Magnetic Nanoparticles for Biomedical Applications: A Computational Study

2020-06-15 16:20:44
报告题目: Magnetic Nanoparticles for Biomedical Applications: A Computational Study
报告时间:2020年6月16日(周二)下午3:00
报告地点:高新大厦15楼会议室1
报告人:赵志远 博士 Dr. Zhiyuan Zhao, Courtesy Postdoctoral Researcher, department of chemical engineering, University of Florida
 
Zhiyuan Zhao received his bachelor degree of engineering at the Beijing University of Chemical Technology, China, in 2012. During the following one year, he worked as a research assistant in Tianjin Institute of Industrial Biotechnology, Chinese Academy of Science. Then he attended the University of Florida, USA, and joined in Dr. Carlos Rinaldi’s lab, working on the dynamics simulations of magnetic nanoparticles and rheology of ferrofluids. After completing his degree in Master of Science in 2015, he continued to work as a research assistant in the Rinaldi lab for one year and then was re-admitted to the doctoral program in the department of chemical engineering at the University of Florida. His degree in Doctor of Philosophy was received in December 2019. Currently he works as a courtesy postdoctoral researcher in the Rinaldi lab at the University of Florida. 
 
Over the past decades, magnetic nanoparticles (MNPs) has exhibited increasing potentials in the biomedical applications, such as in magnetic capture/assembly, magnetic hyperthermia, and magnetic particle imaging. However, the computational study on the behaviors of the MNPs in these applications were still limited. In Zhiyuan’s work, computational simulations based on the Brownian dynamics and Landau-Lifshitz-Gilbert equation were executed to study the dynamics and magnetization dynamics of MNPs with different properties and interaction conditions in various patterns of magnetic field. The simulation results provide significant theoretical understanding of mechanisms of the MNPs in the applications of magnetic capture/assembly, magnetic hyperthermia, and magnetic particle imaging. These theoretical predictions can guide to synthesize MNPs of optimal performance and to design better experiment protocols. 

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