Isolation of ellagic acid from pomegranate peel extract by hydrophobic interaction chromatography using graphene oxide grafted cotton fiber adsorbent
Bibliographic record
Abstract
Ellagic acid, a natural polyphenol, was isolated from pomegranate peel extract by hydrophobic interaction using graphene oxide grafted cotton fiber as a stationary adsorbent. The grafted graphene oxide moieties served as hydrophobic interaction-binding sites for ellagic acid adsorption. The graphene oxide grafted cotton fiber was made into a membrane-like sheet in order to complete ellagic acid purification by using a binding-elution mode. The effects of operational parameters, such as the composition of the binding buffer/elution buffer, buffer pH, and buffer concentration, on the isolation process were investigated. It was found that 5 mmol/L sodium carbonate aqueous solution is a proper-binding buffer, and sodium hydroxide aqueous solution ranging from 0.04 to 0.06 mol/L is a suitable elution solution for ellagic acid purification. Under the optimized condition, the purity of ellagic acid increased significantly from 7.5% in the crude extract to 75.0-80.0%. The pH value was found to be a key parameter that determines the adsorption and desorption of ellagic acid. No organic solvent is involved in the entire purification process. Thus, a simple and environmentally friendly method is established for ellagic acid purification using a graphene oxide-modified biodegradable and bio-sourced fibrous adsorbent.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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".