Synthesis of novel catalysts for hydrodeoxygenation of bio-oil: guaiacol as a model component
Bibliographic record
Abstract
In this work, NiMo/Support catalysts have been prepared by impregnation technique and evaluated for hydrodeoxygneation (HDO) reaction of guaiacol (GUA) aiming at the identification of active catalysts.Among various catalysts, NiMo/TiO 2 was chosen to understand the influence of reaction parameters, such as temperature, reaction time, H 2 pressure, and quantity of the catalyst.The influence of the metal components on guaiacol conversion is determined by comparing mono (Ni and Mo) metals deposited TiO 2 .These results signify the alloying nature of metal components.GCMS results showed that phenol is a major component in all the conditions.As there is an increase in the temperature (200, 250, 300, 350C), reaction time (1, 5, 8, 24 h), H 2 /guaiacol molar feed ratio (0, 1:2, 1:1, and 2:1), and catalyst amount (0, 25, 50, 100 mg), the conversion has increased significantly while maintaining the high selectivity to HDO products without ring opening reactions.Under optimized conditions (350C and 2:1 of H 2 :guaiacol ratio), 98% guaiacol is converted on NiMo/TiO 2 resulting phenol, poly methyl substituted phenols, and traces of cyclohexanone and benzene.It is remarkable that a low amount of catechol dimethyl ether and no indication of catechol and creosol were detected.
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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) | 0.000 | 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".