Controlled Hydrogel Surfaces Adhesion via Macrophase Separation Polymerization Triggered by Electrostatic Interaction for Wound Dressing and Bio‐Sensor
Bibliographic record
Abstract
Abstract Asymmetrical hydrogels with selective tissue adhesion represent a significant advancement in biomaterials for preventing postoperative adhesion and restoring internal tissues. In this work, a one‐step macrophase separation polymerization method triggered by electrostatic interactions is developed to fabricate asymmetrical hydrogels (denoted as QAD). Inspired by barnacle cement proteins, phenylboronic acid is incorporated into the top surface of the hydrogel for strong wet tissue adhesion. Meanwhile, quaternary ammonium chitosan (QCS) functionalized with zwitterions and acrylic acids formed bulky monomers, which then underwent macrophase separation polymerization and sank down to achieve the bottom surface with non‐adhesion. Such hydrogels not only effectively mitigated the challenges of postoperative adhesion, but also exhibited excellent hemostatic properties, thereby reducing the bleeding from 243 mg (gauze) to 16.9 mg. Over a period of 14 days, these hydrogels achieved a remarkably enhanced repair rate of 96.7%, as opposed to 85.6% in the control group. Moreover, an abundant quantity of free ions within the QAD hydrogel endowed it with the capacity to record pulse signal waveforms and convert throat sounds into electrical signals. In summary, this research presents a novel approach to asymmetrical hydrogels, offering promising solutions for adhesion prevention, wound management, and clinical monitoring.
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How this classification was reachedexpand
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 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".