Refinement of Crystalline Domains: A Strategy To Toughen Conductive Hydrogels
Bibliographic record
Abstract
The characteristic of possessing both high strength and flexibility is a requisite for the extensive application of conductive hydrogels in the domains of flexible wearable electronic devices and implantable biomedical fields. Nevertheless, the existing strategies for optimizing mechanical properties invariably enhance the strength at the expense of stretchability, thereby causing the hydrogels to still possess relatively low toughness. Herein, we propose a crystalline refinement-driven toughening strategy that overcomes this paradigm, achieving strength of 13.4 ± 1.1 MPa, fracture strain of 2337 ± 246.2%, and toughness of 164.6 ± 25.6 MJ·m –3 . The crux of maintaining flexibility while elevating strength lies in perfecting the energy dissipation mechanism with the concurrent preservation of molecular chain extensibility. To achieve strength–toughness synergy in hydrogel systems, a crystalline domain-featured layered architecture is assembled via a controlled drying process, followed by precise modulation of domain dimensions through high-density hydrogen-bond networks during rehydration, ultimately engineering a hierarchical structure dominated by refined crystalline domains. The hydrogel’s good mechanical properties impart exceptional lubricity and robust sensing capabilities, underscoring its promising applicability in biomedical and wearable device technologies.
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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 | 0.000 |
| 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 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".