Highly sensitive and stretchable fiber strain sensors empowered by synergetic conductive network of silver nanoparticles and carbon nanotubes
Source abstract
Abstract
Abstract Nowadays the demand for flexible strain sensors is booming for human-centered wearable applications, yet it is still challenging to achieve a high sensitivity at large stretchability of human motions (∼100%). Herein, carbon nanotubes (CNTs) and silver nanoparticles (AgNPs) layers were consecutively deposited on stretchable polyurethane (PU) fiber by ultrasonication and polydopamine (PD) assisted chemical deposition, to achieve a fiber sensor with both high sensitivity and stretchability. Specially, the crack mechanism dominated AgNPs offer low initial resistance and generation of abundant cracks under strain, while slippage mechanism dominated CNTs guarantee the integrity of conductive path at large strain. The as-prepared PU/CNTs/PD/AgNPs sensor achieves far better sensing performance than the conventional PU/CNTs sensor, in terms of high sensitivity (gauge factor of up to 1004.5), wide workable stretching range (150%), good stability (3000 cycles @ 150%) and fast response (53 ms) and recovery (80 ms). Moreover, the PU fiber substrate also endows the strain sensor with characteristics of light-weight, small-size, and good postprocessing capability to be easily integrated into textiles by weaving, greatly facilitating the preparation of sensing textiles for wearable human motion detections. This work not only provides an efficient way to achieve trade-off between high sensitivity and large stretchability of strain sensors, but also presents huge potentials of wearable and comfortable sensing textiles for personal health monitoring.
Source: openaire · Quality: candidate unverified · Source record ↗
Research context
OpenAlex topics: Advanced Sensor and Energy Harvesting Materials · Conducting polymers and applications · Electrospun Nanofibers in Biomedical Applications
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
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