A multiple scattering algorithm for three dimensional phase contrast atomic electron tomography.

Atomic electron tomography Multiple scattering Optimization Phase retrieval Transmission electron microscopy

Journal

Ultramicroscopy
ISSN: 1879-2723
Titre abrégé: Ultramicroscopy
Pays: Netherlands
ID NLM: 7513702

Informations de publication

Date de publication:
01 2020
Historique:
received: 28 02 2019
revised: 27 09 2019
accepted: 15 10 2019
pubmed: 11 11 2019
medline: 11 11 2019
entrez: 10 11 2019
Statut: ppublish

Résumé

Electron tomography is used in both materials science and structural biology to image features well below the optical resolution limit. Here, we present a new method for high-resolution 3D transmission electron microscopy (TEM) which approximately reconstructs the electrostatic potential of a sample at atomic resolution in all three dimensions. We use phase contrast images captured through-focus and at varying tilt angles, along with an implicit phase retrieval algorithm that accounts for dynamical and strong scattering, providing more accurate results with much lower electron doses than current atomic electron tomography methods. We test our algorithm using simulated images of a synthetic needle geometry dataset composed of an amorphous silicon dioxide shell around a silicon core. By simulating various levels of electron dose, tilt and defocus, missing projections, and regularization methods, we identify a configuration that allows us to accurately determine both atomic positions and species. We also test the ability of our method to recover randomly positioned vacancies in light elements such as silicon, and to accurately reconstruct strongly-scattering elements such as tungsten.

Identifiants

pubmed: 31704623
pii: S0304-3991(19)30052-X
doi: 10.1016/j.ultramic.2019.112860
pii:
doi:

Types de publication

Journal Article Research Support, U.S. Gov't, Non-P.H.S.

Langues

eng

Sous-ensembles de citation

IM

Pagination

112860

Informations de copyright

Copyright © 2019 The Authors. Published by Elsevier B.V. All rights reserved.

Auteurs

David Ren (D)

Department of Electrical Engineering and Computer Sciences, University of California, Berkeley, CA 94720, USA. Electronic address: david.ren@berkeley.edu.

Colin Ophus (C)

NCEM, Molecular Foundry, Lawrence Berkeley National Laboratory, 1 Cyclotron Road, Berkeley, CA 94720, USA. Electronic address: cophus@gmail.com.

Michael Chen (M)

Department of Electrical Engineering and Computer Sciences, University of California, Berkeley, CA 94720, USA.

Laura Waller (L)

Department of Electrical Engineering and Computer Sciences, University of California, Berkeley, CA 94720, USA.

Classifications MeSH