Tough and Ultrastretchable Liquid‐Free Ion Conductor Strengthened by Deep Eutectic Solvent Hydrolyzed Cellulose Microfibers
Bibliographic record
Abstract
Abstract Ion conductors (ICs) have gained extensive research interest in various advanced application scenarios including sensors, batteries, and supercapacitors. However, stretchable, tough, and long‐term stable ICs are still hard to achieve yet highly demanded. In this study, the authors propose a one‐pot green and sustainable fabrication of cellulose based ICs through polymerizable deep eutectic solvents treated cellulose followed by an in situ photo‐polymerization. The obtained ICs exhibit extremely high stretchability (3210 ± 302%), high toughness (13.17 ± 2.32 MJ m −3 ), high transparency, and self‐healing ability. Notably, the introduction of cellulose fibers greatly enhances the mechanical properties of ICs while eliminating the environmental concerns of traditional nanocellulose fabrication process. More importantly, the ICs possess good long‐term performance stability after 1 month storage. Due to these outstanding properties, the feasibility of applying ICs in human motion sensing and physiological signal detecting is demonstrated. This simple and green method will contribute to the development of tough, self‐healing, transparent, and long‐term stable ICs.
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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".