Gene therapy involves the use of nucleic acids, either DNA or RNA for the treatment, cure, or prevention of human diseases. Synthetic cationic polymers are promising as a tool for gene delivery because of their high level of design flexibility for biomaterial construction and are capable of binding and condensing DNA through electrostatic interactions. Our lab has developed a novel polymer (poly (polyethylene glycol-dodecanoate) (PEGD), a polyester of polyethylene glycol (PEG) and dodecanedioic acid (DDA). PEGD is a linear viscous polymer that self-assembles into a vesicle upon immersion in an aqueous solution. A copolymer of dodecanedioc acid and polyethylene glycol (PEG) was synthesized at a 1:1 ratio. Furmaric (FA) or itaconic acid (IA) was used to suppress DDA in the PEGD copolymer at an 80:20 ratio (DDA: furmaric/itaconic acid) to form the PEGDF/I variant. PEGDF/I are then modified through the Michael addition of Protamine Sulfate (PEGDF/I-PS) and Cys-Arg8 (PEGDF/I-CA) peptide to the carbon-carbon double bond on the polymer backbone to introduce a positive charge. The modified PEGDF/I polymers were capable of binding and condensing DNA. Transfection of HEK 293 cells with pTurboGFP plasmid using modified PEGDF/I polymers was successful but showed varied efficiency. The PEGDF/I-CA polymer had around 30% transfection efficiency and was shown to be non-cytotoxic.

Recent development in portable electronics and electric vehicles have increased the demand for high performance lithium ion batteries. However, it is still challenging to produce high energy and high power lithium ion batteries. The major objective of this research is to fabricate advanced electrode materials with enhanced power density and energy density. Porous Li4Ti5O12 (LTO) and its nanocomposites (with Si and reduced graphene oxide (rGO)) synthesized by electrostatic spray deposition (ESD) technique were mainly studied and promising electrochemical performance was achieved. In chapter 3, porous LTO thin film electrode was synthesized by ESD to solve the low energy density and low power density issues by providing good ionic and electronic conductivities. Electrochemical test results showed that it had a large specific capacity of 357 mAh g-1 at 0.15 A g-1, which was even higher than its theoretical capacity. It also exhibited very high rate capability of 98 mAh g-1 at 6 A g-1. The improved electrochemical performance was due to the advantage of ESD generated porous structures. In order to further enhance the power density of LTO, ESD derived LTO/rGO composite electrodes were studied in chapter 4. In chapter 5, high energy density component Si was introduced viii into LTO composite. The synergistic effect between commercial LTO and Si powder was studied. Then, ESD derived LTO/Si/rGO composite was prepared and evaluated. At 0.15 A g-1, a stable capacity of 624 mAh g-1 was observed, which was much higher than the capacities of LTO and LTO/rGO electrodes. In addition, effect of activation process on electrochemical performance of carbon nanofibers (ACNFs) and feasibility of ion intercalation into 2D MMT montmorillonite clay (MMT) were studied and discussed in chapter 6. In summary, we have successfully synthesized various LTO based electrodes by ESD. Both high energy and high power density were achieved as compared to commercial LTO electrode. Through electrochemical characterization and charge storage distribution analysis, origins of the high rate capability were proposed. This work demonstrates ESD as a powerful tool for fabricating high performance porous structures and nanocomposite electrode materials.

This dissertation describes the development of a label-free, electrochemical immunosensing platform integrated into a low-cost microfluidic system for the sensitive, selective and accurate detection of cortisol, a steroid hormone co-related with many physiological disorders. Abnormal levels of cortisol is indicative of conditions such as Cushing’s syndrome, Addison’s disease, adrenal insufficiencies and more recently post-traumatic stress disorder (PTSD). Electrochemical detection of immuno-complex formation is utilized for the sensitive detection of Cortisol using Anti-Cortisol antibodies immobilized on sensing electrodes. Electrochemical detection techniques such as cyclic voltammetry (CV) and electrochemical impedance spectroscopy (EIS) have been utilized for the characterization and sensing of the label-free detection of Cortisol. The utilization of nanomaterial’s as the immobilizing matrix for Anti-cortisol antibodies that leads to improved sensor response has been explored. A hybrid nano-composite of Polyanaline-Ag/AgO film has been fabricated onto Au substrate using electrophoretic deposition for the preparation of electrochemical immunosening of cortisol. Using a conventional 3-electrode electrochemical cell, a linear sensing range of 1pM to 1µM at a sensitivity of 66µA/M and detection limit of 0.64pg/mL has been demonstrated for detection of cortisol. Alternately, a self-assembled monolayer (SAM) of dithiobis(succinimidylpropionte) (DTSP) has been fabricated for the modification of sensing electrode to immobilize with Anti-Cortisol antibodies. To increase the sensitivity at lower detection limit and to develop a point-of-care sensing platform, the DTSP-SAM has been fabricated on micromachined interdigitated microelectrodes (µIDE). Detection of cortisol is demonstrated at a sensitivity of 20.7µA/M and detection limit of 10pg/mL for a linear sensing range of 10pM to 200nM using the µIDE’s. A simple, low-cost microfluidic system is designed using low-temperature co-fired ceramics (LTCC) technology for the integration of the electrochemical cortisol immunosensor and automation of the immunoassay. For the first time, the non-specific adsorption of analyte on LTCC has been characterized for microfluidic applications. The design, fabrication technique and fluidic characterization of the immunoassay are presented. The DTSP-SAM based electrochemical immunosensor on µIDE is integrated into the LTCC microfluidic system and cortisol detection is achieved in the microfluidic system in a fully automated assay. The fully automated microfluidic immunosensor hold great promise for accurate, sensitive detection of cortisol in point-of-care applications.

Knowledge of cell electronics has led to their integration to medicine either by physically interfacing electronic devices with biological systems or by using electronics for both detection and characterization of biological materials. In this dissertation, an electrical impedance sensor (EIS) was used to measure the electrode surface impedance changes from cell samples of human and environmental toxicity of nanoscale materials in 2D and 3D cell culture models. The impedimetric response of human lung fibroblasts and rainbow trout gill epithelial cells when exposed to various nanomaterials was tested to determine their kinetic effects towards the cells and to demonstrate the biosensor’s ability to monitor nanotoxicity in real-time. Further, the EIS allowed rapid, real-time and multi-sample analysis creating a versatile, noninvasive tool that is able to provide quantitative information with respect to alteration in cellular function. We then extended the application of the unique capabilities of the EIS to do real-time analysis of cancer cell response to externally applied alternating electric fields at different intermediate frequencies and low-intensity. Decreases in the growth profiles of the ovarian and breast cancer cells were observed with the application of 200 and 100 kHz, respectively, indicating specific inhibitory effects on dividing cells in culture in contrast to the non-cancerous HUVECs and mammary epithelial cells. We then sought to enhance the effects of the electric field by altering the cancer cell’s electronegative membrane properties with HER2 antibody functionalized nanoparticles. An Annexin V/EthD-III assay and zeta potential were performed to determine the cell death mechanism indicating apoptosis and a decrease in zeta potential with the incorporation of the nanoparticles. With more negatively charged HER2-AuNPs attached to the cancer cell membrane, the decrease in membrane potential would thus leave the cells more vulnerable to the detrimental effects of the applied electric field due to the decrease in surface charge. Therefore, by altering the cell membrane potential, one could possibly control the fate of the cell. This whole cell-based biosensor will enhance our understanding of the responsiveness of cancer cells to electric field therapy and demonstrate potential therapeutic opportunities for electric field therapy in the treatment of cancer.

A novel biocompatible and biodegradable polymer, termed poly(Glycerol malate co-dodecanedioate) (PGMD), was prepared by thermal condensation method and used for fabrication of nanoparticles (NPs). PGMD NPs were prepared using the single oil emulsion technique and loaded with an imaging/hyperthermia agent (IR820) and a chemotherapeutic agent (doxorubicin, DOX). The size of the void PGMD NPs, IR820-PGMD NPs and DOX-IR820-PGMD NPs were approximately 90 nm, 110 nm, and 125 nm respectively. An acidic environment (pH=5.0) induced higher DOX and IR820 release compared to pH=7.4. DOX release was also enhanced by exposure to laser, which increased the temperature to 42°C. Cytotoxicity of DOX-IR820-PGMD NPs was comparable in MES-SA but was higher in Dx5 cells compared to free DOX plus IR820 (pIn vivomouse studies showed that NP formulation significantly improved the plasma half-life of IR820 after tail vein injection. Significant lower IR820 content was observed in kidney in DOX-IR820-PGMD NP treatment as compared to free IR820 treatment in our biodistribution studies (p

In this research the integration of nanostructures and micro-scale devices was investigated using silica nanowires to develop a simple yet robust nanomanufacturing technique for improving the detection parameters of chemical and biological sensors. This has been achieved with the use of a dielectric barrier layer, to restrict nanowire growth to site-specific locations which has removed the need for post growth processing, by making it possible to place nanostructures on pre-pattern substrates. Nanowires were synthesized using the Vapor-Liquid-Solid growth method. Process parameters (temperature and time) and manufacturing aspects (structural integrity and biocompatibility) were investigated. Silica nanowires were observed experimentally to determine how their physical and chemical properties could be tuned for integration into existing sensing structures. Growth kinetic experiments performed using gold and palladium catalysts at 1050 ˚C for 60 minutes in an open-tube furnace yielded dense and consistent silica nanowire growth. This consistent growth led to the development of growth model fitting, through use of the Maximum Likelihood Estimation (MLE) and Bayesian hierarchical modeling. Transmission electron microscopy studies revealed the nanowires to be amorphous and X-ray diffraction confirmed the composition to be SiO2 . Silica nanowires were monitored in epithelial breast cancer media using Impedance spectroscopy, to test biocompatibility, due to potential in vivo use as a diagnostic aid. It was found that palladium catalyzed silica nanowires were toxic to breast cancer cells, however, nanowires were inert at 1µg/mL concentrations. Additionally a method for direct nanowire integration was developed that allowed for silica nanowires to be grown directly into interdigitated sensing structures. This technique eliminates the need for physical nanowire transfer thus preserving nanowire structure and performance integrity and further reduces fabrication cost. Successful nanowire integration was physically verified using Scanning electron microscopy and confirmed electrically using Electrochemical Impedance Spectroscopy of immobilized Prostate Specific Antigens (PSA). The experiments performed above serve as a guideline to addressing the metallurgic challenges in nanoscale integration of materials with varying composition and to understanding the effects of nanomaterials on biological structures that come in contact with the human body.

A typical electronic or photonic device may consist of several materials each one potentially meeting at an interface or terminating with a free-surface boundary. As modern device dimensions reach deeper into the nanoscale regime, interfaces and boundaries become increasingly influential to both electrical and thermal energy transport. While a large majority of the device community focuses on the former, we focus here on the latter issue of thermal transport which is of great importance in implementing nanoscale devices as well as developing solutions for on-chip heat removal and waste heat scavenging. In this document we will discuss how modern performance enhancing techniques (strain, nanostructuring, alloying, etc.) affect thermal transport at boundaries and across interfaces through the avenue of three case studies. We use first-principles Density Functional Perturbation Theory to obtain the phonon spectrum of the materials of interest and then use the dispersion data as input to a phonon Boltzmann Transport model. First, we investigate the combined effects of strain and boundary scattering on the in-plane and cross-plane thermal conductivity of thin-film silicon and germanium. Second, we review a recently developed model for cross-dimensional (2D-3D) phonon transport and apply it to 3D-2D-3D stacked interfaces involving graphene and molybdenum disulfide 2D-layers. Third, we combine relevant models from earlier Chapters to study extrinsic effects, such as line edge roughness and substrate effects, on in-plane and through-plane thermal transport in 1H-phase transition metal dichalcogenide (TMD) alloys. Through these investigations we show that: (1) biaxial strain in Si and Ge thin-films can modulate cross-plane conductivity due to strong boundary scattering, (2) the thermal boundary conductance between 2D-3D materials can be enhanced in the presence of an encapsulating layer, and (3) the thermal conductivity of 1H-phase TMDs can be reduced by an order of magnitude through the combination of nanostructuring, alloying, and substrate effects.

With a growing population and continuous accumulation of pollutants, water resources worldwide are quickly being depleted. Drastic improvements need to be made in both water conservation and treatment. Advanced oxidation processes (AOPs) have been developed to go above and beyond the capabilities of traditional wastewater treatment facilities to eliminate emerging contaminants from our water systems. AOPs increase the generation of hydroxyl radicals (•OH) in oxidation reactions, which are less selective and more reactive than other oxidants, such as ozone, so they are more effective at degrading persistent compounds. This study explored an AOP that utilizes ozonated multi-walled carbon nanotubes (MWCNTs) to generate •OH; past research has proven the success of this method of water treatment, showing a significant decrease in the effluent concentration of the tested compounds. However, these previous studies used a batch system with an initial aliquot of ozone, which would not be a feasible option in a commercial application. This research compares results from a semi-batch system with a continuous injection of ozone to these previous batch system studies to determine if continuous ozonation improves •OH generation capability, contaminant degradation, and the associated reaction kinetics. Results from batch studies had shown limitations to both •OH generation and contaminant degradation which were suspected to be due to ozone degradation; however, these results suggest that ozone availability is in fact not a limiting factor to •OH or contaminant degradation, and another mechanism must be at play. Further, to advance the AOP toward a commercially feasible design, a continuous flow-through system with a MWCNT embedded membrane was explored. The continuous system achieved 80% contaminant degradation in some cases, however, with varying retention times and efficiencies over time, the results were inconclusive and additional experimentation is required.

Despite of all the research going on for the treatment of ocular diseases, age-related macular degeneration (AMD) remains one of the serious vision threatening disease worldwide. Choroidal neovascularization, a pathophysiological characteristic of wet AMD, is the growth of anomalous blood vessels in the eye choroidal layer. Neovascularization is a key factor in AMD and thus anti-angiogenic therapy is beneficial in reducing the development of new abnormal blood vessels to prevent progression of AMD. Axitinib, multi-receptor tyrosine kinase inhibitor, is a small molecule that works by blocking vascular endothelial growth factor receptors (VEGFR) and platelet derived growth factor receptors (PDGFR) responsible for developing neovascularization. Thus, goal of this study was to develop and characterise a sustained release formulation of Axitinib loaded poly (lactic-co-glycolic) acid (PLGA) nanoparticles. The nanoparticles were characterized for particle size and zeta potential as well as using DSC, TEM and in vitro drug release profile. The cytotoxicity of the formulation was evaluated on human retinal pigmented epithelium ARPE19 cells by MTT assay. The cellular uptake, anti-migration assay, and VEGF expression levels were found out in vitro using cells. The optimized formulation was 131.33 ± 31.20 nm in size with -4.63± 0.76 mV zeta potential. Entrapment efficiency was found to be 87.9 ± 2.7%. The cytotoxicity of ARPE19 cells was less than 12% for nanoparticles suggesting the in vitro compatibility at 10 µM concentration of drug. Cellular uptake, anti-migration assay and VEGF expression levels for the nanoparticles had greater uptake, had significant anti-angiogenic potential and exhibited inhibition of VEGF activity. The results showed successful development of axitinib loaded PLGA nanoparticles as an alternative potential treatment option for AMD.

Neuromorphic engineering is the research field dedicated to the study and design of brain-inspired hardware and software tools. Recent advances in emerging nanoelectronics promote the implementation of synaptic connections based on memristive devices. Their non-volatile modifiable conductance was shown to exhibit the synaptic properties often used in connecting and training neural layers. With their nanoscale size and non-volatile memory property, they promise a next step in designing more area and energy efficient neuromorphic hardware. My research deals with the challenges of harnessing memristive device properties that go beyond the behaviors utilized for synaptic weight storage. Based on devices that exhibit non-linear state changes and volatility, I present novel architectures and algorithms that can harness such features for computation. The crossbar architecture is a dense array of memristive devices placed in-between horizontal and vertical nanowires. The regularity of this structure does not inherently provide the means for nonlinear computation of applied input signals. Introducing a modulation scheme that relies on nonlinear memristive device properties, heterogeneous state patterns of applied spatiotemporal input data can be created within the crossbar. In this setup, the untrained and dynamically changing states of the memristive devices offer a useful platform for information processing. Based on the MNIST data set I'll demonstrate how the temporal aspect of memristive state volatility can be utilized to reduce system size and training complexity for high dimensional input data. With 3 times less neurons and 15 times less synapses to train as compared to other memristor-based implementations, I achieve comparable classification rates of up to 93%. Exploiting dynamic state changes rather than precisely tuned stable states, this approach can tolerate device variation up to 6 times higher than reported levels. Random assemblies of memristive networks are analyzed as a substrate for intrinsic computation in connection with reservoir computing; a computational framework that harnesses observations of inherent dynamics within complex networks. Architectural and device level considerations lead to new levels of task complexity, which random memristive networks are now able to solve. A hierarchical design composed of independent random networks benefits from a diverse set of topologies and achieves prediction errors (NRMSE) on the time-series prediction task NARMA-10 as low as 0.15 as compared to 0.35 for an echo state network. Physically plausible network modeling is performed to investigate the relationship between network dynamics and energy consumption. Generally, increased network activity comes at the cost of exponentially increasing energy consumption due to nonlinear voltage-current characteristics of memristive devices. A trade-off, that allows linear scaling of energy consumption, is provided by the hierarchical approach. Rather than designing individual memristive networks with high switching activity, a collection of less dynamic, but independent networks can provide more diverse network activity per unit of energy. My research extends the possibilities of including emerging nanoelectronics into neuromorphic hardware. It establishes memristive devices beyond storage and motivates future research to further embrace memristive device properties that can be linked to different synaptic functions. Pursuing to exploit the functional diversity of memristive devices will lead to novel architectures and algorithms that study rather than dictate the behavior of such devices, with the benefit of creating robust and efficient neuromorphic hardware.