A Bionic Electronic Skin Based on Phase-Separated Ionogels and Macroscopic Triangular Geometric Optimization for AI-Assisted Tactile-Thermal Perception

While bionic electronic skins are essential for multimodal tactile and thermal perception, distinguishing complex topographies with nonarrayed single-point sensors continues to present technical difficulties. In this study, we developed a bionic electronic skin utilizing phase-separated ionogels and a macroscopic triangular geometry to achieve reliable AI-assisted tactile-thermal sensing, where dynamic mechanical stimuli and quasi-static thermal variations can be decoupled in the time domain. By controlling the aggregation of the ionogel’s internal polymer networks, we created a flexible substrate where spontaneously formed surface micropatterns enhance pressure sensitivity. The resulting device detects a broad pressure range (0–1500 kPa) and maintains stable performance across 3500 compression cycles. Additionally, the phase-separated network provides the sensor with distinct temperature responsiveness, yielding a thermal index ( B ) of 2727.5 K and a temperature resolution of 0.1 K near physiological human body temperature. At the device level, replacing conventional square sensing planes with macroscopic triangular geometric optimization enables effective spatial recognition. Because the slanted edges break spatial symmetry, the sensor interacts with aligned Braille dots sequentially and dense physical stimuli are converted into asynchronous temporal signals. Coupling this geometric optimization with a hybrid convolutional neural network and long short-term memory (CNN-LSTM) algorithm enabled a Braille recognition system that reached 99.1% accuracy under laboratory conditions. The synergy of material phase-programming, macroscopic device geometry optimization, and AI-assisted algorithm offers a potential approach for developing multimodal electronic skin systems in dynamic real-world applications.