The Manufacture and Characterization of Silver Diammine Fluoride and Silver Salt Crosslinked Nanocrystalline Cellulose Films as Novel Antibacterial Materials
Bibliographic record
Abstract
There is an unmet need for biocompatible, anti-infective, and mechanically strong hydrogels. This study investigated the use of poly vinyl alcohol (PVA), polysaccharides, and nanocrystalline cellulose (CNC) to deliver silver in a controlled manner for possible use against oral or wound bacteria. Silver was included in solvent cast films as silver diammine fluoride (SDF) or as nitrate, sulphate, or acetate salts. Hydrogel formation was assessed by swelling determinations and silver release was measured using inductively coupled plasma methods. Antibacterial studies were performed using Gram-positive and negative bacteria turbidity assays. PVA formed homogenous, strong films with SDF and swelled gently (99% hydrolyzed) or vigorously with dissolution (88% hydrolyzed) and released silver slowly or quickly, respectively. CNC-SDF films swelled over a week and formed robust hydrogels whereas CNC alone (no silver) disintegrated after two days. SDF loaded CNC films released silver slowly over 9 days whereas films crosslinked with silver salts were less robust and swelled and released silver more quickly. All silver loaded films showed good antibacterial activity. CNC may be crosslinked with silver in the form of SDF (or any soluble silver salt) to form a robust hydrogel suitable for dental use such as for exposed periodontal debridement areas.
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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".