Date of Graduation

12-2021

Document Type

Dissertation

Degree Name

Doctor of Philosophy in Engineering (PhD)

Degree Level

Graduate

Department

Mechanical Engineering

Advisor

David Huitink

Committee Member

Rick Couvillion

Second Committee Member

Paul Millett

Third Committee Member

Robert Coridan

Fourth Committee Member

Jingyi Chen

Fifth Committee Member

Lauren Greenlee

Keywords

Magnetic Nanoparticle Hyperthermia, Nanoparticle Induction Heating, Nanoscale Heat Transfer, Thermometry

Abstract

Induction heating causes the release of enormous amounts of heat from dispersed magnetic nanoparticles. While the rate of heat transfer can be easily quantified calorimetrically, measuring the temperature of the nanoparticles on the nanoscale presents experimental challenges. Fully characterizing the temperature and thermal output of these magnetic particles is necessary to gauge overall heating efficiency and to provide a more holistic understanding of heat transfer on the nanoscale. Herein, this dissertation seeks to develop a novel nanoparticle thermometry technique, which correlates diffusion behavior in core-shell nanoparticles to local temperature. Initial measurements suggested that heating silica capped ferrous nanoparticles (SCNPs) via induction in a saline environment encouraged the diffusion of dissolved sodium ions into the silica shell. The concentration gradient of sodium ions within the shell underwent an observable transition after only a few seconds of heating, thus implying that the increased core temperature was the driving force behind the diffusion event. Calculating nanoscale temperature required a three-prong approach, which combined experimental and theoretical analyses. First, a computational model of the core-shell system was developed to accurately depict diffusion into the core-shell structure. Experimental X-ray methods then analyzed the relationship between diffusivity and temperature for the material system and also measured nanoscale concentration gradients within physical SCNPs. By comparing the experimental diffusion data to the theoretical model, the estimated nanoscale temperature was able to be extracted. Understanding nanoscale temperature provides insight into more encompassing thermal models for nanoparticle induction heating, which will ultimately lead to advancements in numerous applications.

Available for download on Saturday, February 17, 2024

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