Green and selective supercritical fluid extraction of essential oil and cannabidiol from <scp><i>Cannabis sativa</i></scp> L.
Bibliographic record
Abstract
Abstract In this study, supercritical fluid extraction (SFE) was used to extract cannabis essential oil and cannabinoids from the same matrix by changing decarboxylation conditions and CO2 density. Cannabis essential oil was extracted at 90 bar and 50°C (0.288 g/cm3 CO2 density), and it was mainly composed of α‐pinene, eucalyptol, β‐caryophyllene, and selinene. Subsequently, decarboxylation was carried out on the vegetable matter operating at 105°C for 15 min and, then, at 120°C for 1 h. The largest cannabidiol (2.96% w/w) yield was obtained when SFE was performed at 100 bar and 40°C (0.623 g/cm3 CO2 density). An effective online fractional separation of the extracted compounds was also obtained by SFE, since no cuticular waxes were detected in GC–MS traces of the products of interest. Operating in this way, expensive, time‐consuming, and polluting post‐processing steps of separation were not required, producing a ready‐to‐use extract for the market.
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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".