Fabrication of Cellulosic Nonwoven Material Coated with Polyvinyl Alcohol and Zinc Oxide/Mesoporous Silica Nanoparticles for Wound Dressing Purposes with Cephalexin Delivery
Bibliographic record
Abstract
Wound dressings with antibacterial properties have emerged as a promising material to accelerate wound healing treatments. The present study explores the fabrication of non-woven fabric treated with polyvinyl alcohol (PVA) hydrogel including zinc oxide nanoparticles (ZnO-NPs), and mesoporous silica nanoparticles (MS-NPs) to develop wound dressings that can help to heal the wound. In addition, the antibiotic cephalexin was loaded to the composite coating to aid in mitigating the establishment of opportunistic bacterial infection. Accordingly, the antibacterial efficiency was evaluated against two common pathogenic bacteria: Staphylococcus aureus (S. aureus) and Escherichia coli (E.coli) . To characterize the coated nonwoven, SEM images, XRD pattern, FTIR spectra, swelling ratio, drug release, and MTT assays were employed to describe the potential wound dressing. It was observed that the fabricated nanocomposite possesses a considerable capacity to take up water through swelling, and incorporation of ZnO-NPs and MS-NPs into the hydrogel network increased the swelling ratio of the samples to about 8 times. Moreover, the fabricated composite appeared to have significant properties of degradation: the release of the loaded drugs from the nanocomposite displayed a burst release at the first hours and by 80% release of the cephalexin happened after 32 h. Lastly, the treated composite demonstrated excellent antibacterial properties against the selected bacteria. The results of this study indicate that the novel nanocomposite wound dressing may be a significant innovation for the medical treatment of infected skin wounds.
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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".