A response surface methodology study on the development of pH-sensitive wound dressings using Rhodamine B-loaded chitosan nanoparticles and sodium alginate-based films
Bibliographic record
Abstract
Purpose: This study aimed to develop an innovative, intelligent wound dressing capable of signaling infections through color changes. Design/methodology/approach: Using response surface methodology, the Rhodamine B fluorescence colorant was encapsulated within colloidal nanoparticles and integrated into a sodium alginate patch at various concentrations. The physical and chemical characteristics of the nanoparticles and the wound dressing were thoroughly analyzed via dynamic light scattering (DLS), zeta potential measurements, scanning electron microscopy (SEM), and Fourier transform infrared spectroscopy (FTIR). Additionally, the biodegradability, hydrophilicity, swelling behavior, release kinetics, porosity, mechanical properties, biocompatibility, and infection detection capability of the wound dressing were evaluated. Findings: The results indicated that the average diameter of the synthesized colloidal nanoparticles was 300 nm before loading with Rhodamine B and increased to 400 nm after loading, with zeta potentials of 52 mV and -6 mV, respectively. The Rhodamine B-loaded wound dressing demonstrated adequate levels of swelling and hydrophilicity. Release studies revealed the gradual release of Rhodamine B at low pH. Cytotoxicity assays confirmed the high biocompatibility of the engineered wound dressing with the L929 cell line. Furthermore, bacterial exposure experiments indicated that the color change was activated in the presence of infection, making it visible under UV-A light. Originality/value: This research presents a novel approach to wound care by developing a smart wound dressing that can detect infections via color changes. These findings underscore the potential of this innovative wound dressing to improve infection management in clinical settings through its responsive and biocompatible design.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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 | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".