Self-Driven Hybrid Piezomagnetic-Iontronic Mechanoreceptors for Bimodal SA/RA Perception and Tactile Synthesis

Artificial tactile systems increasingly use independent transduction of slow-adapting (SA) signals from static pressure and rapid-adapting (RA) signals from dynamic vibrations, aiming to mimic the human skin's mechanosensory pathways for enhanced perceptual processing. Here, we present a tactile sensing system based on hybrid materials that integrate an Fe₃O₄-based piezomagnetic elastomer and a poly(vinyl chloride) (PVC)-based iontronic gel in a unified layered architecture, enabling the orthogonal encoding of SA and RA mechanotransduction. The Fe₃O₄ elastomer exhibits piezomagnetic coupling, yielding a magnetic flux density of ∼1.5 mT and a peak voltage modulation of ∼15 mV at 3.2 N load, while effectively capturing RA signals over a wide bandwidth up to 1 kHz. Concurrently, the iontronic PVC gel is self-driven by the potential of the Fe₃O₄ elastomer and delivers stable SA signal outputs with a sensitivity of 1.6/0.48 mV N^{- 1} (whereas the RA channel exhibits 11.4/0.75 mV N^-1). By combining these decoupled signal modalities, we construct a haptic mapping framework that generates distinctive tactile fingerprints of object surfaces. This multimodal self-driven sensing approach enables accurate classification of material texture, reliable slip detection, and identification of surface anomalies with different groove widths. This work offers a scalable materials strategy for intelligent robotics and human-machine interfaces.