A new model of iron oxide nanoparticle magnetic properties to guide design of novel nanomaterials

Abstract

The goal of this work is to develop and demonstrate a novel model of superparamagnetic iron oxide nanoparticle (SPION) magnetic properties based on physical first principles and experimental mathematical relationships. SPIONs exhibit magnetic properties that differ from the bulk properties of iron oxide due to scale affects unique to nanoparticles. The developed model is able to predict the magnetic properties of any type of SPION at a given temperature and applied field strength based solely on the particle size. By predicting SPION magnetization and induced magnetic field, the model is a useful engineering tool for nanomaterials design. Using the model, it is possible to predict the magnetic behavior of even complex SPION based nanomaterials, facilitating materials design rather than pure discovery using costly high throughput methods. Using this model, we have investigated the magnetic properties of a clustered system of SPIONs to potentially be used as a magnetic detection device and image contrast agent. Using the model, it is possible to predict the ideal particle size for these particular nanomaterials by optimizing key magnetic parameters with regards to a specific application.