How chains and rings affect the dynamic magnetic susceptibility of a highly clustered ferrofluid

Philip J. Camp, Alexey O. Ivanov, and Julien O. Sindt
Phys. Rev. E 103, 062611 – Published 21 June 2021

Abstract

The dynamic magnetic susceptibility, χ(ω), of a model ferrofluid at a very low concentration (volume fraction, approximately 0.05%), and with a range of dipolar coupling constants (1λ8), is examined using Brownian dynamics simulations. With increasing λ, the structural motifs in the system change from unclustered particles, through chains, to rings. This gives rise to a nonmonotonic dependence of the static susceptibility χ(0) on λ and qualitative changes to the frequency spectrum. The behavior of χ(0) is already understood, and the simulation results are compared to an existing theory. The single-particle rotational dynamics are characterized by the Brownian time, τB, which depends on the particle size, carrier-liquid viscosity, and temperature. With λ5.5, the imaginary part of the spectrum, χ(ω), shows a single peak near ωτB1, characteristic of single particles. With λ5.75, the spectrum is dominated by the low-frequency response of chains. With λ7, new features appear at high frequency, which correspond to intracluster motions of dipoles within chains and rings. The peak frequency corresponding to these intracluster motions can be computed accurately using a simple theory.

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  • Received 3 May 2021
  • Accepted 3 June 2021

DOI:https://doi.org/10.1103/PhysRevE.103.062611

©2021 American Physical Society

Physics Subject Headings (PhySH)

  1. Physical Systems
Polymers & Soft Matter

Authors & Affiliations

Philip J. Camp*

  • School of Chemistry, University of Edinburgh, David Brewster Road, Edinburgh EH9 3FJ, Scotland, and Department of Theoretical and Mathematical Physics, Institute of Natural Sciences and Mathematics, Ural Federal University, 51 Lenin Avenue, Ekaterinburg 620000, Russia

Alexey O. Ivanov

  • Department of Theoretical and Mathematical Physics, Ural Mathematical Center, Institute of Natural Sciences and Mathematics, Ural Federal University, 51 Lenin Avenue, Ekaterinburg 620000, Russia

Julien O. Sindt

  • EPCC, Bayes Centre, University of Edinburgh, 47 Potterrow, Edinburgh EH8 9BT, Scotland

  • *philip.camp@ed.ac.uk

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Issue

Vol. 103, Iss. 6 — June 2021

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