Extraction of macauba kernel oil using supercritical carbon dioxide and compressed propane
Bibliographic record
Abstract
Abstract Macauba kernel oil (MKO) was extracted using supercritical CO 2 and compressed propane as the solvent. The influence of temperature (313–353 K for CO 2 and 303–333 K for propane) and pressure (18–22 MPa for CO 2 and 8–12 MPa for propane) on oil yield was investigated, and the results were compared with conventional extraction. A characterization of oil and defatted meal was performed. Temperature and pressure, in the evaluated range, influenced oil yield with the use of CO 2 . For propane, the oil yield was not influenced by the experimental conditions. In the evaluated experimental range, the use of propane and CO 2 provided 100 % and ∼93 % oil yield compared to conventional extraction, respectively. MKO showed a predominance of saturated fatty acids, among which lauric acid was the major constituent. CO 2 presented greater efficiency in the removal of phytosteroids and tocopherols from the macauba kernel, however, the oil extracted with propane presented longer oxidative stability. Defatted meals presented modified physico‐chemical composition and potentiated technological characteristics due to oil removal.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".