Distribution functions of magnetic nanoparticles determined by a numerical inversion method. (10th July 2017)

• Record Type: Journal Article
• Authors: Bender, P; Balceris, C; Ludwig, F; Posth, O; Bogart, L K; Szczerba, W; Castro, A; Nilsson, L; Costo, R; Gavilán, H; González-Alonso, D; Pedro, I de; Barquín, L Fernández; Johansson, C
• Is Part Of: New journal of physics. Volume 19:Number 7(2017:Jul.)
• Publication Date: 2017-07-10
• Subjects: magnetic nanoparticles -- numerical inversion -- SAXS -- magnetization measurements -- AC susceptibility -- distribution functions; Physics -- Periodicals; Physics; Periodicals; 530.05
• DOI: 10.1088/1367-2630/aa73b4
• Languages: English
• ISSNs: 1367-2630

Abstract

Abstract: 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.