Structural and magnetic properties of multi-core nanoparticles analysed using a generalised numerical inversion method

Abstract

The structural and magnetic properties of magnetic multi-core particles were determined by numerical inversion of small angle scattering and isothermal magnetisation data. The investigated particles consist of iron oxide nanoparticle cores (9 nm) embedded in poly(styrene) spheres (160 nm). A thorough physical characterisation of the particles included transmission electron microscopy, X-ray diffraction and asymmetrical flow field-flow fractionation. Their structure was ultimately disclosed by an indirect Fourier transform of static light scattering, small angle X-ray scattering and small angle neutron scattering data of the colloidal dispersion. The extracted pair distance distribution functions clearly indicated that the cores were mostly accumulated in the outer surface layers of the poly(styrene) spheres. To investigate the magnetic properties, the isothermal magnetisation curves of the multi-core particles (immobilised and dispersed in water) were analysed. The study stands out by applying the same numerical approach to extract the apparent moment distributions of the particles as for the indirect Fourier transform. It could be shown that the main peak of the apparent moment distributions correlated to the expected intrinsic moment distribution of the cores. Additional peaks were observed which signaled deviations of the isothermal magnetisation behavior from the non-interacting case, indicating weak dipolar interactions.

Dipolar-coupled moment correlations in clusters of magnetic nanoparticles

“…From the SAXS data we derived the underlying correlation function C(r)r 2 by an indirect Fourier transform (IFT) [41,[66][67][68][69] of the radially averaged scattering intensity I(q) ( Fig. 1(b)).…”

“…A special class of 3D ensembles of magnetic nanoparticles are particle core aggregates or clusters, also referred to as multi-core nanoparticles [37]. Investigation of such particles has attracted much interest in recent years [38][39][40][41], mainly motivated by their large potential for biomedical applications [42,43].…”

Distribution functions of magnetic nanoparticles determined by a numerical inversion method

“…In these cases no a priori assumptions regarding the line shape of the extracted distribution have to be made and data analysis is ultimately performed by interpreting the obtained apparent distributions. Similar numerical approaches are also commonly used to infer the hydrodynamic size distribution of nanoparticles from DLS measurements [30,31] or to analyze the small-angle scattering data of nanoparticle ensembles [32][33][34][35][36].…”

“…In the current work we use the same numerical approach as applied in [36] for the analysis of magnetic multi-core nanoparticles to systematically evaluate SAXS, DCM and ACS data of a dilute, colloidal dispersion of single-domain IONPs. Initially, we extracted the discrete particle size distribution from the SAXS data, the moment distribution from the DCM data and the relaxation time distribution from the ACS data.…”

Magnetic small-angle neutron scattering on bulk metallic glasses: A feasibility study for imaging displacement fields

“…For the computation of the correlation function, one may also use Glatter's indirect Fourier transformation method [24,[27][28][29][30][31]. However, this technique, which was originally developed for particle scattering, requires the rather precise knowledge of the maximum particle size in the system.…”