POLYMER-NANOPARTICLE COMPOSITES FOR APPLICATIONS IN FLEXIBLE ELECTRONICS

The aim of this dissertation was to investigate flexible polymer-nanoparticle composites with unique magnetic and electrical properties. Toward this goal, two distinct projects were carried out. The first project explored the magneto-dielectric properties and morphology of flexible polymer-nanoparticle composites that possess high permeability (µ), high permittivity (ε) and minimal dielectric, and magnetic loss (tan δε, tan δµ). The main materials challenges were the synthesis of magnetic nanoparticle fillers displaying high saturation magnetization (Ms), limited coercivity, and their homogeneous dispersion in a polymeric matrix. Nanostructured magnetic fillers including polycrystalline iron core-shell nanoparticles, and constructively assembled superparamagnetic iron oxide nanoparticles were synthesized, and dispersed uniformly in an elastomer matrix to minimize conductive losses. The resulting composites have demonstrated promising permittivity (22.3), permeability (3), and sustained low dielectric (0.1), magnetic (0.4) loss for frequencies below 2 GHz. This study demonstrated nanocomposites with tunable magnetic resonance frequency, which can be used to develop compact and flexible radio frequency devices with high efficiency. The second project focused on fundamental research regarding methods for the design of highly conductive polymer-nanoparticle composites that can maintain high electrical conductivity under tensile strain exceeding 100%. We investigated a simple solution spraying method to fabricate stretchable conductors based on elastomeric block copolymer fibers and silver nanoparticles. Silver nanoparticles were assembled both in and around block copolymer fibers forming interconnected dual nanoparticle networks, resulting in both in-fiber conductive pathways and additional conductive pathways on the outer surface of the fibers. Stretchable composites with conductivity values reaching 9000 S/cm maintained 56% of their initial conductivity after 500 cycles at 100% strain. The developed manufacturing method in this research could pave the way towards direct deposition of flexible electronic devices on any shaped substrate. The electrical and electromechanical properties of these dual silver nanoparticle network composites make them promising materials for the future construction of stretchable circuitry for displays, solar cells, antennas, and strain and tactility sensors.

MICROSCALE DIELECTRIC ANTI-REFLECTION COATINGS FOR PHOTOVOLTAICS

In order to power our planet for the next century, clean energy technologies need to be developed and deployed. Photovoltaic solar cells, which convert sunlight into electricity, are a clear option; however, they currently supply 0.1% of the US electricity due to the relatively high cost per Watt of generation. Thus, our goal is to create more power from a photovoltaic device, while simultaneously reducing its price. To accomplish this goal, we are creating new high efficiency anti-reflection coatings that allow more of the incident sunlight to be converted to electricity, using simple and inexpensive coating techniques that enable reduced manufacturing costs. Traditional anti-reflection coatings (consisting of thin layers of non-absorbing materials) rely on the destructive interference of the reflected light, causing more light to enter the device and subsequently get absorbed. While these coatings are used on nearly all commercial cells, they are wavelength dependent and are deposited using expensive processes that require elevated temperatures, which increase production cost and can be detrimental to some temperature sensitive solar cell materials. We are developing two new classes of anti-reflection coatings (ARCs) based on textured dielectric materials: (i) a transparent, flexible paper technology that relies on optical scattering and reduced refractive index contrast between the air and semiconductor and (ii) silicon dioxide (SiO2) nanosphere arrays that rely on collective optical resonances. Both techniques improve solar cell absorption and ultimately yield high efficiency, low cost devices. For the transparent paper-based ARCs, we have recently shown that they improve solar cell efficiencies for all angles of incident illumination reducing the need for costly tracking of the sun’s position. For a GaAs solar cell, we achieved a 24% improvement in the power conversion efficiency using this simple coating. Because the transparent paper is made from an earth abundant material (wood pulp) using an easy, inexpensive and scalable process, this type of ARC is an excellent candidate for future solar technologies. The coatings based on arrays of dielectric nanospheres also show excellent potential for inexpensive, high efficiency solar cells. The fabrication process is based on a Meyer rod rolling technique, which can be performed at room-temperature and applied to mass production, yielding a scalable and inexpensive manufacturing process. The deposited monolayer of SiO2 nanospheres, having a diameter of 500 nm on a bare Si wafer, leads to a significant increase in light absorption and a higher expected current density based on initial simulations, on the order of 15-20%. With application on a Si solar cell containing a traditional anti-reflection coating (Si3N4 thin-film), an additional increase in the spectral current density is observed, 5% beyond what a typical commercial device would achieve. Due to the coupling between the spheres originated from Whispering Gallery Modes (WGMs) inside each nanosphere, the incident light is strongly coupled into the high-index absorbing material, leading to increased light absorption. Furthermore, the SiO2 nanospheres scatter and diffract light in such a way that both the optical and electrical properties of the device have little dependence on incident angle, eliminating the need for solar tracking. Because the layer can be made with an easy, inexpensive, and scalable process, this anti-reflection coating is also an excellent candidate for replacing conventional technologies relying on complicated and expensive processes.

Effects of Information and Time of Use Pricing on Irish Electricity Demand and Supply

In this dissertation I quantify residential behavior response to interventions designed to reduce electricity demand at different periods of the day. In the first chapter, I examine the effect of information provision coupled with bimonthly billing, monthly billing, and in-home displays, as well as a time-of-use (TOU) pricing scheme to measure consumption over each month of the Irish Consumer Behavior Trial. I find that time-of-use pricing with real time usage information reduces electricity usage up to 8.7 percent during peak times at the start of the trial but the effect decays over the first three months and after three months the in-home display group is indistinguishable from the monthly treatment group. Monthly and bi-monthly billing treatments are not found to be statistically different from another. These findings suggest that increasing billing reports to the monthly level may be more cost effective for electricity generators who wish to decrease expenses and consumption, rather than providing in-home displays. In the following chapter, I examine the response of residential households after exposure to time of use tariffs at different hours of the day. I find that these treatments reduce electricity consumption during peak hours by almost four percent, significantly lowering demand. Within the model, I find evidence of overall conservation in electricity used. In addition, weekday peak reductions appear to carry over to the weekend when peak pricing is not present, suggesting changes in consumer habit. The final chapter of my dissertation imposes a system wide time of use plan to analyze the potential reduction in carbon emissions from load shifting based on the Ireland and Northern Single Electricity Market. I find that CO2 emissions savings are highest during the winter months when load demand is highest and dirtier power plants are scheduled to meet peak demand. TOU pricing allows for shifting in usage from peak usage to off peak usage and this shift in load can be met with cleaner and cheaper generated electricity from imports, high efficiency gas units, and hydro units.

Ceramic Materials Development for Intermediate Temperature Solid Oxide Fuel Cell (IT-SOFC)

Solid oxide fuel cell (SOFC) is an electrochemical device that converts chemical energy into electric power with high efficiency. Traditional SOFC has its disadvantages, such as redox cycling instability and carbon deposition while using hydrocarbon fuels. It is because traditional SOFC uses Ni-cermet as anode. In order to solve these problems, ceramic anode is a good candidate to replace Ni. However, the conductivity of most ceramic anode materials are much lower than Ni metal, and it introduces high ohmic resistance. How to increase the conductivity is a hot topic in this research field. Based on our proposed mechanism, several types of ceramic materials have been developed. Vanadium doped perovskite, Sr1-x/2VxTi1-xO3 (SVT) and Sr0.2Na0.8Nb1-xVxO3 (SNNV), achieved the conductivity as high as 300 S*cm-1 in hydrogen, without any high temperature reduction. GDC electrolyte supported cell was fabricated with Sr0.2Na0.8Nb0.9V0.1O3 and the performance was measured in hydrogen and methane respectively. Due to vanadium’s intrinsic problems, the anode supported cell is not easy. Fe doped double perovskite Sr2CoMoO6 (SFCM) was also developed. By carefully doping Fe, the conductivity was improved over one magnitude, without any vigorous reducing conditions. SFCM anode supported cell was successfully fabricated with GDC as the electrolyte. By impregnating Ni-GDC nano particles into the anode, the cell can be operated at lower temperatures while having higher performance than the traditional Ni-cermet cells. Meanwhile, this SFCM anode supported SOFC has long term stability in the reformate containing methane. During the anode development, cathode improvement caused by a thin Co-GDC layer was observed. By adding this Co-GDC layer between the electrolyte and the cathode, the interfacial resistance decreases due to fast oxygen ion transport. This mechanism was confirmed via isotope exchange. This Co-GDC layer works with multiple kinds of cathodes and the modified cell’s performance is 3 times as the traditional Ni-GDC cell. With this new method, lowering the SOFC operation temperature is feasible.

Learning Binary Code Representations for Effective and Efficient Image Retrieval

The size of online image datasets is constantly increasing. Considering an image dataset with millions of images, image retrieval becomes a seemingly intractable problem for exhaustive similarity search algorithms. Hashing methods, which encodes high-dimensional descriptors into compact binary strings, have become very popular because of their high efficiency in search and storage capacity. In the first part, we propose a multimodal retrieval method based on latent feature models. The procedure consists of a nonparametric Bayesian framework for learning underlying semantically meaningful abstract features in a multimodal dataset, a probabilistic retrieval model that allows cross-modal queries and an extension model for relevance feedback. In the second part, we focus on supervised hashing with kernels. We describe a flexible hashing procedure that treats binary codes and pairwise semantic similarity as latent and observed variables, respectively, in a probabilistic model based on Gaussian processes for binary classification. We present a scalable inference algorithm with the sparse pseudo-input Gaussian process (SPGP) model and distributed computing. In the last part, we define an incremental hashing strategy for dynamic databases where new images are added to the databases frequently. The method is based on a two-stage classification framework using binary and multi-class SVMs. The proposed method also enforces balance in binary codes by an imbalance penalty to obtain higher quality binary codes. We learn hash functions by an efficient algorithm where the NP-hard problem of finding optimal binary codes is solved via cyclic coordinate descent and SVMs are trained in a parallelized incremental manner. For modifications like adding images from an unseen class, we propose an incremental procedure for effective and efficient updates to the previous hash functions. Experiments on three large-scale image datasets demonstrate that the incremental strategy is capable of efficiently updating hash functions to the same retrieval performance as hashing from scratch.

Design of Nanophotonic Antireflection Coatings for III-V Thin-Film Photovoltaics

Thin-film photovoltaics have provided a critical design avenue to help decrease the overall cost of solar power. However, a major drawback of thin-film solar cell technology is decreased optical absorption, making compact, high-quality antireflection coatings of critical importance to ensure that all available light enters the cell. In this thesis, we describe high efficiency thin-film InP and GaAs solar cells that utilize a periodic array of nanocylinders as antireflection coatings. We use coupled optical and electrical simulations to find that these nanophotonic structures reduce the solar-weighted average reflectivity of InP and GaAs solar cells to around 1.3 %, outperforming the best double-layer antireflection coatings. The coupling between Mie scattering resonances and thin-film interference effects accurately describes the optical enhancement provided by the nanocylinders. The spectrally resolved reflectivity and J-V characteristics of the devices under AM1.5G solar illumination are determined via the coupled optical and electrical simulations, resulting in predicted power conversion efficiencies > 23 %. We conclude that the nanostructured coatings reduce reflection without negatively affecting the electronic properties of the InP and GaAs solar cells by separating the nanostructured optical components from the active layer of the device.

Improving Photovoltaics with High Luminescence Efficiency Quantum Dot Layers

A solar cell relies on its ability to turn photons into current. Because short wavelength photons are typically absorbed near the top surface of a cell, the generated charge carriers recombine before being collected. But when a layer of quantum dots (nanoscale semiconductor particles) is placed on top of the cell, it absorbs short wavelength photons and emits them into the cell at longer wavelengths, which enables more efficient carrier collection. However, the resulting power conversion efficiency of the system depends critically on the quantum dot luminescence efficiency – the nature of this relationship was previously unknown. Our calculations suggest that a quantum dot layer must have high luminescence efficiency (at least 80%) to improve the current output of existing photovoltaic (PV) cells; otherwise, it may worsen the cell’s efficiency. Our quantum dot layer (using quantum dots with over 85% quantum yield) slightly reduced the efficiency of our PV cells. We observed a decrease in short circuit current of a commercial-grade cell from 0.1977 A to 0.1826 A, a 7.6% drop, suggesting that improved optical coupling from the quantum dot emission into the solar cell is needed. With better optical coupling, we predict current enhancements between ~6% and ~8% for a solar cell that already has an antireflection coating. Such improvements could have important commercial impacts if the coating could be deployed in a scalable fashion.