Engineering and modifying two-dimensional materials by electron beams

Zhao, Xiaoxu; Kotakoski, Jani; Meyer, Jannik C.; Sutter, Eli; Sutter, Peter; Krasheninnikov, Arkady V.; Kaiser, Ute; Zhou, Wu

doi:10.1557/mrs.2017.184

Cited by 73 publications

(84 citation statements)

References 92 publications

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“…In the experimental STEM‐ADF image, the periodicity in the image may be interrupted by lattice distortion and phase boundaries . Fourier filtering will modify the actual structure information, especially along boundaries or edges.…”

Section: Identification Of Mos2 Polymorphs and Stacking Polytypes By mentioning

confidence: 99%

Differentiating Polymorphs in Molybdenum Disulfide via Electron Microscopy

et al. 2018

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The ORCID identification number(s) for the author(s) of this article can be found under https://doi.org/10.1002/adma.201802397. The presence of rich polymorphs and stacking polytypes in molybdenum disulfide (MoS 2 ) endows it with a diverse range of electrical, catalytic, optical, and magnetic properties. This has stimulated a lot of interest in the unique properties associated with each polymorph. Most techniques used for polymorph identification in MoS 2 are macroscopic techniques that sample averaged properties due to their limited spatial resolution. A reliable way of differentiating the atomic structure of different polymorphs is needed in order to understand their growth dynamics and establish the correlation between structure and properties. Herein, the use of electron microscopy for identifying the atomic structures of several important polymorphs in MoS 2 , some of which are the subjects of mistaken assignment in the literature, is discussed. In particular, scanning transmission electron microscopy-annular dark field imaging has emerged as the most effective and reliable approach for identifying the different phases in MoS 2 and other 2D materials because its images can be directly correlated to the atomic structures. Examples of the identification of polymorphs grown under different conditions in molecular beam epitaxy or chemical vapor deposition, for example, 3R, 1T, 1T′-phases, and 1T′-edges, are presented, including their atomic structures, fascinating properties, growth methods, and corresponding thermodynamic stabilities.

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Section: Identification Of Mos2 Polymorphs and Stacking Polytypes By mentioning

confidence: 99%

Differentiating Polymorphs in Molybdenum Disulfide via Electron Microscopy

et al. 2018

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“…8,9 However, advances in low-voltage probes have enabled studies in which only a small number of atomic species and chemical bonds change during imaging, enabling dynamic studies of beam-induced transformations. [10][11][12][13] Phenomena such as oxygen vacancy ordering, 14 dopant atom dynamics, 12 formation of topological defects and bond formation and breaking, [15][16][17] and vacancy formation 18 etc. have been visualized with atomic resolution.…”

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confidence: 99%

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

Ziatdinov

Dyck

Jesse

et al. 2019

Adv Funct Materials

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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.

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“…In the last several years, advances in (scanning) transmission electron microscopy ((S)TEM) have enabled visualization the atomic dynamics during multiple solid state processes, 6,7 including vacancy ordering in cobaltites, 8 phase transformation at crystalline oxide interfaces, 9 restructuring in 2D silica glass, 10 single point defect motion, [11][12][13] single atom catalytic activity, [14][15][16] elastic-plastic transitions, 17 and dislocation migration. 18 The atomic dynamics were observed in response to classical macroscopic stimuli such as temperature and pressure, but also as a response to e-beam irradiation.…”

Section: Introductionmentioning

confidence: 99%

Mapping mesoscopic phase evolution during E-beam induced transformations via deep learning of atomically resolved images

et al. 2018

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Understanding transformations under electron beam irradiation requires mapping the structural phases and their evolution in real time. To date, this has mostly been a manual endeavor comprising of difficult frame-by-frame analysis that is simultaneously tedious and prone to error. Here, we turn towards the use of deep convolutional neural networks (DCNN) to automatically determine the Bravais lattice symmetry present in atomically-resolved images. A DCNN is trained to identify the Bravais lattice class given a 2D fast Fourier transform of the input image. Monte-Carlo dropout is used for determining the prediction probability, and results are shown for both simulated and real atomically-resolved images from scanning tunneling microscopy and scanning transmission electron microscopy. A reduced representation of the final layer output allows to visualize the separation of classes in the DCNN and agrees with physical intuition. We then apply the trained network to electron beam-induced transformations in WS2, which allows tracking and determination of growth rate of voids. These results are novel in two ways: (1) It shows that DCNNs can be trained to recognize diffraction patterns, which is markedly different from the typical 'real image' cases, and (2) it provides a method with in-built uncertainty quantification, allowing the real-time analysis of phases present in atomically resolved images.

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Engineering and modifying two-dimensional materials by electron beams

Abstract: Abstract

Cited by 73 publications

References 92 publications

Differentiating Polymorphs in Molybdenum Disulfide via Electron Microscopy

Differentiating Polymorphs in Molybdenum Disulfide via Electron Microscopy

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

Mapping mesoscopic phase evolution during E-beam induced transformations via deep learning of atomically resolved images

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