Non-uniform magnetic fields for collective behavior of self-assembled magnetic pillars

IF 2.1 4区 计算机科学 Q3 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
Juan J. Huaroto, Franco N. Piñan Basualdo, Dionne Lisa Roos Ariëns, Sarthak Misra
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Abstract

Programmable and self-assembled magnetic pillars are essential to expanding the application domain of magnetic microparticle collectives. Typically, the collective behavior of self-assembled magnetic pillars is carried out by generating uniform and time-varying magnetic fields. However, magnetic field-shaping capabilities employing non-uniform fields have not been explored for magnetic pillars. In this study, we generate non-uniform magnetic fields using a nine-coil electromagnetic system to achieve object manipulation, upstream/downstream locomotion, and independent actuation. We begin analyzing the static magnetic self-assembly of reduced iron microparticles and experimentally derive the average dimensions (height and diameter) of the resulting pillars. Subsequently, we delve into the collective dynamic response under non-uniform and time-varying magnetic fields, unveiling four distinct modalities. In order to demonstrate the versatility of our approach, we extend our study to the two-dimensional manipulation of a millimeter-sized glass bead using a precessing magnetic field describing a Lissajous curve. Moreover, we showcase the ability of magnetic pillars to adapt to confined and dynamic conditions within fluidic tubes. We finally present a noteworthy case where the nine-coil electromagnetic system independently actuates two clusters of magnetic pillars. Our study shows the potential of using non-uniform magnetic fields to actuate self-assembled magnetic pillars, enabling morphology reconfiguration capabilities, object manipulation, locomotion, and independent actuation.

Abstract Image

自组装磁柱集体行为的非均匀磁场
可编程自组装磁柱对于拓展磁性微粒子集合体的应用领域至关重要。通常,自组装磁柱的集合行为是通过产生均匀和时变磁场来实现的。然而,人们尚未探索过利用非均匀磁场对磁柱进行磁场塑造的能力。在本研究中,我们利用九线圈电磁系统产生非均匀磁场,以实现物体操纵、上下游运动和独立驱动。我们首先分析了还原铁微粒的静态磁性自组装,并通过实验得出了所产生磁柱的平均尺寸(高度和直径)。随后,我们深入研究了非均匀和时变磁场下的集体动态响应,揭示了四种不同的模式。为了展示我们方法的多功能性,我们将研究扩展到利用描述利萨如曲线的前冲磁场对毫米大小的玻璃珠进行二维操纵。此外,我们还展示了磁柱适应流体管内封闭和动态条件的能力。最后,我们介绍了一个值得注意的案例,即九线圈电磁系统可独立驱动两组磁柱。我们的研究显示了利用非均匀磁场驱动自组装磁柱的潜力,从而实现形态重构能力、物体操纵、运动和独立驱动。
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来源期刊
Swarm Intelligence
Swarm Intelligence COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE-ROBOTICS
CiteScore
5.70
自引率
11.50%
发文量
11
审稿时长
>12 weeks
期刊介绍: Swarm Intelligence is the principal peer-reviewed publication dedicated to reporting on research and developments in the multidisciplinary field of swarm intelligence. The journal publishes original research articles and occasional review articles on theoretical, experimental and/or practical aspects of swarm intelligence. All articles are published both in print and in electronic form. There are no page charges for publication. Swarm Intelligence is published quarterly. The field of swarm intelligence deals with systems composed of many individuals that coordinate using decentralized control and self-organization. In particular, it focuses on the collective behaviors that result from the local interactions of the individuals with each other and with their environment. It is a fast-growing field that encompasses the efforts of researchers in multiple disciplines, ranging from ethology and social science to operations research and computer engineering. Swarm Intelligence will report on advances in the understanding and utilization of swarm intelligence systems, that is, systems that are based on the principles of swarm intelligence. The following subjects are of particular interest to the journal: • modeling and analysis of collective biological systems such as social insect colonies, flocking vertebrates, and human crowds as well as any other swarm intelligence systems; • application of biological swarm intelligence models to real-world problems such as distributed computing, data clustering, graph partitioning, optimization and decision making; • theoretical and empirical research in ant colony optimization, particle swarm optimization, swarm robotics, and other swarm intelligence algorithms.
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