Green synthesis of AgNPs catalyzing free radical polymerization as wound dressing with an anti-bacterial effect
Bibliographic record
Abstract
The development of wound dressing materials remains a critical need, driven by the complexity of wound environments and the intricate nature of the healing process. One of the most common wound dressing materials in research is hydrogel polymerized through different initiators and catalysts. However, not all catalysts can ensure low cytotoxicity, hence, the potential of designing hydrogel-based wound dressing materials is restricted in certain aspects. In this report, we have demonstrated a facile and green synthesis method to polymerize gelatin methacrylate (GelMA) by silver nanoparticles (AgNPs) and polydopamine (PDA) without additional catalysts. AgNPs and silver ions are long known to have a catalytic ability that is proven to work on free radical polymerization in this report. AgNPs were successfully obtained by reducing AgNO 3 through catechol groups from PDA. The materials adopted in this project are ensured to be bio-friendly and green. The final product (GelMA-AgPDA) had good mechanical strength and swelling ability with a negligible difference with GelMA catalyzed by tetramethylethylenediamine (TEMED). In vivo test was then conducted which show the superior wound healing, antibacterial and anti-inflammation properties of the GelMA-AgPDA wound dressing.
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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".