NiNbO catalyst deposited on anodized aluminum monoliths for the oxidative dehydrogenation of ethane
Bibliographic record
Abstract
Abstract Aluminum monoliths were used as substrates to prepare structured catalysts. A rough alumina layer was generated on the surface of the substrates by anodizing followed by hydrothermal treatments. The dip‐coating technique was used for coating the monolithic substrates. Aqueous suspensions with 0.15 and 0.30 g/g of Ni‐Nb mixed oxides catalysts were prepared for that purpose. Colloidal SiO2 was added as a binder in order to obtain homogeneous and adherent coatings. The samples were characterized by SEM, TPR, XPS, XRD, and N2 adsorption and tested in the oxidative dehydrogenation (ODH) of ethane to ethylene. The silica particles produced a drop in catalytic activity without affecting ethylene selectivity. The former effect was attributed mainly to a decrease in surface nickel concentration and an increase in reduction temperature. The presence of anodized aluminum substrates in the reaction environment did not have a significant influence on catalytic activity and product distribution, as observed for the coated monoliths used in this work, thus being a useful material to prepare structured catalysts for low‐temperature ethane ODH.
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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".