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Records |
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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 |
| |
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 |
Thesis |
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Wos |
000995063900001 |
Publication Date |
2022-12-28 |
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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 |
Jain, N.; Hao, Y.; Parekh, U.; Kaltenegger, M.; Pedrazo-Tardajos, A.; Lazzaroni, R.; Resel, R.; Geerts, Y.H.; Bals, S.; Van Aert, S. |
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Title |
Exploring the effects of graphene and temperature in reducing electron beam damage: A TEM and electron diffraction-based quantitative study on Lead Phthalocyanine (PbPc) crystals |
Type |
A1 Journal article |
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Year |
2023 |
Publication |
Micron |
Abbreviated Journal |
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Volume |
169 |
Issue |
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Pages |
103444 |
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Keywords |
A1 Journal article; Electron microscopy for materials research (EMAT) |
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Abstract |
High-resolution transmission electron microscopy (TEM) of organic crystals, such as Lead Phthalocyanine (PbPc), is very challenging since these materials are prone to electron beam damage leading to the breakdown of the crystal structure during investigation. Quantification of the damage is imperative to enable high-resolution imaging of PbPc crystals with minimum structural changes. In this work, we performed a detailed electron diffraction study to quantitatively measure degradation of PbPc crystals upon electron beam irradiation. Our study is based on the quantification of the fading intensity of the spots in the electron diffraction patterns. At various incident dose rates (e/Å2/s) and acceleration voltages, we experimentally extracted the decay rate (1/s), which directly correlates with the rate of beam damage. In this manner, a value for the critical dose (e/Å2) could be determined, which can be used as a measure to quantify beam damage. Using the same methodology, we explored the influence of cryogenic temperatures, graphene TEM substrates, and graphene encapsulation in prolonging the lifetime of the PbPc crystal structure during TEM investigation. The knowledge obtained by diffraction experiments is then translated to real space high-resolution TEM imaging of PbPc. |
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Wos |
000965998800001 |
Publication Date |
2023-03-21 |
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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 |
0968-4328 |
ISBN |
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Additional Links |
UA library record; WoS full record; WoS citing articles |
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Impact Factor |
2.4 |
Times cited |
1 |
Open Access |
OpenAccess |
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Notes |
This work is supported by FWO and FNRS within the 2Dto3D network of the EOS (Excellence of Science) program (grant number 30489208) and ERC-CoGREALNANO-815128 (to Prof. Dr. Sara Bals). N.J. would like to thank Dr. Kunal S. Mali and Dr. Da Wang for useful and interesting discussions on sample preparation procedures. |
Approved |
Most recent IF: 2.4; 2023 IF: 1.98 |
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Call Number |
EMAT @ emat @c:irua:196069 |
Serial |
7379 |
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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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Abstract |
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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 |
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Language |
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Wos |
001041792100007 |
Publication Date |
2023-05-30 |
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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 |
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.; 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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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 |
OpenAccess |
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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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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 |
N P J 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 research (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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Corporate Author |
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Thesis |
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Publisher |
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Place of Publication |
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Wos |
001138183000001 |
Publication Date |
2024-01-09 |
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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 |
2057-3960 |
ISBN |
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Additional Links |
UA library record; WoS full record |
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Impact Factor |
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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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Author |
Friedrich, T.; Yu, C.-P.; Verbeeck, J.; Van Aert, S. |
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Title |
Phase object reconstruction for 4D-STEM using deep learning |
Type |
A1 Journal article |
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Year |
2023 |
Publication |
Microscopy and microanalysis |
Abbreviated Journal |
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Volume |
29 |
Issue |
1 |
Pages |
395-407 |
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Keywords |
A1 Journal article; Electron microscopy for materials research (EMAT) |
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Abstract |
In this study, we explore the possibility to use deep learning for the reconstruction of phase images from 4D scanning transmission electron microscopy (4D-STEM) data. The process can be divided into two main steps. First, the complex electron wave function is recovered for a convergent beam electron diffraction pattern (CBED) using a convolutional neural network (CNN). Subsequently, a corresponding patch of the phase object is recovered using the phase object approximation. Repeating this for each scan position in a 4D-STEM dataset and combining the patches by complex summation yields the full-phase object. Each patch is recovered from a kernel of 3x3 adjacent CBEDs only, which eliminates common, large memory requirements and enables live processing during an experiment. The machine learning pipeline, data generation, and the reconstruction algorithm are presented. We demonstrate that the CNN can retrieve phase information beyond the aperture angle, enabling super-resolution imaging. The image contrast formation is evaluated showing a dependence on the thickness and atomic column type. Columns containing light and heavy elements can be imaged simultaneously and are distinguishable. The combination of super-resolution, good noise robustness, and intuitive image contrast characteristics makes the approach unique among live imaging methods in 4D-STEM. |
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Corporate Author |
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Place of Publication |
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Language |
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Wos |
001033590800038 |
Publication Date |
2023-01-12 |
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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 |
1431-9276 |
ISBN |
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Additional Links |
UA library record; WoS full record; WoS citing articles |
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Impact Factor |
2.8 |
Times cited |
1 |
Open Access |
OpenAccess |
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Notes |
We acknowledge funding from the European Research Council (ERC) under the European Union's Horizon 2020 research and innovation program (grant agreement no. 770887 PICOMETRICS) and funding from the European Union's Horizon 2020 research and innovation program under grant agreement No. 823717 ESTEEM3. J.V. and S.V.A acknowledge funding from the University of Antwerp through a TOP BOF project. The direct electron detector (Merlin, Medipix3, Quantum Detectors) was funded by the Hercules fund from the Flemish Government. This work was supported by the FWO and FNRS within the 2Dto3D project of the EOS program (grant number 30489208). |
Approved |
Most recent IF: 2.8; 2023 IF: 1.891 |
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Call Number |
UA @ admin @ c:irua:198221 |
Serial |
8912 |
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Author |
Şentürk, DG.; Yu, CP.; De Backer, A.; Van Aert, S. |
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Title |
Atom counting from a combination of two ADF STEM images |
Type |
A1 Journal article |
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Year |
2024 |
Publication |
Ultramicroscopy |
Abbreviated Journal |
Ultramicroscopy |
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Volume |
255 |
Issue |
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Pages |
113859 |
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Keywords |
A1 Journal article; Electron microscopy for materials research (EMAT) |
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Abstract |
To understand the structure–property relationship of nanostructures, reliably quantifying parameters, such as the number of atoms along the projection direction, is important. Advanced statistical methodologies have made it possible to count the number of atoms for monotype crystalline nanoparticles from a single ADF STEM image. Recent developments enable one to simultaneously acquire multiple ADF STEM images. Here, we present an extended statistics-based method for atom counting from a combination of multiple statistically independent ADF STEM images reconstructed from non-overlapping annular detector collection regions which improves the accuracy and allows one to retrieve precise atom-counts, especially for images acquired with low electron doses and multiple element structures. |
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Corporate Author |
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Place of Publication |
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Wos |
001089064200001 |
Publication Date |
2023-09-23 |
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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 |
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, G0A7723N, and EOS 40007495) and a postdoctoral grant to A. De Backer. S. Van Aert acknowledges funding from the University of Antwerp Research fund (BOF). |
Approved |
Most recent IF: 2.2; 2024 IF: 2.843 |
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Call Number |
EMAT @ emat @c:irua:201008 |
Serial |
8964 |
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Author |
Hugenschmidt, M.; Jannis, D.; Kadu, A.A.; Grünewald, L.; De Marchi, S.; Perez-Juste, J.; Verbeeck, J.; Van Aert, S.; Bals, S. |
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Title |
Low-dose 4D-STEM tomography for beam-sensitive nanocomposites |
Type |
A1 Journal article |
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Year |
2023 |
Publication |
ACS materials letters |
Abbreviated Journal |
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Volume |
6 |
Issue |
1 |
Pages |
165-173 |
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Keywords |
A1 Journal article; Electron microscopy for materials research (EMAT) |
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Abstract |
Electron tomography is essential for investigating the three-dimensional (3D) structure of nanomaterials. However, many of these materials, such as metal-organic frameworks (MOFs), are extremely sensitive to electron radiation, making it difficult to acquire a series of projection images for electron tomography without inducing electron-beam damage. Another significant challenge is the high contrast in high-angle annular dark field scanning transmission electron microscopy that can be expected for nanocomposites composed of a metal nanoparticle and an MOF. This strong contrast leads to so-called metal artifacts in the 3D reconstruction. To overcome these limitations, we here present low-dose electron tomography based on four-dimensional scanning transmission electron microscopy (4D-STEM) data sets, collected using an ultrafast and highly sensitive direct electron detector. As a proof of concept, we demonstrate the applicability of the method for an Au nanostar embedded in a ZIF-8 MOF, which is of great interest for applications in various fields, including drug delivery. |
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Corporate Author |
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Place of Publication |
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Wos |
001141178500001 |
Publication Date |
2023-12-11 |
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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 |
2639-4979 |
ISBN |
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Additional Links |
UA library record; WoS full record |
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Impact Factor |
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Times cited |
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Open Access |
Not_Open_Access |
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Notes |
This work was supported by the European Research Council (Grant 815128 REALNANO to S.B., Grant 770887 PICOMETRICS to S.V.A.). J.P.-J. and S.M. acknowledge financial support from the MCIN/AEI/10.13039/501100011033 (Grants No. PID2019-108954RB-I00) and EU Horizon 2020 research and innovation program under grant agreement no. 883390 (SERSing). J.V., S.B., S.V.A., and L.G. acknowledge funding from the Flemish government (iBOF-21-085 PERsist). |
Approved |
Most recent IF: NA |
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Call Number |
UA @ admin @ c:irua:202771 |
Serial |
9053 |
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