Hydrodenitrogenation and Hydrogenation of Aromatic Compounds over Titania Supported Cobalt Molybdenum Catalysts
Bibliographic record
Abstract
Newly developed CoMo titania catalysts for ultra-deep hydrodesulfurization (HDS) of diesel oil have higher activities for both HDS and hydrodenitrogenation (HDN), whereas the chemical hydrogen consumption is almost the same or lower than that of alumina supported catalysts. The HDN reaction routes of a model nitrogen compound and the hydrogenation activities of a model aromatic hydrocarbon compound over CoMo titania catalysts and commercial CoMo and NiMo alumina catalysts were investigated using carbazole dissolved in toluene as the feedstock. Toluene was used as the representative of aromatic hydrocarbon compounds which account for about 30% of diesel oil. The HDN reactions over each catalyst proceeded by the same reaction routes for hydrogenation of the aromatic rings of carbazole. However, the hydrogenation activity of toluene over the CoMo titania catalysts was lower than that over alumina supported catalysts. It is considered that the chemical hydrogen consumption of the CoMo titania catalyst is less than that of alumina supported catalysts, because hydrogenation of aromatic hydrocarbon compounds in diesel oil is selectively restricted over the CoMo titania catalyst.
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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".