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Bioinspired Sensory Transduction for Magnetic Profile Recognition and Encryption
Ziyue Miao1,2,3, Xichen Hu1,2, Kai Liu2
1College of Smart Materials and Future Energy, State Key Laboratory of Coatings for Advanced Equipment and Advanced Coating Research Center of Ministry of Education of China, Fudan University, Shanghai, China.
Advanced Materials (Deerfield Beach, Fla.)
|March 26, 2026
Summary
Researchers developed a novel artificial sensory system using magnetic soft composites to recognize object profiles via electromagnetic induction. This bioinspired technology achieves high accuracy in decoding magnetic information for advanced materials and robotics.
Area of Science:
- Materials Science
- Bioinspired Engineering
- Robotics
Background:
- Biological systems use sensory transduction to convert environmental stimuli into electrical signals for processing.
- Bioinspired artificial systems require methods to transduce diverse stimuli into electrical signals for recognition.
- Magnetoreception in elasmobranchs provides a model for sensing magnetic fields for navigation.
Purpose of the Study:
- To introduce an artificial sensory transduction system for magnetic profile recognition of objects.
- To utilize electromagnetic induction for generating electrical intermediate signaling.
- To employ machine learning for decoding the transduced signals.
Main Methods:
- Design and fabrication of moldable magnetic soft composites (MSCs) with magnetic particles in a zwitterionic polymer matrix.
- Encoding multidimensional features including static (shape, rheology, magnetization) and dynamic (magnetization decay) properties.
- Translocation of MSCs through a receiving coil to generate transient induced electrical signals.
- Application of machine learning algorithms for decoding static and dynamic magnetic information.
Main Results:
- MSCs successfully generated distinct transient electrical signals upon translocation.
- Machine learning achieved ~100% accuracy in decoding static magnetic information.
- Machine learning achieved 87.5% accuracy in decoding dynamic magnetic information.
- The system demonstrated a recognition strength of 3 bits and a high information-carrying capacity (10^62-10^934 states).
Conclusions:
- Electromagnetic induction in soft composites is a viable and generalizable concept for sensory transduction.
- This approach is applicable to adaptive dissipative bioinspired materials, haptic systems, and soft robotics.
- The developed system offers a novel pathway for creating advanced sensory capabilities in artificial systems.
