Maximizing hysteretic losses in magnetic ferrite nanoparticles via model-driven synthesis and materials optimization

Abstract

This article develops a set of design guidelines for maximizing heat dissipation characteristics of magnetic ferrite MFe2O4 (M = Mn, Fe, Co) nanoparticles in alternating magnetic fields. Using magnetic and structural nanoparticle characterization, we identify key synthetic parameters in the thermal decomposition of organometallic precursors that yield optimized magnetic nanoparticles over a wide range of sizes and compositions. The developed synthetic procedures allow for gram-scale production of magnetic nanoparticles stable in physiological buffer for several months. Our magnetic nanoparticles display some of the highest heat dissipation rates, which are in qualitative agreement with the trends predicted by a dynamic hysteresis model of coherent magnetization reversal in single domain magnetic particles. By combining physical simulations with robust scalable synthesis and materials characterization techniques, this work provides a pathway to a model-driven design of magnetic nanoparticles tailored to a variety of biomedical applications ranging from cancer hyperthermia to remote control of gene expression.

Polina Anikeeva

Professor in Materials Science and Engineering
Professor in Brain and Cognitive Sciences
Associate Director, Research Laboratory of Electronics

My goal is to combine the current knowledge of biology and nanoelectronics to develop materials and devices for minimally invasive treatments for neurological and neuromuscular diseases.