Micromagnetic simulation of neutron scattering from spherical nanoparticles: Effect of pore-type defects

in Physical Review. B (2023), 107

Resolving the complex spin structure in Fe-based soft magnetic nanocrystalline material by magnetic small-angle neutron scattering

Scientific Conference (2022, September 13)

Resolving the Complex Spin Structure in Fe-Based Soft Magnetic Nanocrystalline Material by Magnetic Small-Angle Neutron Scattering

in Neutron News (2022), 33(2), 15-17

Revealing defect-induced spin disorder in nanocrystalline

Scientific Conference (2022, January)

MuMag2022: a software tool for analyzing magnetic field dependent unpolarized small-angle neutron scattering data of bulk ferromagnets

in Journal of Applied Crystallography (2022), 55

Magnetic small-angle neutron scattering - a powerful probe of the magnetization configuration

Scientific Conference (2021, October 16)

Revealing defect-induced spin disorder in nanocrystalline Ni

in Physical Review Materials (2021), 5(4), 044409

Unraveling Nanostructured Spin Textures in Bulk Magnets

in Small Science (2021), 1(1), 2000003

One of the key challenges in magnetism remains the determination of the nanoscopic magnetization profile within the volume of thick samples, such as permanent ferromagnets. Thanks to the large penetration ... [more ▼]

One of the key challenges in magnetism remains the determination of the nanoscopic magnetization profile within the volume of thick samples, such as permanent ferromagnets. Thanks to the large penetration depth of neutrons, magnetic small-angle neutron scattering (SANS) is a powerful technique to characterize bulk samples. The major challenge regarding magnetic SANS is accessing the real-space magnetization vector field from the reciprocal scattering data. In this letter, a fast iterative algorithm is introduced that allows one to extract the underlying two-dimensional magnetic correlation functions from the scattering patterns. This approach is used here to analyze the magnetic microstructure of Nanoperm, a nanocrystalline alloy which is widely used in power electronics due to its extraordinary soft magnetic properties. It can be shown that the computed correlation functions clearly reflect the projection of the three-dimensional magnetization vector field onto the detector plane, which demonstrates that the used methodology can be applied to probe directly spin-textures within bulk samples with nanometer-resolution. This article is protected by copyright. All rights reserved. [less ▲]

Toward Understanding Complex Spin Textures in Nanoparticles by Magnetic Neutron Scattering

in Physical Review Letters (2020), 125

The benefits of a Bayesian analysis for the characterization of magnetic nanoparticles

in Nanotechnology (2020), 31(43), 435704

Magnetic nanoparticles offer a unique potential for various biomedical applications, but prior to commercial usage a standardized characterization of their structural and magnetic properties is required ... [more ▼]

Magnetic nanoparticles offer a unique potential for various biomedical applications, but prior to commercial usage a standardized characterization of their structural and magnetic properties is required. For a thorough characterization, the combination of conventional magnetometry and advanced scattering techniques has shown great potential. In the present work, we characterize a powder sample of high-quality iron oxide nanoparticles that are surrounded with a homogeneous thick silica shell by DC magnetometry and magnetic small-angle neutron scattering (SANS). To retrieve the particle parameters such as their size distribution and saturation magnetization from the data, we apply standard model fits of individual data sets as well as global fits of multiple curves, including a combination of the magnetometry and SANS measurements. We show that by combining a standard least-squares fit with a subsequent Bayesian approach for the data refinement, the probability distributions of the model parameters and their cross correlations can be readily extracted, which enables a direct visual feedback regarding the quality of the fit. This prevents an overfitting of data in case of highly correlated parameters and renders the Bayesian method as an ideal component for a standardized data analysis of magnetic nanoparticle samples. [less ▲]