STRAINING ENGINEERING OF GRAPHENE AND GRAPHENE-BASED NANOSTRUCTURES

Graphene has emerged as an extraordinary material with its capability to accommodate an array of remarkable electronic, mechanical and chemical properties. Extra-large surface-to-volume ratio renders graphene a highly flexible morphology, giving rise to intriguing observations such as ripples, wrinkles and folds as well as the potential to transform into other novel carbon nanostructures. Ultra-thin, mechanically tough, electrically conductive graphene films promise to enable a wealth of possible applications ranging from hydrogen storage scaffolds, electronic transistors, to bottom-up material designs. Enthusiasm for graphene-based applications aside, there are still significant challenges to their realization, largely due to the difficulty of precisely controlling the graphene properties. Controlling the graphene morphology over large areas is crucial in enabling future graphene-based applications and material design. This dissertation aims to shed lights on potential mechanisms to actively manipulate the graphene morphology and properties and therefore enable the material design principle that delivers desirable mechanical and electronic functionalities of graphene and its derivatives.

Dynamic Resource Allocation in Wireless Heterogeneous Networks

Deployment of low power basestations within cellular networks can potentially increase both capacity and coverage. However, such deployments require efficient resource allocation schemes for managing interference from the low power and macro basestations that are located within each other’s transmission range. In this dissertation, we propose novel and efficient dynamic resource allocation algorithms in the frequency, time and space domains. We show that the proposed algorithms perform better than the current state-of-art resource management algorithms. In the first part of the dissertation, we propose an interference management solution in the frequency domain. We introduce a distributed frequency allocation scheme that shares frequencies between macro and low power pico basestations, and guarantees a minimum average throughput to users. The scheme seeks to minimize the total number of frequencies needed to honor the minimum throughput requirements. We evaluate our scheme using detailed simulations and show that it performs on par with the centralized optimum allocation. Moreover, our proposed scheme outperforms a static frequency reuse scheme and the centralized optimal partitioning between the macro and picos. In the second part of the dissertation, we propose a time domain solution to the interference problem. We consider the problem of maximizing the alpha-fairness utility over heterogeneous wireless networks (HetNets) by jointly optimizing user association, wherein each user is associated to any one transmission point (TP) in the network, and activation fractions of all TPs. Activation fraction of a TP is the fraction of the frame duration for which it is active, and together these fractions influence the interference seen in the network. To address this joint optimization problem which we show is NP-hard, we propose an alternating optimization based approach wherein the activation fractions and the user association are optimized in an alternating manner. The subproblem of determining the optimal activation fractions is solved using a provably convergent auxiliary function method. On the other hand, the subproblem of determining the user association is solved via a simple combinatorial algorithm. Meaningful performance guarantees are derived in either case. Simulation results over a practical HetNet topology reveal the superior performance of the proposed algorithms and underscore the significant benefits of the joint optimization. In the final part of the dissertation, we propose a space domain solution to the interference problem. We consider the problem of maximizing system utility by optimizing over the set of user and TP pairs in each subframe, where each user can be served by multiple TPs. To address this optimization problem which is NP-hard, we propose a solution scheme based on difference of submodular function optimization approach. We evaluate our scheme using detailed simulations and show that it performs on par with a much more computationally demanding difference of convex function optimization scheme. Moreover, the proposed scheme performs within a reasonable percentage of the optimal solution. We further demonstrate the advantage of the proposed scheme by studying its performance with variation in different network topology parameters.

Controlling Nanostructures for in-situ TEM Characterization

Low dimensional nanostructures, such as nanotubes and 2D sheets, have unique and promising material properties both from a fundamental science and an application standpoint. Theoretical modelling and calculations predict previously unobserved phenomena that experimental scientists often struggle to reproduce because of the difficulty in controlling and characterizing the small structures under real-world constraints. The goal of this dissertation is to controlling these structures so that nanostructures can be characterized in-situ in transmission electron microscopes (TEM) allowing for direct observation of the actual physical responses of the materials to different stimuli. Of most interest to this work are the thermal and electrical properties of carbon nanotubes, boron nitride nanotubes, and graphene. The first topic of the dissertation is using surfactants for aqueous processing to fabricate, store, and deposit the nanostructures. More specifically, thorough characterization of a new surfactant, ammonium laurate (AL), is provided and shows that this new surfactant outperforms the standard surfactant for these materials, sodium dodecyl sulfate (SDS), in almost all tested metrics. New experimental set-ups have been developed by combining specialized in-situ TEM holders with innovative device fabrication. For example, electrical characterization of graphene was performed by using an STM-TEM holder and depositing graphene from aqueous solutions onto lithographically patterned, electron transparent silicon nitride membranes. These experiments produce exciting information about the interaction between graphene and metal probes and the substrate that it rests on. Then, by adding indium to the backside of the membrane and employing the electron thermal microscopy (EThM) technique, the same type of graphene samples could be characterized for thermal transport with high spatial resolution. It is found that reduced graphene oxide sheets deposited onto a silicon nitride membrane and displaying high levels of wrinkling have higher than expected electrical and thermal conduction properties. We are clearly able to visualize the ability of graphene to spread heat away from an electronic hot spot and into the substrate.

Exploiting Nested Parallelism on Heterogeneous Processors

Heterogeneous computing systems have become common in modern processor architectures. These systems, such as those released by AMD, Intel, and Nvidia, include both CPU and GPU cores on a single die available with reduced communication overhead compared to their discrete predecessors. Currently, discrete CPU/GPU systems are limited, requiring larger, regular, highly-parallel workloads to overcome the communication costs of the system. Without the traditional communication delay assumed between GPUs and CPUs, we believe non-traditional workloads could be targeted for GPU execution. Specifically, this thesis focuses on the execution model of nested parallel workloads on heterogeneous systems. We have designed a simulation flow which utilizes widely used CPU and GPU simulators to model heterogeneous computing architectures. We then applied this simulator to non-traditional GPU workloads using different execution models. We also have proposed a new execution model for nested parallelism allowing users to exploit these heterogeneous systems to reduce execution time.

MOLECULAR MASS MANIPULATION ENABLED BY GRAPHENE BASED NANOSTRUCTURES

The surge of interest in graphene, as epitomized by the Nobel Prize in Physics in 2010, is attributed to its extraordinary properties. Graphene is ultrathin, mechanically tough, and has amendable surface chemistry. These features make graphene and graphene based nanostructure an ideal candidate for the use of molecular mass manipulation. The controllable and programmable molecular mass manipulation is crucial in enabling future graphene based applications, however is challenging to achieve. This dissertation studies several aspects in molecular mass manipulation including mass transportation, patterning and storage. For molecular mass transportation, two methods based on carbon nanoscroll are demonstrated to be effective. They are torsional buckling instability assisted transportation and surface energy induced radial shrinkage. To achieve a more controllable transportation, a fundamental law of direction transport of molecular mass by straining basal graphene is studied. For molecular mass patterning, we reveal a barrier effect of line defects in graphene, which can enable molecular confining and patterning in a domain of desirable geometry. Such a strategy makes controllable patterning feasible for various types of molecules. For molecular mass storage, we propose a novel partially hydrogenated bilayer graphene structure which has large capacity for mass uptake. Also the mass release can be achieved by simply stretching the structure. Therefore the mass uptake and release is reversible. This kind of structure is crucial in enabling hydrogen fuel based technology. Lastly, spontaneous nanofluidic channel formation enabled by patterned hydrogenation is studied. This novel strategy enables programmable channel formation with pre-defined complex geometry.