Continuous Hydrothermal Decarboxylation of Fatty Acids and Their Derivatives into Liquid Hydrocarbons Using Mo/Al<sub>2</sub>O<sub>3</sub> Catalyst
Bibliographic record
Abstract
Abstract In this study, we report a single-step continuous production of straight-chain liquid hydrocarbons from oleic acid and other fatty acid derivatives of interest including castor oil, frying oil, and palm oil using Mo, MgO, and Ni on Al2O3 as catalysts in subcritical water. Straight-chain hydrocarbons were obtained via decarboxylation and hydrogenation reactions with no added hydrogen. Mo/Al2O3 catalyst was found to exhibit a higher degree of decarboxylation (92%) and liquid yield (71%) compared to the other two examined catalysts (MgO/Al2O3, Ni/Al2O3) at the maximized conditions of 375 °C, 4 h of space time, and a volume ratio of 5:1 of water to oleic acid. The obtained liquid product has a similar density (0.85 kg/m3 at 15.6 °C) and high heating value (44.7 MJ/kg) as commercial fuels including kerosene (0.78–0.82 kg/m3 and 46.2 MJ/kg), jet fuel (0.78–0.84 kg/m3 and 43.5 MJ/kg), and diesel fuel (0.80–0.96 kg/m3 and 44.8 MJ/kg). The reaction conditions including temperature, volume ratio of water-to-feed, and space time were maximized for the Mo/Al2O3 catalyst. Characterization of the spent catalysts showed that a significant amount of amorphous carbon deposited on the catalyst could be removed by simple carbon burning in air with the catalyst recycled and reused.
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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".