材料科学
石墨烯
人工肌肉
复合材料
韧性
复合数
氧化物
纺纱
乙二醇
液晶
纤维
聚合物
弯曲
执行机构
纳米技术
化学工程
光电子学
计算机科学
工程类
冶金
人工智能
作者
Yuchong Gao,Jiaqi Liu,Shu Yang
标识
DOI:10.1016/j.mattod.2023.08.003
摘要
Soft materials are known for their compliance, adaptivity and dexterity. They have been well-pursued to create artificial muscles for robotic applications, however, at the cost of load bearing, bit rates and energy efficiency compared with their rigid body counterparts. Despite significant efforts to improve their performance, very few can match the biological muscles, achieving fast speed, high elasticity, and high power density simultaneously. Here, we self-assemble composite fibers by wet-spinning graphene oxide (GO) nanosheets in their lyotropic liquid crystal (LLC) phase mixed with conducting polymers and depleting agent, poly(ethylene glycol), followed by chemical reduction of GO. The resulting reduced GO (rGO) nanosheets are highly aligned and closely packed, which is essential to their high mechanical strength and toughness and actuation behaviors. The composite fiber exhibits fast (80 ms) and reversible bending via electrostatic repulsion between the rGO sheets. When the fibers are plied with nylon yarns, our actuators can achieve 75 J/kg work capacity and 924 W/kg power density without diminishing the bending strain and efficiency up to 10,000 cycles.
科研通智能强力驱动
Strongly Powered by AbleSci AI