Volatility of oil prices along with major concerns about climate change, oil supply security and depleting reserves have sparked renewed interest in the production of biofuels and biochemicals. While the carbohydrate portion of edible crops is currently used as the primary feedstock in the biological production of fuels and chemicals, the availability of fatty acid (FA)-rich feedstocks and recent progress in the development of oil-accumulating organisms have drawn the attention to FAs as an attractive alternative. However, microbial platforms to enable this were nearly absent. To this end, we engineered native and heterologous fermentative pathways in E. coli to enable the efficient synthesis of fuels and chemicals from FAs. The current de facto strategy for the synthesis of non-native products in model organisms is He terologous M etabolic E ngineering (HeME), which consists of recruiting foreign genes from native producers. However, the relative incompatibility of the heterologous pathways with the host metabolism may be considered a drawback. As an alternative approach, the HoME ( Ho mologous M etabolic E ngineering) strategy that we propose overcomes this limitation by harnessing the metabolic potential of the host strain. HoME aims at reconstructing heterologous pathways to enable biosynthesis of non-natural products by identifying and assembling native functional surrogates. Implementation of both HeME and HoME strategies in the context of fuels and chemicals biosynthesis has usually been directed to the conversion of feedstocks constituents into a specific product. However, we demonstrated a novel metabolic platform based on a functional reversal of the fatty acid catabolic pathway (β-oxidation) as a means of synthesizing a wide array of products with various chain lengths and functionalities.

The evolution of nanotechnology over the past 20 years has allowed researchers to use a wide variety of techniques and instruments to synthesize and characterize new materials on the nano scale. Due to their size, these nano materials have a wide variety of interesting properties, including, high tensile strength, novel electronic and optical properties and high surface areas. In any absorption system, a high surface areas is desirable, making carbon nano materials ideal candidates for use in absorption systems. To that end, we have prepared a variety of nano carbons, single walled carbon nanotubes, multi walled carbon nanotubes, graphite intercalation compounds, graphite oxide, phenylalanine modified graphite and fullerenes, for the absorption of carbon dioxide. These nano carbons are functionalized with the polymer, polyethyleneimine, and fully characterized using Raman spectroscopy, x-ray photoelectron spectroscopy, scanning electron microscopy, atomic force microscopy, solid state 13 C NMR, and thermogravimetric analysis. The carbon dioxide absorption potential of the PEI-nano carbons was evaluated using thermogravimetric analysis at standard room temperature and pressure. We have demonstrated the high gravimetric capacity of carbon dioxide capture on these materials with extremely high capacities for PEI-C 60 .

Strongly coupled plasmon-exciton hybrid nanostructures are fabricated and their optical properties are studied. The plasmonic and excitonic systems are gold nanoshells and J-aggregates, respectively. Gold nanoshells are tunable plasmonic core-shell nanoparticles which can sustain distinct dipole and quadrupole plasmons with resonant energies dependent on core-size/shell-thickness ratio. J-aggregates are organic semiconducting material with excitons that possess very high oscillator strength making them suitable for coherent interaction with other kinds of excitations. The J-aggregates are formed on the surface of the nanoshells when a water/ethanol (50:50) solution of the dye molecules (2,2'-dimethyl-8-phenyl-5,6,5',6'-dibenzothiacarbocyanine chloride) is added to an aqueous solution of nanoshells. These nanoshell-J-aggregate complexes exhibit coherent coupling between localized plasmons of the nanoshell and excitons of the molecular J-aggregates. Coherent coupling strengths of 120 meV and 100 meV have been measured for dipole and quadrupole plasmon interactions with excitons, respectively. Femtosecond time-resolved transmission spectroscopy studies are carried out in order to understand the possible sources of optical nonlinearities in the nanoshell-J-aggregate hybrid. Transient absorption of the interacting plasmon-exciton system is observed, in dramatic contrast to the photoinduced transmission of the pristine J-aggregate. An additional, transient Fano-shaped modulation within the Fano dip is also observable. The transient behavior of the J-aggregate-Au nanoshell complex is described by a combined one-exciton and two-exciton state model coupled to the nanoshell plasmon.

We investigate spectral properties of limit-periodic Schrödinger operators in [cursive l] 2 ([Special characters omitted.] ). Our goal is to exhibit as rich a spectral picture as possible. We regard limit-periodic potentials as generated by continuous sampling along the orbits of a minimal translation of a procyclic group. This perspective was first proposed by Avila and further exploited by the author, which allows one to separate the base dynamics and the sampling function. Starting from this point of view, we conclude that all the spectral types (i.e. purely absolutely continuous, purely singular continuous, and pure point) can appear within the class of limit-periodic Schrödinger operators. We furthermore answer questions regarding how often a certain type of spectrum would occur and discuss the corresponding Lyapunov exponent. In the regime of pure point spectrum, we exhibit the first almost periodic examples that are uniformly localized across the hull and the spectrum.

This thesis describes work on building numerical models of the Earth's magnetosphere using magnetohydrodynamics (MHD) and other related modeling methods. For many years, models that solve the MHD equations have been the main tool for improving our theoretical understanding of the large-scale dynamics of the Earth's magnetosphere. While the MHD models have been very successful in capturing many large-scale features, they fail to adequately represent the important drift physics in the inner magnetosphere. Consequently, the ring current, which contains most of the particle energy in the inner magnetosphere, is not realistically represented in MHD models. In this thesis, Chapter 2 and 3 will describe in detail our effort to couple the OpenGGCM (Open Geospace General Circulation Model), one of the major MHD models, to the Rice Convection Model (RCM), an inner magnetosphere ring current model, with the goal of including energy dependent drift physics into the MHD model. In Chapter 4, we will describe an initial attempt to use a direct-integration method to calculate Birkeland currents in the MHD code. Another focus of the thesis work, presented in Chapter 5, addresses a longstanding problem on how a geomagnetic substorm can occur within the closed field line region of the tail. We find a scenario of a bubble-blob pair formation in an OpenGGCM simulation just before the expansion phase of the substorm begins and the subsequent separation of the bubble and the blob decreases the normal component of the magnetic field until finally an X-line occurs. Thus the formation of the bubble-blob pair may play an important role in changing the magnetospheric configuration from a stretched field to the X-line formation that is believed to be the major signature of a substorm.

Compared to 'conventional' materials made from metal, glass, or ceramics, protein-based materials have unique mechanical properties. Furthermore, the morphology, mechanical properties, and functionality of protein-based materials may be optimized via sequence engineering for use in a variety of applications, including textile materials, biosensors, and tissue engineering scaffolds. The development of recombinant DNA technology has enabled the production and engineering of protein-based materials ex vivo . However, harsh production conditions can compromise the mechanical properties of protein-based materials and diminish their ability to incorporate functional proteins. Developing a new generation of protein-based materials is crucial to (i) improve materials assembly conditions, (ii) create novel mechanical properties, and (iii) expand the capacity to carry functional protein/peptide sequences. This thesis describes development of novel protein-based materials using Ultrabithorax, a member of the Hox family of proteins that regulate developmental pathways in Drosophila melanogaster . The experiments presented (i) establish the conditions required for the assembly of Ubx-based materials, (ii) generate a wide range of Ubx morphologies, (iii) examine the mechanical properties of Ubx fibers, (iv) incorporate protein functions to Ubx-based materials via gene fusion, (v) pattern protein functions within the Ubx materials, and (vi) examine the biocompatibility of Ubx materials in vitro . Ubx-based materials assemble at mild conditions compatible with protein folding and activity, which enables Ubx chimeric materials to retain the function of appended proteins in spatial patterns determined by materials assembly. Ubx-based materials also display mechanical properties comparable to existing protein-based materials and demonstrate good biocompatibility with living cells in vitro . Taken together, this research demonstrates the unique features and future potential of novel Ubx-based materials.

One of the oldest and most fundamental problems in statistics is the analysis of cross-classified data called contingency tables. Analyzing contingency tables is typically a question of association - do the variables represented in the table exhibit special dependencies or lack thereof? The statistical models which best capture these experimental notions of dependence are the categorical conditional independence models; however, until recent discoveries concerning the strongly algebraic nature of the conditional independence models surfaced, the models were widely overlooked due to their unwieldy implicit description. Apart from the inferential question above, this thesis asks the more basic question - suppose such an experimental model of association is known, how can one incorporate this information into the estimation of the joint distribution of the table? In the traditional parametric setting several estimation paradigms have been developed over the past century; however, traditional results are not applicable to arbitrary categorical conditional independence models due to their implicit nature. After laying out the framework for conditional independence and algebraic statistical models, we consider three aspects of estimation in the models using the minimum Euclidean (L2E), minimum Pearson chi-squared, and minimum Neyman modified chi-squared distance paradigms as well as the more ubiquitous maximum likelihood approach (MLE). First, we consider the theoretical properties of the estimators and demonstrate that under general conditions the estimators exist and are asymptotically normal. For small samples, we present the results of large scale simulations to address the estimators' bias and mean squared error (in the Euclidean and Frobenius norms, respectively). Second, we identify the computation of such estimators as an optimization problem and, for the case of the L2E, propose two different methods by which the problem can be solved, one algebraic and one numerical. Finally, we present an R implementation via two novel packages, mpoly for symbolic computing with multivariate polynomials and catcim for fitting categorical conditional independence models. It is found that in general minimum distance estimators in categorical conditional independence models behave as they do in the more traditional parametric setting and can be computed in many practical situations with the implementation provided.

Multi-scale ordering of the components is of utmost importance for the preparation of any functional system. This is particularly interesting for the assembly of plamonic nanoparticles which show drastic differences in their optical properties compared to the individual counterparts, giving rise to the unique opportunity to perform enhanced spectroscopies, sensing, and transporting optical information below the diffraction limitation of light. The control over ordering of nanoscale materials is therefore of paramount importance. Template based bottom up approaches such as using nematic liquid crystals promise a long range, reversible ordering of nanomaterials. It also promises active control over plasmonic properties of metal nanoparticles due to the electric field induced reorientation of liquid crystals, resulting in a change of the local refractive index. This thesis discusses the possibility of ordering anisotropic metal nanoparticles and performing active modulaton of the plasmonics response using a nematic liquid crystals. While long polymer chains can be solvated and aligned in liquid crystal solvents, anisotropic metal nanoparticles could not be dissolved in the nematic liquid crystal phase because of their poor solubility. Here, I show that appropriate surface functionalization can increase the otherwise low solubility of plasmonic nanoparticles in a nematic liquid crystal matrix. I also show that it is possible to reversibly modulate the polarized scattering of individual gold nanorods through an electric field induced phase transition of the liquid crystal. In this thesis, I also studied the motion of a molecular machine, commonly known as nanocars, over different solid surface. I show that individual nanocars, which consist of four carborane wheels attached to an aromatic backbone chassis, can move up to several micrometers over a glass surface at ambient temperature. Their movement is consistent with the rolling of the carborane wheels and can be controlled by tuning the interaction between the surface and the wheels.

Quantile regression, a method used to estimate conditional quantiles of a set of data ( X, Y ), was popularized by Koenker and Bassett (1978). For a particular quantile q , the q th quantile estimate of Y given X = x can be found using an asymmetrically-weighted, absolute-loss criteria. This form of regression is considered to be robust, in that it is less affected by outliers in the data set than least-squares regression. However, like standard L 1 regression, this form of quantile regression can still be affected by multiple outliers. In this thesis, we propose a method for improving robustness in quantile regression through an application of Scott's L 2 Estimation (2001). Theoretic and asymptotic results are presented and used to estimate properties of our method. Along with simple linear regression, semiparametric extensions are examined. To verify our method and its extensions, simulated results are considered. Real data sets are also considered, including estimating the effect of various factors on the conditional quantiles of child birth weight, using semiparametric quantile regression to analyze the relationship between age and personal income, and assessing the value distributions of Major League Baseball players.

Over the last decade, much research has been done to understand the role of nanoparticles in heat transfer fluids. While experimental results have shown "anomalous" thermal enhancements and non-linear behavior with respect to CNT loading percentage, little has been done to replicate this behavior from an analytical or computational standpoint. This study is aimed towards using molecular dynamics to augment our understanding of the physics at play in CNT-nanofluid systems. This research begins with a heat transfer study of individual CNTs in a vacuum environment. Temperature gradients are imposed or induced via various methods. Tersoff and AIREBO potentials are used for the carbon-carbon interactions in the CNTs. Various chirality CNTs are explored, along with several different lengths and temperatures. The simulations have shown clear dependencies upon CNT length, CNT chirality, and temperature. Subsequent studies simulate individual CNTs solvated in a simple fluidic box domain. A heat flux is applied to the domain, and various tools are employed to study the resulting heat transfer. The results from these simulations are contrasted against the earlier control simulations of the CNT-only domain. The degree by which the solvation dampens the effect of physical parameters is discussed. Effective thermal conductivity values are computed, however the piecewise nature of the temperature gradient makes Fourier's law insufficient in interpretting the heat transfer. Nevertheless, the computed effective thermal conductivities are applied to classical models and better agreement with experimental results is evident. Phonon spectra of solvated and unsolvated CNTs are compared. However, a unique method utilizing the Irving-Kirkwood relations reveals the spatially-localized heat flux mapping that fully illuminates the heat transfer pathways in the solid-fluid composite material. This method confirms why conventional models fail at predicting effective thermal conductivity. Specifically, it reveals the volume of influence that the CNT has on its surrounding fluid.