Ingenieurwissenschaften und zugeordnete Tätigkeiten
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- Core-clusters (1)
- Magnetic hyperthermia (1)
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- Multi-core particles (1)
- Nanoparticle (1)
- SAXS (1)
- Small-angle X-ray scattering (1)
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Clustering of magnetic nanoparticles can drastically change their collective magnetic properties, which in turn may influence their performance in technological or biomedical applications. Here, we investigate a commercial colloidal dispersion (FeraSpinTMR), which contains dense clusters of iron oxide cores (mean size around 9 nm according to neutron diffraction) with varying cluster size (about 18–56 nm according to small angle x-ray diffraction), and its individual size fractions (FeraSpinTMXS, S, M, L, XL, XXL). The magnetic properties of the colloids were characterized by isothermal magnetization, as well as frequency-dependent optomagnetic and AC susceptibility measurements. From these measurements we derive the underlying moment and Relaxation frequency distributions, respectively. Analysis of the distributions shows that the clustering of the initially superparamagnetic cores leads to remanent magnetic moments within the large clusters. At frequencies below 105 rad s−1, the relaxation of the clusters is dominated by Brownian (rotation) relaxation. At higher frequencies, where Brownian relaxation is inhibited due to viscous friction, the clusters still show an appreciable magnetic relaxation due to internal moment relaxation within the clusters. As a result of the internal moment relaxation, the colloids with the large clusters (FSL, XL, XXL) excel in magnetic hyperthermia experiments.
In the present study, we applied a regularized inversion method to extract the particle size, magnetic moment and relaxation-time distribution of magnetic nanoparticles from small-angle x-ray scattering (SAXS), DC magnetization (DCM) and AC susceptibility (ACS) measurements. For the measurements the particles were colloidally dispersed in water. At first approximation the particles could be assumed to be spherically shaped and homogeneously magnetized single-domain particles. As model functions for the inversion, we used the particle form factor of a sphere (SAXS), the Langevin function (DCM) and the Debye model (ACS). The extracted distributions exhibited features/peaks that could be distinctly attributed to the individually dispersed and non-interacting nanoparticles. Further analysis of these peaks enabled, in combination with a prior characterization of the particle ensemble by electron microscopy and dynamic light scattering, a detailed structural and magnetic characterization of the particles. Additionally, all three extracted distributions featured peaks, which indicated deviations of the scattering (SAXS), magnetization (DCM) or relaxation (ACS) behavior from the one expected for individually dispersed, homogeneously magnetized nanoparticles. These deviations could be mainly attributed to partial agglomeration (SAXS, DCM, ACS), uncorrelated surface spins (DCM) and/or intra-well relaxation processes (ACS). The main advantage of the numerical inversion method is that no ad hoc assumptions regarding the line shape of the extracted distribution functions are required, which enabled the detection of these contributions. We highlighted this by comparing the results with the results obtained by standard model fits, where the functional form of the distributions was a priori assumed to be log-normal shaped.