|   | 
Details
   web
Records
Author Zhang, Z.; Lobato, I.; De Backer, A.; Van Aert, S.; Nellist, P.
Title Fast generation of calculated ADF-EDX scattering cross-sections under channelling conditions Type A1 Journal article
Year 2023 Publication (up) Ultramicroscopy Abbreviated Journal
Volume 246 Issue Pages 113671
Keywords A1 Journal article; Electron microscopy for materials research (EMAT)
Abstract Advanced materials often consist of multiple elements which are arranged in a complicated structure. Quantitative scanning transmission electron microscopy is useful to determine the composition and thickness of nanostructures at the atomic scale. However, significant difficulties remain to quantify mixed columns by comparing the resulting atomic resolution images and spectroscopy data with multislice simulations where dynamic scattering needs to be taken into account. The combination of the computationally intensive nature of these simulations and the enormous amount of possible mixed column configurations for a given composition indeed severely hamper the quantification process. To overcome these challenges, we here report the development of an incoherent non-linear method for the fast prediction of ADF-EDX scattering cross-sections of mixed columns under channelling conditions. We first explain the origin of the ADF and EDX incoherence from scattering physics suggesting a linear dependence between those two signals in the case of a high-angle ADF detector. Taking EDX as a perfect incoherent reference mode, we quantitatively examine the ADF longitudinal incoherence under different microscope conditions using multislice simulations. Based on incoherent imaging, the atomic lensing model previously developed for ADF is now expanded to EDX, which yields ADF-EDX scattering cross-section predictions in good agreement with multislice simulations for mixed columns in a core–shell nanoparticle and a high entropy alloy. The fast and accurate prediction of ADF-EDX scattering cross-sections opens up new opportunities to explore the wide range of ordering possibilities of heterogeneous materials with multiple elements.
Address
Corporate Author Zezhong Zhang Thesis
Publisher Place of Publication Editor
Language Wos 000995063900001 Publication Date 2022-12-28
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN 0304-3991 ISBN Additional Links UA library record; WoS full record; WoS citing articles
Impact Factor 2.2 Times cited Open Access OpenAccess
Notes European Research Council 770887 PICOMETRICS; Fonds Wetenschappelijk Onderzoek No.G.0502.18N; Horizon 2020, 770887 ; Horizon 2020 Framework Programme; European Research Council, 823717 ESTEEM3 ; esteem3reported; esteem3JRa Approved Most recent IF: 2.2; 2023 IF: 2.843
Call Number EMAT @ emat @c:irua:195890 Serial 7251
Permanent link to this record
 

 
Author Lobato, I.; De Backer, A.; Van Aert, S.
Title Real-time simulations of ADF STEM probe position-integrated scattering cross-sections for single element fcc crystals in zone axis orientation using a densely connected neural network Type A1 Journal article
Year 2023 Publication (up) Ultramicroscopy Abbreviated Journal Ultramicroscopy
Volume 251 Issue Pages 113769
Keywords A1 Journal article; Electron microscopy for materials research (EMAT)
Abstract Quantification of annular dark field (ADF) scanning transmission electron microscopy (STEM) images in terms

of composition or thickness often relies on probe-position integrated scattering cross sections (PPISCS). In

order to compare experimental PPISCS with theoretically predicted ones, expensive simulations are needed for

a given specimen, zone axis orientation, and a variety of microscope settings. The computation time of such

simulations can be in the order of hours using a single GPU card. ADF STEM simulations can be efficiently

parallelized using multiple GPUs, as the calculation of each pixel is independent of other pixels. However, most

research groups do not have the necessary hardware, and, in the best-case scenario, the simulation time will

only be reduced proportionally to the number of GPUs used. In this manuscript, we use a learning approach and

present a densely connected neural network that is able to perform real-time ADF STEM PPISCS predictions as

a function of atomic column thickness for most common face-centered cubic (fcc) crystals (i.e., Al, Cu, Pd, Ag,

Pt, Au and Pb) along [100] and [111] zone axis orientations, root-mean-square displacements, and microscope

parameters. The proposed architecture is parameter efficient and yields accurate predictions for the PPISCS

values for a wide range of input parameters that are commonly used for aberration-corrected transmission

electron microscopes.
Address
Corporate Author Thesis
Publisher Place of Publication Editor
Language Wos 001011617200001 Publication Date 2023-06-01
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 This work was supported by the European Research Council (Grant 770887 PICOMETRICS to S. Van Aert). The authors acknowledge financial support from the Research Foundation Flanders (FWO, Belgium) through project fundings (G034621N and G0A7723N) and a postdoctoral grant to A. De Backer. S. Van Aert acknowledges funding from the University of Antwerp Research fund (BOF), Belgium. Approved Most recent IF: 2.2; 2023 IF: 2.843
Call Number EMAT @ emat @c:irua:197275 Serial 8812
Permanent link to this record