Using Subcritical Water for Decarboxylation of Oleic Acid into Fuel-Range Hydrocarbons
Bibliographic record
Abstract
Current interest in renewable fuel production is focused on high-performance fuels such as jet fuel because of their premium value in the marketplace. Currently, lower-value fuels such as biodiesel can be obtained using a variety of feedstocks, but contain significant amounts of oxygen, hence lowering their fuel value. In this work, we examined a one-pot catalytic hydrothermal process for the decarboxylation with an activated carbon catalyst of oleic acid as a model compound for free fatty acids. Temperature (350–400 °C), water-to-oleic acid ratio (2:1–4:1, v/v), catalyst, catalyst-to-total feed ratio (0.15–0.75), and residence time (1–2 h) were found to be key factors for removing oxygen from oleic acid. The complete removal of the carboxylic group from the upgraded liquid phase was achieved at 400 °C with a water-to-oleic acid ratio of 4:1 (v/v) and a residence time of 2 h as confirmed by FTIR and 13 C NMR results. The pseudo-first-order reaction rate constant was found to follow Arrhenius behavior with the activation energy determined to be 90.6 ± 3 kJ/mol. GC-FID results showed a high selectivity to heptadecane conversion, whereas the GC-TCD results indicated that decarboxylation was the dominating chemical reaction. High heating values and fuel densities in the range of commercial jet fuels were obtained using this approach without the addition of high-pressure hydrogen or a hydrogen-donor solvent.
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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) | 0.001 | 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 | 0.001 |
| 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".