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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 teacher head, 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".