Stretchable and Bioadhesive Gelatin Methacryloyl-Based Hydrogels Enabled by <i>in Situ</i> Dopamine Polymerization
Bibliographic record
Abstract
Hydrogel patches with high toughness, stretchability, and adhesive properties are critical to healthcare applications including wound dressings and wearable devices. Gelatin methacryloyl (GelMA) provides a highly biocompatible and accessible hydrogel platform. However, low tissue adhesion and poor mechanical properties of cross-linked GelMA patches ( i.e ., brittleness and low stretchability) have been major obstacles to their application for sealing and repair of wounds. Here, we show that adding dopamine (DA) moieties in larger quantities than those of conjugated counterparts to the GelMA prepolymer solution followed by alkaline DA oxidation could result in robust mechanical and adhesive properties in GelMA-based hydrogels. In this way, cross-linked patches with ∼140% stretchability and ∼19 000 J/m 3 toughness, which correspond to ∼5.7 and ∼3.3× improvement, respectively, compared to that of GelMA controls, were obtained. The DA oxidization in the prepolymer solution was found to play an important role in activating adhesive properties of cross-linked GelMA patches (∼4.0 and ∼6.9× increase in adhesion force under tensile and shear modes, respectively) due to the presence of reactive oxidized quinone species. We further conducted a parametric study on the factors such as UV light parameters, the photoinitiator type ( i.e ., lithium phenyl-2,4,6-trimethylbenzoylphosphinate, LAP, versus 2-hydroxy-4′-(2-hydroxyethoxy)-2-methylpropiophenone, Irgacure 2959), and alkaline DA oxidation to tune the cross-linking density and thereby hydrogel compliance for better adhesive properties. The superior adhesion performance of the resulting hydrogel along with in vitro cytocompatibility demonstrated its potential for use in skin-attachable substrates.
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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".