Miao Jiaqi, Sun Siqi, Zhang Tieshan, Li Gen, Ren Hao, Shen Yajing
Shenzhen Research Institute of City University of Hong Kong, Shenzhen518057, China.
Department of Biomedical Engineering, City University of Hong Kong, Hong Kong999077, China.
ACS Appl Mater Interfaces. 2022 Nov 9;14(44):50296-50307. doi: 10.1021/acsami.2c12434. Epub 2022 Oct 25.
Natural structures and motion behaviors open new avenues for effective small-scale transport, such as the plant-inspired energy-free liquid transport surfaces and cilia-inspired propulsion systems. However, they are restricted by either the fixed structure or nonself-regulating beating modes, making many complex tasks remain challenging, e.g., the controllable multidirectional liquid transport and flexible propulsion. Herein, inspired by pine needles and natural cilia, we report an asymmetric-structured intelligent magnetic pillar actuator (AI-MPA) with both the "passive" and "active" transport features. Under the control of the magnetic field, the AI-MPA shows an all-space liquid transport ability toward arbitrary directions. Moreover, benefiting from the material's magnetoelasticity and asymmetric-structured design, the AI-MPA enables self-regulation of two-dimensional (2D)/three-dimensional (3D) cilia-like beating modes and can be further developed for robotic crawling and self-rotatable motion. The AI-MPA integrates the superiority of static and dynamic systems in nature and exhibits intelligent self-regulation that could not be achieved before. Confirmed theoretically and demonstrated experimentally, this work provides insights into increasingly functional and intelligent miniature biomimetic systems, with applications from directional liquid transport to robotic locomotion.
自然结构和运动行为为有效的小规模运输开辟了新途径,例如受植物启发的无能源液体运输表面和受纤毛启发的推进系统。然而,它们要么受到固定结构的限制,要么受到非自调节跳动模式的限制,这使得许多复杂任务仍然具有挑战性,例如可控的多向液体运输和灵活的推进。在此,受松针和天然纤毛的启发,我们报道了一种具有“被动”和“主动”运输特征的不对称结构智能磁柱致动器(AI-MPA)。在磁场控制下,AI-MPA显示出向任意方向的全空间液体运输能力。此外,受益于材料的磁弹性和不对称结构设计,AI-MPA能够实现二维(2D)/三维(3D)纤毛状跳动模式的自我调节,并可进一步发展用于机器人爬行和自旋转运动。AI-MPA整合了自然界中静态和动态系统的优势,并展现出以前无法实现的智能自我调节。通过理论验证和实验证明,这项工作为功能越来越强大和智能的微型仿生系统提供了见解,其应用范围从定向液体运输到机器人运动。