Preparation and Characterization of Eco-Friendly Transparent Antibacterial Starch/Polyvinyl Alcohol Materials for Use as Wound-Dressing
Bibliographic record
Abstract
In this study, eco-friendly and transparent starch-based/polyvinyl alcohol/citric acid composite films are evaluated for their efficacy as wound dressing materials. The starch/polyvinyl alcohol (PVA) materials with added citric acid (0.46-1.83 wt%) and glycerol were made and handled based on the modified casting method. This new formulation decreases the amount of PVA used in the conventional preparation method. Citric acid ensures an appropriate antibacterial environment for wound-dressing materials. The mechanical, chemical, and surface morphological properties of such films were assessed and analyzed by tensile strength tests, UV-Vis spectrometry, swelling index, and scanning electron microscopy (SEM). Furthermore, the water vapor transmission (WVT) quantity was measured for an ideal wound-healing process to investigate an optimal moisture environment around the wound bed. Moreover, the pH level of the dressings was measured to examine the possibility of bacterial growth around these starch-based films. Additionally, the films' in-vitro antibacterial activities were studied against the two most common Gram-positive and Gram-negative bacteria (Escherichia coli and Staphylococcus aureus). The new starch-based dressings demonstrated suitable degradation, antibacterial activity, fluid absorption, and adequate mechanical strength, representing wound-dressing materials' vital features.
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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".