Please use this identifier to cite or link to this item: http://hdl.handle.net/1942/38702
Title: Three Approaches for Representing the Statistical Uncertainty on Atom-Counting Results in Quantitative ADF STEM
Authors: De Wael, Annelies
De Backer , Annick
Yu, Chu-Ping
Sentuerk, Duygu Gizem
Lobato, Ivan
FAES, Christel 
Van Aert, Sandra
Issue Date: 2022
Publisher: CAMBRIDGE UNIV PRESS
Source: MICROSCOPY AND MICROANALYSIS,
Status: Early view
Abstract: A decade ago, a statistics-based method was introduced to count the number of atoms from annular dark-field scanning transmission electron microscopy (ADF STEM) images. In the past years, this method was successfully applied to nanocrystals of arbitrary shape, size, and composition (and its high accuracy and precision has been demonstrated). However, the counting results obtained from this statistical framework are so far presented without a visualization of the actual uncertainty about this estimate. In this paper, we present three approaches that can be used to represent counting results together with their statistical error, and discuss which approach is most suited for further use based on simulations and an experimental ADF STEM image.
Notes: Van Aert, S (corresponding author), Univ Antwerp, EMAT, Antwerp, Belgium.; Van Aert, S (corresponding author), Univ Antwerp, NANOlab Ctr Excellence, Antwerp, Belgium.
sandra.vanaert@uantwerpen.be
Keywords: model averaging;quantitative electron microscopy;scanning transmission electron microscopy;statistical parameter estimation theory;statistical uncertainty
Document URI: http://hdl.handle.net/1942/38702
ISSN: 1431-9276
e-ISSN: 1435-8115
DOI: 10.1017/S1431927622012284
ISI #: 000854930500001
Rights: The Author(s), 2022. Published by Cambridge University Press on behalf of the Microscopy Society of America. This is an Open Access article, distributed under the terms of the Creative Commons Attribution licence (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted re-use, distribution, and reproduction in any medium, provided the original work is properly cited.
Category: A1
Type: Journal Contribution
Validations: ecoom 2023
Appears in Collections:Research publications

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