Hydrodeoxygenation of Pyrolysis Oil in Supercritical Ethanol with Formic Acid as an In Situ Hydrogen Source over NiMoW Catalysts Supported on Different Materials
Bibliographic record
Abstract
Hydrodeoxygenation (HDO) is one of the most promising approaches to upgrading pyrolysis oils, but this process normally operates over expensive noble metal catalysts (e.g., Ru/C, Pt/Al2O3) under high-pressure hydrogen gas, which raises processing costs and safety concerns. In this study, a wood-derived pyrolysis oil was upgraded in supercritical ethanol using formic acid as an in situ hydrogen source at 300 °C and 350 °C, over a series of nickel–molybdenum-tungsten (NiMoW) catalysts supported on different materials, including Al2O3, activated carbon, sawdust carbon, and multiwalled nanotubes (MWNTs). The upgrading was also conducted under hydrogen gas (an ex situ hydrogen source) for comparison. The upgrading process was evaluated by oil yield, degree of deoxygenation (DOD), and oil qualities. The NiMoW/MWNT catalyst showed the best HDO performance among all the catalysts tested at 350 °C, with 74.8% and 70.9% of oxygen in the raw pyrolysis oil removed under in situ and ex situ hydrogen source conditions, respectively, which is likely owing to the large pore size and volume of the MWNT support material, while the in situ hydrogen source outperformed the ex situ hydrogen source in terms of upgraded oil yields and qualities, regardless of the catalysts employed.
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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".