Bimetallic Bi‐Ni oxides over carbide supports for oxidative dehydrogenation of <i>n</i>‐butane: Experimental and kinetic modelling
Bibliographic record
Abstract
Abstract n ‐butane oxidative dehydrogenation was studied over Bi‐Ni/TiC, Bi‐Ni/SiC, Bi‐Ni/Silicalite‐1, and unsupported Bi‐Ni catalyst. The co‐impregnation technique was followed, with fixed 0.30 and 0.20 g/g for Bi and Ni respectively. The physico‐chemical properties were measured by means of BET, XRD, CO 2 /NH 3 ‐TPD, and H 2 ‐TPR techniques. The catalytic test results revealed that bimetallic Bi‐Ni/TiC catalyst exhibited the highest n ‐butane conversion of 16.6 % with dehydrogenation selectivity of 83.7 % (1,3‐butadiene and butenes selectivity of 50.7 % and 33 % respectively). As regards Bi‐Ni/TiC catalyst, the main operating parameters including O 2 /n‐C 4 H 10 feed ratio, space velocity, and reaction temperature were varied. The Bi‐Ni/TiC catalyst showed adequate stability for a 10 h time on stream test. The high performance of Bi‐Ni‐O/TiC catalyst is attributed to the synergistic role of reducibility, presence of strong basic sites and weak acidic sites, and an increased metal species dispersion over the TiC support. A kinetic study of n ‐butane oxidative dehydrogenation was carried out. The apparent activation energy for formation of butadiene was estimated to be the lowest (15 kJ/mol), while the highest activation energy (105 kJ/mol) is required for the undesirable cracking reaction.
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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".