Infection Control with Antimicrobial Solid-State ZnO Nanoparticles on Silk Fibroin Gauze
Bibliographic record
Abstract
Conventional antibiotic treatments for wound infections have the risk of developing microbial resistance, necessitating the search for innovative, alternative solutions like nanoparticles as advanced antibiotics. This work introduces a novel approach for managing acute and chronic wounds by creating an active wound dressing designed to both protect and eliminate bacteria from the injury site. We focused on the electroless deposition of large zinc oxide nanoparticles (ZnO NPs,), aiming for a particle size of around 200 nm to reduce cytotoxicity, onto electrospinned silk fibroin (SF) gauze. We assessed the biocompatibility and antimicrobial effectiveness of the ZnO NP-embedded silk dressing against gram-positive (Staphylococcus aureus) and gram-negative (Pseudomonas aeruginosa) bacteria. Our analysis indicates that incorporating ZnO nanoparticles into silk wound dressings maintains biocompatibility, achieving 70% cell viability while suppressing the growth of S. aureus and P. aeruginosa, particularly during the initial 24 h after application. By employing 200 nm particle sizes, we facilitated a significant release of zinc ions without producing harmful reactive oxygen species (ROSs) that could damage both bacteria and host tissues. These findings emphasize the therapeutic potential of bioresorbable bandages enhanced with large ZnO nanoparticles, presenting an innovative approach to clinical wound treatment.
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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".