Optimization of artemisinin extraction from <i>Artemisia annua</i> L. with supercritical carbon dioxide + ethanol using response surface methodology
Bibliographic record
Abstract
Abstract Malaria is a high priority life‐threatening public health concern in developing countries, and therefore there is a growing interest to obtain artemisinin for the production of artemisinin‐based combination therapy products. In this study, artemisinin was extracted from the Artemisia annua L. plant using supercritical carbon dioxide (SC‐CO2) modified with ethanol. Response surface methodology based on central composite rotatable design was employed to investigate and optimize the extraction conditions of pressure (9.9–30 MPa), temperature (33–67°C), and co‐solvent (ethanol, 0–12.6 wt.%). Optimum SC‐CO2 extraction conditions were found to be 30 MPa and 33°C without ethanol. Under optimized conditions, the predicted artemisinin yield was 1.09% whereas the experimental value was 0.71 ± 0.07%. Soxhlet extraction with hexane resulted in higher artemisinin yields and there was no significant difference in the purity of the extracts obtained with SC‐CO2 and Soxhlet extractions. Results indicated that SC‐CO2 and SC‐CO2+ethanol extraction is a promising alternative for the extraction of artemisinin to eliminate the use of organic solvents, such as hexane, and produce extracts that can be used for the production of antimalarial products.
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 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) | 0.000 | 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 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".