Characterization of Cellulose Nanocrystals and PLA Based Thin Films with either Silver or Antimicrobial Peptide
Bibliographic record
Abstract
Sustainable polylactic acid (PLA) antimicrobial-based plastics were engineered with a cellulose nanocrystals (CNC) filler.The small weight fractions (< 10 % w/w in total of CNC) and antibacterial agents were added in the different systems.Silver (Ag) nanomaterial was used as a positive control.A peptide sequence known to exhibit antimicrobial activity was integrated into the PLA based systems.The plastics were previously characterized in an earlier publication from our group.That publication focused on estimating the thermal and mechanical stability of the plastics, the oxygen and water vapour permeability, and the morphological arrangement of the components within the plastics.Hence, in this study, our team completed antimicrobial testing against four strains of organisms commonly encountered in the food industry.All the systems with either Ag (higher concentration systems only) or peptide were characterized with significant inhibition of microbial growth over different time periods.Further, another important parameter was to estimate whether exposure to a medium concentration within the package will reduce the integrity of the plastic systems.Thus, plastics exposed to different solvents were assessed for migration rates.The thermal and mechanical properties after soaking were determined.All systems were characterized with migration rates below values reported by international bodies.Finally, in most cases, when the plastics were exposed to the solvents, they were maintained their mechanical and thermal properties overtime.
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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) | 0.001 | 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".