Accurate EELS background subtraction – an adaptable method in MATLAB

Fung, Kayleigh L. Y.; Fay, Michael W.; Collins, Sean M.; Kepaptsoglou, Demie; Skowron, Stephen T.; Ramasse, Quentin M.; Khlobystov, Andrei N.

doi:10.1016/j.ultramic.2020.113052

Cited by 13 publications

(12 citation statements)

References 24 publications

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“…Hence similar conclusions can be obtained albeit that typically for EDX the SBR is significantly higher than for EELS explaining why it is commonly accepted that EDX is preferable when it comes to trace element detection even though the detection efficiency is considerably lower than for EELS [33]. Please note that this derivation takes only Poisson noise statistics into account and assumes an ideal detector and perfect background subtraction with no extrapolation and fitting error [34][35][36][37]. From Figure 6c one can conclude that also with conventional EELS we can detect low-concentration species in a surrounding matrix (low SBR situation) by choosing the current high enough.…”

Section: Low Concentration Detectionsupporting

confidence: 70%

Coincidence Detection of EELS and EDX Spectral Events in the Electron Microscope

et al. 2021

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Recent advances in the development of electron and X-ray detectors have opened up the possibility to detect single events from which its time of arrival can be determined with nanosecond resolution. This allows observing time correlations between electrons and X-rays in the transmission electron microscope. In this work, a novel setup is described which measures individual events using a silicon drift detector and digital pulse processor for the X-rays and a Timepix3 detector for the electrons. This setup enables recording time correlation between both event streams while at the same time preserving the complete conventional electron energy loss (EELS) and energy dispersive X-ray (EDX) signal. We show that the added coincidence information improves the sensitivity for detecting trace elements in a matrix as compared to conventional EELS and EDX. Furthermore, the method allows the determination of the collection efficiencies without the use of a reference sample and can subtract the background signal for EELS and EDX without any prior knowledge of the background shape and without pre-edge fitting region. We discuss limitations in time resolution arising due to specificities of the silicon drift detector and discuss ways to further improve this aspect.

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Section: Low Concentration Detectionsupporting

confidence: 70%

Coincidence Detection of EELS and EDX Spectral Events in the Electron Microscope

et al. 2021

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“…The ZLPs were removed from experimental spectra using two-term exponential functions on a window below 0.12 eV, preceding the first observable spectral feature of interest, using scripts written in MATLAB. 48 STEM images shown in Fig 3a,c were processed using a "difference of Gaussians" filter, 49 see Supporting Information S11 for original unprocessed images. Finally, fingerprinting of the single F atoms using EELS spectrum imaging was carried out on a non-monochromated Nion UltraSTEM100 microscope, operated at 60 kV.…”

Section: Materials Preparationmentioning

confidence: 99%

Bond Dissociation and Reactivity of HF and H₂O in a Nano Test Tube

et al. 2020

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Molecular motion and bond dissociation are two of the most fundamental phenomena underpinning properties of molecular materials. We have entrapped HF and H2O molecules within the fullerene C60 cage, encapsulated within a single-walled carbon nanotube (X@C60)@SWNT where X = HF or H2O. (X@C60)@SWNT represents a class of molecular nanomaterial composed of a guest within a molecular host within a nanoscale host, enabling investigations of the interactions of isolated single di-or tri-atomic molecules with the electron beam. The use of the electron beam simultaneously as a stimulus of chemical reactions in molecules and as a sub-Å resolution imaging probe allows investigations of the molecular dynamics and reactivity in real time and at the atomic scale, which are probed directly by chromatic and spherical aberration corrected high resolution transmission electron microscopy (Cc/Cs-corrected HRTEM) imaging, or indirectly by vibrational electron energy loss spectroscopy (EELS) in situ during scanning transmission electron microscopy (STEM) experiments.Experimental measurements indicate that the electron beam triggers homolytic dissociation of the H-F or H-O bonds, respectively, causing the expulsion of the hydrogen atoms from the fullerene cage, leaving fluorine or oxygen behind. Due to a difference in the mechanisms of penetration 2 through the carbon lattice available for F or O atoms, atomic fluorine inside the fullerene cage appears to be more stable than the atomic oxygen under the same conditions. The use of (X@C60)@SWNT, where each molecule X is 'packaged' separately from each other, in combination with the electron microscopy methods and density functional theory (DFT) modelling in this work, enable bond dynamics and reactivity of individual atoms to be probed directly at the singlemolecule level.

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“…The ZLP is the peak in the EEL spectrum containing all elastic scattering and non‐interacting (no energy loss) electrons and for thin samples it is by far the dominant feature in the EEL spectrum (typically 3–5 orders of magnitude larger than the low‐loss signal); thus, small variations in the ZLP are dominant mathematically with respect to the real plasmonic peaks. This can be done in a number of ways, [ 30 ] either by simply cutting the spectral axis off at a channel above the ZLP or by performing a power law fit to the tail of the ZLP and subtracting it from each pixel in the SI. Furthermore, datasets must be cleaned to remove the small clusters of spectrometer channels with signal orders of magnitude higher than the surrounding channels that are caused by stray X‐rays hitting the scintillator in the spectrometer.…”

Section: Resultsmentioning

confidence: 99%

Separating Physically Distinct Mechanisms in Complex Infrared Plasmonic Nanostructures via Machine Learning Enhanced Electron Energy Loss Spectroscopy

Kalinin

Roccapriore

Cho

et al. 2021

Advanced Optical Materials

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Electron energy loss spectroscopy (EELS) enables direct exploration of plasmonic phenomena at the nanometer level. To isolate individual plasmon modes, linear unmixing methods can be used to separate different physical mechanisms, but in larger and more complex systems the interpretability of the components becomes uncertain. Here, infrared plasmonic resonances in self‐assembled heterogeneous monolayer films of doped‐semiconductor nanoparticles are examined beyond linear unmixing techniques, and both supervised and unsupervised machine‐learning‐based analyses of hyperspectral EELS datasets are demonstrated. In the supervised approach, a human operator labels a small number of pixels in the hyperspectral dataset corresponding to features of interest which are then propagated across the entire dataset. In the unsupervised approach, non‐linear autoencoders are used to create a highly‐reduced latent‐space representation of the dataset, within which insight into the relevant physics can be gleaned from straightforward distance metrics that do not depend on operator input and bias. The advantage of these approaches is that the labeling separates physical mechanisms without altering the data, enabling robust analyses of the influence of heterogeneities in mesoscale complex systems.

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Accurate EELS background subtraction – an adaptable method in MATLAB

Cited by 13 publications

References 24 publications

Coincidence Detection of EELS and EDX Spectral Events in the Electron Microscope

Coincidence Detection of EELS and EDX Spectral Events in the Electron Microscope

Bond Dissociation and Reactivity of HF and H₂O in a Nano Test Tube

Separating Physically Distinct Mechanisms in Complex Infrared Plasmonic Nanostructures via Machine Learning Enhanced Electron Energy Loss Spectroscopy

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Accurate EELS background subtraction – an adaptable method in MATLAB

Cited by 13 publications

References 24 publications

Coincidence Detection of EELS and EDX Spectral Events in the Electron Microscope

Coincidence Detection of EELS and EDX Spectral Events in the Electron Microscope

Bond Dissociation and Reactivity of HF and H2O in a Nano Test Tube

Separating Physically Distinct Mechanisms in Complex Infrared Plasmonic Nanostructures via Machine Learning Enhanced Electron Energy Loss Spectroscopy

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Bond Dissociation and Reactivity of HF and H₂O in a Nano Test Tube