Application of excitation emission matrix fluorescence monitoring in the assessment of spatial and seasonal drivers of dissolved organic matter composition: Sources and physical disturbance controls

The environmental dynamics of dissolved organic matter (DOM) were characterized for a shallow, subtropical, seagrass-dominated estuarine bay, namely Florida Bay, USA. Large spatial and seasonal variations in DOM quantity and quality were assessed using dissolved organic C (DOC) measurements and spectrophotometric properties including excitation emission matrix (EEM) fluorescence with parallel factor analysis (PARAFAC). Surface water samples were collected monthly for 2 years across the bay. DOM characteristics were statistically different across the bay, and the bay was spatially characterized into four basins based on chemical characteristics of DOM as determined by EEM-PARAFAC. Differences between zones were explained based on hydrology, geomorphology, and primary productivity of the local seagrass community. In addition, potential disturbance effects from a very active hurricane season were identified. Although the overall seasonal patterns of DOM variations were not significantly affected on a bay-wide scale by this disturbance, enhanced freshwater delivery and associated P and DOM inputs (both quantity and quality) were suggested as potential drivers for the appearance of algal blooms in high impact areas. The application of EEM-PARAFAC proved to be ideally suited for studies requiring high sample throughput methods to assess spatial and temporal ecological drivers and to determine disturbance-induced impacts in aquatic ecosystems.

Positron emission tomography (PET) tumor segmentation and quantification: Development of new algorithms

Tumor functional volume (FV) and its mean activity concentration (mAC) are the quantities derived from positron emission tomography (PET). These quantities are used for estimating radiation dose for a therapy, evaluating the progression of a disease and also use it as a prognostic indicator for predicting outcome. PET images have low resolution, high noise and affected by partial volume effect (PVE). Manually segmenting each tumor is very cumbersome and very hard to reproduce. To solve the above problem I developed an algorithm, called iterative deconvolution thresholding segmentation (IDTS) algorithm; the algorithm segment the tumor, measures the FV, correct for the PVE and calculates mAC. The algorithm corrects for the PVE without the need to estimate camera's point spread function (PSF); also does not require optimizing for a specific camera. My algorithm was tested in physical phantom studies, where hollow spheres (0.5-16 ml) were used to represent tumors with a homogeneous activity distribution. It was also tested on irregular shaped tumors with a heterogeneous activity profile which were acquired using physical and simulated phantom. The physical phantom studies were performed with different signal to background ratios (SBR) and with different acquisition times (1-5 min). The algorithm was applied on ten clinical data where the results were compared with manual segmentation and fixed percentage thresholding method called T50 and T60 in which 50% and 60% of the maximum intensity respectively is used as threshold. The average error in FV and mAC calculation was 30% and -35% for 0.5 ml tumor. The average error FV and mAC calculation were ~5% for 16 ml tumor. The overall FV error was ∼10% for heterogeneous tumors in physical and simulated phantom data. The FV and mAC error for clinical image compared to manual segmentation was around -17% and 15% respectively. In summary my algorithm has potential to be applied on data acquired from different cameras as its not dependent on knowing the camera's PSF. The algorithm can also improve dose estimation and treatment planning.^

Synthesis and electron emission properties of aligned carbon nanotube arrays

Carbon nanotubes (CNTs) have become one of the most interesting allotropes of carbon due to their intriguing mechanical, electrical, thermal and optical properties. The synthesis and electron emission properties of CNT arrays have been investigated in this work. Vertically aligned CNTs of different densities were synthesized on copper substrate with catalyst dots patterned by nanosphere lithography. The CNTs synthesized with catalyst dots patterned by spheres of 500 nm diameter exhibited the best electron emission properties with the lowest turn-on/threshold electric fields and the highest field enhancement factor. Furthermore, CNTs were treated with NH3 plasma for various durations and the optimum enhancement was obtained for a plasma treatment of 1.0 min. CNT point emitters were also synthesized on a flat-tip or a sharp-tip to understand the effect of emitter geometry on the electron emission. The experimental results show that electron emission can be enhanced by decreasing the screening effect of the electric field by neighboring CNTs. In another part of the dissertation, vertically aligned CNTs were synthesized on stainless steel (SS) substrates with and without chemical etching or catalyst deposition. The density and length of CNTs were determined by synthesis time. For a prolonged growth time, the catalyst activity terminated and the plasma started etching CNTs destructively. CNTs with uniform diameter and length were synthesized on SS substrates subjected to chemical etching for a period of 40 minutes before the growth. The direct contact of CNTs with stainless steel allowed for the better field emission performance of CNTs synthesized on pristine SS as compared to the CNTs synthesized on Ni/Cr coated SS. Finally, fabrication of large arrays of free-standing vertically aligned CNT/SnO2 core-shell structures was explored by using a simple wet-chemical route. The structure of the SnO2 nanoparticles was studied by X-ray diffraction and electron microscopy. Transmission electron microscopy reveals that a uniform layer of SnO2 is conformally coated on every tapered CNT. The strong adhesion of CNTs with SS guaranteed the formation of the core-shell structures of CNTs with SnO2 or other metal oxides, which are expected to have applications in chemical sensors and lithium ion batteries.

Enhancement of field emission from multistage structure of carbon nanotube arrays

The field emission measurements for the multistage structured nanotubes (i.e., thin-multiwall and single wall carbon nanotubes grown on multiwall carbon nanotubes) were carried out and a low turn-on field of ~0.45 V/ μm, high emission current of 450 μA at a field of IV/μm and a large field enhancement factor of ~26200 were obtained. The thin multiwall carbon nanotubes (thin-MWNTs) and single wall carbon nanotubes (SWNTs) were grown on the regular arrays of vertically aligned multi wall carbon nanotubes (MWNTs) on porous silicon substrate by Chemical Vapor Deposition (CVD) method. The thin-MWNTs and SWNTs grown on MWNTs in this way have a multistage structure which gives higher enhancement of the electric field and hence the electron field emission.

Design and Synthesis of 4-N-Alkanoyl and 4-N-Alkyl Gemcitabine Analogues Suitable for Positron Emission Tomography

Gemcitabine is a highly potent chemotherapeutic nucleoside agent used in the treatment of several cancers and solid tumors. However, it is therapeutically limitated because of toxicity to normal cells and its rapid intracellular deamination by cytidine deaminase into the inactive uracil derivative. Modification at the 4-(N) position of gemcitabine's exocyclic amine to an -amide functionality is a well reported prodrug strategy which has been that confers a resistance to intracellular deamination while also altering pharmacokinetics of the parent drug. Coupling of gemcitabine to carboxylic acids with varying terminal moieties afforded the 4-N-alkanoylgemcitabines whereas reaction of 4-N-tosylgemcitabine with the corresponding alkyl amines gave the 4-N-alkylgemcitabines. The 4-N-alkanoyl and 4-N-alkyl gemcitabine analogues with a terminal hydroxyl group on the 4-N-alkanoyl or 4-N-alkyl chain were efficiently fluorinated either with diethylaminosulfur trifluoride or under conditions that are compatible with the synthetic protocols for 18F labeling, such as displacement of the corresponding mesylate with KF/Kryptofix 2.2.2. The 4-N-alkanoylgemcitabine analogues displayed potent cytostatic activities against murine and human tumor cell lines with 50% inhibitory concentration (IC50) values in the range of low nM, whereas cytotoxicity of the 4-N-alkylgemcitabine derivatives were in the low to modest µM range. The cytostatic activity of the 4-N-alkanoylgemcitabines was reduced by several orders of magnitude in the 2'-deoxycytidine kinase (dCK)-deficient CEM/dCK- cell line while the 4-N-alkylgemcitabines were only lowered by 2-5 times. None of the 4-N-modified gemcitabines were found to be substrates for cytosolic dCK, however all were found to inhibit DNA synthesis. As such, the 4-N-alkanoyl gemcitabine derivatives likely need to be converted to gemcitabine prior to achieving their significant cytostatic potential, whereas the 4-N-alkylgemcitabines reach their modest activity without "measurable" conversion to gemcitabine. Thus, the 4-N-alkylgemcitabines provide valuable insight on the metabolism of 4-N-modified gemcitabine prodrugs.

Positron Emission Tomography (PET) Tumor Segmentation and Quantification: Development of New Algorithms

Tumor functional volume (FV) and its mean activity concentration (mAC) are the quantities derived from positron emission tomography (PET). These quantities are used for estimating radiation dose for a therapy, evaluating the progression of a disease and also use it as a prognostic indicator for predicting outcome. PET images have low resolution, high noise and affected by partial volume effect (PVE). Manually segmenting each tumor is very cumbersome and very hard to reproduce. To solve the above problem I developed an algorithm, called iterative deconvolution thresholding segmentation (IDTS) algorithm; the algorithm segment the tumor, measures the FV, correct for the PVE and calculates mAC. The algorithm corrects for the PVE without the need to estimate camera’s point spread function (PSF); also does not require optimizing for a specific camera. My algorithm was tested in physical phantom studies, where hollow spheres (0.5-16 ml) were used to represent tumors with a homogeneous activity distribution. It was also tested on irregular shaped tumors with a heterogeneous activity profile which were acquired using physical and simulated phantom. The physical phantom studies were performed with different signal to background ratios (SBR) and with different acquisition times (1-5 min). The algorithm was applied on ten clinical data where the results were compared with manual segmentation and fixed percentage thresholding method called T50 and T60 in which 50% and 60% of the maximum intensity respectively is used as threshold. The average error in FV and mAC calculation was 30% and -35% for 0.5 ml tumor. The average error FV and mAC calculation were ~5% for 16 ml tumor. The overall FV error was ~10% for heterogeneous tumors in physical and simulated phantom data. The FV and mAC error for clinical image compared to manual segmentation was around -17% and 15% respectively. In summary my algorithm has potential to be applied on data acquired from different cameras as its not dependent on knowing the camera’s PSF. The algorithm can also improve dose estimation and treatment planning.

Effect of Pavement-Vehicle Interaction on Highway Fuel Consumption and Emission

Vehicle fuel consumption and emission are two important effectiveness measurements of sustainable transportation development. Pavement plays an essential role in goals of fuel economy improvement and greenhouse gas (GHG) emission reduction. The main objective of this dissertation study is to experimentally investigate the effect of pavement-vehicle interaction (PVI) on vehicle fuel consumption under highway driving conditions. The goal is to provide a better understanding on the role of pavement in the green transportation initiates. Four study phases are carried out. The first phase involves a preliminary field investigation to detect the fuel consumption differences between paired flexible-rigid pavement sections with repeat measurements. The second phase continues the field investigation by a more detailed and comprehensive experimental design and independently investigates the effect of pavement type on vehicle fuel consumption. The third study phase calibrates the HDM-IV fuel consumption model with data collected in the second field phase. The purpose is to understand how pavement deflection affects vehicle fuel consumption from a mechanistic approach. The last phase applies the calibrated HDM-IV model to Florida’s interstate network and estimates the total annual fuel consumption and CO2 emissions on different scenarios. The potential annual fuel savings and emission reductions are derived based on the estimation results. Statistical results from the two field studies both show fuel savings on rigid pavement compared to flexible pavement with the test conditions specified. The savings derived from the first phase are 2.50% for the passenger car at 112km/h, and 4.04% for 18-wheel tractor-trailer at 93km/h. The savings resulted from the second phase are 2.25% and 2.22% for passenger car at 93km/h and 112km/h, and 3.57% and 3.15% for the 6-wheel medium-duty truck at 89km/h and 105km/h. All savings are statistically significant at 95% Confidence Level (C.L.). From the calibrated HDM-IV model, one unit of pavement deflection (1mm) on flexible pavement can cause an excess fuel consumption by 0.234-0.311 L/100km for the passenger car and by 1.123-1.277 L/100km for the truck. The effect is more evident at lower highway speed than at higher highway speed. From the network level estimation, approximately 40 million gallons of fuel (combined gasoline and diesel) and 0.39 million tons of CO2 emission can be saved/reduced annually if all Florida’s interstate flexible pavement are converted to rigid pavement with the same roughness levels. Moreover, each 1-mile of flexible-rigid conversion can result in a reduction of 29 thousand gallons of fuel and 258 tons of CO2 emission yearly.