Bactericidal Effect and Cytotoxicity of Graphene Oxide/Silver Nanocomposites
Bibliographic record
Abstract
To tackle the proliferation of pathogenic microorganisms without relying on antibiotics, innovative materials boasting antimicrobial properties have been engineered. This study focuses on the development of graphene oxide/silver (GO/Ag) nanocomposites, derived from partially reduced graphene oxide adorned with silver nanoparticles. Various nanocomposites with different amounts of silver (GO/Ag-1, GO/Ag-2, GO/Ag-3, and GO/Ag-4) were synthesized, and their antibacterial efficacy was systematically studied. The silver nanoparticles were uniformly deposited on the partially reduced graphene oxide surface, exhibiting spherical morphologies with an average size of 25 nm. The nanocomposites displayed potent antibacterial properties against both gram-positive bacteria ( S. aureus and B. subtilis ) and gram-negative bacteria ( E. coli and S. enterica ) as confirmed by minimum inhibition concentration (MIC) studies and time-dependent experiments. The optimal MIC for Gram-positive bacteria was 62.5 μg/mL and for Gram-negative bacteria was 125 μg/mL for the GO/Ag nanocomposites. Bacterial cells that encountered the nanocomposite films exhibited significantly greater inhibitory effects compared to those exposed to conventional antibacterial materials. Furthermore, the cytotoxicity of these nanocomposites was assessed using human epithelial cells (HEC), revealing that GO/Ag-1 and GO/Ag-2 exhibited lower toxicity levels toward HEC and remained compatible even at higher dilution rates. This study underscores the potential of GO/Ag-based nanocomposites as versatile materials for antibacterial applications, particularly as biocompatible wound dressings, offering promising prospects for wound healing and infection control.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 teacher head, 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".