Zein-based nanoparticles and nanofibers: Co-encapsulation, characterization, and application in food and biomedicine
Bibliographic record
Abstract
Background Zein-based nanoparticles and nanofibers have attracted considerable attention because of their ability to co-encapsulate and deliver multiple bioactive compounds . Zein has unique properties, including amphiphilicity, renewability, nontoxicity, biodegradability, and biocompatibility , making it a highly suitable carrier for enhancing the stability, bioavailability, and efficacy of small molecules in the field of functional food ingredients and smart biomedicine. Scope and approach This review highlights recent advancements in zein-based delivery systems, focusing on the synergistic effects of co-encapsulated bioactive compounds, improved stability, bioavailability, and controlled release mechanisms. The integration of zein with other biopolymers for hybrid systems is also discussed. Key findings and conclusion Zein nanoparticles and nanofibers, typically ranging in size from 50 to 300 nm, achieved a co-encapsulation efficiency of greater than 90%, facilitating the controlled and prolonged release of bioactive compounds, such as vitamins, lipids, and antioxidants for over 21 days. Future research could optimize multifunctional delivery systems and scalable production methods such as microfluidics and solution blow spinning processes to advance zein-based applications across the food and biomedical sectors.
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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".