Supercritical carbon dioxide extraction of <scp> <i>Polygonum cuspidatum</i> </scp> powders: Experiments and modelling
Bibliographic record
Abstract
Abstract In recent years, the natural anticancer components resveratrol and emodin have attracted significant attention. This study employs supercritical carbon dioxide (ScCO 2 ) extraction, using ethanol as a cosolvent, to extract resveratrol and emodin from Polygonum cuspidatum powders. Experiments were conducted at temperatures ranging from 308 to 328 K, pressures from 15 to 30 MPa, ethanol contents of 60 to 160 mL/L, and extraction times between 1800 and 7200 s. The maximum extraction yields of resveratrol and emodin were 2.516 and 2.765 mg/g, respectively, under optimal conditions (temperature: 323 K, pressure: 25 MPa, ethanol content: 100 mL/L, extraction time: 3600 s), determined through one‐way experiments. Additionally, a mathematical model of the ScCO 2 extraction process was developed. The mass transfer coefficient ( K f ) was used as a fitting parameter and the kinetic model, based on mass conservation, was validated with experimental data. The model demonstrated good accuracy, with a low average absolute relative deviation (AARD) of 4.05%. This model provides theoretical support for industrial scaling and process optimization, achieving maximum extraction efficiency while minimizing CO₂ and ethanol consumption, thereby reducing costs and enhancing environmental benefits. Its establishment framework and methodology offer valuable references for optimizing similar ScCO 2 extraction processes.
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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) | 0.000 | 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".