First-principles and machine-learning study of electronic and phonon transport in carbon-based AA-stacked bilayer biphenylene nanosheets

Abstract

In this study, the structural, electronic and thermal transport properties of AA-stacked bilayer biphenylene sheet (BPN) are systematically investigated in the framework of first-principles calculations in addition to machine-learning interatomic potential approaches. Optimized geometry of AA-stacked bilayer satisfies all the necessary stability criteria which further infers that structure becomes feasible for experimental design. Similar to monolayer BPN, its bilayer variant also exhibits a metallic band structure. Thermal transport characteristics of the AA-stacked bilayer have been analyzed from thermal conductivity, the Seebeck coefficient, and electrical conductivity variations. The electronic part of the thermal conductivity for AA-stacked bilayer Biphenylene exhibits linearly enhancing character with temperature, whereas lattice contributions possess an inverse function of temperature. Moreover, the negative values of the Grüneisen (γ) parameter for AA-stacked bilayer BPN dictate negative thermal expansion which can have potential application to tailor the thermal expansion coefficient in many practical situations.