Factors Affecting the Extraction of (Poly)Phenols from Natural Resources Using Deep Eutectic Solvents Combined with Ultrasound-Assisted Extraction
Bibliographic record
Abstract
Replacing conventional solvents with deep eutectic solvents (DES) has shown promising effects on the extraction yield of (poly)phenols.DES can be combined with ultrasound-assisted extraction (UAE) to further increase the extraction efficiency of (poly)phenols from natural resources compared to conventional methods.This review discusses the factors associated with DES (composition, solvent-tosample ratio, extraction duration, and temperature) and UAE (ultrasound frequency, power, intensity, and duty cycle) methods that influence the extraction of (poly)phenols and informs future improvements required in the optimization of the extraction process.For the optimum (poly)phenol extraction from natural resources, the following parameters shall be considered: ultrasound frequency should be in the range of 20-50 kHz, ultrasound intensity in the range of 60-120 W/cm 2 , ultrasound duty cycle in the range of 40-80%, ultrasound duration for 10-30 minutes, and ultrasound temperature for 25-50 C.Among the reported DES systems, choline chloride with glycerol or lactic acid, with a solvent-to-sample mass ratio of 10-30:1 shown to be effective.The solvent composition and solvent-to-sample mass ratio should be selected according to the target compound and the source material.However, the high viscosity of DES is among the major limitations.Optimizing these factors can help to increase the yield of extracted (poly)phenols and their applications.
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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| 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".