High-sensitivity omnidirectional recognition strain sensor based on two-dimensional materials

Abstract

Flexible strain sensors are essential in fields such as medicine, sports, robotics, and virtual reality but face challenges in achieving excellent sensing performance and accurate multi-directional detection simultaneously. To address this issue, we have developed a spider-web structured multi-directional flexible strain sensor using Ti₃C₂Tₓ (MXene) conductive ink and 3D printing technology. Combined with a multi-class, multi-output neural network model algorithm, the sensor achieves signal decoupling from the sensor array, allowing for precise detection of strain direction and intensity. It exhibits good sensitivity (gauge factor ~ 26.3), a moderate sensing range (0-10%), and high reliability (1000 stretching cycles). Using neural network algorithms, a four-unit spider-web sensor array achieves approximately 97% accuracy in identifying strain intensity and direction within the 0-10% strain range under various surface stimuli. Additionally, it can track complex human motions, demonstrating significant potential in applications such as motion monitoring and human-machine interaction.