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

材料科学 自愈水凝胶 佩多:嘘 纳米纤维 导电体 标度系数 韧性 导电聚合物 纳米技术 复合材料 聚合物 制作 高分子化学 医学 替代医学 病理
作者
Yalei Wang,Shulong Zeng,Shaohong Shi,Yuheng Jiang,Zhiwei Du,Bingzhen Wang,Xiurong Li
出处
期刊:Journal of Materials Science & Technology [Elsevier]
卷期号:169: 1-10 被引量:9
标识
DOI:10.1016/j.jmst.2023.05.064
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 is feasible to integrate the strain sensor on the joints of robots to recognize complicated machine motion signals, showing potential in advanced human-machine interactions.
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