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Author |
Samal, D.; Gauquelin, N.; Takamura, Y.; Lobato, I.; Arenholz, E.; Van Aert, S.; Huijben, M.; Zhong, Z.; Verbeeck, J.; Van Tendeloo, G.; Koster, G. |
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Title |
Unusual structural rearrangement and superconductivity in infinite layer cuprate superlattices |
Type |
A1 Journal Article |
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Year |
2023 |
Publication |
Physical review materials |
Abbreviated Journal |
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Volume |
7 |
Issue |
5 |
Pages |
054803 |
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Keywords |
A1 Journal article; Electron microscopy for materials research (EMAT) |
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Wos  |
001041792100007 |
Publication Date |
2023-05-30 |
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Edition |
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ISSN |
2475-9953 |
ISBN |
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Additional Links |
UA library record; WoS full record |
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Impact Factor |
3.4 |
Times cited |
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Open Access |
OpenAccess |
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Notes |
Air Force Office of Scientific Research; European Office of Aerospace Research and Development, FA8655-10-1-3077 ; Office of Science, DE-AC02-05CH11231 ; National Science Foundation, DMR-1745450 ; Seventh Framework Programme, 278510 ; Bijzonder Onderzoeksfonds UGent; |
Approved |
Most recent IF: 3.4; 2023 IF: NA |
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Call Number |
EMAT @ emat @c:irua:196973 |
Serial |
8790 |
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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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Times cited |
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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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