Thermal conductivity of two-dimensional BC<sub>3</sub>: a comparative study with two-dimensional C<sub>3</sub>N

Song, Jieren; Xu, Zhonghai; He, Xiaodong; Bai, Yujiao; Miao, Linlin; Cai, Chaocan; Wang, Rongguo

doi:10.1039/c9cp01943j

Cited by 34 publications

(15 citation statements)

References 52 publications

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“…/mK is reasonably matching the value of 64.8 W/mK reported byMortazavi et al in 2016 [43]. Moreover, a thermal conductivity value of 451±30 W/mK was obtained for C3B structures which is close to the value reported by Song et al (~ 488 W/mK) in 2019 using reverse NEMD method[50]. The values of 𝑘 ∞ for all structures in the X and Y directions are shown in Figures 9 to 11 for the carbon structures having nitrogen atoms, the pure carbon structures and the carbon structures having boron atoms, respectively.In order to have a complete picture, Fig.12(a) gathers the thermal conductivities of all structures for two main chirality directions in one figure.…”

supporting

confidence: 90%

Thermal conductivity of graphene polymorphs and compounds: From C3N to graphdiyne lattices

Hatam-Lee

Rajabpour

Volz

2020

Carbon

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Tremendous experimental and theoretical attempts to find carbon based two-dimensional semiconductors have yielded a wide variety of graphene polymorphs, such as carbon-nitride, carbonboride, graphyne and graphdiyne 2D materials with highly attractive physical and chemical properties.In this study, by conducting extensive non-equilibrium molecular dynamics simulations, we have calculated and compared the thermal conductivity of thirteen prominent carbon-based structures at different lengths and two main chirality directions. Acquired results show that the structures of C3N, C3B and C2N exhibit the highest thermal conductivity, respectively, which suggest them as suitable candidates for thermal management systems in order to enhance the heat dissipation rates. In contrast, generally graphdiyne lattices and in particular 18-6-Gdy graphdiyne yields the lowest thermal conductivity, which can be a promising feature for thermoelectric applications. As a remarkable finding, we could establish connections between the thermal conductivity and density or Young's modulus of carbon based 2D systems, which can be employed to estimate the thermal conductivity of other polymorphs. Those results can provide a comprehensive viewpoint on the thermal transport properties of the nonporous and exceedingly porous carbon based 2D materials and may be used as useful guides for future designs in thermal management. * Corresponding authors, Email: Rajabpour@eng.ikiu.ac.ir (A. Rajabpour), volz@iis.u-tokyo.ac.jp (S. Volz)

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confidence: 90%

Thermal conductivity of graphene polymorphs and compounds: From C3N to graphdiyne lattices

Hatam-Lee

Rajabpour

Volz

2020

Carbon

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“…On this basis, to simulate the thermal conductivity of carbon-nitride 2D systems, commonly the researchers use the Tersoff parameters constructed over Optimized Tersoff [22] proposed by the Kinarci et al [73]. Using the aforementioned interatomic potential setup along with the NEMD approach, the thermal conductivity of C 3 N monolayer with infinite length was predicted to be 805-520 [54], 810-826 [74], 775 [75], 806 [76], 800 [77] and 780 [78] W mK −1 . In a latest NEMD study [79] authors also reported consistent values for the length effect on the thermal conductivity of C 3 N monolayer with previous studies [54,74,76].…”

Section: Resultsmentioning

confidence: 99%

Efficient machine-learning based interatomic potentialsfor exploring thermal conductivity in two-dimensional materials

et al. 2020

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It is well-known that the calculation of thermal conductivity using classical molecular dynamics (MD) simulations strongly depends on the choice of the appropriate interatomic potentials. As proven for the case of graphene, while most of the available interatomic potentials estimate the structural and elastic constants with high accuracy, when employed to predict the lattice thermal conductivity they however lead to a variation of predictions by one order of magnitude. Here we present our results on using machine-learning interatomic potentials (MLIPs) passively fitted to computationally inexpensive ab-initio molecular dynamics trajectories without any tuning or optimizing of hyperparameters. These first-attempt potentials could reproduce the phononic properties of different two-dimensional (2D) materials obtained using density functional theory (DFT) simulations. To illustrate the efficiency of the trained MLIPs, we consider polyaniline C 3 N nanosheets. C 3 N monolayer was selected because the classical MD and different first-principles results contradict each other, resulting in a scientific dilemma. It is shown that the predicted thermal conductivity of 418 ± 20 W mK −1 for C 3 N monolayer by the non-equilibrium MD simulations on the basis of a first-attempt MLIP evidences an improved accuracy when compared with the commonly employed MD models. Moreover, MLIP-based prediction can be considered as a solution to the debated reports in the literature. This study highlights that passively fitted MLIPs can be effectively employed as versatile and efficient tools to obtain accurate estimations of thermal conductivities of complex materials using classical MD simulations. In response to remarkable growth of 2D materials family, the devised modeling methodology could play a fundamental role to predict the thermal conductivity.

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“…Nonetheless, among the aforementioned lattices, only the C3N shows a densely packed structure, and the rest include low-density nanoporous lattices. 2D polyaniline C3N is already known to show outstandingly high thermal conductivity [36,37,46,[38][39][40][41][42][43][44][45] and mechanical properties [47][48][49][50][51], stemming from its densely packed lattice and strong covalent interactions between carbon and nitrogen atoms. In contrast, the presence of nanoporosity in the atomic structure promotes phonon scattering and can lead to substantial suppression of lattice thermal conductivity, which has been theoretically confirmed [15,[52][53][54][55].…”

Section: Introductionmentioning

confidence: 99%

Ultrahigh thermal conductivity and strength in direct-gap semiconducting graphene-like BC6N: A first-principles and classical investigation

Mortazavi

2021

Carbon

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Thermal conductivity of two-dimensional BC₃: a comparative study with two-dimensional C₃N

Abstract: The thermal conductivities of single-layer BC3 (SLBC) sheets and their responses to environmental temperature, vacancy defects and external strain have been studied and compared with those of single-layer C3N (SLCN) sheets by molecular dynamics simulations.

Cited by 34 publications

References 52 publications

Thermal conductivity of graphene polymorphs and compounds: From C3N to graphdiyne lattices

Thermal conductivity of graphene polymorphs and compounds: From C3N to graphdiyne lattices

Efficient machine-learning based interatomic potentialsfor exploring thermal conductivity in two-dimensional materials

Ultrahigh thermal conductivity and strength in direct-gap semiconducting graphene-like BC6N: A first-principles and classical investigation

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Thermal conductivity of two-dimensional BC3: a comparative study with two-dimensional C3N

Abstract: The thermal conductivities of single-layer BC3 (SLBC) sheets and their responses to environmental temperature, vacancy defects and external strain have been studied and compared with those of single-layer C3N (SLCN) sheets by molecular dynamics simulations.

Cited by 34 publications

References 52 publications

Thermal conductivity of graphene polymorphs and compounds: From C3N to graphdiyne lattices

Thermal conductivity of graphene polymorphs and compounds: From C3N to graphdiyne lattices

Efficient machine-learning based interatomic potentialsfor exploring thermal conductivity in two-dimensional materials

Ultrahigh thermal conductivity and strength in direct-gap semiconducting graphene-like BC6N: A first-principles and classical investigation

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Thermal conductivity of two-dimensional BC₃: a comparative study with two-dimensional C₃N