Welcome to the EELSFitter website!
EELSFitter is an open-source Python-based framework developed for the analysis and interpretation of Electron Energy Loss Spectroscopy (EELS) measurements in Transmission Electron Microscopy (TEM). EELSfitter is based on the machine learning techniques developed by the NNPDF Collaboration in the context of applications in high energy physics, in particular feed-forward neural networks for unbiased regression in multidimensional problems.
Fig. 1 Spatially-resolved map of the bandgap for the WS2 nanoflower specimen, where a mask has been applied to remove the vacuum and pure substrate pixels.
Publications
EELSFitter has been used in the following scientific publications:
Charting the low-loss region in Electron Energy Loss Spectroscopy with machine learning, Laurien I. Roest, Sabrya E. van Heijst, Luigi Maduro, Juan Rojo, Sonia Conesa-Boj [Roest et al., 2021].
Illuminating the Electronic Properties of WS2 Polytypism with Electron Microscopy, Sabrya E. van Heijst, Masaki Mukai, Eiji Okunishi, Hiroki Hashiguchi, Laurien I. Roest, Luigi Maduro, Juan Rojo, Sonia Conesa-Boj [van Heijst et al., 2021].
Spatially-resolved band gap and dielectric function in 2D materials from Electron Energy Loss Spectroscopy, Abel Brokkelkamp, Jaco ter Hoeve, Isabel Postmes, Sabrya E. van Heijst, Luigi Maduro, Albert Davydov, Sergiy Krylyuk, Juan Rojo, Sonia Conesa-Boj [Brokkelkamp et al., 2022].
Localized exciton anatomy and band gap energy modulation in 1D MoS2 nanostructures Stijn van der Lippe, Abel Brokkelkamp, Juan Rojo, Sonia Conesa-Boj [van der Lippe et al., 2023].
Edge-induced excitations in Bi2Te3 from spatially-resolved electron energy-gain spectroscopy, Helena La, Abel Brokkelkamp, Stijn van der Lippe, Jaco ter Hoeve, Juan Rojo, Sonia Conesa-Boj [La et al., 2023]
The code from the these publications is available in the Code section.
Team description
The EELSFitter collaboration is currently composed by the following members:
Abel Brokkelkamp, Kavli Institiute of Nanoscience, Delft University of Technology
Jaco ter Hoeve, VU Amsterdam and Nikhef Theory Group
Stijn van der Lippe, Kavli Institiute of Nanoscience, Delft University of Technology
Juan Rojo, VU Amsterdam and Nikhef Theory Group
Sonia Conesa Boj, Kavli Institiute of Nanoscience, Delft University of Technology
Former members of the EELSFitter project include
Laurien Roest, Business Analyst at Picnic
Isabel Postmes, Electron Optics Engineer at ASML
Luigi Maduro, PostDoc at University of Manchester
Helena La, Design Engineer at ASML
Citation policy
If you use EELSFitter in a scientific publication, please make sure to cite:
Charting the low-loss region in Electron Energy Loss Spectroscopy with machine learning, Roest, Laurien I. and van Heijst, Sabrya E. and Maduro, Luigi and Rojo, Juan and Conesa-Boj, Sonia [Roest et al., 2021].
Spatially-resolved band gap and dielectric function in 2D materials from Electron Energy Loss Spectroscopy, Brokkelkamp, Abel, and ter Hoeve, Jaco, and Postmes, Isabel, and van Heijst, Sabrya E. and Maduro, Luigi, and Davydov, Albert, and Krylyuk, Sergiy, and Rojo, Juan and Conesa-Boj, Sonia, [Brokkelkamp et al., 2022].