UC San Diego

Superparamagnetic nanoparticles represent an exciting area of research within the broader field of nanoscale materials. Synthetic control over physical properties, such as nanoparticle size, shape, and crystalline phase strongly impact the magnetic properties, ultimately dictating the functionality in potential applications.

Chapter 1 provides a brief background of nanoscale magnetic materials. It discusses the high degree of control possible through colloidal nanoparticle synthesis techniques, as well as common challenges encountered. The chapter includes specific examples on the structure-property relationship, including implications for magnetism, concluding with a summary of superparamagnetism.

Chapter 2 discusses the efforts to enhance the reproducibility in magnetic nanoparticle synthesis. The chapter introduces two novel forms of iron oleate precursor that offer long-term stability, well-defined stoichiometry, and large-scale availability, addressing issues with reproducibility. These advancements enable the synthesis of magnetite nanoparticles with tunable sizes ranging from 4 to 16 nm and low size dispersity, providing consistent results in the superparamagnetic size regime.

Chapter 3 details the development of a new technique for the quantification and parametrization of superparamagnetic nanoparticle magnetization curves. The size-dependent magnetic properties were elucidated by fitting the data to a statistical model, leading to a better understanding of these materials' behavior and potential for applications. It emphasizes the challenges in interpreting magnetization data and the importance of simplified models in advancing the field.

Chapter 4, in collaboration with Angelica Orlova, presents the extension of the statistical analysis of magnetization data to a molecular system. Three ErCOT2 compounds with varying structures, including the angle, and spacing between magnetic units, were analyzed. The temperature dependence and effect of dipolar coupling between ErCOT2 units on the magnetization curve were analyzed. Each curve was deconvoluted into its components, which were tracked and quantified across the parameter space.