Pd–Ni bimetallic nanoparticles supported on TiO<sub>2</sub> as an efficient catalyst for catalytic transfer hydrodeoxygenation of guaiacol
Bibliographic record
Abstract
Bio-oil is a sustainable energy source, produced from pyrolysis of lignocellulosic materials or algae. It is, however, difficult to directly use the bio-oil for fuels due to several drawbacks, such as its viscosities and high oxygen content. One of the ways to solve this is upgrading of the bio-oil through hydrodeoxygenation (HDO). In this study, guaiacol was used as a bio-oil model compound in HDO reaction via catalytic transfer hydrogenation. The reaction was conducted in the presence of TiO 2 -supported palladium catalysts, with isopropyl alcohol as a hydrogen source. Nickel and molybdenum were added into the Pd catalyst to investigate their effects on the catalyst activity. The prepared catalysts were characterized using XRD, N 2 physisorption, hydrogen temperature-programmed reduction, and transmission electron microscopy. HDO reaction was carried out using a pressurized batch reactor at 250 °C for 1 h with 30 bar of helium. Liquid products were analyzed by GC–MS and GC–FID to identify and quantify the conversions and product yields. The result showed that the presence of nickel on the catalyst could improve the catalytic activity of Pd/TiO 2 . Guaiacol conversion over Pd–Ni/TiO 2 was 32.2%, while the conversion of guaiacol over Pd/TiO 2 was only 17.9%. In addition, Pd–Ni/TiO 2 showed good selectivity to produce cyclohexanol, while Pd/TiO 2 showed good selectivity to produce one oxygenated compound such as 2-methylphenol and phenol.
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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".