Propane Oxidative Dehydrogenation Using Consecutive Feed Injections and Fluidizable VO<sub><i>x</i></sub>/γAl<sub>2</sub>O<sub>3</sub> and VO<sub><i>x</i></sub>/ZrO<sub>2</sub>–γAl<sub>2</sub>O<sub>3</sub> Catalysts
Bibliographic record
Abstract
Propane oxidative dehydrogenation (PODH) was studied using VO x /γAl 2 O 3 and VO x /ZrO 2 –γAl 2 O 3 catalysts and consecutive propane injections. These catalysts were synthesized with 2.5, 5, and 7.5 wt % vanadium (V) loadings. Temperature-programmed reduction by hydrogen displayed one reduction peak for VO x /γAl 2 O 3 and two for VO x /ZrO 2 –γAl 2 O 3 (5 and 7.5 wt % vanadium). Temperature-programmed desorption of ammonia (NH 3 -TPD) and pyridine Fourier transform infrared spectroscopy showed that zirconia on γAl 2 O 3 reduces the catalyst acidity. NH 3 -TPD kinetics gave for VO x /ZrO 2 –γAl 2 O 3 higher desorption activation energies than those for VO x /γAl 2 O 3 . PODH runs in the Chemical Reactor Engineering Center Riser Simulator were developed under an oxygen-free atmosphere at 550 °C, close to 1 atm, 20 s, and a 42.0 catalyst/propane weight ratio (g/g). PODH runs for the 7.5% V/ZrO 2 –γAl 2 O 3 showed (a) 93% propylene selectivity and 25% propane conversion (based on propane converted into gaseous carbon-containing products), (b) 85% propylene selectivity at 28% propane conversion (based on propane converted including coke). The CO x selectivity remains at 2%. This makes the 7.5% V/ZrO 2 –γAl 2 O 3 catalyst a promising one for anticipated PODH industrial applications.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".