An Ultrastretchable and Self-Healable Nanocomposite Conductor Enabled by Autonomously Percolative Electrical Pathways

• Published in: ACS nano 2019-06, Vol.13 (6), p.6531-6539
• Main Authors: Kim, Sun Hong, Seo, Hyunseon, Kang, Jiheong, Hong, Jaeyoung, Seong, Duhwan, Kim, Han-Jin, Kim, Jaemin, Mun, Jaewan, Youn, Inchan, Kim, Jinseok, Kim, Yu-Chan, Seok, Hyun-Kwang, Lee, Changhee, Tok, Jeffrey B.-H, Bao, Zhenan, Son, Donghee
• Format: Article
• Language: English
• ISSN: 1936-0851; 1936-086X
• DOI: 10.1021/acsnano.9b00160

Both self-healable conductors and stretchable conductors have been previously reported. However, it is still difficult to simultaneously achieve high stretchability, high conductivity, and self-healability. Here, we observed an intriguing phenomenon, termed “electrical self-boosting”, which enables reconstructing of electrically percolative pathways in an ultrastretchable and self-healable nanocomposite conductor (over 1700% strain). The autonomously reconstructed percolative pathways were directly verified by using microcomputed tomography and in situ scanning electron microscopy. The encapsulated nanocomposite conductor shows exceptional conductivity (average value: 2578 S cm–1; highest value: 3086 S cm–1) at 3500% tensile strain by virtue of efficient strain energy dissipation of the self-healing polymer and self-alignment and rearrangement of silver flakes surrounded by spontaneously formed silver nanoparticles and their self-assembly in the strained self-healing polymer matrix. In addition, the conductor maintains high conductivity and stretchability even after recovered from a complete cut. Besides, a design of double-layered conductor enabled by the self-bonding assembly allowed a conducting interface to be located on the neutral mechanical plane, showing extremely durable operations in a cyclic stretching test. Finally, we successfully demonstrated that electromyogram signals can be monitored by our self-healable interconnects. Such information was transmitted to a prosthetic robot to control various hand motions for robust interactive human-robot interfaces.