Dual-Functional Capacitive Flexible Sensors with Bionic Microstructures and Magnetic Nanocomposites for Pressure Sensing and Magnetic Ternary Encoding.

In advanced robotics and human-machine interfaces, there is a critical demand for flexible sensors that can bridge the gap between noncontact perception and physical interaction. Integrating noncontact magnetic sensing for proximity detection with contact-based pressure sensing for tactile feedback in a single device is a key approach to meeting this demand. However, achieving high performance in both modalities is challenging due to a fundamental trade-off: materials and structures optimized for high pressure sensitivity are often compromised by the integration of magnetic components required for field detection, and vice versa. To address the above issues, this paper proposes a rabbit leg-inspired flexible pressure-magnetic sensor (RF-PMS) designed to provide a highly integrated, performance-balanced solution for advanced human-machine interaction. The sensor's high performance is rooted in its unique design: a rabbit-leg bioinspired microstructure enables efficient stress concentration to significantly enhance sensitivity, while a composite of NdFeB nanoparticles in a silicone rubber (SR) matrix facilitates highly sensitive magnetic field detection and novel ternary encoding through field-induced changes in both permittivity and electrode distance, featuring three key advantages. First, the integration of biomimetic microstructures with multiwalled carbon nanotubes (MWCNTs)/NdFeB/silicone rubber (SR) nanocomposites enables simultaneous detection of pressure and magnetic fields. Second, the sensor exhibits a sensitivity of 1.0648 kPa (0-1 kPa), with an ultralow detection limit of 7 Pa. Third, a ternary signal encoding system (-1, 0, + 1) supports contactless information encryption. Fabricated via precision 3D printing, the RF-PMS demonstrates a fast response time of 62 ms and good mechanical durability (>4,500 cycles). For practical applications, it enables robotic object classification through grasp-induced capacitive signals, achieving 95.2% accuracy via a CNN-based framework. Additionally, the device supports secure data transmission using a programmable 4 × 4 magnetic array. This compact, multifunctional platform addresses key limitations in current flexible sensors and opens new opportunities for next-generation wearable devices and human-machine interfaces.