Hybrid nanocomposite packaging films from cellulose nanocrystals, zinc sulfide quantum dots reinforced polylactic acid with fluorescent and antibacterial properties
Bibliographic record
Abstract
Abstract In this study, nanocomposite thin films from cellulose nanocrystals (CNCs), zinc sulfide (ZnS) quantum dots, and polylactic acid (PLA) with fluorescent and antibacterial properties were constructed via a simple, and fast route. ZnS nanoparticles were added to CNC following the hydrothermal process. The film casting method was used to form thin nanocomposite films. The transmission electron microscope analysis revealed the uniform dispersion and ZnS nanoparticles through CNC suspension and FTIR results confirmed the interaction between –OH group on CNC and ZnS nanoparticles. A noticeable emission peak at 475 nm for CNC‐ZnS signifies the suitability of CNC‐ZnS for the assembly of ZnS QDs. The addition of ZnS‐coated CNC into PLA film resulted in a bright blue fluorescence when exposed to ultraviolet light. The incorporation of 5 wt% CNCs loaded with ZnS quantum dots into PLA notably decreased bacterial growth in Escherichia coli and Salmonella, respectively. Data obtained from dynamic mechanical analysis underlined that the presence of 1 wt% ZnS‐coated CNC in PLA increased the storage modulus of nanocomposite films by 25%. The CNCs loaded with ZnS quantum dots showed effective fluorescent and antimicrobial properties to be used as food packaging material.
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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".