Silica-supported ionic liquid-based dispersive microextraction of grapefruit peel flavonoid (hesperidin) and assessment of its antioxidant activities
Bibliographic record
Abstract
Abstract The present research explores the application of dispersive liquid–liquid microextraction (DLLME) of hesperidin (HSP) from grapefruit peel (GFP) while using silica-supported ionic liquids (ILs) as a stationary phase. The ILs [Et3NH][HSO4], Benzalkonium chloride, and didecyldimethyl ammonium chloride were immobilised on mesoporous silica support of particle size 150 micron. The parameters were optimised for the preparation of crude extracts. The extraction of GFP with a 50-mesh particle size using 1:1 (v/v) of 1% (v/v) acidified methanol (glacial acetic acid) and DMSO for 80 min at a liquid to solid ratio of 20 and 350 rpm shaking speed produced 18 g of crude extract/100 g of GFP. After using silica-supported IL as the stationary phase in a DLLME-based clean-up, an extract with 0.54 mg of HSP/g of crude extract was obtained upon elution with DMSO. Remarkably, 0.80 μg of HSP/g of crude extract was similarly recovered when IL was used as an eluent against a non-polar stationary phase. An enrichment factor for HSP equivalent to 2.1–3.4 was obtained by employing [Et3NH][HSO4] as a mobile phase against mesoporous silica or as a stationary phase supported on mesoporous silica. The findings support the great potential of silica-supported ILs as the stationary phase for DLLME.
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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".