Comparison of the Two Common Solvents for THC and CBDExtractions
Bibliographic record
Abstract
Cannabis extract (Cannabis sativa L.) has been widely used for both medical and recreational purposes. The ability of cannabis to exert effects on health varies depending on different amounts of the active compound, cannabinoids. The important ingredients of interest are namely Delta-9-tetrahydro cannabinoids (THC) and Cannabidiol (CBD). It is known that the common solvent used for the extraction process is ethanol, among a variety of organic solvents. Accounted for the low polarity of those interest cannabinoids, other common solvents of higher carbon chain such as isopropanol are subjected to study for comparing the extracted concentrations of THC and CBD at the same condition with ethanol. The experiment was conducted using the same amount of dried cannabis leave and flowers in two solvents; ethanol and isopropanol. The filtrate was dried under vacuum using Rotary Evaporator and subjected to the Liquid-Chromatography techniques. Fractions were collected and tested with the thin layer chromatography technique (TLC) with respect to the standard solution. Liquid Chromatography was applied to separate the constituents, followed by the highperformance chromatography technique (HPLC) for the quantification of THC and CBD. The results showed that CBD, which is higher polarity, was obtained in the ethanol extract more than that of isopropanol. Whist, isopropanol solvent provided the higher amount of THC attributed to the more compatibility between lower polarities of substances. Therefore, it is recommended that the selection of solvent depends on the main target of the ingredients required in the extract.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".