Pyrolytic Decarboxylation and Cracking of Stearic Acid
Bibliographic record
Abstract
The primary objective of this work was to study the pyrolytic conversion of fatty acids to produce deoxygenated, liquid hydrocarbon products for use as renewable chemicals or fuels. Stearic acid ( n -octadecanoic acid) was chosen as a model compound for the free fatty acids liberated through the hydrolysis of beef tallow. Batch pyrolysis of stearic acid was conducted over a range of temperatures and times, and the reaction products were extracted and identified through gas chromatography and mass spectrometry. Under mild conditions, n -heptadecane was the main product, with concurrent production of CO 2, showing that decarboxylation was likely the first reaction to occur. Distinct series of n -alkanes and 1-alkenes developed and shifted to lower carbon numbers with increased temperature and time, consistent with hydrocarbon cracking reactions. Eventually, the series decomposed to aromatics, insoluble solids, and unidentified low-molecular-weight species. Semiquantitative analysis of the chromatographic data confirmed the predominance of n -heptadecane in the product mixture, the development and decomposition of the aliphatic species, the selectivity for n -alkanes over 1-alkenes, and the shift in product distribution toward lower carbon numbers. This work demonstrates the feasibility of producing liquid hydrocarbons through the pyrolysis of free fatty acids hydrolyzed from lipid feeds.
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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".