Harnessing Disparities in Magnetic Microswarms: From Construction to Collaborative Tasks.

Journal: Advanced science (Weinheim, Baden-Wurttemberg, Germany)
PMID:

Abstract

Individual differences in size, experience, and task specialization in natural swarms often result in heterogeneity and hierarchy, facilitating efficient and coordinated task accomplishment. Drawing inspiration from this phenomenon, a general strategy is proposed for organizing magnetic micro/nanorobots (MNRs) with apparent differences in size, shape, and properties into cohesive microswarms with tunable heterogeneity, controlled spatial hierarchy, and collaborative tasking capability. In this strategy, disparate magnetic MNRs can be manipulated to show reversible transitions between synchronization and desynchronization by elaborately regulating parameter sets of the rotating magnetic field. Utilizing these transitions, alongside local robust hydrodynamic interactions, diverse heterospecific pairings of disparate magnetic MNRs can be organized into heterogeneous microswarms, and their spatial organization can be dynamically adjusted from egalitarian to leader-follower-like hierarchies on the fly, both in open space and complex microchannels. Furthermore, when specializing the disparate MNRs with distinct functions ("division of labor") such as sensing and drug carrying, they can execute precise drug delivery targeting unknown sites in a collaborative sensing-navigating-cargo dropping sequence, demonstrating significant potential for precise tumor treatment. These findings highlight the critical roles of attribute differences and hierarchical organization in designing efficient swarming micro/nanorobots for biomedical applications.

Authors

  • Chuan Cao
    State Key Laboratory of Advanced Technology for Materials Synthesis and Processing International School of Materials Science and Engineering, Wuhan University of Technology, 122 Luoshi Road, Wuhan, 430070, P. R. China.
  • Fangzhi Mou
    State Key Laboratory of Advanced Technology for Materials Synthesis and Processing International School of Materials Science and Engineering, Wuhan University of Technology, 122 Luoshi Road, Wuhan, 430070, P. R. China.
  • Manyi Yang
    State Key Laboratory of Advanced Technology for Materials Synthesis and Processing International School of Materials Science and Engineering, Wuhan University of Technology, 122 Luoshi Road, Wuhan, 430070, P. R. China.
  • Shuming Zhang
  • Di Zhang
    College of Food Science, Fujian Agriculture and Forestry University, Fuzhou 350002, China.
  • Luolin Li
    State Key Laboratory of Advanced Technology for Materials Synthesis and Processing International School of Materials Science and Engineering, Wuhan University of Technology, 122 Luoshi Road, Wuhan, 430070, P. R. China.
  • Tong Lan
    State Key Laboratory of Advanced Technology for Materials Synthesis and Processing International School of Materials Science and Engineering, Wuhan University of Technology, 122 Luoshi Road, Wuhan, 430070, P. R. China.
  • Dunyi Xiao
    State Key Laboratory of Advanced Technology for Materials Synthesis and Processing International School of Materials Science and Engineering, Wuhan University of Technology, 122 Luoshi Road, Wuhan, 430070, P. R. China.
  • Wei Luo
    Centre for Pattern Recognition and Data Analytics, School of Information Technology, Deakin University, Geelong, Victoria, Australia.
  • Huiru Ma
    Wuhan Institute of Photochemistry and Technology, 7 North Bingang Road, Wuhan, 430083, P. R. China.
  • Jianguo Guan
    State Key Laboratory of Advanced Technology for Materials Synthesis and Processing International School of Materials Science and Engineering, Wuhan University of Technology, 122 Luoshi Road, Wuhan, 430070, P. R. China.