Medium amplitude field susceptometry (MAFS) for magnetic nanoparticles

Ilg, P. ORCID: https://orcid.org/0000-0002-7518-5543 (2024) Medium amplitude field susceptometry (MAFS) for magnetic nanoparticles. Journal of Magnetism and Magnetic Materials, 610. 172540. ISSN 1873-4766 doi: 10.1016/j.jmmm.2024.172540

Abstract/Summary

The linear dynamic susceptibility is arguably the most important property to specify the magnetization dynamics of a given system. Therefore, this quantity has been studied in great detail and the corresponding measurements known as susceptometry are well-established. Notwithstanding its relevance, the linear susceptibility is inherently limited to describe the magnetization response to weak external fields only. Here, we suggest the framework of Medium Amplitude Field Susceptometry (MAFS) to study the non-linear response which complements the linear susceptibility and provides additional information on the magnetic properties of the system and is applicable to magnetic fields of medium amplitudes. In particular, we introduce the general third-order nonlinear susceptibility cˆ3 as a central quantity that completely specifies the lowest-order non-linear response to arbitrary time-dependent magnetic fields. We show that response functions in medium amplitude oscillatory magnetic fields and parallel superposition susceptometry are contained in cˆ3 as special cases. Also included in cˆ3 are interesting intermodulation effects when the system is probed by a superposition of oscillating magnetic fields with different frequencies. We work out the explicit form of cˆ3 for several model systems for the dynamics of magnetic nanoparticles (MNPs). We expect this unifying framework to be not only of theoretical interest, but also useful for a deeper characterization of MNP systems, giving additional information on their suitability for various applications.

Item Type	Article
URI	https://reading-clone.eprints-hosting.org/id/eprint/118485
Identification Number/DOI	10.1016/j.jmmm.2024.172540
Refereed	Yes
Divisions	Science > School of Mathematical, Physical and Computational Sciences > Department of Mathematics and Statistics
Publisher	Elsevier
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