| People | Locations | Statistics |
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| Naji, M. |
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| Motta, Antonella |
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| Aletan, Dirar |
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| Mohamed, Tarek |
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| Ertürk, Emre |
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| Taccardi, Nicola |
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| Kononenko, Denys |
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| Petrov, R. H. | Madrid |
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| Alshaaer, Mazen | Brussels |
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| Bih, L. |
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| Casati, R. |
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| Muller, Hermance |
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| Kočí, Jan | Prague |
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| Šuljagić, Marija |
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| Kalteremidou, Kalliopi-Artemi | Brussels |
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| Azam, Siraj |
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| Ospanova, Alyiya |
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| Blanpain, Bart |
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| Ali, M. A. |
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| Popa, V. |
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| Rančić, M. |
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| Ollier, Nadège |
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| Azevedo, Nuno Monteiro |
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| Landes, Michael |
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| Rignanese, Gian-Marco |
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Honecker, Dirk
Universidad de Cantabria
in Cooperation with on an Cooperation-Score of 37%
Topics
Publications (28/28 displayed)
- 2024Small-angle neutron scattering analysis in Sn-Ag Lead-free solder alloyscitations
- 2022Uniaxial polarization analysis of bulk ferromagnets: theory and first experimental resultscitations
- 2022Magnetic nanoprecipitates and interfacial spin disorder in zero-field-annealed Ni50Mn45In5 Heusler alloys as seen by magnetic small-angle neutron scatteringcitations
- 2022Magnetic nanoprecipitates and interfacial spin disorder in zero-field-annealed Ni<sub>50</sub>Mn<sub>45</sub>In<sub>5</sub> Heusler alloys as seen by magnetic small-angle neutron scatteringcitations
- 2022Controlling the rotation modes of hematite nanospindles using dynamic magnetic fields
- 2021TaC Precipitation Kinetics During Cooling of Co−Re‐Based Alloyscitations
- 2021Clustering in Ferronematics - the Effect of Magnetic Collective Orderingcitations
- 2021Unraveling Nanostructured Spin Textures in Bulk Magnetscitations
- 2020Field Dependence of Magnetic Disorder in Nanoparticlescitations
- 2020Magnetic Guinier lawcitations
- 2020Magnetic structure factor of correlated moments in small-angle neutron scatteringcitations
- 2020The benefits of a Bayesian analysis for the characterization of magnetic nanoparticlescitations
- 2020The benefits of a Bayesian analysis for the characterization of magnetic nanoparticlescitations
- 2020Unraveling Nanostructured Spin Textures in Bulk Magnets
- 2019Field Dependence of Magnetic Disorder in Nanoparticlescitations
- 2019Evidence for the formation of nanoprecipitates with magnetically disordered regions in bulk $mathrm{Ni}_{50}mathrm{Mn}_{45}mathrm{In}_{5}$ Heusler alloys
- 2019Using the singular value decomposition to extract 2D correlation functions from scattering patterns
- 2019Experimental observation of third-order effect in magnetic small-angle neutron scatteringcitations
- 2019The magnetic structure factor of correlated moments in small-angle neutron scatteringcitations
- 2019The magnetic structure factor of correlated nanoparticle moments in small-angle neutron scattering
- 2019Magnetic ordering of the martensite phase in Ni-Co-Mn-Sn-based ferromagnetic shape memory alloyscitations
- 2019Transverse and longitudinal spin-fluctuations in INVAR Fe0.65Ni0.35.citations
- 2018Dipolar-coupled moment correlations in clusters of magnetic nanoparticlescitations
- 2018Dipolar-coupled moment correlations in clusters of magnetic nanoparticlescitations
- 2018Dipolar-coupled moment correlations in clusters of magnetic nanoparticlescitations
- 2016Magnetic small-angle neutron scattering on bulk metallic glasses
- 2013Magnetization reversal in Nd-Fe-B based nanocomposites as seen by magnetic small-angle neutron scatteringcitations
- 2013Analysis of magnetic neutron-scattering data of two-phase ferromagnetscitations
Places of action
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document
Unraveling Nanostructured Spin Textures in Bulk Magnets
Abstract
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 wi thin bulk samples with nanometer-resolution.