| |
Records |
Links |
|
Author |
Annys, A.; Jannis, D.; Verbeeck, J. |
|
| |
Title |
Core-loss EELS dataset and neural networks for element identification |
Type |
Dataset |
| |
Year |
2023 |
Publication |
|
Abbreviated Journal |
|
|
| |
Volume |
|
Issue |
|
Pages |
|
|
| |
Keywords |
Dataset; Electron microscopy for materials research (EMAT) |
|
| |
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) |
|
| |
Address |
|
|
| |
Corporate Author |
|
Thesis |
|
|
| |
Publisher |
|
Place of Publication |
|
Editor |
|
|
| |
Language |
|
Wos |
|
Publication Date |
|
|
| |
Series Editor |
|
Series Title |
|
Abbreviated Series Title |
|
|
| |
Series Volume |
|
Series Issue |
|
Edition  |
|
|
| |
ISSN |
|
ISBN |
|
Additional Links |
UA library record |
|
| |
Impact Factor |
|
Times cited |
|
Open Access |
|
|
| |
Notes |
|
Approved |
no |
|
| |
Call Number |
UA @ admin @ c:irua:203391 |
Serial |
9015 |
|
| Permanent link to this record |
| |
|
| |
|
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 |
|
Abbreviated Journal |
|
|
| |
Volume |
|
Issue |
|
Pages |
|
|
| |
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. |
|
| |
Address |
|
|
| |
Corporate Author |
|
Thesis |
|
|
| |
Publisher |
|
Place of Publication |
|
Editor |
|
|
| |
Language |
|
Wos |
|
Publication Date |
|
|
| |
Series Editor |
|
Series Title |
|
Abbreviated Series Title |
|
|
| |
Series Volume |
|
Series Issue |
|
Edition |
|
|
| |
ISSN |
|
ISBN |
|
Additional Links |
UA library record |
|
| |
Impact Factor |
|
Times cited |
|
Open Access |
Not_Open_Access |
|
| |
Notes |
|
Approved |
no |
|
| |
Call Number |
UA @ admin @ c:irua:203392 |
Serial |
9042 |
|
| Permanent link to this record |
| |
|
| |
|
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 |
|
|
| |
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. |
|
| |
Address |
|
|
| |
Corporate Author |
|
Thesis |
|
|
| |
Publisher |
|
Place of Publication |
|
Editor |
|
|
| |
Language |
|
Wos |
001141178500001 |
Publication Date |
2023-12-11 |
|
| |
Series Editor |
|
Series Title |
|
Abbreviated Series Title |
|
|
| |
Series Volume |
|
Series Issue |
|
Edition |
|
|
| |
ISSN |
2639-4979 |
ISBN |
|
Additional Links |
UA library record; WoS full record |
|
| |
Impact Factor |
|
Times cited |
|
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 |
no |
|
| |
Call Number |
UA @ admin @ c:irua:202771 |
Serial |
9053 |
|
| Permanent link to this record |
