Enhanced-performance flexible pressure sensors enabled by synergistic effect of hierarchical porous structures for motion sensing and deep learning-assisted speech recognition.

Amid the global wave of intelligentization, flexible pressure sensors have emerged as core sensing components owing to their excellent flexibility, portability, and highly sensitive response to pressure signals in fields such as human-computer interaction, health monitoring, smart wearable devices, and artificial-intelligence terminals. Herein, we developed a flexible pressure sensor with a hierarchically porous structure via a hybrid manufacturing strategy integrating three-dimensional (3D) printing and electrospinning. A MXene/silver nanowires/polydimethylsiloxane (MAP) conductive ink modified with nano silicon dioxide (SiO2) was formulated to optimize printability, and the multi-scale porous sensing unit was constructed by direct ink writing (DIW) combined with a sacrificial template method, followed by encapsulation with a fibrous film electrode. Benefiting from the synergistic effect of macro-micro porous structure and the structural-mechanical matching design, the sensor exhibits high sensitivity (0.813 kPa-1), fast response (37 ms/30 ms), and an extremely low detection limit (1.47 Pa), thus enabling the effective monitoring of various human physiological motion signals and demonstrating significant potential in health monitoring applications. Furthermore, by integrating deep learning algorithms, high-accuracy recognition (94.9 %) of several spoken phrases is achieved, extending the sensor's applicability to human-computer interaction and intelligent speech perception. In summary, this study proposes a strategy based on a hybrid manufacturing technology for fabricating hierarchically porous flexible pressure sensors, demonstrating broad application prospects in health monitoring and smart human-computer interaction, thus providing new pathways for the innovative design and functional expansion of wearable electronic devices.