Catalytic Oxidative Dehydrogenation of <i>n</i>‐Butane on Gallium Nitride‐Containing Titanosilicate Catalyst
Bibliographic record
Abstract
GaN‐containing titanosilicate catalysts were used for the first time for the oxidative dehydrogenation (ODH) of n‐butane at a relatively low reaction temperature (460 °C). Commercially available GaN powder with a wurtzite crystal structure showed superior reactivity and stability for the ODH of n‐butane. The catalytic property of GaN catalyst for ODH strongly depends on the GaN particle size. The effects of the GaN weight percentage and GaN particle size on the catalytic performance are investigated in a fixed bed reactor. Based on the physicochemical properties of the catalyst characterized via TEM, DLS, N2 adsorption‐desorption, XRF, O2‐TPD, XRD, XPS, and in‐situ FTIR, the textural and structural properties of catalyst were obtained. The catalytic results reveal that the presence of GaN increases the activity of the catalysts, indicating that GaN can be used as a new active phase for the ODH of n‐butane. XRD, XPS, O2‐TPD, DLS, TEM, and in‐situ FTIR results show that activated O species exist on the surface of the GaN catalyst and enhance the catalytic performance with a decreasing GaN particle size, suggesting that smaller GaN particles possess a remarkable capability to activate O species in O2 and C‐H bonds in light alkanes.
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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".