Department of Physics, The University of York, Heslington, York YO10 5DD, UK.
Faculty of Engineering and the Environment, University of Southampton, Highfield, Southampton, UK.
Sci Rep. 2017 Mar 24;7:45218. doi: 10.1038/srep45218.
The generic problem of extracting information on intrinsic particle properties from the whole class of interacting magnetic fine particle systems is a long standing and difficult inverse problem. As an example, the Switching Field Distribution (SFD) is an important quantity in the characterization of magnetic systems, and its determination in many technological applications, such as recording media, is especially challenging. Techniques such as the first order reversal curve (FORC) methods, were developed to extract the SFD from macroscopic measurements. However, all methods rely on separating the contributions to the measurements of the intrinsic SFD and the extrinsic effects of magnetostatic and exchange interactions. We investigate the underlying physics of the FORC method by applying it to the output predictions of a kinetic Monte-Carlo model with known input parameters. We show that the FORC method is valid only in cases of weak spatial correlation of the magnetisation and suggest a more general approach.
从整个相互作用的磁性细颗粒系统类别中提取关于内在粒子特性的信息,这是一个长期存在且困难的反问题。例如,开关场分布(SFD)是磁性系统特征化的一个重要数量,在记录介质等许多技术应用中,其确定特别具有挑战性。诸如一阶反转曲线(FORC)方法等技术被开发用于从宏观测量中提取 SFD。然而,所有方法都依赖于分离测量的固有 SFD 和静磁和交换相互作用的外在影响的贡献。我们通过将 FORC 方法应用于具有已知输入参数的动力学蒙特卡罗模型的输出预测,研究了 FORC 方法的基础物理。我们表明,只有在磁化的弱空间相关性的情况下,FORC 方法才有效,并提出了一种更通用的方法。