Luminescent Electro‐Spun Nanofibers Crosslinked with Boronic Esters Exhibiting Controlled Release of Carbon Dots for Detection of Wound pHs and Enhanced Antimicrobial
Bibliographic record
Abstract
Timely and accurate assessment of wounds during the healing process is crucial for proper diagnosis and treatment. Conventional wound dressings lack both real-time monitoring capabilities and active therapeutic functionalities, limiting their effectiveness in dynamic wound environments. Herein, we report our proof-of-concept approach exploring the unique emission properties and antimicrobial activities of carbon nanodots (CNDs) for simultaneous detection and treatment of bacteria. This approach centers on the fabrication of well-defined CND-embedded poly(vinyl alcohol) (PVA) e-spun nanofibrous mats, which are crosslinked with degradable boronic ester (BE) crosslinks. The BE-CND/PVA mats exhibit stimuli-responsive degradation to pHs and hydrogen peroxide as well as pH-responsive release of CNDs. Promisingly, the mats turn out to be hemocompatible with blood and biocompatible with skin cells. Furthermore, they exhibit notable antimicrobial activity against Gram-negative bacteria and demonstrate great potential for real-time monitoring of wound pH to assess the wound status. These results suggest that BE-CND/PVA mats could significantly enhance wound healing by providing localized therapeutic action, reducing the risk of bacterial infections, and enabling non-invasive monitoring of wound progress.
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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".