| Records |
| Author |
De Backer, A.; Van Aert, S.; Faes, C.; Arslan Irmak, E.; Nellist, P.D.; Jones, L. |
| Title |
Experimental reconstructions of 3D atomic structures from electron microscopy images using a Bayesian genetic algorithm |
Type |
A1 Journal article |
Year  |
2022 |
Publication |
N P J Computational Materials |
Abbreviated Journal |
npj Comput Mater |
| Volume |
8 |
Issue |
1 |
Pages |
216 |
| Keywords |
A1 Journal article; Electron microscopy for materials research (EMAT) |
| Abstract |
We introduce a Bayesian genetic algorithm for reconstructing atomic models of monotype crystalline nanoparticles from a single projection using Z-contrast imaging. The number of atoms in a projected atomic column obtained from annular dark field scanning transmission electron microscopy images serves as an input for the initial three-dimensional model. The algorithm minimizes the energy of the structure while utilizing a priori information about the finite precision of the atom-counting results and neighbor-mass relations. The results show promising prospects for obtaining reliable reconstructions of beam-sensitive nanoparticles during dynamical processes from images acquired with sufficiently low incident electron doses. |
| Address |
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| Corporate Author |
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Thesis |
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| Publisher |
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Place of Publication |
|
Editor |
|
| Language |
|
Wos |
000866500900001 |
Publication Date |
2022-10-12 |
| Series Editor |
|
Series Title |
|
Abbreviated Series Title |
|
| Series Volume |
|
Series Issue |
|
Edition |
|
| ISSN |
2057-3960 |
ISBN |
|
Additional Links |
UA library record; WoS full record; WoS citing articles |
| Impact Factor |
|
Times cited |
|
Open Access |
OpenAccess |
| Notes |
This work was supported by the European Research Council (Grant 770887 PICOMETRICS to S.V.A. and Grant 823717 ESTEEM3). The authors acknowledge financial support from the Research Foundation Flanders (FWO, Belgium) through project fundings (G.0267.18N, G.0502.18N, G.0346.21N) and a postdoctoral grant to A.D.B. L.J. acknowledges Science Foundation Ireland (SFI – grant number URF/RI/191637), the Royal Society, and the AMBER Centre. The authors acknowledge Aakash Varambhia for his assistance and expertise with the experimental recording and use of characterization facilities within the David Cockayne Centre for Electron Microscopy, Department of Materials, University of Oxford, and in particular the EPSRC (EP/K040375/1 South of England Analytical Electron Microscope).; esteem3reported; esteem3JRA |
Approved |
Most recent IF: NA |
| Call Number |
EMAT @ emat @c:irua:191398 |
Serial |
7114 |
| Permanent link to this record |
| |
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| |
| Author |
De wael, A.; De Backer, A.; Yu, C.-P.; Sentürk, D.G.; Lobato, I.; Faes, C.; Van Aert, S. |
| Title |
Three Approaches for Representing the Statistical Uncertainty on Atom-Counting Results in Quantitative ADF STEM |
Type |
A1 Journal article |
| Year |
2022 |
Publication |
Microscopy and microanalysis |
Abbreviated Journal |
Microsc Microanal |
| Volume |
|
Issue |
|
Pages |
1-9 |
| Keywords |
A1 Journal article; Electron microscopy for materials research (EMAT) |
| 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. |
| Address |
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| Corporate Author |
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Thesis |
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| Publisher |
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Place of Publication |
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Editor |
|
| Language |
|
Wos |
000854930500001 |
Publication Date |
2022-09-19 |
| Series Editor |
|
Series Title |
|
Abbreviated Series Title |
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| Series Volume |
|
Series Issue |
|
Edition |
|
| ISSN |
1431-9276 |
ISBN |
|
Additional Links |
UA library record; WoS full record |
| Impact Factor |
2.8 |
Times cited |
|
Open Access |
OpenAccess |
| Notes |
This project has received funding from the European Research Council (ERC) under the European Union's Horizon 2020 research and innovation programme (Grant Agreement No. 770887 and No. 823717 ESTEEM3). The authors acknowledge financial support from the Research Foundation Flanders (FWO, Belgium) through grants to A.D.w. and A.D.B. and projects G.0502.18N, G.0267.18N, and EOS 30489208. S.V.A. acknowledges TOP BOF funding from the University of Antwerp. The authors are grateful to L.M. Liz-Marzán (CIC biomaGUNE and Ikerbasque) for providing the samples. esteem3reported; esteem3jra |
Approved |
Most recent IF: 2.8 |
| Call Number |
EMAT @ emat @c:irua:190585 |
Serial |
7119 |
| Permanent link to this record |
