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Author |
Lobato, I.; Friedrich, T.; Van Aert, S. |
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Title |
Deep convolutional neural networks to restore single-shot electron microscopy images |
Type |
A1 Journal Article |
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Year |
2024 |
Publication |
npj Computational Materials |
Abbreviated Journal |
npj Comput Mater |
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Volume |
10 |
Issue |
1 |
Pages |
10 |
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Keywords |
A1 Journal Article; Electron Microscopy for Materials Science (EMAT) ; |
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Abstract |
Advanced electron microscopy techniques, including scanning electron microscopes (SEM), scanning transmission electron microscopes (STEM), and transmission electron microscopes (TEM), have revolutionized imaging capabilities. However, achieving high-quality experimental images remains a challenge due to various distortions stemming from the instrumentation and external factors. These distortions, introduced at different stages of imaging, hinder the extraction of reliable quantitative insights. In this paper, we will discuss the main sources of distortion in TEM and S(T)EM images, develop models to describe them, and propose a method to correct these distortions using a convolutional neural network. We validate the effectiveness of our method on a range of simulated and experimental images, demonstrating its ability to significantly enhance the signal-to-noise ratio. This improvement leads to a more reliable extraction of quantitative structural information from the images. In summary, our findings offer a robust framework to enhance the quality of electron microscopy images, which in turn supports progress in structural analysis and quantification in materials science and biology. |
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Wos |
001138183000001 |
Publication Date |
2024-01-09 |
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ISSN |
2057-3960 |
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Additional Links |
UA library record; WoS full record |
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Open Access |
OpenAccess |
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Notes |
This work was supported by the European Research Council (Grant 770887 PICOMETRICS to S.V.A.). The authors acknowledge financial support from the Research Foundation Flanders (FWO, Belgium) through project fundings (G034621N, G0A7723N and EOS 40007495). S.V.A. acknowledges funding from the University of Antwerp Research Fund (BOF). The authors thank Lukas Grünewald for data acquisition and support for Fig. 7. |
Approved |
Most recent IF: NA |
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Call Number |
EMAT @ emat @c:irua:202714 |
Serial |
8994 |
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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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Corporate Author |
Zezhong Zhang |
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Wos |
000995063900001 |
Publication Date |
2022-12-28 |
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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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