Investigation of Deep Eutectic Solvent-Based Microwave-Assisted Extraction and Efficient Recovery of Natural Products
Bibliographic record
Abstract
A systematic study of the principles of deep eutectic solvent-based microwave-assisted extraction (DES-MAE) was performed. It was found that the heating rates of most DESs decreased (heat capacity increased) under microwave irradiation with increasing water content, allowing high-efficiency extraction for thermally sensitive compounds. In addition, DESs containing carboxylic acids reacted with hydroxyl groups of sugar and choline chloride, resulting in cell wall destruction and inhibition of cellulose, hemicellulose, and lignin reconnection in cell walls through hydrogen bonds, thus leading to better extraction performance. This was verified by extracting anthraquinones from Rheum palmatum using DES-MAE and optimizing extraction conditions. DES with citric acid as the hydrogen bonding donor gave the highest extraction efficiency under the optimized conditions. In addition, anthraquinones in the DES extract were recovered using three kinds of silica modified by different functional groups. The results showed that material containing a phenyl group is beneficial to the recovery of anthraquinones in acid-based DESs because it can facilitate strong hydrophobic and π–π interactions. This study showcases the green chemistry applications of DES-MAE in a laboratory and industry alike, and demonstrates the recovery of natural products from DES extracts. The findings also provide valuable information for green extraction, modification, and application of cellulose, hemicellulose, and lignin.
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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".