Title

Quantitative Scanning Transmission Electron Microscopy For The Measurement Of Thicknesses And Volumes Of Individual Nanoparticles

Abstract

In Scanning Transmission Electron Microscopy (STEM) the High-Angle Annular Dark-Field (HAADF) signal increases with atomic number and sample thickness, while dynamic scattering effects and sample orientation have little influence on the contrast. The sensitivity of the HAADF detector for a FEI F30 transmission electron microscope has been calibrated. Additionally, a nearly linear relationship of the HAADF signal with the incident electron current is confirmed. Cross sections of multilayered samples for contrast calibration were obtained by focused ion-beam (FIB) preparation. These cross sections contained several layers with known composition. A database with several pure elements and compounds has been compiled, containing experimental data on the fraction of electrons scattered onto the HAADF detector for each nanometer of sample thickness. Contrast simulations are based on the multi-slice formalism and confirm the differences in HAADF-scattering contrast for the elements and compounds. TEM offers high lateral resolution, but contains little or no information on the thickness of samples. Thickness maps in energy-filtered transmission electron microscopy, convergent-beam electron diffraction and tilt series are so far the only methods to determine thicknesses of particles in a transmission electron microscope. We show that the calibrated HAADF contrast can be used to determine the thicknesses of individual nanoparticles deposited on carbon films. With this information the volumes of nanoparticles with known composition were determined. © 2009 Materials Research Society.

Publication Date

1-1-2009

Publication Title

Materials Research Society Symposium Proceedings

Volume

1184

Number of Pages

119-124

Document Type

Article; Proceedings Paper

Personal Identifier

scopus

DOI Link

https://doi.org/10.1557/proc-1184-hh01-06

Socpus ID

74049105232 (Scopus)

Source API URL

https://api.elsevier.com/content/abstract/scopus_id/74049105232

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