Bibliographic record
Abstract
Cellulose nanocrystals (CNCs), highly recognized for its reinforcing properties, are an organic nanosized material extracted from natural cellulose sources. CNC \t≤ were used for the development of active edible coatings and films packaging development based on natural polymers like caseinate, chitosan, alginate, methylcellulose or biodegradable polymers like polycaprolactone. Bio-polymeric diffusion devices encapsulated with plant derived essential oils or fruit extracts based nano-emulsions were evaluated against pathogens, fungal and weevil, Sitophillus oryzae in different food systems. The addition of CNCs as reinforcing filler improved significantly the tensile strength of the nanocomposite based films and decreased water barrier properties. In addition, combined treatment of bioactive films with an irradiation treatment showed more pronounced insecticide, antifungal and antimicrobial properties than treatment with the bioactive film alone. The effect of γ-irradiation on the surface chemistry of CNCs was also evaluated in order to develop films with antioxidant properties. Finally, CNCs was used to develop a biopolymer support membrane for the detection of E. coli O157: H7 and L. monocytogenes. The presentation will summarize the most important results obtained in these studies.
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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".