Anisotropic Thermal Transport in Phase-Transition Layered 2D Alloys WSe2(1-x)Te2x

Abstract

Transition metal dichalcogenide (TMD) alloys have attracted great interests in recent years due to their tunable electronic properties, especially the semiconductor-metal phase transition, along with their potential applications in solid-state memories and thermoelectrics. However, the thermal conductivity of layered two-dimensional (2D) TMD alloys remains largely unexplored despite that it plays a critical role in the reliability and functionality of TMD-enabled devices. In this work, we study the temperature-dependent anisotropic thermal conductivity of the phase-transition 2D TMD alloys WSe2(1-x)Te2x in both the in-plane direction (parallel to the basal planes) and the cross-plane direction (along the c-axis) using time-domain thermoreflectance measurements. In the WSe2(1-x)Te2x alloys, the cross-plane thermal conductivity is observed to be dependent on the heating frequency (modulation frequency of the pump laser) due to the non-equilibrium transport between different phonon modes. Using a two-channel heat conduction model, we extracted the anisotropic thermal conductivity at the equilibrium limit. A clear discontinuity in both the cross-plane and the in-plane thermal conductivity is observed as x increases from 0.4 to 0.6 due to the phase transition from the 2H to Td phase in the layered 2D alloys. The temperature dependence of thermal conductivity for the TMD alloys was found to become weaker compared with the pristine 2H WSe2 and Td WTe2 due to the atomic disorder. This work serves as an important starting point for exploring phonon transport in layered 2D alloys.

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Cited by in corpus (5)

• Tutorial: Time-domain thermoreflectance (TDTR) for thermal property characterization of bulk and thin film materials
• Phononic thermal properties of two-dimensional materials
• Efficient method for calculating Raman spectra of solids with impurities and alloys and its application to two-dimensional transition metal dichalcogenides
• High-throughput computational characterization of two-dimensional compositionally complex transition-metal chalcogenide alloys
• Machine learning enables robust prediction of thermal boundary conductance of 2D substrate interfaces