Ultrahigh thermal conductivity and strength in direct-gap semiconducting graphene-like BC6N: A first-principles and classical investigation
arXiv:2106.07090 · doi:10.1016/j.carbon.2021.06.038
Abstract
In recent years, graphene-like boron carbide and carbon nitride nanosheets have attracted remarkable attentions, owing to their semiconducting electronic nature and outstanding mechanical and heat transport properties. Graphene-like BC6N is an experimentally realized layered material and most recently has been the focus of numerous theoretical studies. Interestingly, the most stable form of BC6N monolayer remains unexplored and limited information are known concerning its intrinsic physical properties. Herein, on the basis of density functional theory (DFT) calculations we confirm that the most stable form of BC6N nanosheet shows a rectangular unitcell, in accordance with an overlooked experimental finding. We found that BC6N monolayer is a semiconductor with 1.19 eV direct gap and yields anisotropic and excellent absorption of visible light. First-principles results highlight that BC6N nanosheet exhibits anisotropic and ultrahigh tensile strength and lattice thermal conductivity, outperforming all other fabricated 2D semiconductors. We moreover develop classical molecular dynamic models for the evaluation of heat transport and mechanical properties of BC6N nanomembranes. The presented results in this work not only shed light on the most stable configuration of BC6N nanosheet, but also confirm its outstandingly appealing electronic, optical, heat conduction and mechanical properties, extremely motivating for further theoretical and experimental endeavors.
References in corpus (13)
- Electric Field Effect in Atomically Thin Carbon Films
- The electronic properties of graphene
- Ultrathin epitaxial graphite: 2D electron gas properties and a route toward graphene-based nanoelectronics
- High Electron Mobility, Quantum Hall Effect and Anomalous Optical Response in Atomically Thin InSe
- Accelerating first-principles estimation of thermal conductivity by machine-learning interatomic potentials: A MTP/ShengBTE solution
- Outstanding strength, optical characteristics and thermal conductivity of graphene-like BC and BCN semiconductors
- Nanoporous C3N4, C3N5 and C3N6 nanosheets; Novel strong semiconductors with low thermal conductivities and appealing optical/electronic properties
- Exploring Phononic Properties of Two-Dimensional Materials using Machine Learning Interatomic Potentials
- Suppression of coherent thermal transport in quasiperiodic graphene-hBN superlattice ribbons
- Thermo-mechanical properties of nitrogenated holey graphene (C2N): A comparison of machine-learning-based and classical interatomic potentials
- Thermal Transport in Amorphous Graphene with Varying Structural Quality
- Ultrahigh carrier mobility, Dirac cone and high stretchability in pyrenyl and pyrazinoquinoxaline graphdiyne/graphyne nanosheets confirmed by first-principles
- Strain Tunable Photocatalytic Ability of Monolayer
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