<scp>Mussel‐Inspired</scp> Adhesive Hydrogels: Chemistry and Biomedical Applications<sup>†</sup>
Bibliographic record
Abstract
Comprehensive Summary Adhesive hydrogels are an emerging class of hydrogels that combine three‐dimensional hydrated networks with adhesive properties. These properties facilitate intimate tissue‐material contact in diverse biomedical applications, enhancing tissue joining, drug transport, and signal transmission. Inspired by the universal adhesiveness of mussel foot proteins, 3,4‐dihydroxyphenyl‐ L ‐alanine (DOPA) and its analogs have been extensively exploited for the fabrication of adhesive hydrogels, within which the DOPA moieties can not only serve as cross‐linking mediators but also participate in various intermolecular and surface interactions to mediate wet adhesion. This mini‐review highlights recent achievements in the development of mussel‐inspired adhesive hydrogels, focusing on: (1) elucidating DOPA‐mediated adhesion mechanisms through nanomechanical characterizations, (2) designing injectable adhesive hydrogels toward applications in drug delivery, hemostasis, and wound closure, which includes in situ gelling liquids and shear‐thinning preformed hydrogels, and (3) fabricating tough adhesive hydrogels with enhanced mechanical properties for use in tissue regeneration, biosensing, and bioimaging, with typical examples of nanocomposite and double‐network hydrogels. The challenges and prospects in this rapidly developing field are also discussed.
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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) | 0.000 | 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) | 0.006 | 0.002 |
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".