Abstract

The self-assembly of monomers, chains, and bundles is often observed in magnetic mixtures. The most probable interaction between the aggregates in a superparamagnetic colloidal suspension is associated with the minimum of the effective magnetic potential in the two and three dimensional relative shift space, which yields a concrete criterion to be employed for prediction of the cohesive energy and the mean length of the aggregates in the fluid bulk.

Highlights

  • Understanding the physical bases of soft and hard speck coalescence is relevant in a great number of research fields ranging from embryonic development, nanotechnology, and material science

  • Magnetic suspensions present a challenge in equilibrium thermodynamics, out-of-equilibrium physics, and fluid mechanics

  • II A, the first numerical results confirm that δ = −3 and δ = 2 are associated with the minima of the cohesive energy of a 2-chain regular aggregate. 3-chain bundles are classified in zigzag and scale types

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Summary

INTRODUCTION

Understanding the physical bases of soft and hard speck coalescence is relevant in a great number of research fields ranging from embryonic development, nanotechnology, and material science. In order to predict the dynamics of the coalescence process, there have been numerous previous simulation efforts, see, for instance, Ref. for generic coalescence methods and scaling laws, Ref. for ferromagnetic agglomeration under magnetic fields, Ref. for dielectric particles under electric fields and chain formation of superparamagnetic beads, Ref. 15 for superparamagnetic aggregation, and recent numerical findings where the magnetic saturation range was achieved.. The goal of this contribution is to establish criteria and appropriate assumptions to compute and fit the magnetic energy per particle Ud in bundles formed by like sized and unlike sized chains This is employed, for instance, to compute and predict the mean length of the aggregates in a colloidal mixture as a function of magnetic field.

LIKE SIZED CHAINS
Discussion
Zig-zag ribbons
Scale bundles
UNLIKE SIZED CHAINS
Findings
AGGREGATE MEAN LENGTH
CONCLUSIONS
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