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March 3, 2026Advanced Science3 citationsOpen Access

Ultrahigh‐Linear Bio‐Inspired Janus Elastomeric Strain Sensor with High Sensitivity and Stretchability via Surface Wrinkle Engineering

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JLJing LinYLYe LiSYSimi Yu

Key Points

  • The sensor achieves an ultra-high linearity with R² greater than 0.999 and sensitivity with gauge factors exceeding 14.
  • Designed with a micro-wrinkled natural rubber and graphene top layer, it addresses key challenges in strain sensing.
  • Leveraging a combination of wrinkle-guided microcrack and parallel conductive circuits enhances performance across various strain levels.
  • Potential applications include human motion detection and intelligent systems, indicating broad utility in wearable devices.

Abstract

In pursuit of high-performance flexible strain sensors, achieving an optimal trade-off among linearity, sensitivity, and strain sensing range remains a critical challenge. Inspired by the wrinkled-leaf viburnum, we develop a Janus sensor that replicates its asymmetric structure. It comprises a dense, micro-wrinkled natural rubber (NR)/graphene (GRs) top layer and a loose NR/carbon nanotubes (CNTs) bottom layer, fabricated via facile layer-by-layer filtration and pre-stretching strategy. This bio-inspired design enables the sensor with a synergistic sensing mechanism: wrinkle-guided microcrack ensures highly sensitive linear response at low strains; strain-phase division maintains signal continuity at medium strains; and parallel conductive circuits provide robustness at high strains. As a result, the sensor achieves an exceptional combination of ultra-high linearity (R2 > 0.999) and sensitivity (gauge factors, GF > 14) across 0-100% strain, with a wide sensing range (> 400%) and fast response (0.16 s). We demonstrate its practical value in human motion detection, physiological signal monitoring, and an intelligent glove system for gesture recognition and human-machine interaction, highlighting its promising potential for advanced wearable devices and human-machine interactive systems.

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Cite This Study

Lin et al. (2026) studied this question.

synapsesocial.com/papers/69a75b7bc6e9836116a22de5https://doi.org/10.1002/advs.202524269
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