A pH-Indicating Colorimetric Tough Hydrogel Patch towards Applications in a Substrate for Smart Wound Dressings
Bibliographic record
Abstract
The physiological milieu of healthy skin is slightly acidic, with a pH value between 4 and 6, whereas for skin with chronic or infected wounds, the pH value is above 7.3. As testing pH value is an effective way to monitor the status of wounds, a novel smart hydrogel wound patch incorporating modified pH indicator dyes was developed in this study. Phenol red (PR), the dye molecule, was successfully modified with methacrylate (MA) to allow a copolymerization with the alginate/polyacrylamide (PAAm) hydrogel matrix. This covalent attachment prevented the dye from leaching out of the matrix. The prepared pH-responsive hydrogel patch exhibited a porous internal structure, excellent mechanical property, and high swelling ratio, as well as an appropriate water vapour transmission rate. Mechanical responses of alginate/P(AAm-MAPR) hydrogel patches under different calcium and water contents were also investigated to consider the case of exudate accumulation into hydrogels. Results showed that increased calcium amount and reduced water content significantly improved the Young's modulus and elongation at break of the hydrogels. These characteristics indicated the suitability of hydrogels as wound dressing materials. When pH increased, the color of the hydrogel patches underwent a transition from yellow (pH 5, 6 and 7) to orange (7.4 and 8), and finally to red (pH 9). This range of color change matches the clinically-meaningful pH range of chronic or infected wounds. Therefore, our developed hydrogels could be applied as promising wound dressing materials to monitor the wound healing process by a simple colorimetric display, thus providing a desirable substrate for printed electronics for smart wound dressing.
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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) | 0.001 | 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".