Piezoionic sensors perceive the physical world based on polymer ionogels with advantages of flexibility, lightweight, and high sensitivity, and are suitable for physical signal extraction and virtual space construction in artificial intelligence. However, the electrode-electrolyte interface of conventional sensors presents mechanical modulus mismatch, which is prone to interface cracking under external strain and affects cyclic stability. Here, we engineer a gradient sensor interface based on graphene and ionogel that alleviates modulus mismatch by eliminating the interface of the electrode and electrolyte. The piezoionic sensor displays superior cyclic stability in an air environment with signal retention as high as 97% over 4000 bending cycles. It also delivers millisecond-level rapid response and sensitive strain perception in a complex environment for detecting diverse human joint movements. Meanwhile, we integrate the flexible sensors with a large language model for accurate path recognition and realize feature extraction and correlation analysis of the large number of sensor signals. Our study provides an insight into the interface optimization of electrochemical devices and will shed light on the development of flexible sensors in artificial intelligence.
Ling et al. (2026) studied this question.