Insight into the Deep Eutectic Solvent Extraction Mechanism of Flavonoids from Natural Plant
Bibliographic record
Abstract
Deep eutectic solvents (DESs) are a new class of green solvents with potential applications for the extraction of target compounds from both liquid and solid samples. However, current research in the field has focused on demonstrating the advantages in extraction efficiency in terms of more extracted material and shortened time, while the mechanism of the increased efficiency has not been systematically studied. Herein, we explored the solvent and solute interaction mechanisms with the use of three extraction methods (heating, microwave-assisted, and mechanochemical extraction) and different types of DESs. Choline chloride (ChCl) was used as the hydrogen-bond acceptor, while hydrogen-bond donors used are malonic acid, methylurea, and glycerin. Flavonoids from Flos Sophorae were extracted. 1H nuclear magnetic resonance (NMR) spectrometry, ultraviolet–visible (UV–vis) spectrometry, scanning electron microscopy (SEM), and ultrahigh-performance liquid chromatography (UPLC) analyses were performed to investigate the interactions between the flavonoids and the plant cell walls with DESs, and chemical reactions between the DESs and flavonoids. We also systematically evaluated the influence of several key factors on the extraction efficiency, which was consistent with the experimental results. The influence of DES in the sample on qualitative and quantitative UPLC analyses was systematically studied, and conditions were optimized. This study should provide insights into the interactions of specific DESs with various target compounds and help design more efficient extraction methods.
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 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".