Abstract
The thermal conductivity of graphene nanoribbons (GNRs) has been investigated using equilibrium molecular dynamics (EMD) simulation based on Green-Kubo (GK) method to compare two interatomic potentials namely optimized Tersoff and 2nd generation Reactive Empirical Bond Order (REBO). Our comparative study includes the estimation of thermal conductivity as a function of temperature, length and width of GNR for both the potentials. The thermal conductivity of graphene nanoribbon decreases with the increase of temperature. Quantum correction has been introduced for thermal conductivity as a function of temperature to include quantum effect below Debye temperature. Our results show that for temperatures up to Debye temperature, thermal conductivity increases, attains its peak and then falls off monotonically. Thermal conductivity is found to decrease with the increasing length for optimized Tersoff potential. However, thermal conductivity has been reported to increase with length using 2nd generation REBO potential for the GNRs of same size. Thermal conductivity, for the specified range of width, demonstrates an increasing trend with the increase of width for both the concerned potentials. In comparison with 2nd generation REBO potential, optimized Tersoff potential demonstrates a better modeling of thermal conductivity as well as provides a more appropriate description of phonon thermal transport in graphene nanoribbon. Such comparative study would provide a good insight for the optimization of the thermal conductivity of graphene nanoribbons under diverse conditions.
Highlights
In recent years, high density integration and size minimization have taken a tremendous turn in device technology
Our results show that 2nd generation Reactive Empirical Bond Order (REBO) potential underestimates the thermal conductivity of graphene nanoribbon by a considerable margin in comparison with that of optimized Tersoff potential
With the 2nd generation REBO potential parameters, the velocities of the transverse acoustic (TA) branch and longitudinal acoustic (LA) branch are found to be very low while dispersion of the out of plane branch is underestimated
Summary
High density integration and size minimization have taken a tremendous turn in device technology. GK method is advantageous over NEMD because of inclusion of the entire thermal conductivity tensor from one simulation along with some additional parameters like heat current autocorrelation function (HCACF) [20] This is why, in order to systematically observe the thermal conductivity of GNRs, EMD simulation has been carried out in this paper. In this study, we have taken into account two of the MD potential fields: optimized Tersoff potential and 2nd generation REBO potential with a view to interpreting the heat transport mechanism in the graphene nanoribbon and thereby discussing the comparative reliability of the two potentials to provide the more appropriate description of the thermal conductivity of graphene nanoribbon Using these two potential fields, we have performed a comparative analysis for the size dependence, i.e. varying lengths (8 nm to 14 nm) and widths (1 nm to 2.5 nm ) and temperature dependence of thermal conductivity of graphene nanoribbons
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