Robust Metallized Liquid Crystal Elastomer Fiber Arrays Toward a Machine Learning‐Assisted Artificial Neuromuscular System with Perceptual Function

作者：Chuang Zhu, Yajie Zhang, Guanliang He, Yuze Shi, Yi Wu, Yingjia Yu, Xuqing Liu · 发表于：Advanced Functional Materials · 年份：2024 · DOI：10.1002/adfm.202413845 · 被引用次数：19 · 研究领域：Advanced Sensor and Energy Harvesting Materials、Advanced Materials and Mechanics、Dielectric materials and actuators

Abstract Endowing artificial muscles with perceptual function, as an octopus does, is highly desired but still suffering from interfacing mismatch between actuating and sensing units in a thin fiber. Herein, an artificial neuromuscular fiber capable of electrically responding to external strain/temperature and actuation path with power supply is reported by using polymer‐assisted metal deposition to firmly coat Cu nanoparticles on the surface of liquid crystal elastomer (LCE). The LCE core acts as an actuator, while the wrinkled Cu sheath provides environment interaction and actuation monitoring. Benefiting from the levodopa/polyethyleneimine interface design, this fiber exhibits large reversible contraction (47.61%), fast strain rate (370% s −1 ), high output power density (663.75 W kg −1 ) and reliable durability (1000 cycles) under electrical stimulation. Furthermore, by combining as‐made fiber arrays with electronic system and computing algorithm, this machine learning‐assisted artificial neuromuscular system can actuate the Chinese shadow puppetry and recognize its body movements with high accuracy. This work paves a revolutionary way for fabricating next‐generation flexible robots.