| Records |
| Author |
Zheng, Y.-R.; Vernieres, J.; Wang, Z.; Zhang, K.; Hochfilzer, D.; Krempl, K.; Liao, T.-W.; Presel, F.; Altantzis, T.; Fatermans, J.; Scott, S.B.; Secher, N.M.; Moon, C.; Liu, P.; Bals, S.; Van Aert, S.; Cao, A.; Anand, M.; Nørskov, J.K.; Kibsgaard, J.; Chorkendorff, I. |
| Title |
Monitoring oxygen production on mass-selected iridium–tantalum oxide electrocatalysts |
Type |
A1 Journal article |
| Year |
2021 |
Publication |
Nature Energy |
Abbreviated Journal |
Nat Energy |
| Volume |
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Issue |
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Pages |
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| Keywords |
A1 Journal article; Engineering sciences. Technology; Electron microscopy for materials research (EMAT); Applied Electrochemistry & Catalysis (ELCAT) |
| Abstract |
Development of low-cost and high-performance oxygen evolution reaction catalysts is key to implementing polymer electrolyte membrane water electrolyzers for hydrogen production. Iridium-based oxides are the state-of-the-art acidic oxygen evolution reactio catalysts but still suffer from inadequate activity and stability, and iridium's scarcity motivates the discovery of catalysts with lower iridium loadings. Here we report a mass-selected iridium-tantalum oxide catalyst prepared by a magnetron-based cluster source with considerably reduced noble-metal loadings beyond a commercial IrO2 catalyst. A sensitive electrochemistry/mass-spectrometry instrument coupled with isotope labelling was employed to investigate the oxygen production rate under dynamic operating conditions to account for the occurrence of side reactions and quantify the number of surface active sites. Iridium-tantalum oxide nanoparticles smaller than 2 nm exhibit a mass activity of 1.2 ± 0.5 kA “g” _“Ir” ^“-1” and a turnover frequency of 2.3 ± 0.9 s-1 at 320 mV overpotential, which are two and four times higher than those of mass-selected IrO2, respectively. Density functional theory calculations reveal that special iridium coordinations and the lowered aqueous decomposition free energy might be responsible for the enhanced performance. |
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| Language |
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Wos |
000728458000001 |
Publication Date |
2021-12-09 |
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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 |
2058-7546 |
ISBN |
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Additional Links |
UA library record; WoS full record; WoS citing articles |
| Impact Factor |
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Times cited |
95 |
Open Access |
OpenAccess |
| Notes |
Y.-R.Z. and Z.W acknowledge funding from the Toyota Research Institute. This project has received funding from VILLUM FONDEN (grant no. 9455) and the European Research Council under the European Union’s Horizon 2020 research and innovation programme (grants no. 741860-CLUNATRA, no. 815128−REALNANO and no. 770887−PICOMETRICS). S.B. and S.V.A. acknowledge funding from the Research Foundation Flanders (FWO, G026718N and G050218N). T.A. acknowledges the University of Antwerp Research Fund (BOF). STEM measurements were supported by the European Union's Horizon 2020 Research Infrastructure-Integrating Activities for Advanced Communities under grant agreement No 823717 – ESTEEM3.; sygmaSB |
Approved  |
Most recent IF: NA |
| Call Number |
EMAT @ emat @c:irua:184794 |
Serial |
6903 |
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| Author |
Friedrich, T.; Yu, C.-P.; Verbeek, J.; Pennycook, T.; Van Aert, S. |
| Title |
Phase retrieval from 4-dimensional electron diffraction datasets |
Type |
P1 Proceeding |
| Year |
2021 |
Publication |
Proceedings
T2 – IEEE International Conference on Image Processing (ICIP), SEP 19-22, 2021, Electr. network |
Abbreviated Journal |
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| Volume |
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Issue |
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Pages |
3453-3457 |
| Keywords |
P1 Proceeding; Engineering sciences. Technology; Electron microscopy for materials research (EMAT) |
| Abstract |
We present a computational imaging mode for large scale electron microscopy data, which retrieves a complex wave from noisy/sparse intensity recordings using a deep learning approach and subsequently reconstructs an image of the specimen from the Convolutional Neural Network (CNN) predicted exit waves. We demonstrate that an appropriate forward model in combination with open data frameworks can be used to generate large synthetic datasets for training. In combination with augmenting the data with Poisson noise corresponding to varying dose-values, we effectively eliminate overfitting issues. The U-NET[1] based architecture of the CNN is adapted to the task at hand and performs well while maintaining a relatively small size and fast performance. The validity of the approach is confirmed by comparing the reconstruction to well-established methods using simulated, as well as real electron microscopy data. The proposed method is shown to be effective particularly in the low dose range, evident by strong suppression of noise, good spatial resolution, and sensitivity to different atom types, enabling the simultaneous visualisation of light and heavy elements and making different atomic species distinguishable. Since the method acts on a very local scale and is comparatively fast it bears the potential to be used for near-real-time reconstruction during data acquisition. |
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Wos |
000819455103114 |
Publication Date |
2021-08-23 |
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Abbreviated Series Title |
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Series Issue |
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Edition |
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| ISSN |
978-1-6654-4115-5 |
ISBN |
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Additional Links |
UA library record; WoS full record; WoS citing articles |
| Impact Factor |
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Times cited |
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Open Access |
OpenAccess |
| Notes |
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Approved |
Most recent IF: NA |
| Call Number |
UA @ admin @ c:irua:189462 |
Serial |
7089 |
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| Author |
De Backer, A.; Van Aert, S.; Faes, C.; Arslan Irmak, E.; Nellist, P.D.; Jones, L. |
| Title |
Experimental reconstructions of 3D atomic structures from electron microscopy images using a Bayesian genetic algorithm |
Type |
A1 Journal article |
| Year |
2022 |
Publication |
N P J Computational Materials |
Abbreviated Journal |
npj Comput Mater |
| Volume |
8 |
Issue |
1 |
Pages |
216 |
| Keywords |
A1 Journal article; Electron microscopy for materials research (EMAT) |
| Abstract |
We introduce a Bayesian genetic algorithm for reconstructing atomic models of monotype crystalline nanoparticles from a single projection using Z-contrast imaging. The number of atoms in a projected atomic column obtained from annular dark field scanning transmission electron microscopy images serves as an input for the initial three-dimensional model. The algorithm minimizes the energy of the structure while utilizing a priori information about the finite precision of the atom-counting results and neighbor-mass relations. The results show promising prospects for obtaining reliable reconstructions of beam-sensitive nanoparticles during dynamical processes from images acquired with sufficiently low incident electron doses. |
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Place of Publication |
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Wos |
000866500900001 |
Publication Date |
2022-10-12 |
| 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; WoS citing articles |
| Impact Factor |
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Times cited |
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Open Access |
OpenAccess |
| Notes |
This work was supported by the European Research Council (Grant 770887 PICOMETRICS to S.V.A. and Grant 823717 ESTEEM3). The authors acknowledge financial support from the Research Foundation Flanders (FWO, Belgium) through project fundings (G.0267.18N, G.0502.18N, G.0346.21N) and a postdoctoral grant to A.D.B. L.J. acknowledges Science Foundation Ireland (SFI – grant number URF/RI/191637), the Royal Society, and the AMBER Centre. The authors acknowledge Aakash Varambhia for his assistance and expertise with the experimental recording and use of characterization facilities within the David Cockayne Centre for Electron Microscopy, Department of Materials, University of Oxford, and in particular the EPSRC (EP/K040375/1 South of England Analytical Electron Microscope).; esteem3reported; esteem3JRA |
Approved |
Most recent IF: NA |
| Call Number |
EMAT @ emat @c:irua:191398 |
Serial |
7114 |
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| Author |
Lobato, I.; Friedrich, T.; Van Aert, S. |
| Title |
Deep convolutional neural networks to restore single-shot electron microscopy images |
Type |
A1 Journal article |
| Year |
2024 |
Publication |
N P J Computational Materials |
Abbreviated Journal |
npj Comput Mater |
| Volume |
10 |
Issue |
1 |
Pages |
10 |
| Keywords |
A1 Journal article; Electron microscopy for materials research (EMAT) |
| 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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Series Title |
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Abbreviated Series Title |
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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 |
| Impact Factor |
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Times cited |
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Open Access |
OpenAccess |
| 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 |
| Call Number |
EMAT @ emat @c:irua:202714 |
Serial |
8994 |
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| Author |
Zhang, Z.; Lobato, I.; Brown, H.; Jannis, D.; Verbeeck, J.; Van Aert, S.; Nellist, P. |
| Title |
Generalised oscillator strength for core-shell electron excitation by fast electrons based on Dirac solutions |
Type |
Dataset |
| Year |
2023 |
Publication |
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Abbreviated Journal |
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| Volume |
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Issue |
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Pages |
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| Keywords |
Dataset; Electron microscopy for materials research (EMAT) |
| Abstract |
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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UA library record |
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Not_Open_Access |
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Approved |
Most recent IF: NA |
| 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. |
| Title |
Low-dose 4D-STEM tomography for beam-sensitive nanocomposites |
Type |
A1 Journal article |
| Year |
2023 |
Publication |
ACS materials letters |
Abbreviated Journal |
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| Volume |
6 |
Issue |
1 |
Pages |
165-173 |
| Keywords |
A1 Journal article; Electron microscopy for materials research (EMAT) |
| 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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Place of Publication |
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Wos |
001141178500001 |
Publication Date |
2023-12-11 |
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Series Title |
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Abbreviated Series Title |
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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 |
| Impact Factor |
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Times cited |
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Open Access |
Not_Open_Access |
| 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 |
| Call Number |
UA @ admin @ c:irua:202771 |
Serial |
9053 |
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| Author |
Grünewald, L.; Chezganov, D.; De Meyer, R.; Orekhov, A.; Van Aert, S.; Bogaerts, A.; Bals, S.; Verbeeck, J. |
| Title |
Supplementary Information for “In-situ Plasma Studies using a Direct Current Microplasma in a Scanning Electron Microscope” |
Type |
Dataset |
| Year |
2023 |
Publication |
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Abbreviated Journal |
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| Volume |
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Issue |
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Pages |
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| Keywords |
Dataset; Engineering sciences. Technology; Electron microscopy for materials research (EMAT); Plasma Lab for Applications in Sustainability and Medicine – Antwerp (PLASMANT) |
| Abstract |
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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UA library record |
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Open Access |
Not_Open_Access |
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Approved |
Most recent IF: NA |
| Call Number |
UA @ admin @ c:irua:203389 |
Serial |
9100 |
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| Author |
Cioni, M.; Delle Piane, M.; Polino, D.; Rapetti, D.; Crippa, M.; Arslan Irmak, E.; Pavan, G.M.; Van Aert, S.; Bals, S. |
| Title |
Data for Sampling Real‐Time Atomic Dynamics in Metal Nanoparticles by Combining Experiments, Simulations, and Machine Learning |
Type |
Dataset |
| Year |
2024 |
Publication |
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Abbreviated Journal |
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| Volume |
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Issue |
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Pages |
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| Keywords |
Dataset; Engineering sciences. Technology; Electron microscopy for materials research (EMAT) |
| Abstract |
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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UA library record |
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Times cited |
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Open Access |
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Approved |
Most recent IF: NA |
| Call Number |
UA @ admin @ c:irua:205843 |
Serial |
9143 |
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