Ultrastretchable Ionogel with Extreme Environmental Resilience through Controlled Hydration Interactions
Bibliographic record
Abstract
Abstract Ionic conductive gels are widely sought after for applications that require reliable ionic conduction and mechanical performance under extreme conditions, which remains a grand challenge. To address this limitation, water‐induced hydration interactions are deliberately controlled within the ionic liquid (IL)‐based conductive gels (ionogels) to achieve all‐round performance. Specifically, the competitive interactions between IL, water and cellulose nanofibrils (CNF) are balanced to preserve the nanoscale morphology of CNF while avoiding its dissolution. As a result, both mechanical performance and ionic conductivity of the resultant ionogel are synergistically enhanced. For instance, an ultra stretchable ionogel (up to 10250 ± 412% stretchability) with both high toughness (21.8 ± 0.9 MJ m −3 ) and ionic conductivity (0.70 ± 0.06 S m −1 ) is achieved. Furthermore, multimodal sensing functions (strain, compression, temperature, and humidity) are realized by assembling the ionogel as a skin‐like membrane. Due to the low volatility of IL and its strong interaction with water, the ionogel maintains an excellent performance at either ultra‐low temperature (−45 °C), high temperature (75 °C) or low humidity environment (RH < 15%), demonstrating superb anti‐freezing and anti‐drying performance. Overall, a simple yet versatile strategy is introduced that leads to environmentally resilient ionogels to meet the requirements of next‐generation electroactive devices.
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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) | 0.001 | 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".