High-Throughput, Algorithmic Determination of Nanoparticle Structure from Electron Microscopy Images

Christine R. Laramy, Keith A. Brown, Matthew N. O'Brien, Chad A. Mirkin*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

23 Scopus citations

Abstract

Electron microscopy (EM) represents the most powerful tool to directly characterize the structure of individual nanoparticles. Accurate descriptions of nanoparticle populations with EM, however, are currently limited by the lack of tools to quantitatively analyze populations in a high-throughput manner. Herein, we report a computational method to algorithmically analyze EM images that allows for the first automated structural quantification of heterogeneous nanostructure populations, with species that differ in both size and shape. This allows one to accurately describe nanoscale structure at the bulk level, analogous to ensemble measurements with individual particle resolution. With our described EM protocol and our inclusion of freely available code for our algorithmic analysis, we aim to standardize EM characterization of nanostructure populations to increase reproducibility, objectivity, and throughput in measurements. We believe this work will have significant implications in diverse research areas involving nanomaterials, including, but not limited to, fundamental studies of structural control in nanoparticle synthesis, nanomaterial-based therapeutics and diagnostics, optoelectronics, and catalysis.

Original languageEnglish (US)
Pages (from-to)12488-12495
Number of pages8
JournalACS nano
Volume9
Issue number12
DOIs
StatePublished - Nov 20 2015

Keywords

  • automated
  • electron microscopy
  • high-throughput
  • image analysis
  • nanoparticles

ASJC Scopus subject areas

  • Materials Science(all)
  • Engineering(all)
  • Physics and Astronomy(all)

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