Preparation and characterization of silk fibroin/polyethylene oxide nanofiber membranes with antibacterial activity
Bibliographic record
Abstract
Bacterial infection is among the most common diseases that threaten human health. Antibiotics are effective in treating bacterial infections. However, the overuse of antibiotics will lead to an increase in bacterial resistance. To reduce the overuse of antibiotics and improve the effective use of antibiotics through slow release, silk fibroin (SF)/polyethylene oxide (PEO) nanofiber membranes with different SF and PEO proportions were prepared by electrospinning. The ecofriendly solvent ethanol solution was used for electrospinning for better protection of antibiotic activity. The SEM showed that the surface of SF/PEO (2:8) and SF/PEO (3:7) was smoother and more uniform. With the increase of SF content, the thermal stability and hydrophilicity of SF/PEO nanofiber membranes were improved. The SF/PEO (3:7) nanofiber membrane had the best mechanical property and its maximum stress and strain were 4.6 1 ± 0.24 MPa and 16.36 ± 0.41%, respectively. Based on these good properties, SF/PEO (3:7) nanofiber membrane was chosen for loading and releasing gentamicin sulfate (GS). The fabricated (GS)/SF/PEO (3:7) nanofiber membrane exhibited good release efficiency and showed the good antibacterial activity against Staphylococcus aureus and Escherichia coli. These investigations indicated the GS/SF/PEO (3:7) nanofiber membrane (GS/SF/PEO) has a great potential for application in antibacterial materials.
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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 | 0.001 | 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".