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Author |
Zhang, Z.; Lobato, I.; De Backer, A.; Van Aert, S.; Nellist, P. |
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Title |
Fast generation of calculated ADF-EDX scattering cross-sections under channelling conditions |
Type |
A1 Journal article |
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Year |
2023 |
Publication  |
Ultramicroscopy |
Abbreviated Journal |
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Volume |
246 |
Issue |
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Pages |
113671 |
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Keywords |
A1 Journal article; Electron microscopy for materials research (EMAT) |
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Abstract |
Advanced materials often consist of multiple elements which are arranged in a complicated structure. Quantitative scanning transmission electron microscopy is useful to determine the composition and thickness of nanostructures at the atomic scale. However, significant difficulties remain to quantify mixed columns by comparing the resulting atomic resolution images and spectroscopy data with multislice simulations where dynamic scattering needs to be taken into account. The combination of the computationally intensive nature of these simulations and the enormous amount of possible mixed column configurations for a given composition indeed severely hamper the quantification process. To overcome these challenges, we here report the development of an incoherent non-linear method for the fast prediction of ADF-EDX scattering cross-sections of mixed columns under channelling conditions. We first explain the origin of the ADF and EDX incoherence from scattering physics suggesting a linear dependence between those two signals in the case of a high-angle ADF detector. Taking EDX as a perfect incoherent reference mode, we quantitatively examine the ADF longitudinal incoherence under different microscope conditions using multislice simulations. Based on incoherent imaging, the atomic lensing model previously developed for ADF is now expanded to EDX, which yields ADF-EDX scattering cross-section predictions in good agreement with multislice simulations for mixed columns in a core–shell nanoparticle and a high entropy alloy. The fast and accurate prediction of ADF-EDX scattering cross-sections opens up new opportunities to explore the wide range of ordering possibilities of heterogeneous materials with multiple elements. |
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Address |
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Corporate Author |
Zezhong Zhang |
Thesis |
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Publisher |
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Place of Publication |
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Editor |
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Language |
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Wos |
000995063900001 |
Publication Date |
2022-12-28 |
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Series Editor |
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Series Title |
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Abbreviated Series Title |
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Series Volume |
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Series Issue |
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Edition |
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ISSN |
0304-3991 |
ISBN |
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Additional Links |
UA library record; WoS full record; WoS citing articles |
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Impact Factor |
2.2 |
Times cited |
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Open Access |
OpenAccess |
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Notes |
European Research Council 770887 PICOMETRICS; Fonds Wetenschappelijk Onderzoek No.G.0502.18N; Horizon 2020, 770887 ; Horizon 2020 Framework Programme; European Research Council, 823717 ESTEEM3 ; esteem3reported; esteem3JRa |
Approved |
Most recent IF: 2.2; 2023 IF: 2.843 |
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Call Number |
EMAT @ emat @c:irua:195890 |
Serial |
7251 |
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Author |
Lobato, I.; De Backer, A.; Van Aert, S. |
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Title |
Real-time simulations of ADF STEM probe position-integrated scattering cross-sections for single element fcc crystals in zone axis orientation using a densely connected neural network |
Type |
A1 Journal Article |
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Year |
2023 |
Publication |
Ultramicroscopy |
Abbreviated Journal |
Ultramicroscopy |
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Volume |
251 |
Issue |
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Pages |
113769 |
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Keywords |
A1 Journal article; Electron microscopy for materials research (EMAT) |
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Abstract |
Quantification of annular dark field (ADF) scanning transmission electron microscopy (STEM) images in terms
of composition or thickness often relies on probe-position integrated scattering cross sections (PPISCS). In
order to compare experimental PPISCS with theoretically predicted ones, expensive simulations are needed for
a given specimen, zone axis orientation, and a variety of microscope settings. The computation time of such
simulations can be in the order of hours using a single GPU card. ADF STEM simulations can be efficiently
parallelized using multiple GPUs, as the calculation of each pixel is independent of other pixels. However, most
research groups do not have the necessary hardware, and, in the best-case scenario, the simulation time will
only be reduced proportionally to the number of GPUs used. In this manuscript, we use a learning approach and
present a densely connected neural network that is able to perform real-time ADF STEM PPISCS predictions as
a function of atomic column thickness for most common face-centered cubic (fcc) crystals (i.e., Al, Cu, Pd, Ag,
Pt, Au and Pb) along [100] and [111] zone axis orientations, root-mean-square displacements, and microscope
parameters. The proposed architecture is parameter efficient and yields accurate predictions for the PPISCS
values for a wide range of input parameters that are commonly used for aberration-corrected transmission
electron microscopes. |
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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 |
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Language |
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Wos |
001011617200001 |
Publication Date |
2023-06-01 |
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Series Editor |
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Series Title |
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Abbreviated Series Title |
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Series Volume |
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Series Issue |
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Edition |
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ISSN |
0304-3991 |
ISBN |
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Additional Links |
UA library record; WoS full record |
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Impact Factor |
2.2 |
Times cited |
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Open Access |
Open_Access |
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Notes |
This work was supported by the European Research Council (Grant 770887 PICOMETRICS to S. Van Aert). The authors acknowledge financial support from the Research Foundation Flanders (FWO, Belgium) through project fundings (G034621N and G0A7723N) and a postdoctoral grant to A. De Backer. S. Van Aert acknowledges funding from the University of Antwerp Research fund (BOF), Belgium. |
Approved |
Most recent IF: 2.2; 2023 IF: 2.843 |
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Call Number |
EMAT @ emat @c:irua:197275 |
Serial |
8812 |
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| Permanent link to this record |
