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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 |
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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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Wos |
001138183000001 |
Publication Date |
2024-01-09 |
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ISSN |
2057-3960 |
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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 |
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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Wos |
001033590800038 |
Publication Date |
2023-01-12 |
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Edition |
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ISSN |
1431-9276 |
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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 |
Monai, M.; Jenkinson, K.; Melcherts, A.E.M.; Louwen, J.N.; Irmak, E.A.; Van Aert, S.; Altantzis, T.; Vogt, C.; van der Stam, W.; Duchon, T.; Smid, B.; Groeneveld, E.; Berben, P.; Bals, S.; Weckhuysen, B.M. |
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Title |
Restructuring of titanium oxide overlayers over nickel nanoparticles during catalysis |
Type |
A1 Journal article |
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Year |
2023 |
Publication |
Science |
Abbreviated Journal |
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Volume |
380 |
Issue |
6645 |
Pages |
644-651 |
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Keywords |
A1 Journal article; Engineering sciences. Technology; Electron microscopy for materials research (EMAT); Applied Electrochemistry & Catalysis (ELCAT) |
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Abstract |
Reducible supports can affect the performance of metal catalysts by the formation of suboxide overlayers upon reduction, a process referred to as the strong metal-support interaction (SMSI). A combination of operando electron microscopy and vibrational spectroscopy revealed that thin TiOx overlayers formed on nickel/titanium dioxide catalysts during 400 degrees C reduction were completely removed under carbon dioxide hydrogenation conditions. Conversely, after 600 degrees C reduction, exposure to carbon dioxide hydrogenation reaction conditions led to only partial reexposure of nickel, forming interfacial sites in contact with TiOx and favoring carbon-carbon coupling by providing a carbon species reservoir. Our findings challenge the conventional understanding of SMSIs and call for more-detailed operando investigations of nanocatalysts at the single-particle level to revisit static models of structure-activity relationships. |
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000999020900010 |
Publication Date |
2023-05-11 |
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ISSN |
0036-8075; 1095-9203 |
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Additional Links |
UA library record; WoS full record; WoS citing articles |
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Impact Factor |
56.9 |
Times cited |
29 |
Open Access |
OpenAccess |
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Notes |
This work was supported by BASF and NWO CHIPP (research grant to B.M.W.); the MCEC NWO Gravitation Program (B.M.W.); the ARC-CBBC NWO Program (B.M.W.); the European Research Council (grant 770887 PICOMETRICS to S.V.A.); and the European Research Council (grant 815128 REALNANO to S.B.). |
Approved |
Most recent IF: 56.9; 2023 IF: 37.205 |
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Call Number |
UA @ admin @ c:irua:197432 |
Serial |
8923 |
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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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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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Wos |
001089064200001 |
Publication Date |
2023-09-23 |
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ISSN |
0304-3991 |
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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 |
Zhang, Z.; Lobato, I.; Brown, H.; Jannis, D.; Verbeeck, J.; Van Aert, S.; Nellist, P. |
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Title |
Generalised oscillator strength for core-shell electron excitation by fast electrons based on Dirac solutions |
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Dataset |
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Year |
2023 |
Publication |
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Keywords |
Dataset; Electron microscopy for materials research (EMAT) |
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Inelastic excitation as exploited in Electron Energy Loss Spectroscopy (EELS) contains a rich source of information that is revealed in the scattering process. To accurately quantify core-loss EELS, it is common practice to fit the observed spectrum with scattering cross-sections calculated using experimental parameters and a Generalized Oscillator Strength (GOS) database [1]. The GOS is computed using Fermi’s Golden Rule and orbitals of bound and excited states. Previously, the GOS was based on Hartree-Fock solutions [2], but more recently Density Functional Theory (DFT) has been used [3]. In this work, we have chosen to use the Dirac equation to incorporate relativistic effects and have performed calculations using Flexible Atomic Code (FAC) [4]. This repository contains a tabulated GOS database based on Dirac solutions for computing double differential cross-sections under experimental conditions. We hope the Dirac-based GOS database can benefit the EELS community for both academic use and industry integration. Database Details: – Covers all elements (Z: 1-108) and all edges – Large energy range: 0.01 – 4000 eV – Large momentum range: 0.05 -50 Å-1 – Fine log sampling: 128 points for energy and 256 points for momentum – Data format: GOSH [3] Calculation Details: – Single atoms only; solid-state effects are not considered – Unoccupied states before continuum states of ionization are not considered; no fine structure – Plane Wave Born Approximation – Frozen Core Approximation is employed; electrostatic potential remains unchanged for orthogonal states when – core-shell electron is excited – Self-consistent Dirac–Fock–Slater iteration is used for Dirac calculations; Local Density Approximation is assumed for electron exchange interactions; continuum states are normalized against asymptotic form at large distances – Both large and small component contributions of Dirac solutions are included in GOS – Final state contributions are included until the contribution of the previous three states falls below 0.1%. A convergence log is provided for reference. Version 1.1 release note: – Update to be consistent with GOSH data format [3], all the edges are now within a single hdf5 file. A notable change in particular, the sampling in momentum is in 1/m, instead of previously in 1/Å. Great thanks to Gulio Guzzinati for his suggestions and sending conversion script. Version 1.2 release note: – Add “File Type / File version” information [1] Verbeeck, J., and S. Van Aert. Ultramicroscopy 101.2-4 (2004): 207-224. [2] Leapman, R. D., P. Rez, and D. F. Mayers. The Journal of Chemical Physics 72.2 (1980): 1232-1243. [3] Segger, L, Guzzinati, G, & Kohl, H. Zenodo (2023). doi:10.5281/zenodo.7645765 [4] Gu, M. F. Canadian Journal of Physics 86(5) (2008): 675-689. |
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Most recent IF: NA |
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Call Number |
UA @ admin @ c:irua:203392 |
Serial |
9042 |
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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 |
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A1 Journal article |
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Year |
2023 |
Publication |
ACS materials letters |
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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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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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001141178500001 |
Publication Date |
2023-12-11 |
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2639-4979 |
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UA library record; WoS full record |
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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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Grünewald, L.; Chezganov, D.; De Meyer, R.; Orekhov, A.; Van Aert, S.; Bogaerts, A.; Bals, S.; Verbeeck, J. |
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Title |
Supplementary Information for “In-situ Plasma Studies using a Direct Current Microplasma in a Scanning Electron Microscope” |
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Dataset |
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Year |
2023 |
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Dataset; Engineering sciences. Technology; Electron microscopy for materials research (EMAT); Plasma Lab for Applications in Sustainability and Medicine – Antwerp (PLASMANT) |
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Supplementary information for the article “In-situ Plasma Studies using a Direct Current Microplasma in a Scanning Electron Microscope” containing the videos of in-situ SEM imaging (mp4 files), raw data/images, and Jupyter notebooks (ipynb files) for data treatment and plots. Link to the preprint: https://doi.org/10.48550/arXiv.2308.15123 Explanation of the data files can be found in the Information.pdf file. The Videos folder contains the in-situ SEM image series mentioned in the paper. If there are any questions/bugs, feel free to contact me at lukas.grunewaldatuantwerpen.be |
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Most recent IF: NA |
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Call Number |
UA @ admin @ c:irua:203389 |
Serial |
9100 |
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Cioni, M.; Delle Piane, M.; Polino, D.; Rapetti, D.; Crippa, M.; Arslan Irmak, E.; Pavan, G.M.; Van Aert, S.; Bals, S. |
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Title |
Data for Sampling Real‐Time Atomic Dynamics in Metal Nanoparticles by Combining Experiments, Simulations, and Machine Learning |
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2024 |
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Dataset; Engineering sciences. Technology; Electron microscopy for materials research (EMAT) |
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Even at low temperatures, metal nanoparticles (NPs) possess atomic dynamics that are key for their properties but challenging to elucidate. Recent experimental advances allow obtaining atomic‐resolution snapshots of the NPs in realistic regimes, but data acquisition limitations hinder the experimental reconstruction of the atomic dynamics present within them. Molecular simulations have the advantage that these allow directly tracking the motion of atoms over time. However, these typically start from ideal/perfect NP structures and, suffering from sampling limits, provide results that are often dependent on the initial/putative structure and remain purely indicative. Here, by combining state‐of‐the‐art experimental and computational approaches, how it is possible to tackle the limitations of both approaches and resolve the atomistic dynamics present in metal NPs in realistic conditions is demonstrated. Annular dark‐field scanning transmission electron microscopy enables the acquisition of ten high‐resolution images of an Au NP at intervals of 0.6 s. These are used to reconstruct atomistic 3D models of the real NP used to run ten independent molecular dynamics simulations. Machine learning analyses of the simulation trajectories allows resolving the real‐time atomic dynamics present within the NP. This provides a robust combined experimental/computational approach to characterize the structural dynamics of metal NPs in realistic conditions. |
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Most recent IF: NA |
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Call Number |
UA @ admin @ c:irua:205843 |
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9143 |
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