Evaluation of Vanadium Oxide Nanocatalysts over Graphene for Propylene Production Through Oxidative Propane Dehydrogenations
Bibliographic record
Abstract
This study reports an efficient and low-cost hydrothermal method for synthesizing vanadium oxide/graphene nanocatalysts. Field-emission scanning electron microscopy (FESEM) revealed the formation of nanostructured catalysts with consistent and directional shapes, as confirmed by X-ray diffraction (XRD). Fourier transform infrared (FTIR) spectroscopy indicated the presence of V2O5 and graphene, highlighting their bonds and structures. Thermogravimetric analysis (TGA) identified three stages of weight loss in the nanocatalysts, corresponding to water molecule evaporation, decomposition of residual organics, and the formation of yellow vanadium pentoxide particles due to the oxidation of vanadium V4+. Gas chromatography analysis from 450 °C to 600 °C showed that ethylene selectivity increased with temperature, while propylene selectivity showed the opposite trend. The effectiveness of these nanocatalysts was assessed in the oxidative dehydrogenation of propane using temperature programmed reduction. The approach of graphene-based vanadium oxide nanostructures will open up a new insight into the fabrication of high-performance catalysts.
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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) | 0.000 | 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".