Ultrasoft, Elastic, and Ionically Conductive Polyethylene Glycol/Ionic Liquid Bottlebrush Ionogels
Bibliographic record
Abstract
Ultrasoft conductors have revolutionized the field of electronics by achieving a level of softness comparable to that of biological tissues. However, the inherent difference in charge carriers between conventional ultrasoft electronics (utilizing electrons) and tissues (utilizing ions) could yield high contact impedance, hindering electronic performance for physiological signal recordings. Although ionic hydrogels exhibit ionic conductivity, their high-water content could limit their practical applications. This study proposes a new type of ultrasoft and ionically conductive bottlebrush ionogels (BBIs), leveraging polyethylene glycol (PEG) bottlebrushes and ionic liquids (ILs). The incorporation of IL into PEG bottlebrushes results in a simultaneous enhancement of compliance and ionic conductivity. Specifically, the PEG/IL BBI achieves a Young's modulus of 1.08 kPa, akin to the softest biological tissues such as the brain. To the best of our knowledge, this is the softest ionic conductor ever reported. The introduction of ionic liquids enables an ionic conductivity of 0.14 S/m, rendering it well-suited for integration into ultrasoft electronics. The PEG/IL BBI was further applied as sensors on silkworms and as electrodes on the Venus flytrap and human body. These applications facilitated electrocardiogram recordings and plant signal monitoring, showcasing the potential of this innovative ionically conductive BBI in various physiological environments.
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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) | 0.000 | 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 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".