The effect of the change of the structure on oxidative dehydrogenation of propane over cerium–zirconium catalysts
Bibliographic record
Abstract
Abstract A series of pure CeO 2 , ZrO 2 , and CeZrO x mixed metal oxide catalysts were prepared by a wetness impregnation method and were applied to the dehydrogenation of propane to propylene at 500°C and 0.1 MPa. The prepared catalysts were characterized by thermal gravimetric analysis (TGA), Brunauer, Emmett, and Teller (BET), X‐ray photoelectron spectroscopy (XPS), X‐ray diffraction (XRD), scanning electron microscope (SEM), transmission electron microscopes (TEM), Raman spectroscopy, and H 2 ‐TPR. It was observed that the zirconium content of the solid solution of the mixed metal oxide catalyst was 5%–25%, while the zirconium content of the material with phase segregation was higher (50%). The addition of zirconium was proven to decrease the oxygen vacancy concentration on the catalyst surface and change the intensity of (111) crystal of cerium oxide in the catalysts. Among the prepared catalysts, the Ce 0.90 Zr 0.10 O x catalyst with the maximum strength of the (111) crystal plane of cerium oxide exhibited the better catalytic oxidation performance for the dehydrogenation of propane to propylene. Compared with ZrO 2 in the blank experiment, the average propane conversion and propylene selectivity of the Ce 0.90 Zr 0.10 O x catalyst were increased by 10.78% and 17.95%, respectively.
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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".