Analysis of Fatigue Crack Nucleation in Double-Network Hydrogels
Bibliographic record
Abstract
Hydrogel-based devices commonly have a high demand for material durability when subjected to prolonged or cyclic loads. To extend their service life, it is crucial to have a deep understanding of the fatigue mechanisms of hydrogels. It is well-known that double-network (DN) hydrogels are characterized by high strength and toughness and are thus recognized as a promising candidate under load-bearing conditions. However, the existing studies in the literature mainly focus on their resistant capability to fatigue crack growth, while the underlying mechanisms of fatigue crack nucleation are still inconclusive. This work aims to bridge this knowledge gap by formulating a fatigue life predictor for DN hydrogels within the framework of configurational mechanics to elucidate the underlying mechanisms governing fatigue crack nucleation. The fatigue life predictor for DN hydrogels is derived from the configurational stress by incorporating the corresponding constitutive models and the thermodynamic evolution laws for microdamage mechanisms and material viscoelasticity. With the developed fatigue predictor, the effect of the microdamage mechanism on fatigue is elucidated, i.e., the internal damage of the sacrificial network can improve the fatigue life of DN hydrogels. The fatigue life predictor is also adopted to evaluate the effects of some other factors on the fatigue crack nucleation, such as the loading rate, pre-stretching treatment, and water diffusion, identifying feasible loading profiles that could improve material durability. Overall, the theoretical framework and the modeling results in this work are expected to shed light on unveiling the fatigue mechanisms of DN hydrogels and advance the design of hydrogel-based devices.
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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 | 0.001 |
| 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".