A pH-Indicating Colorimetric Tough Hydrogel Patch towards Applications in a Substrate for Smart Wound Dressings
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
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 | 0.000 | 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 it