Deep learning-based denoising for improved dose efficiency in EDX tomography of nanoparticles-Reference-Cited by-同舟云学术

Deep learning-based denoising for improved dose efficiency in EDX tomography of nanoparticles

Published:2021 Issue:28 Volume:13 Page:12242-12249
ISSN:2040-3364
Container-title:Nanoscale
language:en
Short-container-title:Nanoscale

Author:

Skorikov Alexander¹²,Heyvaert Wouter¹²^ORCID,Albecht Wiebke¹²^ORCID,Pelt Daniël M.³^ORCID,Bals Sara¹²^ORCID

Affiliation:

1. EMAT, University of Antwerp, Groenenborgerlaan 171, 2020 Antwerp, Belgium

2. NANOlab Center of Excellence, University of Antwerp, Groenenborgerlaan 171, 2020 Antwerp, Belgium

3. Leiden Institute of Advanced Computer Science, Niels Bohrweg 1, 2333 CA Leiden, The Netherlands

Abstract

Denoising elemental maps of nanoparticles using an artificial neural network trained on simulated data allows for a drastic reduction in acquisition time and electron dose requirements for EDX tomography of nanoparticles.

Funder

Nederlandse Organisatie voor Wetenschappelijk Onderzoek

H2020 European Research Council

H2020 Marie Skłodowska-Curie Actions

H2020 Research Infrastructures

Publisher

Royal Society of Chemistry (RSC)

Subject

General Materials Science

Link

http://pubs.rsc.org/en/content/articlepdf/2021/NR/D1NR03232A

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