Sustained release of CIP from TiO<sub>2</sub>‐PVDF/starch nanocomposite mats with potential application in wound dressing
Bibliographic record
Abstract
ABSTRACT Electrospinning is an economical and alluring method to fabricate wound dressing mats from a variety of natural and synthetic materials. In this study, polyvinylidene fluoride (PVDF) and starch composite mats containing ciprofloxacin (CIP) loaded on titanium dioxide nanoparticles (TiO2) were prepared. Fourier Transform Infrared spectra of CIP, synthesized TiO2 NPs, TiO2/CIP, and PVDF/starch composite mats are analyzed. Scanning electron microscopy images revealed that the fiber diameter of PVDF nanofibers thickens by increasing starch, which varies between 180 nm and 550 nm. Strain at break of PVDF, starch, PVDF/starch (2:1 w:w; P2S1), PVDF/starch (1:1 w:w; P1S1), PVDF/starch (1:2 w:w; P1S2), and nanofibers were 103 ± 11, 3 ± 0.6, 27 ± 4, 52 ± 5.2, 7.7 ± 1%, respectively. Based on strain at break and young modulus, P2S1 was selected as a suitable candidate for wound dressing to which load TiO2/CIP as a bioactive agent. The release rate of CIP showed that about 40% of the drug is released in the first 2 days. Furthermore, the antibacterial activity of dressings was investigated using Escherichia coli and Staphylococcus aureus microorganisms and zones of clearance were obvious around the specimen on the agar plate. © 2020 Wiley Periodicals, Inc. J. Appl. Polym. Sci. 2020, 137, 48916.
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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".