Cellulose-mediated ionic liquid crystallization enables tough-stiff switchable ionogels
Bibliographic record
Abstract
Nature has inspired to fabricate mechanically switchable materials for applications in various aspects, which is, however, unique but challenging to achieve reversible phase transitions using common ionic liquids in ionogels with ambient temperature-triggered crystallization feature. Here, we develop a tough-stiff switchable ionogel through a reversible solvent crystallization design. Cellulose acts as a chemical regulator, competitively binding with polymers to promote the formation of ionic liquid crystals. This results in a tough ionogel with a bulk toughness of 25.7 MJ m−3 and a fracture toughness of 47.1 kJ m−2, which can switch into a stiff ionogel with a tensile modulus of 134.6 MPa and a compressive modulus of 48.9 MPa. Upon heating, the crystallized ionogel reverts to its unconfined as ionic liquid crystals melt. This phase-driven structural and rigidity transition enables dynamical programming, with rapid, reversible and repeatable shape recovery through heating. Our study demonstrates solvent crystallization in ionogels, offering a strategy for creating intelligent, reconfigurable, and performance-switchable materials with customizable functions. Current ionic liquid-based switchable ionogels rely on fluorinated chemicals and require cooling to low temperatures to induce crystallization. Here, the authors use an ionic liquid to create a cellulose containing ionogel with tough-stiff switchable properties through a reversible room temperature crystallization process.
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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 | 0.001 |
| 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".