High-Throughput Fabrication of Antibacterial Starch/PBAT/AgNPs@SiO2 Films for Food Packaging
Bibliographic record
Abstract
In this current work, antimicrobial films based on starch, poly(butylene adipate-co-terephthalate) (PBAT), and a commercially available AgNPs@SiO2 antibacterial composite particle product were produced by using a melt blending and blowing technique. The effects of AgNPs@SiO2 at various loadings (0, 1, 2, 3, and 4 wt%) on the physicochemical properties and antibacterial activities of starch/PBAT composite films were investigated. AgNPs@SiO2 particles were more compatible with starch than PBAT, resulting in preferential distribution of AgNPs@SiO2 in the starch phase. Infusion of starch/PBAT composite films with AgNPs@SiO2 marginally improved mechanical and water vapor barrier properties, while surface hydrophobicity increased as compared with films without AgNPs@SiO2. The composite films displayed superior antibacterial activities against both Gram-positive (Staphylococcus aureus) and Gram-negative (Escherichia coli) bacteria. The sample loaded with 1 wt% AgNPs@SiO2 (SPA-1) showed nearly 90% inhibition efficiency on the tested microorganisms. Furthermore, a preliminary study on peach and nectarine at 53% RH and 24 °C revealed that SPA-1 film inhibited microbial spoilage and extended the product shelf life as compared with SPA-0 and commercial LDPE packaging materials. The high-throughput production method and strong antibacterial activities of the starch/PBAT/AgNPs@SiO2 composite films make them promising as antimicrobial packaging materials for commercial application.
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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) | 0.001 | 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) | 0.001 | 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".