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Author |
de Backer, A.; De wael, A.; Gonnissen, J.; Martinez, G.T.; Béché, A.; MacArthur, K.E.; Jones, L.; Nellist, P.D.; Van Aert, S. |
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Title |
Quantitative annular dark field scanning transmission electron microscopy for nanoparticle atom-counting : what are the limits? |
Type |
A1 Journal article |
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Year |
2015 |
Publication |
Journal of physics : conference series |
Abbreviated Journal |
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Volume |
644 |
Issue |
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Pages |
012034-4 |
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Keywords |
A1 Journal article; Electron microscopy for materials research (EMAT) |
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Abstract |
Quantitative atomic resolution annular dark field scanning transmission electron microscopy (ADF STEM) has become a powerful technique for nanoparticle atom-counting. However, a lot of nanoparticles provide a severe characterisation challenge because of their limited size and beam sensitivity. Therefore, quantitative ADF STEM may greatly benefit from statistical detection theory in order to optimise the instrumental microscope settings such that the incoming electron dose can be kept as low as possible whilst still retaining single-atom precision. The principles of detection theory are used to quantify the probability of error for atom-counting. This enables us to decide between different image performance measures and to optimise the experimental detector settings for atom-counting in ADF STEM in an objective manner. To demonstrate this, ADF STEM imaging of an industrial catalyst has been conducted using the near-optimal detector settings. For this experiment, we discussed the limits for atomcounting diagnosed by combining a thorough statistical method and detailed image simulations. |
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Bristol |
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ISSN |
1742-6588; 1742-6596 |
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UA library record |
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Notes  |
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Most recent IF: NA |
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Call Number |
UA @ lucian @ c:irua:129198 |
Serial |
4506 |
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Author |
Van Aert, S.; Bals, S.; Chang, L.Y.; den Dekker, A.J.; Kirkland, A.I.; Van Dyck, D.; Van Tendeloo, G. |
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Title |
The benefits of statistical parameter estimation theory for quantitative interpretation of electron microscopy data |
Type |
H1 Book chapter |
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Year |
2008 |
Publication |
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Abbreviated Journal |
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Volume |
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Pages |
97-98 |
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Keywords |
H1 Book chapter; Electron microscopy for materials research (EMAT); Vision lab |
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Springer |
Place of Publication |
Berlin |
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Publication Date |
2009-03-17 |
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ISBN |
978-3-540-85154-7 |
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UA library record |
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Notes |
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Most recent IF: NA |
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Call Number |
UA @ lucian @ c:irua:136865 |
Serial |
4493 |
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Author |
Goris, B.; De Beenhouwer, J.; de Backer, A.; Zanaga, D.; Batenburg, J.; Sanchez-Iglesias, A.; Liz-Marzan, L.; Van Aert, S.; Sijbers, J.; Van Tendeloo, G.; Bals, S. |
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Title |
Investigating lattice strain in Au nanodecahedrons |
Type |
P1 Proceeding |
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Year |
2016 |
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Volume |
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Pages |
11-12 |
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Keywords |
P1 Proceeding; Electron microscopy for materials research (EMAT); Vision lab |
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Publication Date |
2016-12-21 |
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ISSN |
978-3-527-80846-5 |
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UA library record |
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Most recent IF: NA |
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Call Number |
UA @ lucian @ c:irua:145813 |
Serial |
5144 |
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Author |
Hao, Y.; Velpula, G.; Kaltenegger, M.; Bodlos, W.R.; Vibert, F.; Mali, K.S.; De Feyter, S.; Resel, R.; Geerts, Y.H.; Van Aert, S.; Beljonne, D.; Lazzaroni, R. |
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Title |
From 2D to 3D : bridging self-assembled monolayers to a substrate-induced polymorph in a molecular semiconductor |
Type |
A1 Journal article |
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Year |
2022 |
Publication |
Chemistry of materials |
Abbreviated Journal |
Chem Mater |
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Volume |
34 |
Issue |
5 |
Pages |
2238-2248 |
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Keywords |
A1 Journal article; Electron microscopy for materials research (EMAT) |
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Abstract |
In this study, a new bottom-up approach is proposed to predict the crystal structure of the substrate-induced polymorph (SIP) of an archetypal molecular semiconductor. In spite of intense efforts, the formation mechanism of SIPs is still not fully understood, and predicting their crystal structure is a very delicate task. Here, we selected lead phthalocyanine (PbPc) as a prototypical molecular material because it is a highly symmetrical yet nonplanar molecule and we demonstrate that the growth and crystal structure of the PbPc SIPs can be templated by the corresponding physisorbed self-assembled molecular networks (SAMNs). Starting from SAMNs of PbPc formed at the solution/graphite interface, the structural and energetic aspects of the assembly were studied by a combination of in situ scanning tunneling microscopy and multiscale computational chemistry approach. Then, the growth of a PbPc SIP on top of the physisorbed monolayer was modeled without prior experimental knowledge, from which the crystal structure of the SIP was predicted. The theoretical prediction of the SIP was verified by determining the crystal structure of PbPc thin films using X-ray diffraction techniques, revealing the formation of a new polymorph of PbPc on the graphite substrate. This study clearly illustrates the correlation between the SAMNs and SIPs, which are traditionally considered as two separate but conceptually connected research areas. This approach is applicable to molecular materials in general to predict the crystal structure of their SIPs. |
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000812125800001 |
Publication Date |
2022-02-17 |
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ISSN |
0897-4756; 1520-5002 |
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Additional Links |
UA library record; WoS full record; WoS citing articles |
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Impact Factor |
8.6 |
Times cited |
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Open Access |
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Notes |
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Approved |
Most recent IF: 8.6 |
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Call Number |
UA @ admin @ c:irua:189086 |
Serial |
7084 |
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Author |
Friedrich, T.; Yu, C.-P.; Verbeek, J.; Pennycook, T.; Van Aert, S. |
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Title |
Phase retrieval from 4-dimensional electron diffraction datasets |
Type |
P1 Proceeding |
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Year |
2021 |
Publication |
Proceedings
T2 – IEEE International Conference on Image Processing (ICIP), SEP 19-22, 2021, Electr. network |
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Pages |
3453-3457 |
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Keywords |
P1 Proceeding; Engineering sciences. Technology; Electron microscopy for materials research (EMAT) |
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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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000819455103114 |
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2021-08-23 |
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978-1-6654-4115-5 |
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UA library record; WoS full record; WoS citing articles |
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Open Access |
OpenAccess |
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Notes |
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Most recent IF: NA |
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Call Number |
UA @ admin @ c:irua:189462 |
Serial |
7089 |
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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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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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Most recent IF: NA |
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Call Number |
UA @ admin @ c:irua:203392 |
Serial |
9042 |
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Author |
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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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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Author |
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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Dataset |
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Year |
2024 |
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Dataset; Engineering sciences. Technology; Electron microscopy for materials research (EMAT) |
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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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Most recent IF: NA |
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Call Number |
UA @ admin @ c:irua:205843 |
Serial |
9143 |
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