Nanowhisker glues for fatigue-resistant bioadhesion and interfacial functionalization
Bibliographic record
Abstract
Fatigue-resistant functional bioadhesion is desired in diverse applications ranging from wound management to wearable devices. Nanoparticle-based bioadhesives offer versatile functionality but suffer from weak adhesion and fatigue vulnerability due to tissue barriers and poor tissue interactions. Polymer chain-based bioadhesives can form tough bioadhesion but remain vulnerable to fatigue fracture. Here we demonstrate that rationally designed chitosan nanowhiskers glues achieve fatigue-resistant bioadhesion and interfacial functionalization via the combined high aspect ratio, rigidity, polymer-binding and network-forming properties. We deploy these glues using chemical enhancers, microneedle rollers, and ultrasound, enabling strong tissue anchorage despite tissue barriers. At low concentrations, the nanowhisker glue paired with a tough hydrogel achieves an interfacial fatigue threshold of 382 J m−2 and adhesion energy exceeding 1000 J m−2. Transmission electron microscopy reveals a sandwiched nanowhisker layer interpenetrated with both hydrogels and tissues, creating an interface of high stiffness and strength that kinks and arrests interfacial cracks, ensuring unprecedented fatigue resistance. Further, the nanowhisker glue allows for versatile functionalization at the interface such as photothermal and sonodynamic effects. This work expands the performance and functionality of bioadhesives, opening new possibilities for medical and engineering applications. Fatigue-resistant functional bioadhesion is desired in diverse applications ranging from wound management to wearable devices but remains elusive. Here, the authors report the design, deployment, and mechanism of nanowhisker glues for fatigue-resistant functional bioadhesion.
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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 | 0.001 | 0.001 |
| 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".