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Title: Atomic Mechanisms for the Si Atom Dynamics in Graphene: Chemical Transformations at the Edge and in the Bulk

Abstract

The dynamic behavior of e-beam irradiated Si atoms in the bulk and at the edges of single-layer graphene is examined using scanning transmission electron microscopy (STEM). A deep learning network is used to convert experimental STEM movies into coordinates of individual Si and carbon atoms. A Gaussian mixture model is further used to establish the elementary atomic configurations of the Si atoms, defining the bonding geometries and chemical species and accounting for the discrete rotational symmetry of the host lattice. The frequencies and Markov transition probabilities between these states are determined. This analysis enables insight into the defect populations and chemical transformation networks from the atomically resolved STEM data. Here, a clear tendency is observed for the formation of a 1D Si crystal along zigzag direction of graphene edges and for the Si impurity coupling to topological defects in bulk graphene.

Authors:
ORCiD logo [1]; ORCiD logo [2]; ORCiD logo [2]; ORCiD logo [2]
+ Show Author Affiliations
  1. Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States). Center for Nanophase Materials Science (CNMS), and Computational Sciences and Engineering Division
  2. Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States). Center for Nanophase Materials Science (CNMS)
Publication Date:
Research Org.:
Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States)
Sponsoring Org.:
USDOE Office of Science (SC), Basic Energy Sciences (BES)
OSTI Identifier:
1607241
Alternate Identifier(s):
OSTI ID: 1575294
Grant/Contract Number:  
AC05-00OR22725
Resource Type:
Accepted Manuscript
Journal Name:
Advanced Functional Materials
Additional Journal Information:
Journal Volume: 29; Journal Issue: 52; Journal ID: ISSN 1616-301X
Publisher:
Wiley
Country of Publication:
United States
Language:
English
Subject:
36 MATERIALS SCIENCE; atom dynamics; electron microscopy; graphene; machine learning

Citation Formats

Ziatdinov, Maxim, Dyck, Ondrej, Jesse, Stephen, and Kalinin, Sergei V. Atomic Mechanisms for the Si Atom Dynamics in Graphene: Chemical Transformations at the Edge and in the Bulk. United States: N. p., 2019. Web. doi:10.1002/adfm.201904480.
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Ziatdinov, Maxim, Dyck, Ondrej, Jesse, Stephen, & Kalinin, Sergei V. Atomic Mechanisms for the Si Atom Dynamics in Graphene: Chemical Transformations at the Edge and in the Bulk. United States. https://doi.org/10.1002/adfm.201904480
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Ziatdinov, Maxim, Dyck, Ondrej, Jesse, Stephen, and Kalinin, Sergei V. Fri . "Atomic Mechanisms for the Si Atom Dynamics in Graphene: Chemical Transformations at the Edge and in the Bulk". United States. https://doi.org/10.1002/adfm.201904480. https://www.osti.gov/servlets/purl/1607241.
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@article{osti_1607241,
title = {Atomic Mechanisms for the Si Atom Dynamics in Graphene: Chemical Transformations at the Edge and in the Bulk},
author = {Ziatdinov, Maxim and Dyck, Ondrej and Jesse, Stephen and Kalinin, Sergei V.},
abstractNote = {The dynamic behavior of e-beam irradiated Si atoms in the bulk and at the edges of single-layer graphene is examined using scanning transmission electron microscopy (STEM). A deep learning network is used to convert experimental STEM movies into coordinates of individual Si and carbon atoms. A Gaussian mixture model is further used to establish the elementary atomic configurations of the Si atoms, defining the bonding geometries and chemical species and accounting for the discrete rotational symmetry of the host lattice. The frequencies and Markov transition probabilities between these states are determined. This analysis enables insight into the defect populations and chemical transformation networks from the atomically resolved STEM data. Here, a clear tendency is observed for the formation of a 1D Si crystal along zigzag direction of graphene edges and for the Si impurity coupling to topological defects in bulk graphene.},
doi = {10.1002/adfm.201904480},
journal = {Advanced Functional Materials},
number = 52,
volume = 29,
place = {United States},
year = {Fri Nov 22 00:00:00 EST 2019},
month = {Fri Nov 22 00:00:00 EST 2019}
}
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Journal Article:
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Publisher's Version of Record
https://doi.org/10.1002/adfm.201904480
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