A semi-transparent strong biomimetic wound healing material: zinc oxide and sodium alginate based bi-layer nanofiber membrane
Bibliographic record
Abstract
Abstract Wound healing is a critical process that significantly impacts patient health and places a substantial burden on healthcare systems. This study aimed to develop and evaluate transparent composite nanofibrous membranes with enhanced biological functionality as advanced wound dressing materials. We hypothesized that the incorporation of sodium alginate (NaAlg) or zinc oxide (ZnO) into electrospun polymer nanofibers, combined with the use of a conductive aluminum mesh during fiber collection, would result in membranes with locally aligned nanofibers, enabling optical transparency, biocompatibility, and mechanical properties. By using a 1.58 mm aluminum square mesh as a fiber collector during electrospinning of polycaprolactone (PCL)/ethylene vinyl alcohol (EVOH), we fabricated composite fiber membranes with varying concentrations of NaAlg (1–5% w/v) or ZnO (1–3% w/v). The use of the conductive mesh led to partial alignment of the nanofibers, enhancing light transmission and achieving notable optical transparency (up to 40% for NaAlg and 22% for ZnO). These membranes also exhibited a bi-layer structural configuration, robust mechanical properties (12–13 MPa), and optimal water vapor transmission rates (WVTR, 1400–1700 g/m $$^{2}$$ 2 /day). Biological assessments, including disc diffusion and cytotoxicity tests, demonstrated excellent biocompatibility (85–100% viability with HaCaT cells) and promising blood-clotting properties. These findings suggest that the developed nanofiber membranes, through their unique alignment-driven transparency and multifunctionality, can effectively monitor wound healing in real-time, absorb substantial exudate, and provide a protective barrier against environmental contaminants. This work highlights the novelty and potential of these nanofiber membranes as advanced biomaterials for diverse wound dressing applications. Graphical abstract
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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".