The benefits of a Bayesian analysis for the characterization of magnetic nanoparticles

TL;DR

This paper demonstrates that combining Bayesian analysis with standard fitting techniques enhances the characterization of magnetic nanoparticles by providing detailed probability distributions of parameters, improving data quality assessment and preventing overfitting.

Contribution

It introduces a Bayesian approach for analyzing magnetic nanoparticle data, improving parameter estimation and fit quality assessment over traditional methods.

Findings

Bayesian analysis provides detailed probability distributions of nanoparticle parameters.

Combining Bayesian methods with standard fits prevents overfitting in correlated data.

The approach enables visual feedback on fit quality and parameter correlations.

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 extracted, 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.