Biomimetic Spun Silk Ionotronic Fibers for Intelligent Discrimination of Motions and Tactile Stimuli

Abstract Innovation in the ionotronics field has significantly accelerated the development of ultraflexible devices and machines. However, it is still challenging to develop efficient ionotronic‐based fibers with necessary stretchability, resilience, and conductivity due to inherent conflict in producing spinning dopes with both high polymer and ion concentrations and low viscosities. Inspired by the liquid crystalline spinning of animal silk, this study circumvents the inherent tradeoff in other spinning methods by dry spinning a nematic silk microfibril dope solution. The liquid crystalline texture allows the spinning dope to flow through the spinneret and form free‐standing fibers under minimal external forces. The resultant silk‐sourced ionotronic fibers (SSIFs) are highly stretchable, tough, resilient, and fatigue‐resistant. These mechanical advantages ensure a rapid and recoverable electromechanical response of SSIFs to kinematic deformations. Further, the incorporation of SSIFs into core–shell triboelectric nanogenerator fibers provides outstanding stable and sensitive triboelectric response to precisely and sensitively perceive small pressures. Moreover, by implementing a combination of machine learning and Internet of Things techniques, the SSIFs can sort objects made of different materials. With these structural, processing, performance, and functional merits, the SSIFs prepared herein are expected to be applied in human–machine interfaces.