Abstract: Motion and interaction of nanoparticles near heterogeneous surfaces play a key role in nanoscale transport processes involved in nanomedicine, environmental remediation, electronics, and sensing. Liquid phase transmission electron microscopy (LPTEM) has emerged as a promising technique for single particle tracking at the nanoscale, enabling us to visualize and characterize the motion and interaction with unprecedented spatiotemporal resolution. Yet, understanding how nanoparticles move in the heterogenous chamber of the LPTEM and in interaction with the electron beam of the microscope has remained elusive. Here, we discuss our recent work on developing physics-inspired deep neural networks to classify and learn the dynamic behavior of nanoparticles in a heterogeneous environment of the LPTEM. We show that the electron beam dose rate of the microscope modulates the dynamic behavior of nanoparticles. We then discuss how such understanding enables us to engineer the dynamic and self-assembly of nanoparticles in the solution environment all while visualizing them at the nanoscale.

Bio: Vida Jamali is an Assistant Professor in the School of Chemical and Biomolecular Engineering at Georgia Tech since August 2022. Before that, she was a postdoctoral researcher at UC Berkeley, working with Prof. A. Paul Alivisatos. She received her PhD in Chemical and Biomolecular Engineering from Rice University in 2017. Her research group at Georgia Tech uses experimental, theoretical, and computational tools such as liquid phase transmission electron microscopy, rheology, statistical and colloidal thermodynamics, and machine learning to study the underlying physical principles that govern the dynamics, statistics, mechanics, and self-organization of nanostructured soft materials, in and out of thermal equilibrium, from both fundamental and technological aspects. Vida is the recipient of NSF CAREER award (2024) and ACS PRF Doctoral New Investigator Award (2023) for her research.

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