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“The notion of resolution”. Van Aert S, den Dekker AJ, van Dyck D, van den Bos A Springer, Berlin, page 1228 (2007).
Keywords: H3 Book chapter; Electron microscopy for materials research (EMAT); Vision lab
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“Obstacles on the road towards atomic resolution tomography”. van Dyck D, Van Aert S, Croitoru MD, Microscoy and microanalysis 11, 238 (2005)
Keywords: A3 Journal article; Electron microscopy for materials research (EMAT); Condensed Matter Theory (CMT); Vision lab
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“Statistical parameter estimation theory : a tool for quantitative electron microscopy”. Van Aert S Wiley-VCH, Weinheim, page 281 (2012).
Keywords: H1 Book chapter; Electron microscopy for materials research (EMAT)
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“High resolution electron microscopy from imaging towards measuring”. Van Aert S, den Dekker AJ, van den Bos A, Van Dyck D ... IEEE International Instrumentation and Measurement Technology Conference
T2 – Rediscovering measurement in the age of informatics : proceedings of the 18th IEEE Instrumentation and Measurement Technology Conference (IMTC), 2001: vol 3. Ieee, page 2081 (2001).
Keywords: H2 Book chapter; Electron microscopy for materials research (EMAT); Vision lab
DOI: 10.1109/IMTC.2001.929564
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“The benefits of statistical parameter estimation theory for quantitative interpretation of electron microscopy data”. Van Aert S, Bals S, Chang LY, den Dekker AJ, Kirkland AI, Van Dyck D, Van Tendeloo G Springer, Berlin, page 97 (2008).
Keywords: H1 Book chapter; Electron microscopy for materials research (EMAT); Vision lab
DOI: 10.1007/978-3-540-85156-1_49
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Zhang Z, Lobato I, Brown H, Jannis D, Verbeeck J, Van Aert S, Nellist P (2023) Generalised oscillator strength for core-shell electron excitation by fast electrons based on Dirac solutions
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.
Keywords: Dataset; Electron microscopy for materials research (EMAT)
DOI: 10.5281/ZENODO.8360240
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Grü,newald L, Chezganov D, De Meyer R, Orekhov A, Van Aert S, Bogaerts A, Bals S, Verbeeck J (2023) Supplementary Information for “In-situ Plasma Studies using a Direct Current Microplasma in a Scanning Electron Microscope”
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
Keywords: Dataset; Engineering sciences. Technology; Electron microscopy for materials research (EMAT); Plasma Lab for Applications in Sustainability and Medicine – Antwerp (PLASMANT)
DOI: 10.5281/ZENODO.8042030
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Cioni M, Delle Piane M, Polino D, Rapetti D, Crippa M, Arslan Irmak E, Pavan GM, Van Aert S, Bals S (2024) Data for Sampling Real‐Time Atomic Dynamics in Metal Nanoparticles by Combining Experiments, Simulations, and Machine Learning
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.
Keywords: Dataset; Engineering sciences. Technology; Electron microscopy for materials research (EMAT)
DOI: 10.5281/ZENODO.10997963
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