Recent progress in synthesis and application of mussel-inspired adhesives
Bibliographic record
Abstract
The rapid and robust adhesion of marine mussels to diverse solid surfaces in wet environments is mediated by the secreted mussel adhesive proteins which are abundant in a catecholic amino acid, l-3,4-dihydroxyphenylalanine (Dopa). Over the last two decades, enormous efforts have been devoted to the development of synthetic mussel-inspired adhesives with water-resistant adhesion and cohesion properties by modifying polymer systems with Dopa and its analogues. In the present review, an overview of the unique features of various mussel foot proteins is provided in combination with an up-to-date understanding of catechol chemistry, which contributes to the strong interfacial binding via balancing a variety of covalent and noncovalent interactions including oxidative cross-linking, electrostatic interaction, metal-catechol coordination, hydrogen bonding, hydrophobic interactions and π-π/cation-π interactions. The recent developments of novel Dopa-containing adhesives with on-demand mechanical properties and other functionalities are then summarized under four broad categories: viscous coacervated adhesives, soft adhesive hydrogels, smart adhesives, and stiff adhesive polyesters, where their emerging applications in engineering, biological and biomedical fields are discussed. Limitations of the developed adhesives are identified and future research perspectives in this field are proposed.
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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 | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".