Unraveling bio-inspired pre-swollen effects of tetra-polyethylene glycol double network hydrogels with ultra-stretchable yielding strain
Bibliographic record
Abstract
Abstract Incorporating flexible cross-links into a brittle network for hydrogel not only significantly improves its toughness, but also effectively provides opportunities to design novel double network (DN) hydrogels with targeted properties and multi-functionalities. However, the principles and toughening mechanisms behind many of experimental studies have not been investigated. In this paper we proposed, for the first time, a phenomenological model for the DN hydrogels, which were derived from tetra-polyethylene glycol (tetra-PEG) first network, molecular stent and polyacrylamide (PAAm) second network, and theoretically and experimentally studied the pre-swollen effects on their ultra-stretchable yielding strains. Based on the Flory–Huggins solution theory, the pre-swollen effect of polar molecular stents on the mechanical properties of tetra-PEG/PAAm DN hydrogels was investigated. Finally, the constitutive stress–strain relationships of two-stage loading process were established for the overall response of mechanical behavior, and a good agreement between the numerically simulated results with the experimental ones has been achieved. This study provides a fundamental understanding of the working mechanism of pre-swollen effects and the design guidance for the ultra-stretchable and toughened DN 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 | 0.000 | 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".