Synthesis, characterization, and evaluation of high selectivity mixed molybdenum and vanadium oxide catalysts for oxidative dehydrogenation of propane
Bibliographic record
Abstract
Abstract Molybdenum and vanadium oxide catalysts with varied compositions (Mo/V = 1/1, 7/3, 8/2, and 9/1) were prepared using a modified citrate‐nitrate auto‐combustion method for the oxidative dehydrogenation of propane to propylene. These catalysts were characterized by BET‐technique, TPR, XRD, SEM, Raman, and UV spectroscopy. The effects of washing and supercritical CO2 drying on catalysts during the preparation steps were investigated. Results show an interaction between the molybdenum and vanadium metal ions in all of these catalysts due to the presence of a peak at 785 cm−1 from the Raman study, which was assigned to a polymolybdovanadate species V‐O‐Mo vibration. This interaction could be efficient for alkane activation reaction. The catalysts were evaluated in a fixed bed micro‐reactor at temperatures in the range of 350–600 °C and at atmospheric pressure. The activity of the catalyst increased by increasing the molybdenum content. All of the catalysts in this study showed 100 % selectivity for propylene in the temperature range of 350–450 °C; however, the propylene selectivity was found to decrease with an increase in the temperature. The highest yield of 4.8 % with 100 % propylene selectivity was obtained for a catalyst with Mo/V ratio of 9:1 at 500 °C.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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 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".