Optimizing Biodegradable Starch-Based Composite Films Formulation for Wound-Dressing Applications
Bibliographic record
Abstract
This paper utilizes response surface methodology (RSM) design-based analyses to optimize starch-based wound dressings that were characterized based on weight loss (WL%), swelling index (SI%), and mechanical strength (TS). The wound-dressing materials were prepared by employing a modified casting method, using various concentrations of starch (0.5–2 w/w%), polyvinyl alcohol (PVA) (0.5–2 weight%), citric acid (1.5–4 weight%), and glycerol (1.5–4 weight%) to yield wound-dressing films with appropriate combinations of in vitro degradation, swelling index, and tensile strength. As a result of the response surface method-based analysis, the swelling index, in vitro degradation, and tensile strength were linearly related to variations in the formulation of organic components. Based on our experimental investigations, the optimized film (formulation: 1 weight% PVA, 2 weight% starch, 1.5 weight% citric acid, and 1.5 weight% glycerol) exhibited an outstanding swelling index (343.52%), suitable in vitro degradation (53.22%), and excellent tensile strength (8.82 MPa). The response surface plots for the dependent variables, swelling index (SI%), weight loss (WL%), and mechanical strength (TS), showed that in all dual relations, the PVA-starch combination significantly affected all dependent variables; however, the PVA-citric acid interaction showed the most excellent effect on the swelling index. As a result, every component of the resulting film had a lesser amount of all ingredients to achieve better properties at a lower material cost. Starch-based/PVA films have been identified in this paper as optimal and more affordable wound-dressing films.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
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 | 0.001 | 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 teacher head, 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".