Engineering thin films of magnetic alloys and semiconductor oxides at the nanoscale

The thesis aims to exploit properties of thin films for applications such as spintronics, UV detection and gas sensing. Nanoscale thin films devices have myriad advantages and compatibility with Si-based integrated circuits processes. Two distinct classes of material systems are investigated, namely ferromagnetic thin films and semiconductor oxides. To aid the designing of devices, the surface properties of the thin films were investigated by using electron and photon characterization techniques including Auger electron spectroscopy (AES), X-ray photoelectron spectroscopy (XPS), grazing incidence X-ray diffraction (GIXRD), and energy-dispersive X-ray spectroscopy (EDS). These are complemented by nanometer resolved local proximal probes such as atomic force microscopy (AFM), magnetic force microscopy (MFM), electric force microscopy (EFM), and scanning tunneling microscopy to elucidate the interplay between stoichiometry, morphology, chemical states, crystallization, magnetism, optical transparency, and electronic properties. Specifically, I studied the effect of annealing on the surface stoichiometry of the CoFeB/Cu system by in-situ AES and discovered that magnetic nanoparticles with controllable areal density can be produced. This is a good alternative for producing nanoparticles using a maskless process. Additionally, I studied the behavior of magnetic domain walls of the low coercivity alloy CoFeB patterned nanowires. MFM measurement with the in-plane magnetic field showed that, compared to their permalloy counterparts, CoFeB nanowires require a much smaller magnetization switching field , making them promising for low-power-consumption domain wall motion based devices. With oxides, I studied CuO nanoparticles on SnO2 based UV photodetectors (PDs), and discovered that they promote the responsivity by facilitating charge transfer with the formed nanoheterojunctions. I also demonstrated UV PDs with spectrally tunable photoresponse with the bandgap engineered ZnMgO. The bandgap of the alloyed ZnMgO thin films was tailored by varying the Mg contents and AES was demonstrated as a surface scientific approach to assess the alloying of ZnMgO. With gas sensors, I discovered the rf-sputtered anatase-TiO2 thin films for a selective and sensitive NO2 detection at room temperature, under UV illumination. The implementation of UV enhances the responsivity, response and recovery rate of the TiO2 sensor towards NO2 significantly. Evident from the high resolution XPS and AFM studies, the surface contamination and morphology of the thin films degrade the gas sensing response. I also demonstrated that surface additive metal nanoparticles on thin films can improve the response and the selectivity of oxide based sensors. I employed nanometer-scale scanning probe microscopy to study a novel gas senor scheme consisting of gallium nitride (GaN) nanowires with functionalizing oxides layer. The results suggested that AFM together with EFM is capable of discriminating low-conductive materials at the nanoscale, providing a nondestructive method to quantitatively relate sensing response to the surface morphology.

A Generalized Methodology to Characterize Composite Materials for Pyrolysis Models

The predictive capabilities of computational fire models have improved in recent years such that models have become an integral part of many research efforts. Models improve the understanding of the fire risk of materials and may decrease the number of expensive experiments required to assess the fire hazard of a specific material or designed space. A critical component of a predictive fire model is the pyrolysis sub-model that provides a mathematical representation of the rate of gaseous fuel production from condensed phase fuels given a heat flux incident to the material surface. The modern, comprehensive pyrolysis sub-models that are common today require the definition of many model parameters to accurately represent the physical description of materials that are ubiquitous in the built environment. Coupled with the increase in the number of parameters required to accurately represent the pyrolysis of materials is the increasing prevalence in the built environment of engineered composite materials that have never been measured or modeled. The motivation behind this project is to develop a systematic, generalized methodology to determine the requisite parameters to generate pyrolysis models with predictive capabilities for layered composite materials that are common in industrial and commercial applications. This methodology has been applied to four common composites in this work that exhibit a range of material structures and component materials. The methodology utilizes a multi-scale experimental approach in which each test is designed to isolate and determine a specific subset of the parameters required to define a material in the model. Data collected in simultaneous thermogravimetry and differential scanning calorimetry experiments were analyzed to determine the reaction kinetics, thermodynamic properties, and energetics of decomposition for each component of the composite. Data collected in microscale combustion calorimetry experiments were analyzed to determine the heats of complete combustion of the volatiles produced in each reaction. Inverse analyses were conducted on sample temperature data collected in bench-scale tests to determine the thermal transport parameters of each component through degradation. Simulations of quasi-one-dimensional bench-scale gasification tests generated from the resultant models using the ThermaKin modeling environment were compared to experimental data to independently validate the models.

Optimal Budget-Constrained Sample Allocation for Selection Decisions with Multiple Uncertain Attributes

A decision-maker, when faced with a limited and fixed budget to collect data in support of a multiple attribute selection decision, must decide how many samples to observe from each alternative and attribute. This allocation decision is of particular importance when the information gained leads to uncertain estimates of the attribute values as with sample data collected from observations such as measurements, experimental evaluations, or simulation runs. For example, when the U.S. Department of Homeland Security must decide upon a radiation detection system to acquire, a number of performance attributes are of interest and must be measured in order to characterize each of the considered systems. We identified and evaluated several approaches to incorporate the uncertainty in the attribute value estimates into a normative model for a multiple attribute selection decision. Assuming an additive multiple attribute value model, we demonstrated the idea of propagating the attribute value uncertainty and describing the decision values for each alternative as probability distributions. These distributions were used to select an alternative. With the goal of maximizing the probability of correct selection we developed and evaluated, under several different sets of assumptions, procedures to allocate the fixed experimental budget across the multiple attributes and alternatives. Through a series of simulation studies, we compared the performance of these allocation procedures to the simple, but common, allocation procedure that distributed the sample budget equally across the alternatives and attributes. We found the allocation procedures that were developed based on the inclusion of decision-maker knowledge, such as knowledge of the decision model, outperformed those that neglected such information. Beginning with general knowledge of the attribute values provided by Bayesian prior distributions, and updating this knowledge with each observed sample, the sequential allocation procedure performed particularly well. These observations demonstrate that managing projects focused on a selection decision so that the decision modeling and the experimental planning are done jointly, rather than in isolation, can improve the overall selection results.

MULTI-UNIT ACCIDENT CONTRIBUTIONS TO U.S. NUCLEAR REGULATORY COMMISSION QUANTITATIVE HEALTH OBJECTIVES: A SAFETY GOAL POLICY ANALYSIS USING MODELS FROM STATE-OF-THE-ART REACTOR CONSEQUENCE ANALYSES

The U.S. Nuclear Regulatory Commission implemented a safety goal policy in response to the 1979 Three Mile Island accident. This policy addresses the question “How safe is safe enough?” by specifying quantitative health objectives (QHOs) for comparison with results from nuclear power plant (NPP) probabilistic risk analyses (PRAs) to determine whether proposed regulatory actions are justified based on potential safety benefit. Lessons learned from recent operating experience—including the 2011 Fukushima accident—indicate that accidents involving multiple units at a shared site can occur with non-negligible frequency. Yet risk contributions from such scenarios are excluded by policy from safety goal evaluations—even for the nearly 60% of U.S. NPP sites that include multiple units. This research develops and applies methods for estimating risk metrics for comparison with safety goal QHOs using models from state-of-the-art consequence analyses to evaluate the effect of including multi-unit accident risk contributions in safety goal evaluations.

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.

Cooperative Design, Cooperative Science: Investigating Collaborative Research through Design with Floracaching

This dissertation presents a case study of collaborative research through design with Floracaching, a gamified mobile application for citizen science biodiversity data collection. One contribution of this study is the articulation of collaborative research through design (CRtD), an approach that blends cooperative design approaches with the research through design methodology (RtD). Collaborative research through design is thus defined as an iterative process of cooperative design, where the collaborative vision of an ideal state is embedded in a design. Applying collaborative research through design with Floracaching illustrates how a number of cooperative techniques—especially contextual inquiry, prototyping, and focus groups—may be applied in a research through design setting. Four suggestions for collaborative research through design (recruit from a range of relevant backgrounds; take flexibility as a goal; enable independence and agency; and, choose techniques that support agreement or consensus) are offered to help others who wish to experiment with this new approach. Applying collaborative research through design to Floracaching yielded a new prototype of the application, accompanied by design annotations in the form of framing constructs for designing to support mobile, place-based citizen science activities. The prototype and framing constructs, which may inform other designers of similar citizen science technologies, are a second contribution of this research.

Joint Optimization for Social Content Delivery in Wireless Networks

Over the last decade, success of social networks has significantly reshaped how people consume information. Recommendation of contents based on user profiles is well-received. However, as users become dominantly mobile, little is done to consider the impacts of the wireless environment, especially the capacity constraints and changing channel. In this dissertation, we investigate a centralized wireless content delivery system, aiming to optimize overall user experience given the capacity constraints of the wireless networks, by deciding what contents to deliver, when and how. We propose a scheduling framework that incorporates content-based reward and deliverability. Our approach utilizes the broadcast nature of wireless communication and social nature of content, by multicasting and precaching. Results indicate this novel joint optimization approach outperforms existing layered systems that separate recommendation and delivery, especially when the wireless network is operating at maximum capacity. Utilizing limited number of transmission modes, we significantly reduce the complexity of the optimization. We also introduce the design of a hybrid system to handle transmissions for both system recommended contents ('push') and active user requests ('pull'). Further, we extend the joint optimization framework to the wireless infrastructure with multiple base stations. The problem becomes much harder in that there are many more system configurations, including but not limited to power allocation and how resources are shared among the base stations ('out-of-band' in which base stations transmit with dedicated spectrum resources, thus no interference; and 'in-band' in which they share the spectrum and need to mitigate interference). We propose a scalable two-phase scheduling framework: 1) each base station obtains delivery decisions and resource allocation individually; 2) the system consolidates the decisions and allocations, reducing redundant transmissions. Additionally, if the social network applications could provide the predictions of how the social contents disseminate, the wireless networks could schedule the transmissions accordingly and significantly improve the dissemination performance by reducing the delivery delay. We propose a novel method utilizing: 1) hybrid systems to handle active disseminating requests; and 2) predictions of dissemination dynamics from the social network applications. This method could mitigate the performance degradation for content dissemination due to wireless delivery delay. Results indicate that our proposed system design is both efficient and easy to implement.

INFORMATION TECHNOLOGY PROJECTS IN NONPROFIT UNIVERSITY: A PROJECT GOVERNANCE PERSPECTIVE

The universities rely on the Information Technology (IT) projects to support and enhance their core strategic objectives of teaching, research, and administration. The researcher’s literature review found that the level of IT funding and resources in the universities is not adequate to meet the IT demands. The universities received more IT project requests than they could execute. As such, universities must selectively fund the IT projects. The objectives of the IT projects in the universities vary. An IT project which benefits the teaching functions may not benefit the administrative functions. As such, the selection of an IT project is challenging in the universities. To aid with the IT decision making, many universities in the United States of America (USA) have formed the IT Governance (ITG) processes. ITG is an IT decision making and accountability framework whose purpose is to align the IT efforts in an organization with its strategic objectives, realize the value of the IT investments, meet the expected performance criteria, and manage the risks and the resources (Weil & Ross, 2004). ITG in the universities is relatively new, and it is not well known how the ITG processes are aiding the nonprofit universities in selecting the right IT projects, and managing the performance of these IT projects. This research adds to the body of knowledge regarding the IT project selection under the governance structure, the maturity of the IT projects, and the IT project performance in the nonprofit universities. The case study research methodology was chosen for this exploratory research. The convenience sampling was done to choose the cases from two large, research universities with decentralized colleges, and two small, centralized universities. The data were collected on nine IT projects from these four universities using the interviews and the university documents. The multi-case analysis was complemented by the Qualitative Comparative Analysis (QCA) to systematically analyze how the IT conditions lead to an outcome. This research found that the IT projects were selected in the centralized universities in a more informed manner. ITG was more authoritative in the small centralized universities; the ITG committees were formed by including the key decision makers, the decision-making roles, and responsibilities were better defined, and the frequency of ITG communication was higher. In the centralized universities, the business units and colleges brought the IT requests to ITG committees; which in turn prioritized the IT requests and allocated the funds and the resources to the IT projects. ITG committee members in the centralized universities had a higher awareness of the university-wide IT needs, and the IT projects tended to align with the strategic objectives. On the other hand, the decentralized colleges and business units in the large universities were influential and often bypassed the ITG processes. The decentralized units often chose the “pet” IT projects, and executed them within a silo, without bringing them to the attention of the ITG committees. While these IT projects met the departmental objectives, they did not always align with the university’s strategic objectives. This research found that the IT project maturity in the university could be increased by following the project management methodologies. The IT project management maturity was found higher in the IT projects executed by the centralized university, where a full-time project manager was assigned to manage the project, and the project manager had a higher expertise in the project management. The IT project executed under the guidance of the Project Management Office (PMO) has exhibited a higher project management maturity, as the PMO set the standards and controls for the project. The IT projects managed by the decentralized colleges by a part-time project manager with lower project management expertise have exhibited a lower project management maturity. The IT projects in the decentralized colleges were often managed by the business, or technical leads, who often lacked the project management expertise. This research found that higher the IT project management maturity, the better is the project performance. The IT projects with a higher maturity had a lower project delay, lower number of missed requirements, and lower number of IT system errors. This research found that the quality of IT decision in the university could be improved by centralizing the IT decision-making processes. The IT project management maturity could be improved by following the project management methodologies. The stakeholder management and communication were found critical for the success of the IT projects in the university. It is hoped that the findings from this research would help the university leaders make the strategic IT decisions, and the university’s IT project managers make the IT project decisions.

Multi-level, Multi-stage and Stochastic Optimization Models for Energy Conservation in Buildings for Federal, State and Local Agencies

Energy Conservation Measure (ECM) project selection is made difficult given real-world constraints, limited resources to implement savings retrofits, various suppliers in the market and project financing alternatives. Many of these energy efficient retrofit projects should be viewed as a series of investments with annual returns for these traditionally risk-averse agencies. Given a list of ECMs available, federal, state and local agencies must determine how to implement projects at lowest costs. The most common methods of implementation planning are suboptimal relative to cost. Federal, state and local agencies can obtain greater returns on their energy conservation investment over traditional methods, regardless of the implementing organization. This dissertation outlines several approaches to improve the traditional energy conservations models. Any public buildings in regions with similar energy conservation goals in the United States or internationally can also benefit greatly from this research. Additionally, many private owners of buildings are under mandates to conserve energy e.g., Local Law 85 of the New York City Energy Conservation Code requires any building, public or private, to meet the most current energy code for any alteration or renovation. Thus, both public and private stakeholders can benefit from this research. The research in this dissertation advances and presents models that decision-makers can use to optimize the selection of ECM projects with respect to the total cost of implementation. A practical application of a two-level mathematical program with equilibrium constraints (MPEC) improves the current best practice for agencies concerned with making the most cost-effective selection leveraging energy services companies or utilities. The two-level model maximizes savings to the agency and profit to the energy services companies (Chapter 2). An additional model presented leverages a single congressional appropriation to implement ECM projects (Chapter 3). Returns from implemented ECM projects are used to fund additional ECM projects. In these cases, fluctuations in energy costs and uncertainty in the estimated savings severely influence ECM project selection and the amount of the appropriation requested. A risk aversion method proposed imposes a minimum on the number of “of projects completed in each stage. A comparative method using Conditional Value at Risk is analyzed. Time consistency was addressed in this chapter. This work demonstrates how a risk-based, stochastic, multi-stage model with binary decision variables at each stage provides a much more accurate estimate for planning than the agency’s traditional approach and deterministic models. Finally, in Chapter 4, a rolling-horizon model allows for subadditivity and superadditivity of the energy savings to simulate interactive effects between ECM projects. The approach makes use of inequalities (McCormick, 1976) to re-express constraints that involve the product of binary variables with an exact linearization (related to the convex hull of those constraints). This model additionally shows the benefits of learning between stages while remaining consistent with the single congressional appropriations framework.

MEMS SENSOR PLATFORMS FOR IN SITU CHARACTERIZATION OF LI-ION BATTERY ELECTRODES

Lithium-ion batteries provide high energy density while being compact and light-weight and are the most pervasive energy storage technology powering portable electronic devices such as smartphones, laptops, and tablet PCs. Considerable efforts have been made to develop new electrode materials with ever higher capacity, while being able to maintain long cycle life. A key challenge in those efforts has been characterizing and understanding these materials during battery operation. While it is generally accepted that the repeated strain/stress cycles play a role in long-term battery degradation, the detailed mechanisms creating these mechanical effects and the damage they create still remain unclear. Therefore, development of techniques which are capable of capturing in real time the microstructural changes and the associated stress during operation are crucial for unravelling lithium-ion battery degradation mechanisms and further improving lithium-ion battery performance. This dissertation presents the development of two microelectromechanical systems sensor platforms for in situ characterization of stress and microstructural changes in thin film lithium-ion battery electrodes, which can be leveraged as a characterization platform for advancing battery performance. First, a Fabry-Perot microelectromechanical systems sensor based in situ characterization platform is developed which allows simultaneous measurement of microstructural changes using Raman spectroscopy in parallel with qualitative stress changes via optical interferometry. Evolutions in the microstructure creating a Raman shift from 145 cm−1 to 154 cm−1 and stress in the various crystal phases in the LixV2O5 system are observed, including both reversible and irreversible phase transitions. Also, a unique way of controlling electrochemically-driven stress and stress gradient in lithium-ion battery electrodes is demonstrated using the Fabry-Perot microelectromechanical systems sensor integrated with an optical measurement setup. By stacking alternately stressed layers, the average stress in the stacked electrode is greatly reduced by 75% compared to an unmodified electrode. After 2,000 discharge-charge cycles, the stacked electrodes retain only 83% of their maximum capacity while unmodified electrodes retain 91%, illuminating the importance of the stress gradient within the electrode. Second, a buckled membrane microelectromechanical systems sensor is developed to enable in situ characterization of quantitative stress and microstructure evolutions in a V2O5 lithium-ion battery cathode by integrating atomic force microscopy and Raman spectroscopy. Using dual-mode measurements in the voltage range of the voltage range of 2.8V – 3.5V, both the induced stress (~ 40 MPa) and Raman intensity changes due to lithium cycling are observed. Upon lithium insertion, tensile stress in the V2O5 increases gradually until the α- to ε-phase and ε- to δ-phase transitions occur. The Raman intensity change at 148 cm−1 shows that the level of disorder increases during lithium insertion and progressively recovers the V2O5 lattice during lithium extraction. Results are in good agreement with the expected mechanical behavior and disorder change in V2O5, highlighting the potential of microelectromechanical systems as enabling tools for advanced scientific investigations. The work presented here will be eventually utilized for optimization of thin film battery electrode performance by achieving fundamental understanding of how stress and microstructural changes are correlated, which will also provide valuable insight into a battery performance degradation mechanism.

Mission and Scenario Planning for Unmanned Aerial Vehicles (Path Planning and Collision Avoidance Systems)

As unmanned autonomous vehicles (UAVs) are being widely utilized in military and civil applications, concerns are growing about mission safety and how to integrate dierent phases of mission design. One important barrier to a coste ective and timely safety certication process for UAVs is the lack of a systematic approach for bridging the gap between understanding high-level commander/pilot intent and implementation of intent through low-level UAV behaviors. In this thesis we demonstrate an entire systems design process for a representative UAV mission, beginning from an operational concept and requirements and ending with a simulation framework for segments of the mission design, such as path planning and decision making in collision avoidance. In this thesis, we divided this complex system into sub-systems; path planning, collision detection and collision avoidance. We then developed software modules for each sub-system