Catechol-functionalized hydrogels: biomimetic design, adhesion mechanism, and biomedical applications
Bibliographic record
Abstract
Hydrogels are a unique class of polymeric materials that possess an interconnected porous network across various length scales from nano- to macroscopic dimensions and exhibit remarkable structure-derived properties, including high surface area, an accommodating matrix, inherent flexibility, controllable mechanical strength, and excellent biocompatibility. Strong and robust adhesion between hydrogels and substrates is highly desirable for their integration into and subsequent performance in biomedical devices and systems. However, the adhesive behavior of hydrogels is severely weakened by the large amount of water that interacts with the adhesive groups reducing the interfacial interactions. The challenges of developing tough hydrogel-solid interfaces and robust bonding in wet conditions are analogous to the adhesion problems solved by marine organisms. Inspired by mussel adhesion, a variety of catechol-functionalized adhesive hydrogels have been developed, opening a door for the design of multi-functional platforms. This review is structured to give a comprehensive overview of adhesive hydrogels starting with the fundamental challenges of underwater adhesion, followed by synthetic approaches and fabrication techniques, as well as characterization methods, and finally their practical applications in tissue repair and regeneration, antifouling and antimicrobial applications, drug delivery, and cell encapsulation and delivery. Insights on these topics will provide rational guidelines for using nature's blueprints to develop hydrogel materials with advanced functionalities and uncompromised adhesive properties.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".