Hydrodeoxygenation of 4-Methylphenol over Unsupported MoP, MoS<sub>2</sub>, and MoO<sub><i>x</i></sub> Catalysts
Bibliographic record
Abstract
A study of the hydrodeoxygenation (HDO) of 4-methylphenol over unsupported, low-surface-area MoS 2, MoO 2, MoO 3, and MoP catalysts is reported. With the exception of MoO 3, the catalysts had the same physicochemical properties before and after the 5 h reaction at 623 K and 4.40 MPa H 2 . The used MoO 3 was partially reduced to a mixed oxide containing Mo 4 O 11, MoO 2, and Mo. Compared to the unused MoO 3, the used MoO 3 CO uptake increased by a factor of 100 following the reaction. The partially reduced Mo oxide catalyst had a high conversion for the HDO of 4-methylphenol because of Brønsted acid sites and the formation of anionic vacancies. The catalyst turnover frequency (TOF) based on CO uptake for the HDO of 4-methylphenol decreased in the order MoP > MoS 2 > MoO 2 > MoO 3, while the activation energy increased in the order of MoP < MoS 2 < MoO 2 < MoO 3 . The activity trends correspond to the increased electron density of the Mo among the catalysts. Two primary reactions, C−O hydrogenolysis to yield toluene and saturation of 4-methylphenol followed by rapid dehydration to produce 4-methylcyclohexene, were identified. The catalysts differed in their hydrogenation and isomerization capabilities. The MoP catalyst displayed the highest selectivity toward hydrogenated products, suggesting that the rate-limiting step over MoO 3, MoO 2, and MoS 2 was the saturation of 4-methylphenol to produce 4-methylcyclohexanol.
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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".