Influence of clustering on the magnetic properties and hyperthermia performance of iron oxide nanoparticles.

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 (FS-L, XL, XXL) excel in magnetic hyperthermia experiments.

Bacterial magnetosomes - nature's powerful contribution to MPI tracer research.

The magnetic particle spectrum (MPS) of bacterial magnetosomes, isolated from Magnetospirillum gryphiswaldense, is measured and compared to that of the current "gold standard", Resovist®. It is shown that the amplitudes of the magnetosomes' harmonics by far exceed that of Resovist®; the amplitude of the third harmonic is higher by a factor of 7, and is the highest value obtained for iron oxide nanoparticles to date.

Size and property bimodality in magnetic nanoparticle dispersions: single domain particles vs. strongly coupled nanoclusters.

The widespread use of magnetic nanoparticles in the biotechnical sector puts new demands on fast and quantitative characterization techniques for nanoparticle dispersions. In this work, we report the use of asymmetric flow field-flow fractionation (AF4) and ferromagnetic resonance (FMR) to study the properties of a commercial magnetic nanoparticle dispersion. We demonstrate the effectiveness of both techniques when subjected to a dispersion with a bimodal size/magnetic property distribution: i.e., a small superparamagnetic fraction, and a larger blocked fraction of strongly coupled colloidal nanoclusters. We show that the oriented attachment of primary nanocrystals into colloidal nanoclusters drastically alters their static, dynamic, and magnetic resonance properties. Finally, we show how the FMR spectra are influenced by dynamical effects; agglomeration of the superparamagnetic fraction leads to reversible line-broadening; rotational alignment of the suspended nanoclusters results in shape-dependent resonance shifts. The AF4 and FMR measurements described herein are fast and simple, and therefore suitable for quality control procedures in commercial production of magnetic nanoparticles.

Synthesis methods to prepare single- and multi-core iron oxide nanoparticles for biomedical applications.

We review current synthetic routes to magnetic iron oxide nanoparticles for biomedical applications. We classify the different approaches used depending on their ability to generate magnetic particles that are either single-core (containing only one magnetic core, i.e. a single domain nanocrystal) or multi-core (containing several magnetic cores, i.e. single domain nanocrystals). The synthesis of single-core magnetic nanoparticles requires the use of surfactants during the particle generation, and careful control of the particle coating to prevent aggregation. Special attention has to be paid to avoid the presence of any toxic reagents after the synthesis if biomedical applications are intended. Several approaches exist to obtain multi-core particles based on the coating of particle aggregates; nevertheless, the production of multi-core particles with good control of the number of magnetic cores per particle, and of the degree of polydispersity of the core sizes, is still a difficult task. The control of the structure of the particles is of great relevance for biomedical applications as it has a major influence on the magnetic properties of the materials.

Comparing neural networks: a benchmark on growing neural gas, growing cell structures, and fuzzy ARTMAP.

This article compares the performance of some recently developed incremental neural networks with the wellknown multilayer perceptron (MLP) on real-world data. The incremental networks are fuzzy ARTMAP (FAM), growing neural gas (GNG) and growing cell structures (GCS). The real-world datasets consist of four different datasets posing different challenges to the networks in terms of complexity of decision boundaries, overlapping between classes, and size of the datasets. The performance of the networks on the datasets is reported with respect to measure classification error, number of training epochs, and sensitivity toward variation of parameters. Statistical evaluations are applied to examine the significance of the results. The overall performance ranks in the following descending order: GNG, GCS, MLP, FAM.