Carbon-dot nanozyme-empowered responsive hydrogels for smart wound dressing
Bibliographic record
Abstract
Smart wound dressings that enable real-time monitoring and responsive therapeutic delivery are essential for advancing personalized wound care. In this work, we present a scalable strategy to develop a multifunctional hydrogel patch by embedding carbon dots (C-dots) into an ionically crosslinked sodium alginate (SA) matrix. The C-dots serve dual roles by providing visible, pH-responsive fluorescence for wound monitoring and functioning as superoxide dismutase (SOD)-like nanozymes to scavenge reactive oxygen species (ROS). The patch’s optical transparency, pH sensitivity, swelling capacity, and mechanical strength can be tuned by varying the C-dot content. Structural analyses using scanning electron microscopy, Fourier-transform infrared spectroscopy, and synchrotron small-angle X-ray scattering reveal hydrogen bonding between C-dots and alginate chains, which underpins the dual stimulus-responsive behavior of the developed hydrogel patch. The patch autonomously releases C-dot nanozymes in response to microenvironmental cues and also allows on-demand release upon gentle mechanical pressure, addressing diverse wound care needs. In vitro studies confirm excellent cytocompatibility, enhanced fibroblast proliferation, and protection against ROS-induced damage. This smart, dual-responsive hydrogel patch presents a promising platform for next-generation wound dressings, suitable for chronic wound management and point-of-care wound management.
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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".