Deep Learning-Assisted Analysis of Anomalous Nanoparticle Surface Diffusion in Liquid Phase TEM

Beginning with Robert Brown’s original observation in 1828, various techniques have been developed to study the hydrodynamics and interactions of particles in solution. These techniques have inspired or been followed by development of theories that are capable of describing these fundamental aspects of micron-scale particles. Yet, many of the underlying assumptions break down at the nanoscale regime, requiring development of new techniques and theories to understand the fundamentals of interactions and dynamics at the nanoscale.

Liquid cell transmission electron microscopy (LCTEM) is a promising technique for studying motion of nanoparticles in liquid with high spatial and temporal resolution. However, the lack of understanding of how the electron beam of a transmission electron microscope affects the particle motion has held back advancement in using LCTEM for in situ single nanoparticle and macromolecule tracking at interfaces.

In this talk, I will present my recent work on studying the anomalous diffusive motion of a model system of gold nanorods dispersed in water and moving near the silicon nitride membrane of a commercial liquid cell in a broad range of electron beam dose rates. By leveraging the power of convolutional deep neural networks as well as canonical statistical tests, I show that there is a crossover in diffusive behavior of nanoparticles in LCTEM from fractional Brownian motion at low dose rates, resembling diffusion in a viscoelastic medium, to continuous time random walk at high dose rates, resembling diffusion on an energy landscape with trapping sites.

I will then discuss how this work forms the foundation to study equilibrium and nonequilibrium processes for a broad range of nanoparticles, interfaces, and fluids in chemical and biological systems.

Speaker: Vida Jamali, UC Berkeley