In vitro characterization of bionanocomposites with green silver nanoparticles: A step towards sustainable wound healing materials
Bibliographic record
Abstract
Abstract This study investigated the characterization, antifungal activity, and biocompatibility of green agar/silver and collagen/silver bionanocomposite films for wound healing and cell growth scaffolds. Silver nanoparticles (AgNPs) are known for their antimicrobial properties, but their toxicity and harsh synthesis limit their applications. To address this, green‐synthesized AgNPs G‐AgNPs were incorporated into agar/collagen suspensions at specific concentrations and three different G‐AgNP‐agar and two different G‐AgNP‐col bionanocomposite films were produced. Nanoparticle homogeneity and film quality were characterized through SEM analysis. Mechanical properties were tested using a uniaxial tensile tester, revealing that the bioplastic control samples exhibited UTS of 3.86 MPa compared to 0.60 MPa for collagen, a 6‐fold improvement. Viable cell metabolic activity derived from MTT assay showed that Col‐4%AgNPs and Bio‐30%AgNPs had a 42.9% and 51.6% increase in net metabolic activity respectively compared to control on day 4. Fluorescence microscopy confirmed enhanced cell adhesion and proliferation in G‐AgNP‐incorporated samples. Antifungal properties were evaluated against Cladosporium spores, able to cause severe diseases when in contact with human skins, following ISO 16869:2008 standards. The demonstrated unique properties and tunability of G‐AgNPs bionanocomposites can be employed in a variety of specialties for wound‐healing applications, to improve rate and quality of healing while reducing the risk of infection.
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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".