Environment‐tolerant ionic hydrogel–elastomer hybrids with robust interfaces, high transparence, and biocompatibility for a mechanical–thermal multimode sensor
Why this work is in the frame
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Bibliographic record
Abstract
Abstract The human skin, an important sensory organ, responds sensitively to external stimuli under various harsh conditions. However, the simultaneous achievement of mechanical/thermal sensitivity and extreme environmental tolerance remains an enormous challenge for skin‐like hydrogel‐based sensors. In this study, a novel skin‐inspired hydrogel–elastomer hybrid with a sandwich structure and strong interfacial bonding for mechanical–thermal multimode sensing applications is developed. An inner‐layered ionic hydrogel with a semi‐interpenetrating network is prepared using sodium carboxymethyl cellulose (CMC) as a nanofiller, lithium chloride (LiCl) as an ionic transport conductor, and polyacrylamide (PAM) as a polymer matrix. The outer‐layered polydimethylsiloxane (PDMS) elastomers fully encapsulating the hydrogel endow the hybrids with improved mechanical properties, intrinsic waterproofness, and long‐term water retention (>98%). The silane modification of the hydrogels and elastomers imparts the hybrids with enhanced interfacial bonding strength and integrity. The hybrids exhibit a high transmittance (~91.2%), fatigue resistance, and biocompatibility. The multifunctional sensors assembled from the hybrids realize real‐time temperature (temperature coefficient of resistance, approximately −1.1% °C −1 ) responsiveness, wide‐range strain sensing capability (gauge factor, ~3.8) over a wide temperature range (from −20°C to 60°C), and underwater information transmission. Notably, the dual‐parameter sensor can recognize the superimposed signals of temperature and strain. The designed prototype sensor arrays can detect the magnitude and spatial distribution of forces and temperatures. The comprehensive performance of the sensor prepared via a facile method is superior to that of most similar sensors previously reported. Finally, this study develops a new material platform for monitoring human health in extreme environments. image
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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 | 0.000 | 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 it