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Order and information in the patterns of spinning magnetic micro-disks at the air-water interface

Authors :
Wang, Wendong
Gardi, Gaurav
Malgaretti, Paolo
Kishore, Vimal
Koens, Lyndon
Son, Donghoon
Gilbert, Hunter
Wu, Zongyuan
Harwani, Palak
Lauga, Eric
Holm, Christian
Sitti, Metin
Sitti, Metin (ORCID 0000-0001-8249-3854 & YÖK ID 297104)
Wang, W.
Gardi, G.
Malgaretti, P.
Kishore, V.
Koens, L.
Son, D.
Gilbert, H.
Wu, Z.
Harwani, P.
Lauga, E.
Holm, C.
College of Engineering
School of Medicine
Department of Mechanical Engineering
Wang, Wendong [0000-0003-3007-1750]
Gardi, Gaurav [0000-0001-6811-4061]
Malgaretti, Paolo [0000-0002-1201-451X]
Kishore, Vimal [0000-0003-4774-6267]
Koens, Lyndon [0000-0003-2059-8268]
Son, Donghoon [0000-0002-6483-1589]
Gilbert, Hunter [0000-0001-8590-2596]
Wu, Zongyuan [0000-0003-2260-7754]
Lauga, Eric [0000-0002-8916-2545]
Holm, Christian [0000-0003-2739-310X]
Sitti, Metin [0000-0001-8249-3854]
Apollo - University of Cambridge Repository
Source :
Science Advances, Science advances 8(2), eabk0685 (2022). doi:10.1126/sciadv.abk0685, Science Advances, 8 (2)
Publication Year :
2022
Publisher :
American Association for the Advancement of Science, 2022.

Abstract

The application of the Shannon entropy to study the relationship between information and structures has yielded insights into molecular and material systems. However, the difficulty in directly observing and manipulating atoms and molecules hampers the ability of these systems to serve as model systems for further exploring the links between information and structures. Here, we use, as a model experimental system, hundreds of spinning magnetic micro-disks self-organizing at the air-water interface to generate various spatiotemporal patterns with varying degrees of order. Using the neighbor distance as the information-bearing variable, we demonstrate the links among information, structure, and interactions. We establish a direct link between information and structure without using explicit knowledge of interactions. Last, we show that the Shannon entropy by neighbor distances is a powerful observable in characterizing structural changes. Our findings are relevant for analyzing natural self-organizing systems and for designing collective robots.<br />Science Advances, 8 (2)<br />ISSN:2375-2548

Details

Language :
English
ISSN :
23752548
Volume :
8
Issue :
2
Database :
OpenAIRE
Journal :
Science Advances
Accession number :
edsair.doi.dedup.....21de5697cca7c7c72684e89ec27dd5ca
Full Text :
https://doi.org/10.1126/sciadv.abk0685