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	Author	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.
	Title	Germanium vacancy centre formation in CVD nanocrystalline diamond using a solid dopant source			Type	A1 Journal Article
	Year	2023	Publication	Science talks	Abbreviated Journal	Science Talks
	Volume	5	Issue		Pages	100157
	Keywords	A3 Journal article; Electron microscopy for materials research (EMAT)
	Abstract
	Address
	Corporate Author				Thesis
	Publisher		Place of Publication		Editor
	Language		Wos		Publication Date	2023-02-09
	Series Editor		Series Title		Abbreviated Series Title
	Series Volume		Series Issue		Edition
	ISSN	2772-5693	ISBN		Additional Links	UA library record
	Impact Factor		Times cited		Open Access	OpenAccess
	Notes				Approved	Most recent IF: NA
	Call Number	EMAT @ emat @c:irua:196969			Serial	8791
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	Author	Denisov, N.; Jannis, D.; Orekhov, A.; Müller-Caspary, K.; Verbeeck, J.
	Title	Characterization of a Timepix detector for use in SEM acceleration voltage range			Type	A1 Journal Article
	Year	2023	Publication	Ultramicroscopy	Abbreviated Journal	Ultramicroscopy
	Volume	253	Issue		Pages	113777
	Keywords	A1 Journal article; Electron microscopy for materials research (EMAT)
	Abstract	Hybrid pixel direct electron detectors are gaining popularity in electron microscopy due to their excellent properties. Some commercial cameras based on this technology are relatively affordable which makes them attractive tools for experimentation especially in combination with an SEM setup. To support this, a detector characterization (Modulation Transfer Function, Detective Quantum Efficiency) of an Advacam Minipix and Advacam Advapix detector in the 15–30 keV range was made. In the current work we present images of Point Spread Function, plots of MTF/DQE curves and values of DQE(0) for these detectors. At low beam currents, the silicon detector layer behaviour should be dominant, which could make these findings transferable to any other available detector based on either Medipix2, Timepix or Timepix3 provided the same detector layer is used.
	Address
	Corporate Author				Thesis
	Publisher		Place of Publication		Editor
	Language		Wos	001026912700001	Publication Date	2023-06-08
	Series Editor		Series Title		Abbreviated Series Title
	Series Volume		Series Issue		Edition
	ISSN	0304-3991	ISBN		Additional Links	UA library record; WoS full record
	Impact Factor	2.2	Times cited		Open Access	OpenAccess
	Notes	The authors acknowledge the financial support of the Research Foundation Flanders (FWO, Belgium) project SBO S000121N. The authors are grateful to Dr. Lobato for productive discussion of methods.			Approved	Most recent IF: 2.2; 2023 IF: 2.843
	Call Number	EMAT @ emat @c:irua:198258			Serial	8815
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	Author	Annys, A.; Jannis, D.; Verbeeck, J.; Annys, A.; Jannis, D.; Verbeeck, J.
	Title	Deep learning for automated materials characterisation in core-loss electron energy loss spectroscopy			Type	A1 Journal article
	Year	2023	Publication	Scientific reports	Abbreviated Journal
	Volume	13	Issue	1	Pages	13724
	Keywords	A1 Journal article; Engineering sciences. Technology; Electron microscopy for materials research (EMAT)
	Abstract	Electron energy loss spectroscopy (EELS) is a well established technique in electron microscopy that yields information on the elemental content of a sample in a very direct manner. One of the persisting limitations of EELS is the requirement for manual identification of core-loss edges and their corresponding elements. This can be especially bothersome in spectrum imaging, where a large amount of spectra are recorded when spatially scanning over a sample area. This paper introduces a synthetic dataset with 736,000 labeled EELS spectra, computed from available generalized oscillator strength tables, that represents 107 K, L, M or N core-loss edges and 80 chemical elements. Generic lifetime broadened peaks are used to mimic the fine structure due to band structure effects present in experimental core-loss edges. The proposed dataset is used to train and evaluate a series of neural network architectures, being a multilayer perceptron, a convolutional neural network, a U-Net, a residual neural network, a vision transformer and a compact convolutional transformer. An ensemble of neural networks is used to further increase performance. The ensemble network is used to demonstrate fully automated elemental mapping in a spectrum image, both by directly mapping the predicted elemental content and by using the predicted content as input for a physical model-based mapping.
	Address
	Corporate Author				Thesis
	Publisher		Place of Publication		Editor
	Language		Wos	001052937600046	Publication Date	2023-08-22
	Series Editor		Series Title		Abbreviated Series Title
	Series Volume		Series Issue		Edition
	ISSN	2045-2322	ISBN		Additional Links	UA library record; WoS full record
	Impact Factor	4.6	Times cited		Open Access	OpenAccess
	Notes	A.A. would like to acknowledge the resources and services used in this work provided by the VSC (Flemish Supercomputer Center), funded by the Research Foundation – Flanders (FWO) and the Flemish Government. J.V. acknowledges the IMPRESS project. The IMPRESS project has received funding from the HORIZON EUROPE framework program for research and innovation under grant agreement n. 101094299.			Approved	Most recent IF: 4.6; 2023 IF: 4.259
	Call Number	UA @ admin @ c:irua:198647			Serial	8846
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