Bioinspired Printable Tough Adhesives with In Situ Benignly Triggered Mechanical Enhancements
Bibliographic record
Abstract
Abstract Adhesives can intimately connect humans to machines, seamlessly bond diverse tissues in the human body, and manage various diseases. However, the precise spatial control of wet and tough adhesion of biocompatible hydrogels on biological tissues remains a major challenge. Inspired by the bioglue secreted by sandcastle worms, the design of printable tough adhesives (PTAs) is proposed, a supramolecular hydrogel that can be printed into defined structures, in situ mechanically reinforced into a tough matrix with physiologically relevant benign triggers, and strongly adhere to diverse substrates. With carefully selected polymer components and ratios, it is discovered that the 3D printed PTAs can achieve a marked increase in toughness, tensile strength, and stiffness after being immersed in water/saline solution or attached to biological tissues. To assess the robust toughening mechanism triggered by the supramolecular interactions, the effects of polymer content and pH on the mechanical performance of PTAs and the kinetics of their triggered reinforcement are thoroughly investigated. The potential of PTAs is further demonstrated for manufacturing tough connective tissue mimetics, controlling patterned bioadhesion, and designing programmable 4D soft robotics. The bioinspired, printable, benignly triggerable, and adhesive supramolecular PTAs are expected to find broad applications in engineering and medicine.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".