Electrospun Zein Fibers as Carriers to Stabilize (−)‐Epigallocatechin Gallate
Bibliographic record
Abstract
In this study, a method was developed for continuous electrospinning of ultrafine corn zein protein fibers with diameters ranging from 150 to 600 nm. Fiber-forming solutions with various zein concentrations (10% to 30%, w/w) and aqueous ethanol concentrations (60% to 90%, w/w) were electrospun at 15 and 20 kV. Scanning electron microscopy results showed that the morphology of zein fibers was affected by aqueous ethanol concentration, zein concentration, and the applied voltage. The optimal condition for forming bead-less fibers was found to be 20% protein, 70% alcohol, and 15 kV. The zein fibers resisted solubilization in water, although swelling and plasticization were apparent after the water treatment. The efficacy of zein fibers was tested for stabilization of a green tea polyphenol, (-)-epigallocatechin gallate (EGCG), by incorporating the EGCG in zein fiber-forming solutions. Freshly spun fibers were less effective at immobilizing the EGCG upon immersion in water (82% recovery) as compared to fibers that were aged at 0% relative humidity for at least 1 d (>98% recovery) before water immersion. Fourier transform infrared spectroscopy studies demonstrated that hydrogen bonding, hydrophobic interactions, and physical encapsulation are the major contributors to the stabilization of EGCG in zein fibers in water.
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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".