Co-encapsulation of Quercetin and α-Tocopherol Bioactives in Zein Nanoparticles: Synergistic Interactions, Stability, and Controlled Release
Bibliographic record
Abstract
This study examined the capabilities of zein-based nanoparticles for the co-delivery of quercetin and α-tocopherol. The results demonstrated an optimal encapsulation efficiency of 96% with an average particle size of 50–320 nm, highlighting the proficiency of the method. Over 60 days, the retention release profiles showed gradual reductions, with the Zein/Que/Toc (20:1:1) and Zein/Que/Toc (20:1:25) formulations exhibiting distinct dynamics. In vitro analyses revealed controlled release, with quercetin reaching 79.7% and α-tocopherol reaching 60.4% after 8 h. The Zein/Que/Toc (20:1:5) combination exhibited a notable release of 73.1% over the same span, indicating a synergistic or stabilizing interplay between the co-encapsulated agents, which is beneficial for digestion. ATR-FTIR, rheology, and fluorescence spectroscopy investigations demonstrated key molecular interactions, including hydrogen bonding and hydrophobic forces. The integration of surfactants enhanced the photostability and retention of both bioactive compounds. This study emphasizes the vast potential of zein-based nanoparticles for bioactive co-delivery, with the Zein/Que/Toc (20:1:5) formulation emerging as a viable candidate. These findings have implications for the pharmaceutical, nutraceutical supplement, drug, and functional food domains.
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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".