Ultra-linear, highly sensitive fabric strain/temperature sensor via the regulation of reduced graphene oxide defect and carrier migration for physiological risk alerts

Abstract

With increasing public attention to health and the rapid development of the Internet of Things, multifunctional wearable sensors capable of detecting diverse physiological signals have emerged as a critical research focus. This study proposes a fabric-based dual-function sensor with both strain and temperature-sensing capabilities. Lycra cotton was selected as the substrate, and a sensing unit was integrated through repeated impregnation and drying processes. Graphene (GR) exhibits excellent mechanical strain characteristics, while reduced graphene oxide (rGO) demonstrates superior thermal conductivity. Silicone rubber (SR) possesses high compatibility with fabric substrate and can work in synergy with rGO and GR to form a three-dimensional conductive network. By adjusting the rGO/GR ratio and dip-coating cycles, the effects on the sensitivity and linearity (strain-sensing) were investigated, and sensors with the optimal ratio were selected for performance evaluation. A physical model inspired by soil microstructure was introduced to elucidate the strain-sensing mechanism. The sensor achieved a high sensitivity of 15.081 and a linearity of 0.9955. GR adheres to rGO interfaces via SR, creating smooth pathways for carrier migration, and the resulting 3D conductive network can be used to explain the temperature-sensing mechanism. The sensor demonstrated a temperature detection range of 22–50 °C with a temperature coefficient of resistance of −1.438 %°C⁻¹, maintaining high linearity (0.9915). As a proof of concept, the sensor was successfully applied to infant sleeping posture monitoring and hazardous motion warning. This dual-mode, fabric-based bioelectronic device holds promise for enabling multimodal sensing strategies in intelligent health monitoring systems.