In a longitudinal cohort study, a group of subjects is chosen based on certain characteristicsand then followed at routine intervals over time. At each visit, some measurements are recorded and the goal is oftentimes to model how they develop over time. During the followup, the collection of the data can be stopped by a terminal event. In this dissertation, we study several statistical methods for modeling longitudinal data while adjusting for the terminal event. In Chapter II, we propose a nonparametric bivariate time-varying coefficient model for longitudinal measurements with the occurrence of a terminal event that is subject to right censoring. The time-varying coefficients capture the longitudinal trajectories of covariate effects along with both the followup time and the residual lifetime, in contrast to the existing work that either models longitudinal measures as a function of only the forward time or the backward time, or poses strong parametric assumptions. We consider a kernel smoothing method for estimating regression coefficients in our model and use cross-validation for bandwidth selection, applying undersmoothing in the final analysis to eliminate the asymptotic bias of the kernel estimator. We show that the kernel estimates follow a finite-dimensional normal distribution asymptotically under mild regularity conditions, and provide an easily computed sandwich covariance matrix estimator. In Chapter III, we study the lifetime Medicare spending patterns of patients with end-stage renal disease (ESRD) stratified by waitlisting and kidney transplant. In addition to the terminal event, a non-terminal event that happened in the follow-up could also have an impact on the longitudinal measures. To study the heterogeneous Medicare cost trajectories across groups stratified by waitlisting and transplant, we proposed two models: a semiparametric regression model with both fixed and bivariate time-varying coefficients to compare unwaitlisted and waitlisted groups, and a bivariate time-varying coefficient model with different starting times (time since the first ESRD service and time since the kidney transplant) to compare untransplanted and transplanted groups. We use sandwich variance estimators to construct confidence intervals and validate inference procedures through simulations. Our analysis of the Medicare claims data reveals that waitlisting is associated with a lower daily medical cost at the beginning of ESRD service among waitlisted patients which gradually increases over time. Averaging over lifespan, however, there is no difference between waitlisted and unwaitlisted groups. A kidney transplant, on the other hand, reduces the medical cost significantly after an initial spike. In Chapter IV, we study how the onset of a non-terminal events is associated with the terminal event. Existing methods under the framework of multi-state model or the semi-competing risks model rely on certain semiparametric assumptions for modeling the joint distribution of these two events, thus may subject to model misspecification and lack clear interpretations of their association. Moreover, they assume the independence between the censoring time C and both event times (S, T), which can be easily violated in realistic situations. We propose to estimate the onset of non-terminal event conditional on the terminal event time using a generalized Beran’s estimator. We consider a left truncation time L in addition to the right censoring time C and only need a relaxed assumption of the independence between (L,C) and the non-terminal event time S conditional on the terminal event time T. Such estimates also enjoy more meaningful interpretations.

Recent advances in the field of organic electronic devices such as supercapacitors, chemical sensors, and field-effect transistors, have brought tremendous attention to conjugated materials. The benefits of these conjugated materials are their significantly lower fabrication cost, low-temperature processing, solution processability, mechanical flexibility, and tunable optoelectronic properties compared to their inorganic counterparts. Among the family of conducting polymers, polyaniline is of particular interest as a result of its ambient stability, distinctive oxidation-reduction chemistry, and the straightforward acid-base doping-dedoping process. However, its properties such as polydispersity, poor processability, complex chain conformation, and poor solubility proved to be challenging for fundamental studies.

Conducting polymers offer the ability to create electronic devices that are lightweight with relatively simple processing compared to their inorganic counterparts. They have recently gained attention in the wearable electronics field due their ability to exhibit flexibility and low stiffness while conducting electricity. However, conducting polymers are not intrinsically flexible and must be modified, usually at the expense of electrical performance, or through the use of expensive and toxic additives. Here, we explore the use of architected materials to tune a conductive polymer's mechanical properties without chemical modification. To do so, a reliable and high-throughput 3D printing system for a conducting polymer-based hydrogel was established. The system utilizes a stereolithography apparatus (SLA) to 3D print a hydrogel of almost any 3D shape. The hydrogel acts as a dopant and structural component for a subsequently grown conducting polymer. This means a conducting polymer, which is usually limited to being a thin film or a stochastic foam, can be made into specific and complex geometries, enabling the tailoring of its mechanical properties through architecture, without compositing or chemical modification. When architected into a lattice, the conducting polymers can withstand compressive strain up to 80% without failure, whereas their bulk counterpart reaches just 25% strain before undergoing brittle fracture. The architected conducting polymers also exhibit a strain rate invariant stress-strain curve, suggesting that a potential strain rate invariant electrical resistance behavior may exist, but further investigation is required.

Perchlorate (ClO4-) has gained attention recently due to its interference with thyroid gland function. In infants and unborn children, inadequate thyroid hormone production can cause mental retardation and thyroid tumors. Since new perchlorate standards will be proposed in 2013, and if a stricter standard is imposed, cost effective technologies will be in high demand. The overall objective of this research was to evaluate two perchlorate bioremediation strategies using indigenous soil bacteria: 1) an autotrophic strategy using zero-valent iron (ZVI) to generate hydrogen as the electron donor and alkalinity in the form of (bi)carbonate as the carbon source for cell growth and maintenance and 2) a heterotrophic strategy using organic substrates as the electron donor and the carbon source for cell growth and maintenance.

The first strategy was evaluated on perchlorate-contaminated groundwater from West Valley Water District Well #2 located in Rialto, CA (Chapter III). A mobile treatment system consisting of a water holding tank, a ZVI packed bed and two parallel sand filters was placed at the site. In the first three months, the system experienced excellent performance, as measured by the tested parameters meeting the California drinking water standards. The effluent concentration of perchlorate was non-detectable (below 4 µg/L), nitrate effluent concentration was less than 0.01 mg/L as N, effluent iron ranged from 0 to 0.05 mg/L. Coliforms, fecal coliforms and E. coli in the reactor effluent were below the detection limit of 2 MPN/100mL. However, significant loss of perchlorate performance was observed after 3 months operation. The reason was attributed to the reduction of hydraulic conductivity and flow channeling.

A laboratory column experiment was conducted to investigate the hydraulic condition change in the ZVI beds (Chapter IV). Effects of flow rate and (bi)carbonate on hydraulic condition were evaluated by performing hydraulic conductivity tests, SEM examination, and tracer tests. The results indicated that the decrease of hydraulic conductivity was more pronounced in the low flow reactors than in the higher flow reactors. This result appeared to contradict the hypothesis that increasing the flow rate will accelerate the hydraulic conductivity reduction. (Bi)carbonate was determined to be the primary cause of the hydraulic conductivity reduction. The decrease in hydraulic conductivity was most severe in the segment receiving the higher concentration of NaHCO3. Hydraulic conductivity decreased from 10-2.73 cm/s to 10 -7.33 cm/s after constantly feeding 24 mM of NaHCO3 for 41 days. The reduction of hydraulic conductivity was caused by the formation of mineral precipitates.

Because of the lack of long-term perchlorate reduction in the autotrophic ZVI-based system, an alternative strategy that utilized organics as both the electron donor and carbon growth source was tested for perchlorate bioremediation (Chapter V). Laboratory microcosm and column tests were employed to assess the effectiveness of selected organic substrates on reducing perchlorate from two different locations of a real perchlorate-contaminated site. One location (source area) had 70 mg/L of perchlorate in groundwater, and another one (plume edge, referred to "biobarrier") had 500 µg/L of perchlorate. The effect of adding nutrients was also examined. For the high concentration source area treatment, emulsified oil substrate (EOS) and glycerin were determined to be the most effective organics from the microcosm testing. Hence, they were selected for column testing. The results revealed that amending soil with EOS had significant advantages over using glycerin as a soil amendment. After a single injection of EOS, perchlorate can be reduced to less than 4 µg/L for 4 months. Perchlorate reduction was not initiated in glycerin-amended soil. Glycerin had to be constantly added into the influent to treat perchlorate to non-detectable level. For the low concentration biobarrier treatment, compost/mulch, EOS, and EHC (a combination of carbon plant-based carbon source and zero-valent iron) had similar perchlorate removal rates in microcosm tests. EOS appeared to have greater longevity than EHC in the column tests. The addition of nutrients had minor benefit on both sites treatments.

Comparing the two strategies, using organic substrate was more feasible for perchlorate bioremediation in terms of overall performance and longevity.

The rapid development of synthetic conjugated materials has enabled organic electronic devices such as solar cells, field-effect transistors and sensors to rival their inorganic counterparts in performance at significantly lower cost. Conducting polymers such as polyaniline constitute an important class of such materials, but their properties are difficult to study due to polydispersity, complex chain conformations and lack of solubility. In particular, assembling conducting polymers into highly crystalline domains similar to that of small molecule (semi)conductors has proven to be challenging, which has limited their integration into electronic devices that require high carrier mobility and stability such as organic field-effect transistors and solar cells. On the other hand, oligomers represent a unique middle ground between conducting polymers and molecular (semi)conductors because oligomers (e.g. oligoanilines) retain the chemical properties of the parent polymer, while also possessing properties typically associated with molecular (semi)conductors especially in regard to monodispersity and self-assembly. In this thesis, an oligomer approach for advancing the field of conducting polymers is presented. A myriad of conjugated oligomers are examined as the more structurally rigid, pure, and soluble model systems to tackle important challenges in this field. This approach has opened new opportunities for (1) understanding the fundamental packing, transport, and self-assembly properties for polymers; (2) the rational design of high performance conducting polymers by analyzing the oligomer building blocks; (3) realizing the long-sought solution-based bottom-up growth for vertically oriented arrays of organic single crystals; and (4) deciphering the role of oligomers in improving the crystallinity of the parent polymers. The advancement of knowledge in these fields has also allowed us to create high performance hybrid solar cells, complex core/shell nanostructures, and microcontact printing methods with nanoscale resolution.

DNA nanotechnology is a rapidly evolving field that exploits the remarkable properties of DNA molecules to create complex and functional nanostructures. One of the key techniques in DNA nanotechnology is self-assembly, wherein DNA molecules are designed to interact and assemble into specific structures with precise control over their size, shape, and composition. This dissertation focuses on the self-assembly of plasmonic, fluorescent, and magnetic nanoparticles in both 2D and 3D using DNA as a programmable scaffold, and explores their applications in various areas, including biosensing and magnetic metamaterials.Chapter 1 provides a comprehensive overview of DNA nanotechnology, self-assembly techniques, and DNA origami. The principles of DNA self-assembly are discussed, including the design rules for creating DNA nanostructures with precise control over their shape and size. The versatility of DNA as a programmable scaffold is highlighted, allowing for the assembly of diverse nanoparticles with unique functionalities. The chapter also discusses the fundamentals of DNA origami, a powerful technique that utilizes the folding of a long single-stranded DNA template to create complex nanostructures with high precision. In Chapter 2, a novel ligand exchange method is presented, which allows for the functionalization of quantum dots (QDs) with DNA to form self-assembled heterodimers. The heterodimers serve as probes for detection, with one QD acting as a reporter and the other AuNP (gold nanoparticle) as a quencher. The chapter elaborates on the design and fabrication of the QD-AuNP heterodimer. The changes in photoluminescence (PL) signals upon binding of the heterodimers to target DNA molecules are investigated. Chapter 3 focuses on the application of the heterodimer probes in the development of a biosensor for nucleic acid detection. The biosensor is designed based on the change in PL signal upon target DNA binding, allowing for sensitive and selective detection. The chapter provides details on the fabrication and characterization of the biosensor, including the optimization of xvi experimental parameters such as probe design and concentration, and target DNA concentration. The performance of the biosensor is also evaluated using different target DNA concentrations. The kinetics of the DNA displacement process in the biosensor are also investigated, shedding light on the dynamics of target DNA binding and release from the heterodimers. In Chapter 4, a novel method for the self-assembly of gold-coated magnetic nanoparticles in 3D using DNA as a scaffold is presented. The chapter discusses the fabrication of DNA-modified magnetic nanoparticles and their subsequent self-assembly into 3D structures by exploiting the programmable base-pairing interactions of DNA molecules. The chapter highlights the unique capabilities of this 3D self-assembly approach and discusses the future prospects and potential directions for further research in this area. In conclusion, this dissertation presents a comprehensive investigation into the use of DNA nanotechnology for the self-assembly of plasmonic, fluorescent, and magnetic nanoparticles in 2D and 3D. The methods and findings presented in this dissertation contribute to the advancement of DNA nanotechnology and demonstrate the potential of self-assembled nanostructures for various applications, including biosensing, nucleic acid detection, DNA data storage and magneto-plasmonic measurements.