Development and characterization of PVA-zein/α-tocopherol nonwoven mats for functional wound dressing applications
Why this work is in the frame
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Bibliographic record
Abstract
Wound healing poses significant clinical challenges due to issues like bacterial infections, oxidative stress, and the need for sustained therapeutic delivery. This study aimed to develop and characterize biocompatible nonwoven fibrous mats composed of poly(vinyl alcohol) (PVA) and zein encapsulating α-tocopherol for wound dressing applications. α-Tocopherol was nano-encapsulated in zein proteins using an antisolvent co-precipitation method, followed by its dispersion in PVA solutions. The resulting composition was then processed using a novel, scalable, and inexpensive solution blow spinning (SBS) process that offers higher throughputs to generate non-woven mats. The resulting fibers in the non-woven mats, ranging in diameter from 350 nm to 796 nm, demonstrate uniform morphology, as confirmed by scanning electron microscopy. Fourier transform infrared (FTIR) spectroscopy validated the successful incorporation of α-tocopherol without altering the chemical structure of the PVA-zein matrix. Rheological assessments revealed Newtonian behavior and a decrease in viscosity with higher tocopherol content, enhancing the processability of the mats. Mechanical testing showed that increasing tocopherol content improved tensile strength, elongation, and Young's modulus. The mats exhibited a biphasic release profile with an initial burst and sustained α-tocopherol release over 24 h, fitting the Korsmeyer-Peppas model and hence indicating a diffusion-controlled mechanism. Cytotoxicity assays confirmed high cell viability (>90 %) and enhanced cell spreading, underscoring their biocompatibility. These findings suggest that PVA-zein/tocopherol fiber mats are promising candidates for functional wound dressing materials, offering sustained antioxidant activity and a favorable wound healing environment. Future work will focus on optimizing fiber composition for antimicrobial properties and conducting in vivo studies to validate their clinical efficacy.
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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 it