Developing Antibacterial Nanocrystalline Cellulose Using Natural Antibacterial Agents
Bibliographic record
Abstract
We used hairy nanocrystalline cellulose functionalized with aldehyde groups, otherwise known as sterically stabilized nanocrystalline cellulose (SNCC), to facilitate the attachment of the antibacterial agents lysozyme and nisin. Immobilization was achieved using a simple, green process that does not require any linker or activator. X-ray photoelectron spectroscopy and Fourier transform infrared spectroscopy analyses showed successful attachment of both nisin and lysozyme onto the SNCC. The efficacy of the conjugated nanocellulose against the model bacteria Bacillus subtilis and Staphylococcus aureus was tested in terms of bacterial growth, cell viability, and biofilm formation/removal. The results show that the minimum inhibitory concentration of the conjugated nanocellulose is higher than that of lysozyme and nisin in free form, which was expected given that immobilization reduces the possible spatial orientations of these proteins. We observed that free nisin is not active against S. aureus after 24 h of exposure due to either deactivation of free nisin or development of resistance in S. aureus against free nisin. Interestingly, we did not observe this phenomenon when the bacteria were exposed to antibacterials immobilized on nanocellulose, suggesting that immobilization of antibacterial agents onto SNCC effectively retains their activity over long time periods. We suggest that antibacterial SNCC is a promising candidate for the development of antibacterial wound dressings.
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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".