Atomic-scale perspective of mechanical properties and fracture mechanisms of graphene/WS2/graphene heterostructure

Oishi, Talukder Musfika Tasnim; Malakar, Prottay; Islam, Mahmudul; Islam, Mahbubul

doi:10.1016/j.cocom.2021.e00612

Cited by 7 publications

(10 citation statements)

References 52 publications

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“…Similar features have been investigated in MoS2/graphene composite, highlighting that lattice mismatch between layers will lead to a spontaneous strain energy in the interface resulting in the decreasing mechanical properties with respect to those of bare graphene [26]. Even in the case of a composite with WS2 and graphene, the mechanical properties, which are essential for optical applications, seem to degrade dramatically with respect to those of bare graphene [27].…”

Section: Introductionsupporting

confidence: 52%

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Mechanical properties of bilayer WS₂ and Graphene-WS₂ Hybrid composites by molecular dynamics simulations

Wu,

Tan,

Palummo

et al. 2024

J. Phys.: Condens. Matter

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In the present work, by using molecular dynamics (MD) simulations, we investigate the mechanical properties of different nanostructures that may be core elements in next generation flexible/wearable photovoltaic devices, namely double layer WS2 nanosheets (DLNS), graphene/WS2 (layer) composites and graphene/WS2 nanotube (NT) composites. Our results reveal that the mechanical properties of DLNS deteriorate when compared to those of monolayer WS2. Owing to graphene’s reinforcement action, the mechanical properties of graphene/WS2 (layer) composite with both layers deformed are superior than those of WS2, even though inferior than those of bare graphene. If stress is applied only to the graphene layer, the graphene/WS2 composite retains the most of the strength and toughness of monolayer graphene, decreasing the fracture strength and Young’s modulus by only 9.7% and 16.3%, respectively. Similarly, in the case of the graphene/WS2 nanotube composite the mechanical strength and toughness experience a reduction compared to monolayer graphene, specifically by 15% and 53% for fracture strength and Young’s modulus, respectively. Considering the market's keen interest in nanomaterials, particularly van der Waals (vdW) ones, for flexible and wearable photovoltaic devices, the findings presented here will significantly enhance the effective utilization of vdW composites.

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Section: Introductionsupporting

confidence: 52%

“…where, r ij is the distance between atoms i and j, ε and σ are the energy and distance parameters, which are 0.0075 eV/3.56 Å for S-S, 0.00291 eV/3.90 Å for S-W and 0.0082 eV/3.14 Å for W-W, respectively [27].…”

Section: Methodsmentioning

confidence: 99%

Mechanical properties of bilayer WS₂ and Graphene-WS₂ Hybrid composites by molecular dynamics simulations

Wu,

Tan,

Palummo

et al. 2024

J. Phys.: Condens. Matter

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“…The SW potential developed by Jiang is employed in this research . This potential has already been used in numerous studies to predict mechanical responses of various layered materials. , The atomic interactions in the SW model are expressed as E = prefix∑ i ∑ j > i ϕ 2 ( r i j ) + prefix∑ i ∑ j ≠ i ∑ k > j ϕ 3 ( r i j , r i k , θ i j k ) where ϕ 2 and ϕ 3 represent the bond stretch (two-body) and bond bending (three-body) interactions, respectively. To eliminate the effect of free edges, the simulation boundary conditions are specified as periodic in the in-plane ( x and y ) directions, and the simulation box is assumed to be an exact multiple of the unit cell in these directions.…”

Section: Methodsmentioning

confidence: 99%

“…66 This potential has already been used in numerous studies to predict mechanical responses of various layered materials. 15,20 The atomic interactions in the SW model are expressed as…”

Section: Simulation Methodmentioning

confidence: 99%

“…Yang et al performed both experimental and first principles calculations for understanding the brittle fracture of 2D MoSe 2 . These computational methods also facilitate the investigation of vertical and lateral heterostructures TMDs. , On the other hand, Bertolazzi et al used the atomic force microscopy (AFM) nanoindentation technique to determine the mechanical properties of exfoliated mono- and bi-layer MoS 2 . They found that in-plane Young’s moduli of monolayer and bi-layer h-MoS 2 were 120–240 and 190–330 N/m, respectively .…”

Section: Introductionmentioning

confidence: 99%

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Data-Driven Machine Learning to Predict Mechanical Properties of Monolayer Transition-Metal Dichalcogenides for Applications in Flexible Electronics

Malakar

Thakur

Nahid

et al. 2022

ACS Appl. Nano Mater.

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Understanding of the material properties of layered transition-metal dichalcogenides (TMDs) is critical for their applications in flexible electronics. Data-driven machine learning (ML)-based approaches are being developed in contrast to the traditional experimental or computational methods to predict and understand material properties under varied operating conditions. In this study, we used two ML algorithms, namely, long short-term memory (LSTM) and feed forward neural network (FFNN), combined with molecular dynamics (MD) simulations to predict the mechanical properties of MX 2 (M = Mo, W and X = S, Se) TMDs. The LSTM model is found to be capable of predicting the entire stress−strain response, whereas the FFNN is used to predict material properties such as fracture stress, fracture strain, and Young's modulus. The effects of operating temperature, chiral orientation, and pre-existing crack size on the mechanical properties are thoroughly investigated. We carried out 1440 MD simulations to produce the input dataset for the neural network models. Our results indicate that both LSTM and FFNN are capable of predicting the mechanical response of monolayer TMDs under different conditions with more than 95% accuracy. The FFNN model exhibits lower computational cost than LSTM; however, the capability of the LSTM model to predict the entire stress−strain curve is advantageous for assessing material properties. The study paves the pathway toward extending this approach to predict other important properties, such as optical, electrical, and magnetic properties of TMDs.

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Temperature-dependent failure of atomically thin MoTe2

Haider,

Hezam,

Islam

et al. 2024

J Mol Model

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Atomic-scale perspective of mechanical properties and fracture mechanisms of graphene/WS2/graphene heterostructure

Cited by 7 publications

References 52 publications

Mechanical properties of bilayer WS₂ and Graphene-WS₂ Hybrid composites by molecular dynamics simulations

Mechanical properties of bilayer WS₂ and Graphene-WS₂ Hybrid composites by molecular dynamics simulations

Data-Driven Machine Learning to Predict Mechanical Properties of Monolayer Transition-Metal Dichalcogenides for Applications in Flexible Electronics

Temperature-dependent failure of atomically thin MoTe2

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Atomic-scale perspective of mechanical properties and fracture mechanisms of graphene/WS2/graphene heterostructure

Cited by 7 publications

References 52 publications

Mechanical properties of bilayer WS2 and Graphene-WS2 Hybrid composites by molecular dynamics simulations

Mechanical properties of bilayer WS2 and Graphene-WS2 Hybrid composites by molecular dynamics simulations

Data-Driven Machine Learning to Predict Mechanical Properties of Monolayer Transition-Metal Dichalcogenides for Applications in Flexible Electronics

Temperature-dependent failure of atomically thin MoTe2

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Mechanical properties of bilayer WS₂ and Graphene-WS₂ Hybrid composites by molecular dynamics simulations

Mechanical properties of bilayer WS₂ and Graphene-WS₂ Hybrid composites by molecular dynamics simulations