A Flexible Ionic Polymer for “Soft Machines” – Where is the Low Temperature Limit?
Bibliographic record
Abstract
Abstract Soft wearable robotics, electronic skins, exoskeletons, implantable drug delivery, medical sensors, fitness trackers as well as flexible power sources (batteries, capacitors, fuel cells) are emerging technologies that require significant innovation to succeed. Conducting hydrogels are one of the promising flexible platforms that can adopt a modular specification of wearable electronics. However, a common difficulty arises from incorporation of water that serves as main facilitator of ionic conductivity for these materials. This results in severe loss of performance when need to operate below freezing point of water. This work shows that the hydrogel with 50 vol % of glycerol with respect to water content can nominally support a modest weight at room temperature (360 g), and a significantly higher weight (1.5 kg) when cooled to −60 °C. Unlike other formulations, this hydrogel remained transparent at extremely low temperatures and thus could be useful in flexible optical devices. At −20 °C, conductivity of hydrogel without anti‐freeze drops below 2×10−4 S cm−1, with 25–50 vol % of glycerol ranging at ∼0.5 to 4×10−3 S cm−1. Furthermore, at −40 °C the hydrogel without glycerol becomes an insulator (∼2×10−6 S cm−1), and the one with 50 vol % glycerol shows ionic conductivity in the range of 1–4 ×10−4 S cm−1. Altogether, we define the operational temperature limit at −40 °C with respect to the ionic conductivity and at −60 °C in terms of sufficient mechanical strength for the hydrogel containing 50 vol % glycerol.
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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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".