Hybrid assembly of conducting nanofiber network for ultra-stretchable and highly sensitive conductive hydrogels

被引:0
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作者
Wang, Yalei [1 ]
Zeng, Shulong [2 ]
Shi, Shaohong [1 ]
Jiang, Yuheng [1 ]
Du, Zhiwei [1 ]
Wang, Bingzhen [1 ]
Li, Xiurong [1 ]
机构
[1] State Key Laboratory of Featured Metal Materials and Life-cycle Safety for Composite Structures, School of Resources, Environment and Materials, Guangxi University, Nanning,530004, China
[2] State Key Laboratory of Polymer Materials Engineering, Polymer Research Institute of Sichuan University, Sichuan University, Chengdu,610065, China
关键词
Conducting polymers;
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摘要
Conductive hydrogels have attracted extensive attention owing to their promising application prospects in flexible and wearable electronics. However, achieving both high sensitivity and mechanical robustness remains challenging. Herein, a novel and versatile conductive hydrogel based on the hybrid assembly of conductive cellulose nanofiber (CNF) networks has been designed and fabricated. Assisted by the templating effect of CNFs and stabilizing effect of negatively charged poly(styrene sulfonate) (PSS), conducting polymer poly (3, 4-ethylenedioxythiophene) (PEDOT) was self-organized into three-dimensional nanostructures which constructed a robust conductive network after in-situ oxidative polymerization. The unique structure derived from CNF bio-template endowed polyacrylamide (PAM) hydrogels with improved electrical conductivity and excellent mechanical performance. As a result, the as-fabricated CNF/PEDOT:PSS/PAM hydrogel exhibited an ultimate tensile strain of 1881% and toughness of 3.72 MJ/m3, which were 4.07 and 8.27 times higher than the CNF-free hydrogel, respectively. More significantly, the resultant hydrogel sensor showed highly desirable sensing properties, including remarkable sensing range (1100%), high gauge factor (GF = 5.16), fast response time (185 ms), and commendable durability, as well as good adhesiveness. Moreover, the hydrogel sensor was able to distinguish subtle physiological activities including phonation and facial expression, and monitor large human body motions such as finger flexion and elbow blending. Besides, it was feasible to integrate the strain sensor on the joints of robots to recognize complicated machine motion signals, showing potential in advanced human-machine interactions. © 2023
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