Nanocomposites of cellulose nanofibers incorporated with carvacrol via stabilizing octenyl succinic anhydride-modified ɛ-polylysine
Bibliographic record
Abstract
Food packaging plays an extremely important role in the global food chain, allowing for products to be shipped across long distances without spoiling. However, there is an increased need to both reduce plastic waste caused by traditional single-use plastic packaging and improve the overall functionality of packaging materials to extend shelf-life even further. Herein, we investigate composite mixtures based on cellulose nanofibers and carvacrol via stabilizing octenyl-succinic anhydride-modified epsilon polylysine (MɛPL-CNF) for active food packaging applications. The effects of epsilon polylysine (εPL) concentration and modification with octenyl-succinic anhydride (OSA) and carvacrol are evaluated with respect to composites morphology, mechanical, optical, antioxidant, and antimicrobial properties. We find that both increased εPL concentration and modification with OSA and carvacrol lead to films with increased antioxidant and antimicrobial properties, albeit at the expense of reduced mechanical performance. Importantly, when sprayed onto the surface of sliced apples, MεPL-CNF-mixtures are able to successfully delay/hinder enzymatic browning, suggesting the potential of such materials for a range of active food packaging applications.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".