A predictor for fatigue crack nucleation in neutral hydrogels consideringwater diffusion effect
Bibliographic record
Abstract
Due to excellent biocompatibility and superior flexibility, hydrogels have shown promise in extensive applications, including soft robotics, drug delivery, and tissue engineering, etc. When hydrogels are subjected to cyclic loads, they are susceptible to fatigue. For durable use of hydrogel instruments, it is essential to investigate their fatigue behaviors. Typical approaches to studying the fatigue of soft materials are based on crack nucleation and crack growth. Most existing work focuses on experimental characterization of crack growth under mechanical loads. However, very few models are proposed for predicting the fatigue damage of hydrogels from the perspective of crack nucleation. Moreover, there is limited work taking into account the impact of water diffusion on the crack nucleation of hydrogels. The present work will develop a fatigue life predictor for the fatigue crack nucleation of single-network neutral hydrogels and unveil the water diffusion effect on fatigue. Borrowing the concept of fatigue crack nucleation from rubber-like materials, the predictor is developed in the framework of configurational mechanics. With the developed predictor, case studies show that water absorption contributes to the fatigue of hydrogels and decreases their fatigue life. The swelling-induced fatigue damage increases with the increase of water content from both the preparation and the loading processes. In addition, the swelling-induced fatigue damage exhibits a rate-dependent trend, i.e., slow deformation induces more fatigue damage than rapid deformation due to more sufficient time for water diffusion. The predictor is capable of estimating the fatigue damage of hydrogels under different loading conditions, providing guidance on designing the loading profiles to improve their fatigue life. Furthermore, by incorporating material viscosity and multiphysics coupling theories, the proposed modeling framework can be further expanded to investigate the fatigue of many other hydrogels, like double-network and stimuli-sensitive hydrogels.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".