Paramagnetic colloids: Chaotic routes to clusters and molecules

Abdi, Hamed; Soheilian, Rasam; Erb, Randall M.; Maloney, Craig

doi:10.1103/physreve.97.032601

Cited by 18 publications

(10 citation statements)

References 52 publications

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“…As can be seen from the figure, at B = 0 Gs, magnetic particles are randomly distributed in the twodimensional simulation area. As shown in Figure 7(a), once an external magnetic field is applied, the magnetic particles gather together within a short time under the action of a magnetic field to form a plurality of short particle chains and clusters of particles [13]. As shown in Figure 7(b), with the increase of magnetic induction intensity, these short chains and clusters will gather together, which will increase the length of the particle chain, thus forming a through chain and forming a bundle chain structure.…”

Section: Comparison and Analysis Of Numerical Simulation And Experimementioning

confidence: 99%

Numerical Simulation and Experimental Analysis of Microstructure of Magnetorheological Fluid

Luo

Ren

et al. 2019

Journal of Nanomaterials

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Magnetorheological fluid is a new type of smart material that is sensitive to magnetic fields and has controllable performance. It is widely regarded for its unique magnetorheological effect and good rheological properties. For materials, the microstructure determines its macroscopic properties. In order to better study its macroscopic properties, it is necessary to have a more comprehensive understanding and deep understanding of its microstructure. In this paper, the magnetization process of magnetorheological fluid is analyzed from a microscopic point of view. Based on Newton’s second law, the dynamic model of particle motion is established. The magnetic force, repulsive force, and viscous resistance of magnetic particles are analyzed. The finite difference numerical calculation method is used. The velocity-Verlet algorithm simulates the static microstructure chaining process of the magnetorheological fluid and the dynamic chaining process under shear force under different influencing factors. At the same time, a static observation device and a shear observation device were developed to observe the microstructure chaining morphology of magnetorheological fluid under different influencing factors, and to study the dynamic chaining law of magnetorheological fluid under the action of a shear force. Therefore, a reasonable contrast index is established, and the numerical simulation results are compared with the experimental observation results.

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Section: Comparison and Analysis Of Numerical Simulation And Experimementioning

confidence: 99%

Numerical Simulation and Experimental Analysis of Microstructure of Magnetorheological Fluid

Luo

Ren

et al. 2019

Journal of Nanomaterials

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“…Hence, each droplet chain is a building block of the microrobotic swarm. The evolution of that swarm is determined by the dynamics of the chain, which is governed by the Mason number [ 44–47 ] (Ma: ratio of viscous to magnetic forces), which can be defined as

\begin{matrix} Ma = \frac{144 η ω}{μ_{0} μ_{s} M^{2}} \end{matrix}

where η, ω, μ 0 , μ s , and M are, respectively, the solvent viscosity, the angular frequency of the magnetic field, the vacuum magnetic permeability, the solvent magnetic permeability, and the magnetization of a ferrofluid droplet. Figure a (Movie S6, Supporting Information) demonstrates the simulation result of the chain dynamics with increasing rotating frequency.…”

Section: Resultsmentioning

confidence: 99%

“…When the input frequency is high ( f ≫ f c , α = 0 or α ≠ 0), before the ferrofluid droplets have a chance to move appreciably, they sample all possible magnetic field orientations, so the magnetostatic interactions average over all angles and become effectively isotropic. The chain‐like structures then collapse into closely packed planar structures, [ 47 ] and the microswarm immediately transforms into closely packed solid‐like planar structures (Figure 3b and Movie S6, Supporting Information).…”

Section: Resultsmentioning

confidence: 99%

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Swarming Microdroplets to a Dexterous Micromanipulator

Sun

Fan

Tian

et al. 2021

Adv Funct Materials

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Many astonishing biological collective behaviors exist in nature, and artificial microrobotic swarms have been developed by emulating these scenarios. However, these microswarms typically have single structures and lack the adaptability that many biological swarms exhibit to thrive in complex environments. Inspired by viscoelastic fire ant aggregations and using a combination of experiment and simulation, a strategy to trigger ferrofluid droplets into forming microswarms exhibiting both liquid‐like and solid‐like behaviors is reported. By spatiotemporally programming an applied magnetic field, microswarms can be liquefied to implement reversible elongation with a high aspect ratio and solidified into entireties to perform overturning and bending behaviors. It is demonstrated that reconfigurability enables the microswarm to be a mobile dexterous micromanipulator, acting not only as a soft “octopus arm” to explore a confined environment and grasp a targeted object but also adaptively navigate multiple terrains, such as uneven surfaces, curved grooves, complex mazes, high steps, narrow channels, and even wide gaps. This microrobotic swarm can reconfigure both shapes and tasks based on the demands of the environment, presenting novel solutions for a variety of applications.

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“…In many of these systems, the cluster formation arises when the particles experience a local confinement or selftrapping due to the nature of the pairwise particleparticle interactions 16,17 . Under various types of driving, these systems can exhibit interesting dynamical effects including self assembly 18,19 , rotating gear behavior 20,21 , and depinning phenomena 22 . In most of these systems, the dynamics is overdamped; however, some systems also include nondissipative effects such as inertia or Magnus forces.…”

Section: Introductionmentioning

confidence: 99%

Dynamics of Magnus Dominated Particle Clusters, Collisions, Pinning and Ratchets

Reichhardt,

Reichhardt

2020

Preprint

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Motivated by the recent work in skyrmions and active chiral matter systems, we examine pairs and small clusters of repulsively interacting point particles in the limit where the dynamics is dominated by the Magnus force. We find that particles with the same Magnus force can form stable pairs, triples and higher ordered clusters or exhibit chaotic motion. For mixtures of particles with opposite Magnus force, particle pairs can combine to form translating dipoles. Under an applied drive, particles with the same Magnus force translate; however, particles with different or opposite Magnus force exhibit a drive-dependent decoupling transition. When the particles interact with a repulsive obstacle, they can form localized orbits with depinning or unwinding transitions under an applied drive. We examine the interaction of these particles with clusters or lines of obstacles, and find that the particles can become trapped in orbits that encircle multiple obstacles. Under an ac drive, we observe a series of ratchet effects, including ratchet reversals, for particles interacting with a line of obstacles due to the formation of commensurate orbits. Finally, in assemblies of particles with mixed Magnus forces of the same sign, we find that the particles with the largest Magnus force become localized in the center of the cluster, while for mixtures with opposite Magnus forces, the motion is dominated by transient local pairs or clusters, where the translating pairs can be regarded as a form of active matter.

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Paramagnetic colloids: Chaotic routes to clusters and molecules

Cited by 18 publications

References 52 publications

Numerical Simulation and Experimental Analysis of Microstructure of Magnetorheological Fluid

Numerical Simulation and Experimental Analysis of Microstructure of Magnetorheological Fluid

Swarming Microdroplets to a Dexterous Micromanipulator

Dynamics of Magnus Dominated Particle Clusters, Collisions, Pinning and Ratchets

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