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Micromagnetic determination of the FORC response of paleomagnetically significant magnetite assemblages
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  • Lesleis Nagy,
  • Roberto Moreno,
  • Adrian Muxworthy,
  • Wyn Williams,
  • Greig Paterson,
  • Lisa Tauxe,
  • Miguel A. Valdez-Grijalva
Lesleis Nagy
Department of Earth, Ocean and Ecological Sciences, University of Liverpool

Corresponding Author:[email protected]

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Roberto Moreno
CONICET, Instituto de Física Enrique Gaviola (IFEG)
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Adrian Muxworthy
Department of Earth Sciences, University College London
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Wyn Williams
School of GeoSciences, University of Edinburgh
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Greig Paterson
Department of Earth, Ocean and Ecological Sciences, University of Liverpool
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Lisa Tauxe
Scripps Institution of Oceanography, University of California San Diego
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Miguel A. Valdez-Grijalva
Instituto Mexicano del Petroleo, Gustavo Adolfo Madero, Mexico


Micromagnetic modelling allows the systematic study of the effects of particle size and shape on the first-order reversal curve (FORC) magnetic hysteresis response for magnetite particles in the single-domain (SD) and pseudo-single domain (PSD) particle size range. The interpretation of FORCs, though widely used, has been highly subjective.  Here, we use micromagnetics to model randomly oriented distributions of particles to allow more physically meaningful interpretations.  We show that one commonly found type of PSD particle - namely single vortex (SV) particles - has far more complex signals than SD particles, with multiple peaks and troughs in the FORC distribution, where the peaks have higher switching fields for larger SV particles. Particles in the SD to SV transition zone have the lowest switching fields. Symmetrical and prolate particles display similar behavior, with distinctive peaks forming near the vertical axis of the FORC diagram. In contrast, highly oblate particles produce `butterfly' structures, suggesting that these are potentially diagnostic of particle morphology. We also consider FORC diagrams for distributions of particle sizes and shapes and produce an online application that users can use to build their own FORC distributions.  There is good agreement between the model predictions for distributions of particle sizes and shapes, and the published experimental literature.
15 Jan 2024Submitted to ESS Open Archive
15 Jan 2024Published in ESS Open Archive