Multifunctional Pressure/Strain Sensor Based on Dual-Layer Mxene/TiO₂/TPU and Its Application in Gesture Recognition

As essential tools for monitoring human activities, flexible pressure and strain sensors have widespread applications in healthcare and smart devices, particularly in scenarios that require lightweight, comfortable, wearable, or shape-adaptive characteristics. However, their practical applications are highly limited by single sensing materials and wearing discomfort. Therefore, it is important to develop breathable and multifunctional composites for pressure/strain sensing. In this work, we prepared a composite material for constructing high-performance pressure/strain sensors to monitor human behavioral activities. Through a one-step electrospinning and in-situ impregnation process, we constructed a dual-layer conductive network of MXene both inside and outside the thermoplastic polyurethane (TPU) fiber, and introduced TiO<sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> nanoparticles for enhanced ultraviolet (UV) protection effect. Using the dual-layer MXene/TiO<sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub>/TPU (DMTPU) composite material, the assembled DMTPU sensor exhibited excellent pressure/strain sensing performance: the pressure sensor had high sensitivity (10.8 kPa<sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">-1</sup>) in a wide detection range of 0-345 kPa, fast response (0.33s) and good reliability (3500 cycles); the strain sensor could be stretched to 97.5%, with a maximum gauge factor (GF) of 7269.18, and maintained excellent sensing performance and stability after more than 1100 stretch/release cycles. Based on the above excellent features, the DMTPU sensor can be used to monitor the pressure and strain deformation caused by human movement, demonstrating bright application prospects in the field of smart wearable devices. By combining machine learning algorithms, the sensor can efficiently recognize different gesture information with an accuracy rate of up to 98.75%, which is of great significance for barrier-free communication for deaf-mute patients and sign language translation.