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• W2990355364 startingPage "1904480" @default.
• W2990355364 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." @default.
• W2990355364 created "2019-12-05" @default.
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• W2990355364 date "2019-11-22" @default.
• W2990355364 modified "2023-10-11" @default.
• W2990355364 title "Atomic Mechanisms for the Si Atom Dynamics in Graphene: Chemical Transformations at the Edge and in the Bulk" @default.
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• W2990355364 doi "https://doi.org/10.1002/adfm.201904480" @default.
• W2990355364 hasPublicationYear "2019" @default.
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