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Birkholzer, Y.A.; Sotthewes, K.; Gauquelin, N.; Riekehr, L.; Jannis, D.; van der Minne, E.; Bu, Y.; Verbeeck, J.; Zandvliet, H.J.W.; Koster, G.; Rijnders, G. |
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Title |
High-strain-induced local modification of the electronic properties of VO₂ thin films |
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A1 Journal article |
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Year |
2022 |
Publication |
ACS applied electronic materials |
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4 |
Issue |
12 |
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6020-6028 |
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Keywords |
A1 Journal article; Electron microscopy for materials research (EMAT) |
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Abstract  |
Vanadium dioxide (VO2) is a popular candidate for electronic and optical switching applications due to its well-known semiconductor-metal transition. Its study is notoriously challenging due to the interplay of long- and short-range elastic distortions, as well as the symmetry change and the electronic structure changes. The inherent coupling of lattice and electronic degrees of freedom opens the avenue toward mechanical actuation of single domains. In this work, we show that we can manipulate and monitor the reversible semiconductor-to-metal transition of VO2 while applying a controlled amount of mechanical pressure by a nanosized metallic probe using an atomic force microscope. At a critical pressure, we can reversibly actuate the phase transition with a large modulation of the conductivity. Direct tunneling through the VO2-metal contact is observed as the main charge carrier injection mechanism before and after the phase transition of VO2. The tunneling barrier is formed by a very thin but persistently insulating surface layer of the VO2. The necessary pressure to induce the transition decreases with temperature. In addition, we measured the phase coexistence line in a hitherto unexplored regime. Our study provides valuable information on pressure-induced electronic modifications of the VO2 properties, as well as on nanoscale metal-oxide contacts, which can help in the future design of oxide electronics. |
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000890974900001 |
Publication Date |
2022-11-18 |
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2637-6113 |
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UA library record; WoS full record |
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Times cited |
2 |
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OpenAccess |
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Notes |
This work received financial support from the project Green ICT (grant number 400.17.607) of the research program NWA, which is financed by the Dutch Research Council (NWO), Research Foundation Flanders (FWO grant number G0F1320N), and the European Union’s Horizon 2020 research and innovation program within a contract for Integrating Activities for Advanced Communities (grant number 823717 − ESTEEM3). The K2 camera was funded through the Research Foundation Flanders (FWO-Hercules grant number G0H4316N – “Direct electron detector for soft matter TEM”).; esteem3reported; esteem3jra |
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Most recent IF: NA |
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Call Number |
UA @ admin @ c:irua:192712 |
Serial |
7309 |
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Author |
Jannis, D.; Müller-Caspary, K.; Béché, A.; Oelsner, A.; Verbeeck, J. |
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Title |
Spectroscopic coincidence experiments in transmission electron microscopy |
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A1 Journal article |
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Year |
2019 |
Publication |
Applied physics letters |
Abbreviated Journal |
Appl Phys Lett |
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Volume |
114 |
Issue |
14 |
Pages |
143101 |
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Keywords |
A1 Journal article; Electron microscopy for materials research (EMAT) |
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Abstract |
We demonstrate the feasibility of coincidence measurements on a conventional transmission electron microscope, revealing the temporal
correlation between electron energy loss spectroscopy (EELS) and energy dispersive X-ray (EDX) spectroscopy events. We make use of a
delay line detector with ps-range time resolution attached to a modified EELS spectrometer. We demonstrate that coincidence between both
events, related to the excitation and deexcitation of atoms in a crystal, provides added information not present in the individual EELS or
EDX spectra. In particular, the method provides EELS with a significantly suppressed or even removed background, overcoming the many
difficulties with conventional parametric background fitting as it uses no assumptions on the shape of the background, requires no user input
and does not suffer from counting noise originating from the background signal. This is highly attractive, especially when low concentrations
of elements need to be detected in a matrix of other elements. |
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000464450200022 |
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2019-04-08 |
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0003-6951 |
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Additional Links |
UA library record; WoS full record; WoS citing articles |
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Impact Factor |
3.411 |
Times cited |
18 |
Open Access |
OpenAccess |
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Notes |
Fonds Wetenschappelijk Onderzoek, G093417 ; Horizon 2020 Framework Programme, 823717 ESTEEM3 ; Helmholtz Association, VH-NG-1327 ; |
Approved |
Most recent IF: 3.411 |
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Call Number |
EMAT @ emat @UA @ admin @ c:irua:159155 |
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5168 |
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Annys, A.; Jannis, D.; Verbeeck, J. |
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Title |
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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Call Number |
UA @ admin @ c:irua:203391 |
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9015 |
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