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Fig. 3 Constructing libraries of defects from STEM data on graphene with Si impurities via deep learning–based analysis of raw experimental data. Constructing.

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Presentation on theme: "Fig. 3 Constructing libraries of defects from STEM data on graphene with Si impurities via deep learning–based analysis of raw experimental data. Constructing."— Presentation transcript:

1 Fig. 3 Constructing libraries of defects from STEM data on graphene with Si impurities via deep learning–based analysis of raw experimental data. Constructing libraries of defects from STEM data on graphene with Si impurities via deep learning–based analysis of raw experimental data. (A and B) Defects containing a single three-fold coordinated Si atom (count, 465) (A) and a single four-fold coordinated Si atom (count, 16) (B). (C and D) Histograms of Si─C bond lengths (C) and Si─C bond angles (D) for three-fold Si defect. (E and F) Examples of distorted threefold Si defect located next to a multivacancy (E) and at topological defect (F). The number of distorted (SD s = ( ∑ dx2/n)1/2 in C─Si─C bond angles above 15) and undistorted three-fold Si defects was 231 and 234, respectively. Maxim Ziatdinov et al. Sci Adv 2019;5:eaaw8989 Copyright © 2019 The Authors, some rights reserved; exclusive licensee American Association for the Advancement of Science. No claim to original U.S. Government Works. Distributed under a Creative Commons Attribution NonCommercial License 4.0 (CC BY-NC).

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