Tough Hydrogels: Toughening Mechanisms and Their Utilization in Stretchable Electronics and in Regenerative Medicines
Bibliographic record
Abstract
Synthetic hydrogels, the key material for use in contact lenses, wound dressings, cosmetics, and scaffolds for tissue engineering, have been considered mechanically weak and brittle materials. Inspired by natural hydrogels that are mechanically tough and robust, such as muscles and cartilages, there has been extensive research in improving mechanical strengths of hydrogels during the last decade. Consequently, proof-of-concept devices of many unprecedented applications, such as bioimplantable electronics, sensors/actuators in soft machines, surgical glues, and gel electrolytes for energy storage devices, are rapidly realized in recent years. In this chapter, we intend to review a comprehensive perspective regarding the field of tough hydrogels: from physical and chemical fundamentals to applications in various fields. We review the concept of fracture toughness to explain design criteria for tough hydrogels. Sticky hydrogel, a promising subgroup of tough hydrogels, requires distinctive chemistry and characterization methods, so it is separately reviewed. Incorporating nanoscale hard material can add desirable mechanical and functional properties to tough hydrogels. We review various interface modification techniques for the hard/soft material integration, as well as for electronic device integration. Finally, advances in the applications of tough hydrogels in stretchable electronics and soft robotics and in regenerative medicine are reviewed.
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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 | 0.001 | 0.001 |
| 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) | 0.005 | 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".