Chemistry, Department of


First Advisor

Xiao-Cheng Zeng

Date of this Version

Summer 8-1-2019


A DISSERTATION Presented to the Faculty of The Graduate College at the University of Nebraska In Partial Fulfillment of Requirements For the Degree of Doctor of Philosophy, Major: Chemistry, Under the Supervision of Professor Xiao Cheng Zeng. Lincoln, Nebraska: July 2019

Copyright 2019 Hong Yang


During the last 30 years, microelectronic devices have been continuously designed and developed with smaller size and yet more functionalities. Today, hundreds of millions of transistors and complementary metal-oxide-semiconductor cells can be designed and integrated on a single microchip through 3D packaging and chip stacking technology. A large amount of heat will be generated in a limited space during the operation of microchips. Moreover, there is a high possibility of hot spots due to non-uniform integrated circuit design patterns as some core parts of a microchip work harder than other memory parts. This issue becomes acute as stacked microchips get thinner. In other applications, laser devices can generate heat fluxes up to 1000 W/cm2 in less than 0.5 mm2 areas. Light-emitting diodes also entail high heat intensities between 300 and 600 W/cm2 due to extremely high power density. Therefore, it is of technological importance that heat dissipation can be well managed and controlled in microelectronics devices.

This thesis is mainly focused on the micro/nanoscale thermal conductivity and interfacial thermal resistance characterization and optimization in two-dimensional (2D) nanostructures, such as graphene, C2N, C3N, phosphorene, stanene, molybdenum disulfide, and molybdenum diselenide. Various approaches including non-equilibrium molecular dynamics (NEMD) simulation, equilibrium molecular dynamics (EMD) simulation, and transient pump-probe approaches have been utilized to explore the thermal properties. Phonon behaviors have also been studied to explain the mechanism of heat transfer. Then various machine learning (ML) models such as linear regression, polynomial regression, decision tree, random forest, and artificial neural network have been employed to predict the thermal properties of 2D materials. In a different area of research, the water desalination performance of carbon nanotube with rim functionalization has been systematically investigated using molecular dynamics (MD) simulations.

Advisor: Xiao Cheng Zeng