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“Optical versus electron diffraction imaging of Twist-angle in 2D transition metal dichalcogenide bilayers”. Psilodimitrakopoulos S, Orekhov A, Mouchliadis L, Jannis D, Maragkakis GM, Kourmoulakis G, Gauquelin N, Kioseoglou G, Verbeeck J, Stratakis E, npj 2D Materials and Applications 5, 77 (2021). http://doi.org/10.1038/S41699-021-00258-5
Abstract: Atomically thin two-dimensional (2D) materials can be vertically stacked with van der Waals bonds, which enable interlayer coupling. In the particular case of transition metal dichalcogenide (TMD) bilayers, the relative direction between the two monolayers, coined as twist-angle, modifies the crystal symmetry and creates a superlattice with exciting properties. Here, we demonstrate an all-optical method for pixel-by-pixel mapping of the twist-angle with a resolution of 0.55(degrees), via polarization-resolved second harmonic generation (P-SHG) microscopy and we compare it with four-dimensional scanning transmission electron microscopy (4D STEM). It is found that the twist-angle imaging of WS2 bilayers, using the P-SHG technique is in excellent agreement with that obtained using electron diffraction. The main advantages of the optical approach are that the characterization is performed on the same substrate that the device is created on and that it is three orders of magnitude faster than the 4D STEM. We envisage that the optical P-SHG imaging could become the gold standard for the quality examination of TMD superlattice-based devices.
Keywords: A1 Journal article; Engineering sciences. Technology; Electron microscopy for materials research (EMAT)
Times cited: 4
DOI: 10.1038/S41699-021-00258-5
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Jannis D, Mü,ller-Caspary K, Bé,ché, A, Oelsner A, Verbeeck J (2019) Spectrocopic coincidence experiment in transmission electron microscopy
Abstract: 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.
Keywords: Dataset; Electron microscopy for materials research (EMAT)
DOI: 10.5281/ZENODO.2563880
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Annys A, Jannis D, Verbeeck J (2023) Core-loss EELS dataset and neural networks for element identification
Abstract: 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)
Keywords: Dataset; Electron microscopy for materials research (EMAT)
DOI: 10.5281/ZENODO.8004912
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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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“Low-dose 4D-STEM tomography for beam-sensitive nanocomposites”. Hugenschmidt M, Jannis D, Kadu AA, Grünewald L, De Marchi S, Perez-Juste J, Verbeeck J, Van Aert S, Bals S, ACS materials letters 6, 165 (2023). http://doi.org/10.1021/ACSMATERIALSLETT.3C01042
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.
Keywords: A1 Journal article; Electron microscopy for materials research (EMAT)
DOI: 10.1021/ACSMATERIALSLETT.3C01042
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