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Mary Joy, R.; Pobedinskas, P.; Bourgeois, E.; Chakraborty, T.; Görlitz, J.; Herrmann, D.; Noël, C.; Heupel, J.; Jannis, D.; Gauquelin, N.; D'Haen, J.; Verbeeck, J.; Popov, C.; Houssiau, L.; Becher, C.; Nesládek, M.; Haenen, K. |
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Title |
Germanium vacancy centre formation in CVD nanocrystalline diamond using a solid dopant source |
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A3 Journal article |
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2023 |
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Science talks |
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Science Talks |
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5 |
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100157 |
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A3 Journal article; Electron microscopy for materials research (EMAT) |
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2023-02-09 |
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2772-5693 |
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EMAT @ emat @c:irua:196969 |
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8791 |
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Jannis, D.; Müller-Caspary, K.; Béché, A.; Oelsner, A.; Verbeeck, J. |
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Title |
Spectrocopic coincidence experiment in transmission electron microscopy |
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2019 |
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Dataset; Electron microscopy for materials research (EMAT) |
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This dataset contains individual EEL and EDX events where for every event (electron or X-ray), their energy and time of arrival is stored. The experiment was performed in a transmission electron microscope (Tecnai Osiris) at 200 keV. The material investigated is an Al-Mg-Si-Cu alloy. The 'full_dataset.mat' contains the full dataset and the 'subset.mat' has the first five frames of the full dataset. The attached 'EELS-EDX.ipynb' is a jupyter notebook file. This file describes the data processing in order to observe the temporal correlation between the electrons and X-rays. |
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UA @ admin @ c:irua:169112 |
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6888 |
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Annys, A.; Jannis, D.; Verbeeck, J. |
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Core-loss EELS dataset and neural networks for element identification |
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2023 |
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Dataset; Electron microscopy for materials research (EMAT) |
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We present a large dataset containing simulated core-loss electron energy loss spectroscopy (EELS) spectra with the elemental content as ground-truth labels. Additionally we present some neural networks trained on this data for element identification. The simulated dataset contains zero padded core-loss spectra from 0 to 3072 eV, which represents 107 core-loss edges through all 80 elements from Be up to Bi. The core-loss edges are calculated from the generalised oscillator strength (GOS) database presented by Zhang et al.[1] Generic fine structures using lifetime broadened peaks are used to imitate fine structure due to solid-state effects in experimental spectra. Generic low-loss regions are used to imitate the effect of multiple scattering. Each spectrum contains at least one edge of a given query element and possibly additional edges depending on samples drawn from The Materials Project [2]. The dataset contains for each of the 80 elements: 7000 training spectra, 1500 test spectra, 600 validation spectra and 100 spectra representing only the query element. This results in a total 736 000 labeled spectra. Code on how to – read the simulated data – transform HDF5 format to TFRecord format – train and evaluate neural networks using the simulated data – use the trained networks for automated element identification is available on GitHub at arnoannys/EELS_ID A full report on the simulation of the dataset and the training and evaluation of the neural networks can be found at: Annys, A., Jannis, D. & Verbeeck, J. Deep learning for automated materials characterisation in core-loss electron energy loss spectroscopy. Sci Rep 13, 13724 (2023). https://doi.org/10.1038/s41598-023-40943-7 [1] Zezhong Zhang, Ivan Lobato, Daen Jannis, Johan Verbeeck, Sandra Van Aert, & Peter Nellist. (2023). Generalised oscillator strength for core-shell electron excitation by fast electrons based on Dirac solutions (1.0) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.7729585 [2] Anubhav Jain, Shyue Ping Ong, Geoffroy Hautier, Wei Chen, William Davidson Richards, Stephen Dacek, Shreyas Cholia, Dan Gunter, David Skinner, Gerbrand Ceder, Kristin A. Persson; Commentary: The Materials Project: A materials genome approach to accelerating materials innovation. APL Mater 1 July 2013; 1 (1): 011002. [https://doi.org/10.1063/1.4812323](https://doi.org/10.1063/1.4812323) |
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UA @ admin @ c:irua:203391 |
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9015 |
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Zhang, Z.; Lobato, I.; Brown, H.; Jannis, D.; Verbeeck, J.; Van Aert, S.; Nellist, P. |
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Generalised oscillator strength for core-shell electron excitation by fast electrons based on Dirac solutions |
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2023 |
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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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UA @ admin @ c:irua:203392 |
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9042 |
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Author |
Jannis, D. |
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Novel detection schemes for transmission electron microscopy |
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Doctoral thesis |
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2021 |
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iv, 208 p. |
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Doctoral thesis; Electron microscopy for materials research (EMAT) |
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Electron microscopy is an excellent tool which provides resolution down to the atomic scale with up to pm precision in locating atoms. The characterization of materials in these length scales is of utmost importance to answer questions in biology, chemistry and material science. The successful implementation of aberration-corrected microscopes made atomic resolution imaging relatively easy, this could give the impression that the development of novel electron microscopy techniques would stagnate and only the application of these instruments as giant magnifying tools would continue. This is of course not true and a multitude of problems still exist in electron microscopy. Two of such issues are discussed below. One of the biggest problems in electron microscopy is the presence of beam damage which occurs due the fact that the highly energetic incoming electrons have sufficient kinetic energy to change the structure of the material. The amount of damage induced depends on the dose, hence minimizing this dose during an experiment is beneficial. This minimizing of the total dose comes at the expense of more noise due to the counting nature of the electrons. For this reason, the implementation of four dimensional scanning transmission electron microscopy (4D STEM) experiments has reduced the total dose needed per acquisition. However, the current cameras used to measure the diffraction patterns are still two orders of magnitude slower than to the conventional STEM methods. Improving the acquisition speed would make the 4D STEM technique more feasible and is of utmost importance for the beam sensitive materials since less dose is used during the acquisition. In TEM there is not only the possibility to perform imaging experiments but also spectroscopic measurements. There are two frequently used methods: electron energy-loss spectroscopy (EELS) and energy dispersive x-ray spectroscopy (EDX). EELS measures the energy-loss spectrum of the incoming electron which gives information on the available excitations in the material providing elemental sensitivity. In EDX, the characteristic x-rays, arising from the decay of an atom which is initially excited due to the incoming electrons, are detected providing similar elemental analysis. Both methods are able to provide comparable elemental information where in certain circumstances one outperforms the other. However, both methods have a detection limit of approximately 100-1000 ppm which is not sufficient for some materials. In this thesis, two novel techniques which can make significant progress for the two problems discussed above. |
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Most recent IF: NA |
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Call Number |
UA @ admin @ c:irua:182404 |
Serial |
6872 |
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