A Universal Strategy to Mitigate Microphase Separation via Cellulose Nanocrystal Hydration in Fabricating Strong, Tough, and Fatigue‐Resistant Hydrogels
Bibliographic record
Abstract
Abstract As a common natural phenomenon, phase separation is exploited for the development of high‐performance hydrogels. Using supersaturated salt to create microphase‐separated hydrogels with strengthened mechanical properties has gained widespread attention. However, such strengthened hydrogel loses its intrinsic flexibility, making the phase separation strategy unsuitable for the fabrication of stretchable and tough hydrogels. Here, a phase‐engineering design strategy is introduced to produce stretchable yet tough hydrogels using supersaturated NaAc salt, by leveraging the hydration effect of cellulose nanocrystal (CNC) to mitigate microphase separation. The CNC‐mitigated microphase‐separated hydrogel presents unprecedented mechanical properties, for example, tensile strength of 1.8 MPa with a fracture strain of 4730%, toughness of 43.1 MJ m −3 , fracture energy of 75.4 kJ m −2 , and fatigue threshold up to 3884.7 J m −2 . Furthermore, this approach is universal in synthesizing various microphase separation‐enhanced polymer gels, including polyacrylic acid, poly(acrylic acid‐co‐acrylamide), gelatin, and alginate. These advancements provide insights into the incorporation of CNC‐mediated microphase separation structures in hydrogels, which will foster the future development of high‐performance soft materials.
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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 | 0.001 | 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 | 0.001 |
| 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".