Duplicating Dynamic Strain-Stiffening Behavior and Nanomechanics of Biological Tissues in a Synthetic Self-Healing Flexible Network Hydrogel
Bibliographic record
Abstract
Biological tissues can accurately differentiate external mechanical stresses and actively select suitable strategies (e.g., reversible strain-stiffening, self-healing) to sustain or restore their integrity and related functionalities as required. Synthetic materials that can imitate the characteristics of biological tissues have a wide range of engineering and bioengineering applications. However, no success has been demonstrated to realize such strain-stiffening behavior in synthetic networks, particularly using flexible polymers, which has remained a great challenge. Here, we present one such synthetic hydrogel material prepared from two flexible polymers (polyethylene glycol and branched polyethylenimine) that exhibits both strain-stiffening and self-healing capabilities. The developed synthetic hydrogel network not only mimics the main features of biological mechanically responsive systems but also autonomously self-heals after becoming damaged, thereby recovering its full capacity to perform its normal physiological functions.
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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 | 0.000 | 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 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".