Ecofriendly Mechanochemical Extraction of Bioactive Compounds from Plants with Deep Eutectic Solvents
Bibliographic record
Abstract
A fast, highly efficient, and ecofriendly extraction method using deep eutectic solvents (DESs) for mechanochemical extraction (MCE) was developed to extract bioactive compounds from plants. Tea leaves containing bioactive compounds such as alkaloids, flavonoids, and catechins were used to evaluate this method. Dozens of DESs and DESs/water mixtures were systematically studied and optimized to select optimized extraction conditions. The results showed that the extractions can be completed within 20 s. Moreover, the developed extraction method is more ecofriendly, faster, gentler, and more efficient than conventional methods. For many compounds, we could simply use the described method without optimization. On the other hand, the target compounds were extracted with various interferences because of the wide ranging high extraction efficiency. Ultrahigh performance liquid chromatography coupled with high-resolution mass spectrometry was therefore used for qualitative and quantitative analysis to characterize the efficiency for individual compounds. To avoid the negative effect of DESs on chromatographic separation, the analytical performances of this method, including reproducibility (RSD, n = 5), correlation of determination ( r 2 ), and the limit of detection, were determined.
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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) | 0.001 | 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".