of yields to butenes and excellent catalytic stability along the cycles, even better than the one of a structured commercial catalyst used industrially for the dehydrogenation of paraffins. The characterization results by temperature‐programmed reduction and X‐ray photoelectron spectroscopy showed some modifications in the metallic phase of the catalysts after the cycles, mainly in the Sn/Pt surface ratios and in segregation effects in some catalysts. However, the transmission electron microscopy (TEM) results are conclusive in the sense that, after the cycles, the bimetallic catalysts maintained a very high proportion of particles with sizes between 1–2 nm, and therefore, preserved a high metallic dispersion.
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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".