Structural characteristics and strain behavior of two-dimensional C3N : First principles calculations

Shi, Li-Bin; Zhang, Yanyan; Xiu, Xiao‐Ming; Dong, Hai-Kuan

doi:10.1016/j.carbon.2018.03.076

Cited by 113 publications

(61 citation statements)

References 52 publications

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“…In figure 1, the studied structures in this study are illustrated, which include full carbon 2D structures of graphene (figure 1(a)), phagraphene [56] (figure 1(b)) and haeckelite [57] (figure 1(c)) and carbon-based binary lattices of C 2 N [58] (figure 1(d)), C 3 N [59] or C 3 B (figure 1(f)) and C 7 N 6 [60] (figure 1(f)) and carbon-free MoS 2 (figure 1(g)) and penta-SiP 2 (figure 1(h)). In this study, we also considered the bulk boron arsenide ( figure 1(k)) and silicon (figure 1(m)) lattices to [56] (c) haeckelite [57] (d) C2N [58] (e) C3N [59] or C3B (F) C7N6 [60] examine the accuracy of MTP for reproducing the phonic properties of bulk crystals. The hexagonal lattice constants of graphene, haeckelite, C 2 N, C 3 N, C 3 B, C 7 N 6 and MoS 2 were found to be 2.4674, 7.0869, 8.3277, 4.8605, 5.1735, 6.7949 and 3.1837 Å, respectively.…”

Section: Resultsmentioning

confidence: 99%

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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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Section: Resultsmentioning

confidence: 99%

“…This structure was selected for the evaluation of thermal conductivity by the NEMD simulations. We remind that the C 3 N was fist synthesized by Mahmood et al [59] in 2016 and can be also considered among the most attractive carbon-based 2D materials, which has been extensively explored recently for various applications [67][68][69][70].…”

Section: Resultsmentioning

confidence: 99%

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

et al. 2020

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“…Therefore, the motivation rises to design and manufacture other materials without graphene limitations. This issue encouraged the people around the world to design and synthesis novel 2D nanosheets such as borophene [7], phospherene [8], Molybdenum disulfide [9], and carbon nitride [10]. As an example, recently a 2D semiconductor nanosheet so-called twin-graphene was predicted by first-principles calculations with 0.981eV band gap [11].…”

Section: Introductionmentioning

confidence: 99%

Thermal conductivity and thermal rectification of nanoporous graphene: A molecular dynamics simulation

Yousefi

Khoeini

Rajabpour

2020

International Journal of Heat and Mass Transfer

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Using non-equilibrium molecular dynamics (NEMD) simulation, we study thermal properties of the so-called nanoporous graphene (NPG) sheet which contains a series of nanoporous in an ordered way and was synthesized recently (Science 360 (2018), 199).The dependence of thermal conductivity on sample size, edge chirality, and porosity concentration are investigated. Our results indicate that the thermal conductivity of NPG is about two orders smaller compared with of pristine graphene. Therefore this sheet can be used as a thermoelectric material. Also, the porosity concentration helps us to tune the thermal conductivity. Moreover, the results show that the thermal conductivity increases with growing sample length due to ballistic transport. On the other hand, along the armchair direction, the thermal conductivity is larger than zigzag direction. We also examined the thermal properties of the interface of NPG and graphene. The temperature drops significantly through the interface leading to the thermal resistance. The thermal resistance changes with imposed heat flux direction, and this difference cause significantly large thermal rectification factor, and heat current prefers one direction to another. Besides, to investigate those quantities fundamentally, we study the phonon density of states and scattering of them.2

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“…Mahmood et al [26], reported the first experimental realization of a densely packed and graphene-like carbon-nitride nanomaterials with a C3N stoichiometry. Likely to g-C3N4 and C2N counterparts, C3N is also an intrinsic semiconductor, however thanks to its nonporous atomic lattice C3N can exhibit remarkably high mechanical and thermal conduction properties [27][28][29][30][31][32]. According to the extensive theoretical studies conducted recently, C3N nanosheet can exhibit desirable properties for various applications, like; nanoelectronics [33][34][35][36][37], nanomagnetics [38,39], catalysis [40,41], superconductivity [42], anode materials for Li-ion batteries [43] and hydrogen storage [44].…”

Section: Introductionmentioning

confidence: 99%

Prediction of C₇N₆ and C₉N₄: stable and strong porous carbon-nitride nanosheets with attractive electronic and optical properties

et al. 2019

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In this work, three novel porous carbon-nitride nanosheets with C7N6, C9N4 and C10N3 stoichiometries are predicted. First-principles simulations were accordingly employed to evaluate stability and explore the mechanical, electronic and optical properties. Phonon dispersions confirm the dynamical stability of all predicted nanosheets. Nonetheless, ab-initio molecular dynamics results indicate that only C7N6 and C9N4 are thermally stable. C7N6, C9N4 and C10N3 nanosheets were predicted to exhibit high elastic modulus of 212, 202 and 208 N/m and maximum tensile strengths of 14.1, 22.4 and 15.8 N/m, respectively. C7N6 monolayer was confirmed to be a direct band-gap semiconductor, with a 2.25 eV gap according to the HSE06 method estimation. Interestingly, C9N4 and C10N3 monolayers show metallic character. The first absorption peaks of optical spectra reveal that C7N6 nanosheet can absorb the visible light, whereas C9N4 and C10N3 monolayers can absorb the Infrared range of light. Moreover, the absorption coefficient and optical conductivity of predicted nanosheets in the visible range of light are larger than those of the graphene. The results provided by this study confirm the stability and highlight very promising properties of C7N6 and C9N4 nanosheets, which may serve as promising candidates for numerous advanced technologies.Corresponding author: *bohayra.mortazavi@gmail.com,

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Structural characteristics and strain behavior of two-dimensional C3N : First principles calculations

Cited by 113 publications

References 52 publications

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

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

Thermal conductivity and thermal rectification of nanoporous graphene: A molecular dynamics simulation

Prediction of C₇N₆ and C₉N₄: stable and strong porous carbon-nitride nanosheets with attractive electronic and optical properties

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Structural characteristics and strain behavior of two-dimensional C3N : First principles calculations

Cited by 113 publications

References 52 publications

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

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

Thermal conductivity and thermal rectification of nanoporous graphene: A molecular dynamics simulation

Prediction of C7N6 and C9N4: stable and strong porous carbon-nitride nanosheets with attractive electronic and optical properties

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Prediction of C₇N₆ and C₉N₄: stable and strong porous carbon-nitride nanosheets with attractive electronic and optical properties