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Nat Commun. 2018 May 3;9(1):1791. doi: 10.1038/s41467-018-04183-y.

Reconfigurable engineered motile semiconductor microparticles.

Author information

1
NSF Research Triangle Materials Research Science and Engineering Center (MRSEC), Durham, NC, 27708, USA.
2
Department of Electrical and Computer Engineering, Duke University, Durham, NC, 27708, USA.
3
Department of Chemical and Biomolecular Engineering, North Carolina State University, Raleigh, NC, 27695, USA.
4
NSF Research Triangle Materials Research Science and Engineering Center (MRSEC), Durham, NC, 27708, USA. odvelev@ncsu.edu.
5
Department of Chemical and Biomolecular Engineering, North Carolina State University, Raleigh, NC, 27695, USA. odvelev@ncsu.edu.
6
NSF Research Triangle Materials Research Science and Engineering Center (MRSEC), Durham, NC, 27708, USA. nan.jokerst@duke.edu.
7
Department of Electrical and Computer Engineering, Duke University, Durham, NC, 27708, USA. nan.jokerst@duke.edu.

Abstract

Locally energized particles form the basis for emerging classes of active matter. The design of active particles has led to their controlled locomotion and assembly. The next generation of particles should demonstrate robust control over their active assembly, disassembly, and reconfiguration. Here we introduce a class of semiconductor microparticles that can be comprehensively designed (in size, shape, electric polarizability, and patterned coatings) using standard microfabrication tools. These custom silicon particles draw energy from external electric fields to actively propel, while interacting hydrodynamically, and sequentially assemble and disassemble on demand. We show that a number of electrokinetic effects, such as dielectrophoresis, induced charge electrophoresis, and diode propulsion, can selectively power the microparticle motions and interactions. The ability to achieve on-demand locomotion, tractable fluid flows, synchronized motility, and reversible assembly using engineered silicon microparticles may enable advanced applications that include remotely powered microsensors, artificial muscles, reconfigurable neural networks and computational systems.

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