Effect of Polyethylene Film Thickness on the Antimicrobial Activity of Embedded Zinc Oxide Nanoparticles
Bibliographic record
Abstract
High Resolution Image Download MS PowerPoint Slide Microbial contamination of most foods occurs primarily at the surface during postprocessing and handling; therefore, preventing cross-contamination by incorporation of antimicrobial substances in contact with the surface of the product is an efficient strategy in reducing food contamination risks. Zinc oxide nanoparticles (ZnONPs) have been used widely to achieve antimicrobial films in various applications including the food industry. This work describes the fabrication of antimicrobial polymeric films containing ZnONPs produced by the coextrusion and dip-coating techniques. Effects of skin layer thicknesses containing ZnONPs on the antimicrobial effectiveness of the film by their capability to inactivate Gram-positive and Gram-negative bacteria were studied for both methods. The antimicrobial properties of the coextruded multilayer LLDPE/ZnONP nanocomposite films evidenced antimicrobial activity in the range 0.5–1.5 log reductions, while in the case of a sandblasted multilayer film, it showed high antimicrobial properties as around 99.99%. The optical properties of coextruded multilayer films were measured and discussed. Furthermore, to achieve a thinner LLDPE thickness, ZnONPs were coated with different concentrations of LLDPE solution by the dip-coating method. TEM confirmed that a homogeneous layer is formed on the surface of ZnONPs. The thickness of the LLDPE layer estimated by TEM was about 2 nm and film produced 3 log and 4 log reductions for E. coli and S. aureus, respectively. The results show that developed films have the potential to be used as food packaging films and can extend shelf life, maintain quality, and assure the safety of food. The antimicrobial mechanisms of ZnONPs were also investigated. It was found that direct contact of particles with products is necessary to assure high antibacterial activity of the films.
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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) | 0.002 | 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".