2020
DOI: 10.1021/acsnano.9b06581
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Direct Visualization of Electric-Field-Induced Structural Dynamics in Monolayer Transition Metal Dichalcogenides

Abstract: Layered transition metal dichalcogenides (TMDs) offer many attractive features for next-generation low-dimensional device geometries. Due to the practical and fabrication challenges related to in situ methods, the atomistic dynamics that give rise to realizable

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Cited by 26 publications
(36 citation statements)
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“…[ 16 ] The detailed role of lattice point defects in the memristive I – V characteristics of MoS 2 devices can likely be revealed by atomically resolved in situ scanning tunneling microscopy [ 17 ] or scanning transmission electron microscopy. [ 8a,18 ]…”
Section: Dual‐gated Mos2 Memtransistor Characteristicsmentioning
confidence: 99%
“…[ 16 ] The detailed role of lattice point defects in the memristive I – V characteristics of MoS 2 devices can likely be revealed by atomically resolved in situ scanning tunneling microscopy [ 17 ] or scanning transmission electron microscopy. [ 8a,18 ]…”
Section: Dual‐gated Mos2 Memtransistor Characteristicsmentioning
confidence: 99%
“…Defects are not limited to vacancies, as adatoms also play a role in the transformation of these materials [105] . Furthermore, observation of electrically‐biased layered materials has revealed the formation of channels due to atom migration at grain boundaries [108] . The role of defects in these transformations warrants careful consideration when investigating transformations in materials being irradiated with the electron beam even when using electron energies below the knock‐on threshold.…”
Section: Structural and Chemical Transformations In Layered Chalcogenmentioning
confidence: 99%
“…Recently, in situ STEM methods have been used to directly visualize and manipulate the atomic arrangements in 2D materials during heating (Fig. 5(b)) [46], laser/electron-beam irradiation [47,48], and in an electrical field [49] to understand the evolution of heterogeneities during exposure to external stimuli. The ever-expanding capabilities for high-resolution data acquisition as well as an exponential increase in the size and quality of data sets produced during microscopy and spectroscopy experiments require the development of machine learning methods to extract critical information from atomically resolved images including the precise locations of atomic species, dopants, and defects in 2D materials.…”
Section: Stem Imaging and Spectroscopymentioning
confidence: 99%