Ultrasensitive wearable sensor with novel hybrid structures of silver nanowires and carbon nanotubes in fluoroelastomer: Multi-directional sensing for human health monitoring and stretchable electronics
Source abstract
Abstract
Today, wearable sensors are being developed for personalized human health monitoring for physical motion detection and physiological signal detection, and well-being. These wearable devices need to be extremely flexible, lightweight, and stretchable to be compatible with the contours of the human body, and mechanically and dynamically stable. Their functionality can be assured by simultaneously increasing conductivity, stretchability, and sensitivity for accurate and fast response. The challenge in fabricating stretchable sensors with these functionalities is embracing current wearable fashion. In this work, for the first time, a new 3D hybrid nanonetwork structures of high aspect ratio silver nanowires (AgNW) and carbon nanotubes (CNT) in fluoroelastomer FKM are developed. An optimized solution mixing (SM) and layer by layer (LBL) assembly technique, and LBL drop-coating process are employed to establish the hierarchical and shell-like nanostructures. The sensor films are ultra-thin and show high conductivity up to 2 × 105 S. m−1, high stretchability of up to 300%, and a maximum sensitivity (gage factor) of up to 2.5 × 107. The multifunctional sensor with hybrid nanonetwork shows high performance in various wearable electronic applications including human motion detection and stretchable light-emitting diode (LED) circuits. These ultra-sensitive materials show capability for multidirectional sensing by special configurations for each human body part movement, and small movements for human heartbeat monitoring.
Source: expertdata · Quality: candidate unverified · Source record ↗
Research context
OpenAlex topics: Advanced Sensor and Energy Harvesting Materials · Tactile and Sensory Interactions · Conducting polymers and applications
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
References (74)
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