Adhesive, Antibacterial, Conductive, Anti-UV, Self-Healing, and Tough Collagen-Based Hydrogels from a Pyrogallol-Ag Self-Catalysis System
Bibliographic record
Abstract
Recently, versatile hydrogels with multifunctionality have been widely developed with emerging applications as wearable and implantable devices. In this work, we reported novel versatile hydrogels by self-catalyzing the gelation of an interpenetrating polymer network consisting of acrylic acid (AA) monomers and GA-modified collagen (GCOL) in situ decorated silver nanoparticles (AgNPs). The resultant hydrogel, namely AgNP@GCOL/PAA, has many desirable features, including good mechanical properties (such as 123 kPa, 916%, and 1961 J m −2 for the fracture stress, strain and tearing energy) that match with those of animal skin, excellent self-healing performance, favorable conductivity and strain sensitivity as a flexible biosensor, and excellent antibacterial and anti-UV properties, as well as the strong adhesiveness on skin. Moreover, AgNP@GCOL/PAA showed excellent biocompatibility via in vitro cell culture. Remarkably, AgNP@GCOL/PAA displayed superior hemostatic properties with sharply decreasing blood loss for a mouse liver incision, closely related to its strong self-adhesion which produced anchoring strength to the bleeding site and thus formed a network barrier with liver tissue. This study provides new opportunities for the facile preparation of widely used multifunctional collagen-based hydrogels based on a simple pyrogallol-Ag system.
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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) | 0.000 | 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 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".