Highly Stretchable, Elastic, and Ionic Conductive Hydrogel for Artificial Soft Electronics

Yang Zhou(Nanyang Technological University), Changjin Wan(Nanyang Technological University), Yongsheng Yang(Wuhan Textile University), Hui Yang(Nanyang Technological University), Shancheng Wang(Nanyang Technological University), Zhendong Dai(Nanjing University of Aeronautics and Astronautics), Keju Ji(Nanjing University of Aeronautics and Astronautics), Hui Jiang(Nanyang Technological University), Xiaodong Chen(Nanyang Technological University), Yi Long(Nanyang Technological University)
Advanced Functional Materials
November 14, 2018
Cited by 930Open Access
Full Text

Abstract

Abstract High conductivity, large mechanical strength, and elongation are important parameters for soft electronic applications. However, it is difficult to find a material with balanced electronic and mechanical performance. Here, a simple method is developed to introduce ion‐rich pores into strong hydrogel matrix and fabricate a novel ionic conductive hydrogel with a high level of electronic and mechanical properties. The proposed ionic conductive hydrogel is achieved by physically cross‐linking the tough biocompatible polyvinyl alcohol (PVA) gel as the matrix and embedding hydroxypropyl cellulose (HPC) biopolymer fibers inside matrix followed by salt solution soaking. The wrinkle and dense structure induced by salting in PVA matrix provides large stress (1.3 MPa) and strain (975%). The well‐distributed porous structure as well as ion migration–facilitated ion‐rich environment generated by embedded HPC fibers dramatically enhances ionic conductivity (up to 3.4 S m −1 , at f = 1 MHz). The conductive hybrid hydrogel can work as an artificial nerve in a 3D printed robotic hand, allowing passing of stable and tunable electrical signals and full recovery under robotic hand finger movements. This natural rubber‐like ionic conductive hydrogel has a promising application in artificial flexible electronics.


Related Papers

No related papers found

Powered by citation graph analysis