Characterization of Trilayer Antimicrobial Diffusion Films (ADFs) Based on Methylcellulose–Polycaprolactone Composites
Bibliographic record
Abstract
Novel trilayer antimicrobial diffusion films (ADFs) were developed for food applications. ADFs were composed of two external layers of polycaprolactone and one internal layer of nanocrystalline cellulose (NCC)-reinforced methylcellulose (MC) matrix. Two antimicrobial mixtures (formulations A and B) were incorporated in the MC layer and compared via the evaluation of film properties. Resulting ADFs were inserted as diffusion devices into vegetable packages, and samples were stored at 4 °C for 14 days. Microbiological diffusion assays in the presence of ADFs were performed on pathogenic bacteria. From this, the study focused on characterizing the structural, physicochemical properties and total phenols (TP) release from ADFs. This TP release was determined by Folin-Ciocalteu's method and by FTIR analysis. Results indicated a controlled release of antimicrobials into the headspace (16.5% for formulation A and 13.4% for formulation B). Good correlations (≥90%) between both methods allowed validating an innovative, accurate, rapid FTIR procedure to quantify the diffusion of TP. SEM micrographs showed fibrillar structure due to NCC and a more compact network due to antimicrobials. Encapsulated antimicrobial formulations induced color changes without affecting visual attributes of films. ADFs containing formulation B exhibited the highest tensile strength (17.3 MPa) over storage.
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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".