Anomalous nanoparticle surface diffusion in LCTEM is revealed by deep learning-assisted analysis

Vida Jamali, Cory Hargus, Assaf Ben-Moshe, Amirali Aghazadeh, Hyun Dong Ha, Kranthi K. Mandadapu, A. Paul Alivisatos

Research output: Contribution to journalArticlepeer-review

31 Scopus citations

Abstract

The motion of nanoparticles near surfaces is of fundamental importance in physics, biology, and chemistry. Liquid cell transmission electron microscopy (LCTEM) is a promising technique for studying motion of nanoparticles with high spatial resolution. Yet, the lack of understanding of how the electron beam of the microscope affects the particle motion has held back advancement in using LCTEM for in situ single nanoparticle and macromolecule tracking at interfaces. Here, we experimentally studied the motion of a model system of gold nanoparticles dispersed in water and moving adjacent to the silicon nitride membrane of a commercial LC in a broad range of electron beam dose rates. We find that the nanoparticles exhibit anomalous diffusive behavior modulated by the electron beam dose rate. We characterized the anomalous diffusion of nanoparticles in LCTEM using a convolutional deep neural-network model and canonical statistical tests. The results demonstrate that the nanoparticle motion is governed by fractional Brownian motion at low dose rates, resembling diffusion in a viscoelastic medium, and continuous-time random walk at high dose rates, resembling diffusion on an energy landscape with pinning sites. Both behaviors can be explained by the presence of silanol molecular species on the surface of the silicon nitride membrane and the ionic species in solution formed by radiolysis of water in presence of the electron beam.

Original languageEnglish
Article numbere2017616118
JournalProceedings of the National Academy of Sciences of the United States of America
Volume118
Issue number10
DOIs
StatePublished - 9 Mar 2021
Externally publishedYes

Bibliographical note

Publisher Copyright:
© 2021 National Academy of Sciences. All rights reserved.

Keywords

  • Anomalous diffusion
  • Deep neural network
  • Liquid cell electron microscopy
  • Single-particle tracking

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