Catalyst-free engineered robust cellulose ionogel for high-performance ionotronic devices
Bibliographic record
Abstract
Ionogels, a newly emerging type of gel material, are considered the most attractive candidate for constructing the next-generation ionotronic devices in the Internet of Things era. However, building robust and sustainable ionogels toward high-performance ionotronic devices in broad scenarios remains a huge challenge. Herein, a mechanically robust cellulose ionogel (RCI) via the facile “catalyst-free” yet chemically cross-linked engineering of cellulose molecules was developed. More specifically, ionic liquid, a typical cellulose solvent, and an ion-conductive component of cellulose ionogel were employed to afford the proton and replace the conventional, additional chemical catalyst, which indeed triggers the chemical reactions between cellulose and glutaraldehyde molecules, and thus creates the chemical-bonded, robust cellulose network of RCI. The prepared RCI (0.4 g glutaraldehyde to 0.6 g cellulose) demonstrated surprisingly high strength of ∼11 MPa with 1 000% improvement and toughness of 2.8 MJ/m 3 with 700% increase compared to the original cellulose ionogel (CI), as well as acceptable conductivity of 29.1 ms/cm, surpassing most ionogel materials. Such RCI easily constructed versatile ionotronic devices with unexpected voltage-pressure sensitivity, wide-range loading, and linear and steady-state output for self-powered, body motion, human health, and Morse-code information communication applications. The catalyst-free engineering paves the way toward easy-to-prepare, robust, and promising ionogels in our sustainable society, beyond the cellulose material.
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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 | 0.000 |
| 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".