期刊
ADVANCED MATERIALS
卷 35, 期 36, 页码 -出版社
WILEY-V C H VERLAG GMBH
DOI: 10.1002/adma.202300447
关键词
biomimetic fibers; ionotronics; sensors; silks
Innovation in ionotronics has accelerated the development of ultraflexible devices. However, developing efficient ionotronic-based fibers with necessary stretchability and conductivity is challenging due to conflicts in spinning dopes. This study overcomes this issue by dry spinning a nematic silk solution, resulting in highly stretchable and resilient silk-sourced ionotronic fibers. These fibers can be applied in human-machine interfaces and demonstrate exceptional triboelectric response.
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.
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