Analysis of White Latex Paints using Laser Induced Breakdown Spectroscopy for Forensic Applications

The analysis of white latex paint is a problem for forensic laboratories because of difficulty in differentiation between samples. Current methods provide limited information that is not suitable for discrimination. Elemental analysis of white latex paints has resulted in 99% discriminating power when using LA-ICP-MS; however, mass spectrometers can be prohibitively expensive and require a skilled operator. A quick, inexpensive, effective method is needed for the differentiation of white latex paints. In this study, LIBS is used to analyze 24 white latex paint samples. LIBS is fast, easy to operate, and has a low cost. Results show that 98.1% of variation can be accounted for via principle component analysis, while Tukey pairwise comparisons differentiated 95.6% with potassium as the elemental ratio, showing that the discrimination capabilities of LIBS are comparable to those of LA-ICP-MS. Due to the many advantages of LIBS, this instrument should be considered a necessity for forensic laboratories.

Development of Advanced Capillary Electrophoresis Techniques with UV and Mass Spectrometry Detection for Forensic, Pharmaceutical and Environmental Applications

Capillary electrophoresis (CE) is a modern analytical technique, which is electrokinetic separation generated by high voltage and taken place inside the small capillaries. In this dissertation, several advanced capillary electrophoresis methods are presented using different approaches of CE and UV and mass spectrometry are utilized as the detection methods. Capillary electrochromatography (CEC), as one of the CE modes, is a recent developed technique which is a hybrid of capillary electrophoresis and high performance liquid chromatography (HPLC). Capillary electrochromatography exhibits advantages of both techniques. In Chapter 2, monolithic capillary column are fabricated using in situ photoinitiation polymerization method. The column was then applied for the separation of six antidepressant compounds. Meanwhile, a simple chiral separation method is developed and presented in Chapter 3. Beta cycodextrin was utilized to achieve the goal of chiral separation. Not only twelve cathinone analytes were separated, but also isomers of several analytes were enantiomerically separated. To better understand the molecular information on the analytes, the TOF-MS system was coupled with the CE. A sheath liquid and a partial filling technique (PFT) were employed to reduce the contamination of MS ionization source. Accurate molecular information was obtained. It is necessary to propose, develop, and optimize new techniques that are suitable for trace-level analysis of samples in forensic, pharmaceutical, and environmental applications. Capillary electrophoresis (CE) was selected for this task, as it requires lower amounts of samples, it simplifies sample preparation, and it has the flexibility to perform separations of neutral and charged molecules as well as enantiomers. Overall, the study demonstrates the versatility of capillary electrophoresis methods in forensic, pharmaceutical, and environmental applications.

Development and Verification of Control and Protection Strategies in Hybrid AC/DC Power Systems for Smart Grid Applications

Modern power networks incorporate communications and information technology infrastructure into the electrical power system to create a smart grid in terms of control and operation. The smart grid enables real-time communication and control between consumers and utility companies allowing suppliers to optimize energy usage based on price preference and system technical issues. The smart grid design aims to provide overall power system monitoring, create protection and control strategies to maintain system performance, stability and security. This dissertation contributed to the development of a unique and novel smart grid test-bed laboratory with integrated monitoring, protection and control systems. This test-bed was used as a platform to test the smart grid operational ideas developed here. The implementation of this system in the real-time software creates an environment for studying, implementing and verifying novel control and protection schemes developed in this dissertation. Phasor measurement techniques were developed using the available Data Acquisition (DAQ) devices in order to monitor all points in the power system in real time. This provides a practical view of system parameter changes, system abnormal conditions and its stability and security information system. These developments provide valuable measurements for technical power system operators in the energy control centers. Phasor Measurement technology is an excellent solution for improving system planning, operation and energy trading in addition to enabling advanced applications in Wide Area Monitoring, Protection and Control (WAMPAC). Moreover, a virtual protection system was developed and implemented in the smart grid laboratory with integrated functionality for wide area applications. Experiments and procedures were developed in the system in order to detect the system abnormal conditions and apply proper remedies to heal the system. A design for DC microgrid was developed to integrate it to the AC system with appropriate control capability. This system represents realistic hybrid AC/DC microgrids connectivity to the AC side to study the use of such architecture in system operation to help remedy system abnormal conditions. In addition, this dissertation explored the challenges and feasibility of the implementation of real-time system analysis features in order to monitor the system security and stability measures. These indices are measured experimentally during the operation of the developed hybrid AC/DC microgrids. Furthermore, a real-time optimal power flow system was implemented to optimally manage the power sharing between AC generators and DC side resources. A study relating to real-time energy management algorithm in hybrid microgrids was performed to evaluate the effects of using energy storage resources and their use in mitigating heavy load impacts on system stability and operational security.

Switching Patterns and Steady-State Analysis of Grid-Connected and Stand-Alone Single-Stage Boost-Inverters for PV Applications

Renewable or sustainable energy (SE) sources have attracted the attention of many countries because the power generated is environmentally friendly, and the sources are not subject to the instability of price and availability. This dissertation presents new trends in the DC-AC converters (inverters) used in renewable energy sources, particularly for photovoltaic (PV) energy systems. A review of the existing technologies is performed for both single-phase and three-phase systems, and the pros and cons of the best candidates are investigated. In many modern energy conversion systems, a DC voltage, which is provided from a SE source or energy storage device, must be boosted and converted to an AC voltage with a fixed amplitude and frequency. A novel switching pattern based on the concept of the conventional space-vector pulse-width-modulated (SVPWM) technique is developed for single-stage, boost-inverters using the topology of current source inverters (CSI). The six main switching states, and two zeros, with three switches conducting at any given instant in conventional SVPWM techniques are modified herein into three charging states and six discharging states with only two switches conducting at any given instant. The charging states are necessary in order to boost the DC input voltage. It is demonstrated that the CSI topology in conjunction with the developed switching pattern is capable of providing the required residential AC voltage from a low DC voltage of one PV panel at its rated power for both linear and nonlinear loads. In a micro-grid, the active and reactive power control and consequently voltage regulation is one of the main requirements. Therefore, the capability of the single-stage boost-inverter in controlling the active power and providing the reactive power is investigated. It is demonstrated that the injected active and reactive power can be independently controlled through two modulation indices introduced in the proposed switching algorithm. The system is capable of injecting a desirable level of reactive power, while the maximum power point tracking (MPPT) dictates the desirable active power. The developed switching pattern is experimentally verified through a laboratory scaled three-phase 200W boost-inverter for both grid-connected and stand-alone cases and the results are presented.

Lightweight Middleware for Software Defined Radio (SDR) Inter-Components Communication

The ability to use Software Defined Radio (SDR) in the civilian mobile applications will make it possible for the next generation of mobile devices to handle multi-standard personal wireless devices and ubiquitous wireless devices. The original military standard created many beneficial characteristics for SDR, but resulted in a number of disadvantages as well. Many challenges in commercializing SDR are still the subject of interest in the software radio research community. Four main issues that have been already addressed are performance, size, weight, and power. This investigation presents an in-depth study of SDR inter-components communications in terms of total link delay related to the number of components and packet sizes in systems based on Software Communication Architecture (SCA). The study is based on the investigation of the controlled environment platform. Results suggest that the total link delay does not linearly increase with the number of components and the packet sizes. The closed form expression of the delay was modeled using a logistic function in terms of the number of components and packet sizes. The model performed well when the number of components was large. Based upon the mobility applications, energy consumption has become one of the most crucial limitations. SDR will not only provide flexibility of multi-protocol support, but this desirable feature will also bring a choice of mobile protocols. Having such a variety of choices available creates a problem in the selection of the most appropriate protocol to transmit. An investigation in a real-time algorithm to optimize energy efficiency was also performed. Communication energy models were used including switching estimation to develop a waveform selection algorithm. Simulations were performed to validate the concept.

Leakage Temperature Dependency Aware Real-Time Scheduling for Power and Thermal Optimization

Catering to society’s demand for high performance computing, billions of transistors are now integrated on IC chips to deliver unprecedented performances. With increasing transistor density, the power consumption/density is growing exponentially. The increasing power consumption directly translates to the high chip temperature, which not only raises the packaging/cooling costs, but also degrades the performance/reliability and life span of the computing systems. Moreover, high chip temperature also greatly increases the leakage power consumption, which is becoming more and more significant with the continuous scaling of the transistor size. As the semiconductor industry continues to evolve, power and thermal challenges have become the most critical challenges in the design of new generations of computing systems. In this dissertation, we addressed the power/thermal issues from the system-level perspective. Specifically, we sought to employ real-time scheduling methods to optimize the power/thermal efficiency of the real-time computing systems, with leakage/ temperature dependency taken into consideration. In our research, we first explored the fundamental principles on how to employ dynamic voltage scaling (DVS) techniques to reduce the peak operating temperature when running a real-time application on a single core platform. We further proposed a novel real-time scheduling method, “M-Oscillations” to reduce the peak temperature when scheduling a hard real-time periodic task set. We also developed three checking methods to guarantee the feasibility of a periodic real-time schedule under peak temperature constraint. We further extended our research from single core platform to multi-core platform. We investigated the energy estimation problem on the multi-core platforms and developed a light weight and accurate method to calculate the energy consumption for a given voltage schedule on a multi-core platform. Finally, we concluded the dissertation with elaborated discussions of future extensions of our research.

Development of Safety Performance Functions for SafetyAnalyst Applications in Florida

In 2010, the American Association of State Highway and Transportation Officials (AASHTO) released a safety analysis software system known as SafetyAnalyst. SafetyAnalyst implements the empirical Bayes (EB) method, which requires the use of Safety Performance Functions (SPFs). The system is equipped with a set of national default SPFs, and the software calibrates the default SPFs to represent the agency’s safety performance. However, it is recommended that agencies generate agency-specific SPFs whenever possible. Many investigators support the view that the agency-specific SPFs represent the agency data better than the national default SPFs calibrated to agency data. Furthermore, it is believed that the crash trends in Florida are different from the states whose data were used to develop the national default SPFs. In this dissertation, Florida-specific SPFs were developed using the 2008 Roadway Characteristics Inventory (RCI) data and crash and traffic data from 2007-2010 for both total and fatal and injury (FI) crashes. The data were randomly divided into two sets, one for calibration (70% of the data) and another for validation (30% of the data). The negative binomial (NB) model was used to develop the Florida-specific SPFs for each of the subtypes of roadway segments, intersections and ramps, using the calibration data. Statistical goodness-of-fit tests were performed on the calibrated models, which were then validated using the validation data set. The results were compared in order to assess the transferability of the Florida-specific SPF models. The default SafetyAnalyst SPFs were calibrated to Florida data by adjusting the national default SPFs with local calibration factors. The performance of the Florida-specific SPFs and SafetyAnalyst default SPFs calibrated to Florida data were then compared using a number of methods, including visual plots and statistical goodness-of-fit tests. The plots of SPFs against the observed crash data were used to compare the prediction performance of the two models. Three goodness-of-fit tests, represented by the mean absolute deviance (MAD), the mean square prediction error (MSPE), and Freeman-Tukey R2 (R2FT), were also used for comparison in order to identify the better-fitting model. The results showed that Florida-specific SPFs yielded better prediction performance than the national default SPFs calibrated to Florida data. The performance of Florida-specific SPFs was further compared with that of the full SPFs, which include both traffic and geometric variables, in two major applications of SPFs, i.e., crash prediction and identification of high crash locations. The results showed that both SPF models yielded very similar performance in both applications. These empirical results support the use of the flow-only SPF models adopted in SafetyAnalyst, which require much less effort to develop compared to full SPFs.

The Eliana Rivero Research Scholarship in Cuban Studies- Call for Applications from Graduate Students

This flyer promotes a call for applications from Graduate Students for the Eliana Rivero Research Scholarship in Cuban Studies. The scholarship provides one graduate student the opportunity to conduct research in Cuban studies- with special emphasis in the humanities- at the Cuban Research Institute. The deadline for applications is February 15, 2016.

An Assessment of Novel Biodegradable Magnesium Alloys for Endovascular Biomaterial Applications

Magnesium alloys have been widely explored as potential biomaterials, but several limitations to using these materials have prevented their widespread use, such as uncontrollable degradation kinetics which alter their mechanical properties. In an attempt to further the applicability of magnesium and its alloys for biomedical purposes, two novel magnesium alloys Mg-Zn-Cu and Mg-Zn-Se were developed with the expectation of improving upon the unfavorable qualities shown by similar magnesium based materials that have previously been explored. The overall performance of these novel magnesium alloys has been assessesed in three distinct phases of research: 1) analysing the mechanical properties of the as-cast magnesium alloys, 2) evaluating the biocompatibility of the as-cast magnesium alloys through the use of in-vitro cellular studies, and 3) profiling the degradation kinetics of the as-cast magnesium alloys through the use of electrochemical potentiodynamic polarization techqnique as well as gravimetric weight-loss methods. As compared to currently available shape memory alloys and degradable as-cast alloys, these experimental alloys possess superior as-cast mechanical properties with elongation at failure values of 12% and 13% for the Mg-Zn-Se and Mg-Zn-Se alloys, respectively. This is substantially higher than other as-cast magnesium alloys that have elongation at failure values that range from 7-10%. Biocompatibility tests revealed that both the Mg-Zn-Se and Mg-Zn-Cu alloys exhibit low cytotoxicity levels which are suitable for biomaterial applications. Gravimetric and electrochemical testing was indicative of the weight loss and initial corrosion behavior of the alloys once immersed within a simulated body fluid. The development of these novel as-cast magnesium alloys provide an advancement to the field of degradable metallic materials, while experimental results indicate their potential as cost-effective medical devices.

Electrochemical Immunosensing of Cortisol in an Automated Microfluidic System Towards Point-of-Care Applications

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.

Enhanced 3-Dimensional Carbon Nanotube Based Anodes for Li-ion Battery Applications

A prototype 3-dimensional (3D) anode, based on multiwall carbon nanotubes (MWCNTs), for Li-ion batteries (LIBs), with potential use in Electric Vehicles (EVs) was investigated. The unique 3D design of the anode allowed much higher areal mass density of MWCNTs as active materials, resulting in more amount of Li+ ion intake, compared to that of a conventional 2D counterpart. Furthermore, 3D amorphous Si/MWCNTs hybrid structure offered enhancement in electrochemical response (specific capacity 549 mAhg-1). Also, an anode stack was fabricated to further increase the areal or volumetric mass density of MWCNTs. An areal mass density of the anode stack 34.9 mg/cm2 was attained, which is 1,342% higher than the value for a single layer 2.6 mg/cm2. Furthermore, the binder-assisted and hot-pressed anode stack yielded the average reversible, stable gravimetric and volumetric specific capacities of 213 mAhg-1 and 265 mAh/cm3, respectively (at 0.5C). Moreover, a large-scale patterned novel flexible 3D MWCNTs-graphene-polyethylene terephthalate (PET) anode structure was prepared. It generated a reversible specific capacity of 153 mAhg-1 at 0.17C and cycling stability of 130 mAhg-1 up to 50 cycles at 1.7C.

Bionano Electronics: Magneto-Electric Nanoparticles for Drug Delivery, Brain Stimulation and Imaging Applications

Nanoparticles are often considered as efficient drug delivery vehicles for precisely dispensing the therapeutic payloads specifically to the diseased sites in the patient’s body, thereby minimizing the toxic side effects of the payloads on the healthy tissue. However, the fundamental physics that underlies the nanoparticles’ intrinsic interaction with the surrounding cells is inadequately elucidated. The ability of the nanoparticles to precisely control the release of its payloads externally (on-demand) without depending on the physiological conditions of the target sites has the potential to enable patient- and disease-specific nanomedicine, also known as Personalized NanoMedicine (PNM). In this dissertation, magneto-electric nanoparticles (MENs) were utilized for the first time to enable important functions, such as (i) field-controlled high-efficacy dissipation-free targeted drug delivery system and on-demand release at the sub-cellular level, (ii) non-invasive energy-efficient stimulation of deep brain tissue at body temperature, and (iii) a high-sensitivity contrasting agent to map the neuronal activity in the brain non-invasively. First, this dissertation specifically focuses on using MENs as energy-efficient and dissipation-free field-controlled nano-vehicle for targeted delivery and on-demand release of a anti-cancer Paclitaxel (Taxol) drug and a anti-HIV AZT 5’-triphosphate (AZTTP) drug from 30-nm MENs (CoFe2O4-BaTiO3) by applying low-energy DC and low-frequency (below 1000 Hz) AC fields to separate the functions of delivery and release, respectively. Second, this dissertation focuses on the use of MENs to non-invasively stimulate the deep brain neuronal activity via application of a low energy and low frequency external magnetic field to activate intrinsic electric dipoles at the cellular level through numerical simulations. Third, this dissertation describes the use of MENs to track the neuronal activities in the brain (non-invasively) using a magnetic resonance and a magnetic nanoparticle imaging by monitoring the changes in the magnetization of the MENs surrounding the neuronal tissue under different states. The potential therapeutic and diagnostic impact of this innovative and novel study is highly significant not only in HIV-AIDS, Cancer, Parkinson’s and Alzheimer’s disease but also in many CNS and other diseases, where the ability to remotely control targeted drug delivery/release, and diagnostics is the key.

Ensemble Stream Model for Data-Cleaning in Sensor Networks

Ensemble Stream Modeling and Data-cleaning are sensor information processing systems have different training and testing methods by which their goals are cross-validated. This research examines a mechanism, which seeks to extract novel patterns by generating ensembles from data. The main goal of label-less stream processing is to process the sensed events to eliminate the noises that are uncorrelated, and choose the most likely model without over fitting thus obtaining higher model confidence. Higher quality streams can be realized by combining many short streams into an ensemble which has the desired quality. The framework for the investigation is an existing data mining tool. First, to accommodate feature extraction such as a bush or natural forest-fire event we make an assumption of the burnt area (BA*), sensed ground truth as our target variable obtained from logs. Even though this is an obvious model choice the results are disappointing. The reasons for this are two: One, the histogram of fire activity is highly skewed. Two, the measured sensor parameters are highly correlated. Since using non descriptive features does not yield good results, we resort to temporal features. By doing so we carefully eliminate the averaging effects; the resulting histogram is more satisfactory and conceptual knowledge is learned from sensor streams. Second is the process of feature induction by cross-validating attributes with single or multi-target variables to minimize training error. We use F-measure score, which combines precision and accuracy to determine the false alarm rate of fire events. The multi-target data-cleaning trees use information purity of the target leaf-nodes to learn higher order features. A sensitive variance measure such as f-test is performed during each node’s split to select the best attribute. Ensemble stream model approach proved to improve when using complicated features with a simpler tree classifier. The ensemble framework for data-cleaning and the enhancements to quantify quality of fitness (30% spatial, 10% temporal, and 90% mobility reduction) of sensor led to the formation of streams for sensor-enabled applications. Which further motivates the novelty of stream quality labeling and its importance in solving vast amounts of real-time mobile streams generated today.

Trajectory Privacy Preservation in Mobile Wireless Sensor Networks

In recent years, there has been an enormous growth of location-aware devices, such as GPS embedded cell phones, mobile sensors and radio-frequency identification tags. The age of combining sensing, processing and communication in one device, gives rise to a vast number of applications leading to endless possibilities and a realization of mobile Wireless Sensor Network (mWSN) applications. As computing, sensing and communication become more ubiquitous, trajectory privacy becomes a critical piece of information and an important factor for commercial success. While on the move, sensor nodes continuously transmit data streams of sensed values and spatiotemporal information, known as ``trajectory information". If adversaries can intercept this information, they can monitor the trajectory path and capture the location of the source node. This research stems from the recognition that the wide applicability of mWSNs will remain elusive unless a trajectory privacy preservation mechanism is developed. The outcome seeks to lay a firm foundation in the field of trajectory privacy preservation in mWSNs against external and internal trajectory privacy attacks. First, to prevent external attacks, we particularly investigated a context-based trajectory privacy-aware routing protocol to prevent the eavesdropping attack. Traditional shortest-path oriented routing algorithms give adversaries the possibility to locate the target node in a certain area. We designed the novel privacy-aware routing phase and utilized the trajectory dissimilarity between mobile nodes to mislead adversaries about the location where the message started its journey. Second, to detect internal attacks, we developed a software-based attestation solution to detect compromised nodes. We created the dynamic attestation node chain among neighboring nodes to examine the memory checksum of suspicious nodes. The computation time for memory traversal had been improved compared to the previous work. Finally, we revisited the trust issue in trajectory privacy preservation mechanism designs. We used Bayesian game theory to model and analyze cooperative, selfish and malicious nodes' behaviors in trajectory privacy preservation activities.