Research Background

In the field of flexible sensing and health monitoring, achieving a wide pressure range, ultra-high sensitivity, and long-term signal stability has always been a technical challenge. Traditional sensors are prone to structural hardening and signal drift under high loads, limiting their reliable application in dynamic biomechanical monitoring. Although some studies have improved performance through microstructure or gradient designs, most still face challenges such as complex fabrication, unstable interfaces, or uneven responses.

Article Introduction

Recently, a research team from the Chongqing Institute of Green and Intelligent Technology, Chinese Academy of Sciences, Chongqing University, and other institutions published their latest findings in *ACS Sensors*. Inspired by the gradient modulus structure of human skin, the team proposed a Gradient Modulus Ionic Electronic Sensor (GMIS). By combining microstructured ionic gels with glass fiber-reinforced matrices, the sensor achieved ultra-high sensitivity (2904 kPa⁻¹) and low signal drift (11.8%) across a broad pressure range of 0–3 MPa with just two-layered architecture. Under dynamic loading, the sensor demonstrated exceptional stability and could be integrated with convolutional neural networks (CNNs) to enable high-precision prediction of ankle joint torque, with a correlation coefficient exceeding 0.91.

Conclusions and Prospects

This study presents a skin-inspired gradient modulus ionic-electronic sensor (GMIS) that achieves synergistic benefits of wide pressure range, high sensitivity, and low drift through material and structural innovation. GMIS offers advantages such as simple fabrication, low cost, and scalability, making it suitable for motion rehabilitation, gait analysis, and long-term health monitoring. It provides a feasible material and system-level solution for next-generation wearable biomechanical sensors.

Source: Sensor Expert Network