Carboxylated Cellulose Nanocrystals Decorated with Varying Molecular Weights of Poly(diallyldimethylammonium chloride) as Sustainable Antibacterial Agents
Bibliographic record
Abstract
Cationic nanomaterials are promising candidates for the development of effective antibacterial agents by taking advantage of the nanoscale effects as well as other exceptional physicochemical properties of nanomaterials. In this study, carboxylated cellulose nanocrystals (cCNCs) derived from softwood pulp were coated with cationic poly(diallyldimethylammonium chloride) of varying molecular weights. The resulting cationic carboxylated cellulose nanocrystals coated with poly(diallyldimethylammonium chloride) (cCNCs–PDDA) nanomaterials were characterized for their structural and morphological properties using Fourier transform infrared spectroscopy, dynamic light scattering, zeta potential, elemental analysis, transmission electron microscopy, and thermogravimetric analysis. Cationic cCNCs–PDDA were investigated for their antibacterial properties against Gram-positive Staphylococcus aureus and Gram-negative Escherichia coli 23934 and Pseudomonas aeruginosa using a bacterial lawn growth inhibition assay. cCNC–PDDA materials displayed marked antibacterial activity, particularly against Gram-positive Staphylococcus aureus. Overall, our results indicated that cCNCs–PDDA could be a potential candidate for antibacterial applications such as antibacterial surfaces or coatings.
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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".