School of Engineering
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Postdoctoral Scholar, Materials Science and Engineering
BioMy research interests have been focusing on how individual building blocks come together resulting in complex functions which are hard to predict, if possible, from the individual identities. Similar to a digital screen displaying a movie, the complicated pattern and story can hardly be interpreted from the dynamic traces of a single pixel. Specifically, I have been studying the general topic of self-assembly and non-equilibrium behaviors in soft matter systems, using both experimental and simulation tools.
I obtained my B.S. degree in physics from University of Science and Technology of China (USTC) in 2015. In my undergraduate research, I tried to use computer simulation to study multiple systems in Prof. Zhonghuai Hou’s group, such as the Viscek model for self-propelled particles. In 2014, I visited Oxford University to study the phase behaviors of active nematics using Lattice-Boltzmann method in Prof. Julia M. Yeomans' group. In 2020, I obtained my Ph.D. degree in Materials Science and Engineering at University of Illinois at Urbana-Champaign (UIUC) under the supervision of Prof. Qian Chen. During my Ph.D. research, we illustrated the nonclassical crystallization pathway of nanoparticles (Nat. Mater., 19, 450–455, 2020) and supracrystal growth kinetics (Nat. Commun., 11, 4555, 2020) using liquid-phase TEM. I also studied other nonequilibrium behaviors in novel colloidal systems, such as shape transformation of metal-organic framework crystals during chemical etching (ACS Appl. Mater. Interfaces, 10, 48, 40990–40995, 2018), application of ferromagnetic colloids in inductor design (Science Adv., 6, 3, eaay4508, 2020) and electron transport in redox-active colloids.
In August 2020, I joined Prof. Guosong Hong’s group at the materials science and engineering department at Stanford University to develop novel nanomaterials that can interact with neurons at the subcellular level. Armed with the knowledge of nanotechnology and theoretical modeling, we are extending the tools that can be used to investigate the challenging questions in neuroscience.