Related Experiment Video
Updated: Jul 8, 2026

A Simple and Scalable Fabrication Method for Organic Electronic Devices on Textiles
Published on: March 13, 2017
Wearable sensors for intelligent sweat monitoring based on extended-gate stretchable organic transistors
Weiyu Wang1, Aoyue Yang2, Huiqi Yang1
1State Key Laboratory of Advanced Materials for Intelligent Sensing, Key Laboratory of Organic Integrated Circuit, Ministry of Education & Tianjin Key Laboratory of Molecular Optoelectronic Sciences, Department of Chemistry, School of Science & Institute of Molecular Aggregation Science, Tianjin University, Tianjin, 300072, China.
Abstract:
Non-invasive sweat analysis holds significant promise for personalized health monitoring, yet existing technologies face limitations in mechanical compatibility, signal fidelity, and multimodal detection. Here, a multimodal sensing platform based on a stretchable organic field-effect transistor (OFET) array is presented to address these challenges. The OFET device exhibits excellent stretchability and operational stability, retaining stable electrical performance after 1000 stretching cycles at 30% strain and showing no significant threshold voltage drift under 3 h of bias stress. With extended-gate electrodes functionalized with selective membranes and enzymes, the system detects Na+, K+, Ca2+, glucose, and lactate over physiologically relevant ranges (1 - 100 mM, 1 - 100 mM, 0.1 - 10 mM, 50 - 250 μM, and 5 - 25 mM, respectively), demonstrating high sensitivity, selectivity, and accuracy. Specifically, the ion sensors exhibited detection errors below 4.5%, while the glucose and lactate sensors showed remarkably low response variations with relative standard deviations (RSD) of only 2.16% and 1.18%, respectively. The sensor retains reliable performance in artificial sweat, demonstrating strong resilience to complex biofluid matrices. Integrated with a miniaturized flexible circuit and wireless module, our platform enables real-time data acquisition, processing, and visualization via a mobile application. This work establishes a robust foundation for future wearable diagnostic systems capable of continuous, multi-parameter physiological monitoring.

