| 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
The benefits of a Bayesian analysis for the characterization of magnetic nanoparticles
Abstract
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 ex tracted, 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.