van der Waals Epitaxial Formation of Atomic Layered α-MoO<sub>3</sub> on MoS<sub>2</sub> by Oxidation

Yoon, Aram; Kim, Jung Hwa; Yoon, Jong Chan; Lee, Yeongdong; Lee, Zonghoon

doi:10.1021/acsami.0c03032

Cited by 32 publications

(31 citation statements)

References 59 publications

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“…Our ab-initio energetics and thermodynamic study show that the origin of these intriguing triangular oxides in TMD systems is due to the preferential O substitution at selenium zigzag edges. This lends credence to experimental results 35 , 50 and can be extended to most 2D TMDs. Moreover, the TMD based oxide heterostructures can have potential use in various electronic and optoelectronic device applications.…”

Section: Discussionsupporting

confidence: 82%

Ab-initio investigation of preferential triangular self-formation of oxide heterostructures of monolayer $$\hbox {WSe}_{2}$$

Das

Wakabayashi

Tsukagoshi

et al. 2020

Sci Rep

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Triangular growth patterns of pristine two-dimensional (2D) transition metal dichalcogenides (TMDs) are ubiquitous in experiments. Here, we use first-principles calculations to investigate the growth of triangular shaped oxide islands upon layer-by-layer controlled oxidation in monolayer and few-layer $$\hbox {WSe}_{2}$$ WSe 2 systems. Pristine 2D TMDs with a trigonal prismatic geometry prefer the triangular growth morphology due to structural stability arising from the edge chalcogen atoms along its three sides. Our ab-initio energetics and thermodynamic study show that, since the Se atoms are more susceptible to oxygen replacement, the preferential oxidation happens along the Se zigzag lines, producing triangular islands of transition metal oxides. The thermodynamic stability arising from the preferential triangular self-formation of TMD based oxide heterostructures and their electronic properties opens a new avenue for their exploration in advanced electronic and optoelectronic devices.

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Section: Discussionsupporting

confidence: 82%

Ab-initio investigation of preferential triangular self-formation of oxide heterostructures of monolayer $$\hbox {WSe}_{2}$$

Das

Wakabayashi

Tsukagoshi

et al. 2020

Sci Rep

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“…This major shortcoming of graphene limits its range of potential device applications. Conversely, molybdenum disulfide (MoS 2 ), which originates from the transition metal dichalcogenide (TMDC) family, is a semiconductor with an indirect bandgap of~1.89 eV [9,10]. In addition, hexagonal boron nitride (h-BN) is an insulator with a wide gap of~5.9 eV [11,12].…”

Section: Introductionmentioning

confidence: 99%

Bandgap prediction of two-dimensional materials using machine learning

Liu

Zhang

et al. 2021

PLoS ONE

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The bandgap of two-dimensional (2D) materials plays an important role in their applications to various devices. For instance, the gapless nature of graphene limits the use of this material to semiconductor device applications, whereas the indirect bandgap of molybdenum disulfide is suitable for electrical and photo-device applications. Therefore, predicting the bandgap rapidly and accurately for a given 2D material structure has great scientific significance in the manufacturing of semiconductor devices. Compared to the extremely high computation cost of conventional first-principles calculations, machine learning (ML) based on statistics may be a promising alternative to predicting bandgaps. Although ML algorithms have been used to predict the properties of materials, they have rarely been used to predict the properties of 2D materials. In this study, we apply four ML algorithms to predict the bandgaps of 2D materials based on the computational 2D materials database (C2DB). Gradient boosted decision trees and random forests are more effective in predicting bandgaps of 2D materials with an R2 >90% and root-mean-square error (RMSE) of ~0.24 eV and 0.27 eV, respectively. By contrast, support vector regression and multi-layer perceptron show that R2 is >70% with RMSE of ~0.41 eV and 0.43 eV, respectively. Finally, when the bandgap calculated without spin-orbit coupling (SOC) is used as a feature, the RMSEs of the four ML models decrease greatly to 0.09 eV, 0.10 eV, 0.17 eV, and 0.12 eV, respectively. The R2 of all the models is >94%. These results show that the properties of 2D materials can be rapidly obtained by ML prediction with high precision.

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“…The humidity resistance and lubrication mechanisms for the MoS 2 and MoS 2 nanosheet–Cu 2 O nanoparticle coatings are displayed in Figure . Due to the loose texture (schematic structure in Figure b) and affinity for vapor and water, the sputtered MoS 2 coatings easily react with moisture and oxygen in the air to reduce their surface energy. ,, Therefore, when the sputtered MoS 2 coating was exposed to a humid environment, the vapor and water may accelerate oxidation. On the contrary, after treatment, the Cu 2 O clusters and layers (Figures f and c) were constructed into coatings that can protect the MoS 2 against water and parts of vapor.…”

Section: Resultsmentioning

confidence: 99%

Superhydrophobic MoS₂ Nanosheet–Cu₂O Nanoparticle Antiwear Coatings

Dong

Jiang

et al. 2021

ACS Appl. Nano Mater.

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The superhydrophobic MoS 2 nanosheet−Cu 2 O nanoparticle antiwear coatings were constructed by immersing the sputtered MoS 2 coatings to a solution of CuSO 4 and sodium citrate. The contact angle (CA) and sliding angle (SA) of the coatings were ∼164 and 2°, respectively. MoS 2 nanosheets wrapped with Cu 2 O layers were ∼54 nm thick. On the nanosheets, the Cu 2 O nanospheres were ∼60 nm in size, and numerous Cu 2 O nanospheres aggregated into Cu 2 O clusters with 1−5 μm length. The cooperation of microstructures (Cu 2 O clusters) and nanostructures (Cu 2 O nanoparticles) and the arrangement of the Cu 2 O clusters on the surface of the sputtered MoS 2 coatings resulted in the superhydrophobic property of the MoS 2 nanosheet−Cu 2 O nanoparticle coating. Besides, the MoS 2 nanosheet−Cu 2 O nanoparticle coating showed outstanding stability and durability (almost 1 year) with superhydrophobic and tribological properties, including a relatively low friction coefficient (as low as 0.008−0.015) and a long antiwear life (∼1.0 × 10 6 r). During friction, Cu 2 O nanospheres can roll at the friction interface, show "nanobearing effects", and then reduce the friction coefficient of the coatings.

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van der Waals Epitaxial Formation of Atomic Layered α-MoO₃ on MoS₂ by Oxidation

Cited by 32 publications

References 59 publications

Ab-initio investigation of preferential triangular self-formation of oxide heterostructures of monolayer $$\hbox {WSe}_{2}$$

Ab-initio investigation of preferential triangular self-formation of oxide heterostructures of monolayer $$\hbox {WSe}_{2}$$

Bandgap prediction of two-dimensional materials using machine learning

Superhydrophobic MoS₂ Nanosheet–Cu₂O Nanoparticle Antiwear Coatings

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van der Waals Epitaxial Formation of Atomic Layered α-MoO3 on MoS2 by Oxidation

Cited by 32 publications

References 59 publications

Ab-initio investigation of preferential triangular self-formation of oxide heterostructures of monolayer $$\hbox {WSe}_{2}$$

Ab-initio investigation of preferential triangular self-formation of oxide heterostructures of monolayer $$\hbox {WSe}_{2}$$

Bandgap prediction of two-dimensional materials using machine learning

Superhydrophobic MoS2 Nanosheet–Cu2O Nanoparticle Antiwear Coatings

Contact Info

Product

Resources

About

van der Waals Epitaxial Formation of Atomic Layered α-MoO₃ on MoS₂ by Oxidation

Superhydrophobic MoS₂ Nanosheet–Cu₂O Nanoparticle Antiwear Coatings