Rational Design of Self-Healing Tough Hydrogels: A Mini Review
Bibliographic record
Abstract
Hydrogels are three-dimensional cross-linked polymer networks which can absorb and retain large amount of water. As representative soft materials with tunable chemical, physical and biological properties, hydrogels with different functions have been developed and utilized in a broad range of applications, from tissue engineering to soft robotics. However, conventional hydrogels usually suffer from weak mechanical properties and they are easily deformed or damaged when they are subjected to mechanical forces. The accumulation of the damage may lead to the permanent structural change and the loss of the functional properties of the hydrogels. Therefore, it is important to develop mechanically robust hydrogels with autonomous self-healing property in order to extend their lifespan for various applications. In this mini review, we focus on the discussion about the appropriate molecular design of the hydrogel network for achieving self-healing and excellent mechanical properties, respectively as well as the corresponding self-healing and toughening mechanisms. We conclude with perspectives on the remaining challenges in the field as well as the recommendations for future development.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".