Modified Cellulose Nanocrystals Enabled Antimicrobial Polymeric Films
Bibliographic record
Abstract
Abstract This study presents an antimicrobial polymeric material comprising cellulose nanocrystals (CNCs) grafted with an antimicrobial oligomer, polyhexamethylene guanidine hydrochloride (PHGH). A one‐pot reaction is implemented to graft PHGH onto CNCs, creating a non‐leaching and nano‐sized antimicrobial additive (mCNC). The mCNC is subsequently incorporated into a model polymer, polylactic acid (PLA), with concentrations of 2.5 to 10 wt.% and tested for its antimicrobial activity during dynamic and static contact with Escherichia coli and Bacillus subtilis bacteria. The grafting of PHGH onto CNC is confirmed with Fourier transform infrared spectroscopy (FTIR), X‐ray spectroscopy (XPS) and elemental analysis. The effect of mCNC incorporation at various loading levels on the morphology and physicomechanical properties of PLA is investigated with polarized optical microscope (POM), scanning electron microscope (SEM), dynamic mechanical analysis (DMA), tensile testing, and differential scanning calorimetry (DSC). In terms of the antimicrobial action, the films exhibited potent efficacy against Gram‐positive bacteria ( B. subtilis ), with growth inhibition of 3.97 to 4.66‐log reduction. However, 10 wt.% of mCNC loading is needed to achieve a significant bacterial inhibition (>6.24‐log) of the Gram‐negative bacteria ( E. coli ). Overall, the incorporation of PHGH grafted CNCs in polymers provided a non‐leaching antimicrobial film that has potential application in food packaging.
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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".