High Sensitivity and Wide Strain Range Flexible Strain Sensor Based on CB/CNT/PDA/TPU Conductive Fiber Membrane
Flexible strain sensors have attracted significant attention due to their critical applications in wearable devices, biological detection, and artificial intelligence. However, achieving both a wide strain range and high sensitivity remains a major challenge in current research. This study aims to d...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2025-01-01
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| Series: | Applied Sciences |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2076-3417/15/3/1461 |
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| Summary: | Flexible strain sensors have attracted significant attention due to their critical applications in wearable devices, biological detection, and artificial intelligence. However, achieving both a wide strain range and high sensitivity remains a major challenge in current research. This study aims to develop a novel composite material with a synergistic conductive network to construct high-performance flexible strain sensors. Thermoplastic polyurethane (TPU) nanofiber membranes were first prepared using electrospinning technology, and their surface was modified with polydopamine (PDA) via in-situ polymerization, which significantly enhanced the fibers’ adsorption capacity for conductive materials. Subsequently, carbon nanotubes (CNTs) and carbon black (CB) were coated onto the PDA-modified TPU fibers through ultrasonic anchoring, forming a CB/CNT/PDA/TPU composite with a synergistic conductive network. The results demonstrated that the flexible strain sensor fabricated from this composite material (with a CB-to-CNT mass ratio of 7:3) achieved ultrahigh sensitivity (gauge factor, GF, up to 1063) over a wide strain range (up to 300%), along with a low detection limit (1% strain), fast response and recovery times (137 ms), and exceptional stability and durability. Further evaluations confirmed that this sensor reliably captured biological signals from various joint movements, highlighting its broad application potential in human motion monitoring, human–machine interaction, and soft robotics. |
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| ISSN: | 2076-3417 |