Charting the low-loss region in electron energy loss spectroscopy with machine learning

Roest, Laurien I.; Heijst, Sabrya E. van; Maduro, Louis; Rojo, Juan; Conesa-Boj, Sònia

doi:10.1016/j.ultramic.2021.113202

Cited by 14 publications

(47 citation statements)

References 56 publications

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“…First, to horizontally-standing WS 2 flakes belonging to flower-like nanostructures (nanoflowers) characterised by a mixed 2H/3R polytypism. This nanomaterial, member of the transition metal dichalcogenide (TMD) family, was already considered in the original study 26,27 and hence provides a suitable benchmark to validate our new strategy. One important property of WS 2 is that the indirect bandgap of its bulk form switches to direct at the monolayer level.…”

Section: Resultsmentioning

confidence: 99%

“…Subsequently to this clustering, we train a deep-learning model parametrising the specimen ZLP by extending the approach that we developed in. 26 The adopted neural network architecture is displayed in Fig. 1(b), where the inputs are the energy loss E and the integrated intensity N tot .…”

Section: Computational Detailsmentioning

confidence: 99%

“…Our approach is amenable to generalisation to other families of nanostructured materials, is suitable for application to higher-dimensional datasets such as momentum-resolved EELS, and is made available as a new release of the EELSfitter open-source framework. 26…”

Section: Introductionmentioning

confidence: 99%

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Spatially-Resolved Band Gap and Dielectric Function in 2D Materials from Electron Energy Loss Spectroscopy

Brokkelkamp,

ter Hoeve,

Postmes

et al. 2022

Preprint

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Section: Resultsmentioning

confidence: 99%

Section: Computational Detailsmentioning

confidence: 99%

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Spatially-Resolved Band Gap and Dielectric Function in 2D Materials from Electron Energy Loss Spectroscopy

Brokkelkamp,

ter Hoeve,

Postmes

et al. 2022

Preprint

Self Cite

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show abstract

“…Subsequent to this clustering, we train a deep-learning model parametrizing the specimen ZLP by extending the approach that we developed in ref ( 26 ). The adopted neural network architecture is displayed in Figure 1 b, where the inputs are the energy loss E and the integrated intensity N tot .…”

Section: Computational Detailsmentioning

confidence: 99%

“…Our approach is amenable to generalization to other families of nanostructured materials, is suitable for application to higher-dimensional data sets such as momentum-resolved EELS, and is made available as a new release of the EELSfitter open-source framework. 26 …”

Section: Introductionmentioning

confidence: 99%

Spatially Resolved Band Gap and Dielectric Function in Two-Dimensional Materials from Electron Energy Loss Spectroscopy

et al. 2022

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The electronic properties of two-dimensional (2D) materials depend sensitively on the underlying atomic arrangement down to the monolayer level. Here we present a novel strategy for the determination of the band gap and complex dielectric function in 2D materials achieving a spatial resolution down to a few nanometers. This approach is based on machine learning techniques developed in particle physics and makes possible the automated processing and interpretation of spectral images from electron energy loss spectroscopy (EELS). Individual spectra are classified as a function of the thickness with K -means clustering, and then used to train a deep-learning model of the zero-loss peak background. As a proof of concept we assess the band gap and dielectric function of InSe flakes and polytypic WS 2 nanoflowers and correlate these electrical properties with the local thickness. Our flexible approach is generalizable to other nanostructured materials and to higher-dimensional spectroscopies and is made available as a new release of the open-source EELSfitter framework.

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Localized Exciton Anatomy and BandGap Energy Modulation in 1D MoS₂ Nanostructures

van der Lippe,

Brokkelkamp,

Rojo

et al. 2023

Adv Funct Materials

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This study presents an in‐depth investigation of the electronic properties and bandgap energy distribution in 1D molybdenum disulfide (1D‐MoS2) nanostructures. Through a combination of high‐angle annular dark‐field scanning transmission electron microscopy (HAADF‐STEM) and electron energy‐loss spectroscopy (EELS), it reveals significant differences between 1D‐MoS2 nanostructures and their 2D counterparts, shedding light on their localized exciton behavior and their bandgap energy modulation within the nanostructures. Excitonic peaks at around 2 and 3 eV appear localized at the ends or along the sides of the 1D‐MoS2 nanostructures, while the plasmonic resonance at 8.3 eV retains its inner‐region localization. It demonstrates the spatial dependence of the bandgap energy, with the central region exhibiting a bandgap of approximately 1.2 eV, consistent with bulk MoS2, while regions characterized by curvature‐induced local strain fields exhibit instead a noticeable reduction. The findings provide valuable insights into the intricate relationship between excitonic behavior and bandgap sensitivity in 1D‐MoS2 nanostructures, streamlining the design and optimization of nanophotonic and optoelectronic devices.

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Charting the low-loss region in electron energy loss spectroscopy with machine learning

Cited by 14 publications

References 56 publications

Spatially-Resolved Band Gap and Dielectric Function in 2D Materials from Electron Energy Loss Spectroscopy

Spatially-Resolved Band Gap and Dielectric Function in 2D Materials from Electron Energy Loss Spectroscopy

Spatially Resolved Band Gap and Dielectric Function in Two-Dimensional Materials from Electron Energy Loss Spectroscopy

Localized Exciton Anatomy and BandGap Energy Modulation in 1D MoS₂ Nanostructures

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Charting the low-loss region in electron energy loss spectroscopy with machine learning

Cited by 14 publications

References 56 publications

Spatially-Resolved Band Gap and Dielectric Function in 2D Materials from Electron Energy Loss Spectroscopy

Spatially-Resolved Band Gap and Dielectric Function in 2D Materials from Electron Energy Loss Spectroscopy

Spatially Resolved Band Gap and Dielectric Function in Two-Dimensional Materials from Electron Energy Loss Spectroscopy

Localized Exciton Anatomy and BandGap Energy Modulation in 1D MoS2 Nanostructures

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Product

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Localized Exciton Anatomy and BandGap Energy Modulation in 1D MoS₂ Nanostructures