Correlating the three-dimensional atomic defects and electronic properties of two-dimensional transition metal dichalcogenides

Tian, Xuezeng; Kim, Dennis; Yang, Shize; Ciccarino, Christopher J.; Gong, Yongji; Yang, Yongsoo; Yang, Yao; Duschatko, Blake; Yuan, Yakun; Ajayan, Pulickel M.; Idrobo, Juan Carlos; Narang, Prineha; Miao, Jianwei

doi:10.1038/s41563-020-0636-5

Cited by 120 publications

(140 citation statements)

References 53 publications

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“…Graphene and related two-dimensional (2D) materials are a family of exciting materials, which are as small as one to three atoms in vertical direction, but extremely large in horizontal space [1][2][3][4][5]. These systems have attracted significant attention for nanoelectronics and optoelectronics [6][7][8][9][10], non-von Neumann architecture computing [11,12], hybrid flexible and stretchable electronics [13][14][15], and many other applications. As a consequence of their extremely high market value, 2D materials are very attractive to many industries.…”

Section: Introductionmentioning

confidence: 99%

RETRACTED: Artificial Intelligence Algorithm Enabled Industrial-Scale Graphene Characterization

2020

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No characterization method is available to quickly perform quality inspection of 2D materials produced on an industrial scale. This hinders the adoption of 2D materials for product manufacturing in many industries. Here, we report an artificial-intelligence-assisted Raman analysis to quickly probe the quality of centimeter-large graphene samples in a non-destructive manner. Chemical vapor deposition of graphene is devised in this work such that two types of samples were obtained: layer-plus-islands and layer-by-layer graphene films, at centimeter scales. Using these samples, we implemented and integrated an unsupervised learning algorithm with an automated Raman spectroscopy to precisely cluster 20,250 and 18,000 Raman spectra collected from layer-plus-islands and layer-by-layer graphene films, respectively, into five and two clusters. Each cluster represents graphene patches with different layer numbers and stacking orders. For instance, the two clusters detected in layer-by-layer graphene films represent monolayer and bilayer graphene based on their Raman fingerprints. Our intelligent Raman analysis is fully automated, with no human operation involved, is highly reliable (99.95% accuracy), and can be generalized to other 2D materials, paving the way towards industrialization of 2D materials for various applications in the future.

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Section: Introductionmentioning

confidence: 99%

RETRACTED: Artificial Intelligence Algorithm Enabled Industrial-Scale Graphene Characterization

2020

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“…One experimental method that can potentially solve this long-standing problem is atomic electron tomography (AET) 34,35 . AET combines high-resolution tomographic tilt series with advanced iterative algorithms to resolve the 3D atomic structure of materials without assuming crystallinity, which has been applied to image grain boundaries, anti-phase boundaries, stacking faults, dislocations, point defects, chemical order/disorder, atomic-scale ripples, bond distortion and strain tensors with unprecedented 3D detail [36][37][38][39][40][41] . More recently, 4D (3D + time) AET has been developed to observe crystal nucleation at atomic resolution, showing that early stage nucleation results are inconsistent with classical nucleation theory 42 .…”

Section: 20mentioning

confidence: 99%

“…1c, d, Supplementary Video 2 and Methods). Since the image contrast in the 3D reconstruction depends on the atomic number [40][41][42] , presently AET is only sensitive enough to classify the eight elements into three different types: Co and Ni as type 1, Ru, Rh, Pd and Ag as type 2, and Ir and Pt as type 3. After atom classification, we obtained the 3D atomic model of the nanoparticle, consisting of 8322, 6896 and 3138 atoms for type 1, 2 and 3, respectively.…”

Section: Determining the 3d Atomic Positions In A Multi-component Metmentioning

confidence: 99%

“…The centres of mass of the images were calculated and the images were shifted so that all the centres of mass coincide with the origin. This image alignment method has been successfully used to achieve sub-pixel accuracy 34,36,[40][41][42] . The Matlab data of the raw, processed and aligned experimental images are provided in where ∇ represents the gradient and % is the rotation matrix at tilt angle θ, which transforms coordinates { , , } to { , , }.…”

Section: Data Acquisitionmentioning

confidence: 99%

“…Using RESIRE, we first performed a large volume reconstruction of the CuTa thin film from the aligned images. Based on the thickness variation of the thin film, we applied scanning AET 41 to conduct multiple local volume reconstructions and then patched them together to produce a full 3D reconstruction. Scanning AET has been previously demonstrated to be effective in improving the 3D reconstruction of 2D materials and/or thin film samples 41 .…”

Section: Determination Of the Solute Centres And The Mromentioning

confidence: 99%

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Determining the three-dimensional atomic structure of a metallic glass

Yang¹,

Zhou²,

Zhu³

et al. 2020

Preprint

Self Cite

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Amorphous solids such as glass are ubiquitous in our daily life and have found broad applications ranging from window glass and solar cells to telecommunications and transformer cores1,2. However, due to the lack of long-range order, the three-dimensional (3D) atomic structure of amorphous solids have thus far defied any direct experimental determination without model fitting3-13. Here, using a multi-component metallic glass as a proof-of-principle, we advance atomic electron tomography to determine the 3D atomic positions in an amorphous solid for the first time. We quantitatively characterize the short-range order (SRO) and medium-range order (MRO) of the 3D atomic arrangement. We find that although the 3D atomic packing of the SRO is geometrically disordered, some SRO connect with each other to form crystal-like networks and give rise to MRO. We identify four crystal-like MRO networks - face-centred cubic, hexagonal close-packed, body-centered cubic and simple cubic - coexisting in the sample, which show translational but no orientational order. These observations confirm that the 3D atomic structure in some parts of the sample is consistent with the efficient cluster packing model8,10-12,20. Looking forward, we anticipate this experiment will open the door to determining the 3D atomic coordinates of various amorphous solids, whose impact on non-crystalline solids may be comparable to the first 3D crystal structure solved by x-ray crystallography over a century ago14.

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Strong Band Bowing Effects and Distinctive Optoelectronic Properties of 2H and 1T′ Phase‐Tunable Mo_xRe_1–_xS₂ Alloys

Deng

et al. 2020

Adv Funct Materials

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Structure and energy band engineering of 2D materials via selective doping or phase modulation provide a significant opportunity to design them for optoelectronic devices. Here, the synthesis of high-quality Mo x Re 1-x S 2 alloys with tunable composition and phase structure via chemical vapor deposition growth is reported, and their novel energy band structures and optoelectronic properties are explored. The phase separation and structure reconstruction, which are found to be two serious problems in the synthesis of these alloys, are successfully suppressed through tuning their growth thermodynamics. As a result, the obtained Mo x Re 1-x S 2 alloys have uniform composition, phase structure, and crystal orientation. Together with X-ray photoelectron spectroscopy analysis and first-principle calculation, the Re/Mo doping-induced Fermi level up-shift/down-shift, new electronic states, and "sub-gap" formation in Mo x Re 1-x S 2 alloys are revealed. Especially, a strong band bowing effect is discovered in the Mo x Re 1-x S 2 alloys with structure transition between 1T′ and 2H phases. Furthermore, these alloys reveal tunable conduction behavior from n-type to bipolar and p-type in 1T′ phase, as well as novel "bipolar-like" electron conduction behavior in 2H alloys. The results highlight the unique alloying effects, which do not exist in the single-phase 2D alloys, and provide the feasibility for potential applications in building novel electronic and optoelectronic devices.

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Correlating the three-dimensional atomic defects and electronic properties of two-dimensional transition metal dichalcogenides

Cited by 120 publications

References 53 publications

RETRACTED: Artificial Intelligence Algorithm Enabled Industrial-Scale Graphene Characterization

RETRACTED: Artificial Intelligence Algorithm Enabled Industrial-Scale Graphene Characterization

Determining the three-dimensional atomic structure of a metallic glass

Strong Band Bowing Effects and Distinctive Optoelectronic Properties of 2H and 1T′ Phase‐Tunable Mo_xRe_1–_xS₂ Alloys

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Correlating the three-dimensional atomic defects and electronic properties of two-dimensional transition metal dichalcogenides

Cited by 120 publications

References 53 publications

RETRACTED: Artificial Intelligence Algorithm Enabled Industrial-Scale Graphene Characterization

RETRACTED: Artificial Intelligence Algorithm Enabled Industrial-Scale Graphene Characterization

Determining the three-dimensional atomic structure of a metallic glass

Strong Band Bowing Effects and Distinctive Optoelectronic Properties of 2H and 1T′ Phase‐Tunable MoxRe1–xS2 Alloys

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Product

Resources

About

Strong Band Bowing Effects and Distinctive Optoelectronic Properties of 2H and 1T′ Phase‐Tunable Mo_xRe_1–_xS₂ Alloys