Ultrasoft Iontronics: Stretchable Diodes Enabled by Ionically Conductive Bottlebrush Elastomers
Bibliographic record
Abstract
Abstract Inspired by the controlled ion migration found in biological systems, ionic diodes that regulate ion flow in iontronic systems have shown great potential for applications in human–machine interfaces, wearable, and implantable devices. However, developing biointegrable ionic diodes with mechanical compliance to biological tissues remains challenging due to the limited availability of ultrasoft materials. Although hydrogel‐based diodes can achieve ultrasoftness, they suffer from dehydration, resulting in instability in mechanical and electrical performance. Here, a solvent‐free, ultrasoft, and stretchable ionic diode enabled by oppositely charged bottlebrush elastomers (BBEs) is presented. The nanostructure of bottlebrush polymers allows the crosslinked BBE diode to achieve tissue‐matched softness. Meanwhile, copolymerized ionic liquids ensure stable ionic conductivity by preventing leaching and thermal evaporation. The BBE diode achieves an ultralow Young's modulus (<23 kPa), stretchability exceeding 400%, and a high rectification ratio of 46. To our knowledge, this is the softest ionic diode ever reported. Its functionality in ionic circuits is demonstrated, including full‐wave rectifiers and logic gates. Furthermore, it is integrated into self‐powered biointerfacing devices for strain sensing, evaluated through finger flexion, eye blinking, and stomach volume changes in an ex vivo model. These results highlight its potential for soft iontronic systems and next‐generation bioelectronic interfaces.
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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 | 0.001 |
| 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".