Ecofriendly Mechanochemical Extraction of Bioactive Compounds from Plants with Deep Eutectic Solvents
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 it