Single-Nanoparticle Orientation Sensing by Deep Learning

Jingtian Hu, Tingting Liu, Priscilla Choo, Shengjie Wang, Thaddeus Reese, Alexander D. Sample, Teri W. Odom*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

1 Scopus citations

Abstract

This paper describes a computational imaging platform to determine the orientation of anisotropic optical probes under differential interference contrast (DIC) microscopy. We established a deep-learning model based on data sets of DIC images collected from metal nanoparticle optical probes at different orientations. This model predicted the in-plane angle of gold nanorods with an error below 20°, the inherent limit of the DIC method. Using low-symmetry gold nanostars as optical probes, we demonstrated the detection of in-plane particle orientation in the full 0-360° range. We also showed that orientation predictions of the same particle were consistent even with variations in the imaging background. Finally, the deep-learning model was extended to enable simultaneous prediction of in-plane and out-of-plane rotation angles for a multibranched nanostar by concurrent analysis of DIC images measured at multiple wavelengths.

Original languageEnglish (US)
Pages (from-to)2339-2346
Number of pages8
JournalACS Central Science
Volume6
Issue number12
DOIs
StatePublished - Dec 23 2020

ASJC Scopus subject areas

  • Chemistry(all)
  • Chemical Engineering(all)

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