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|Title:||Thermal transport in graphene nanostructures||Authors:||Loh, Jarvis Guan Chee.||Keywords:||DRNTU::Engineering::Electrical and electronic engineering::Nanoelectronics||Issue Date:||2012||Source:||Loh, J. G. C. (2012). Thermal transport in graphene nanostructures. Doctoral thesis, Nanyang Technological University, Singapore.||Abstract:||Graphene is an extraordinary material. Its electrical and thermal conductivities are among the highest ever measured for any material, with some experiments recording values higher than those of carbon nanotubes. These excellent transport properties make graphene a promising material in nanoelectronic applications. As the device feature sizes are downscaled to accommodate a greater device packing density, the need for an efficient thermal extraction system is increasingly dire. Graphene could be the solution to these problems.However there are many factors that can vitiate its thermal performance. Lattice discontinuities such as material interfaces and structural defects scatter phonons and impede thermal transport through the material. This affects the heat-extraction performance and in turn diminishes the thermal and electrical reliability of the device. In this thesis, thermal transport in graphene and graphene nanostructures is studied using molecular dynamics (MD) simulation. In particular, structural and topological features, such as interfaces, atomic vacancies, adatoms, and tears, are emulated, and their effects on thermal transport are documented. Thermal phenomena in the diamond-graphene and carbon nanotube-graphene nanostructures are examined in detail.A common analytical tool to calculate the interfacial thermal resistance (or thermal boundary resistance) is the diffuse mismatch model. Although it is widely used, large disparities still exist between its predicted results and experimental measurements. Theoretical evaluation and MD simulation demonstrate the critical role of thermal flux in interfacial thermal transport. An improved model, the flux-mediated diffuse mismatch model (FMDMM) is then developed.||URI:||http://hdl.handle.net/10356/49888||Fulltext Permission:||restricted||Fulltext Availability:||With Fulltext|
|Appears in Collections:||EEE Theses|
Updated on Dec 3, 2020
Updated on Dec 3, 2020
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