The chemical characterization of individual nanoparticles (NPs) </= 100 nm in diameter is one of the current frontiers in analytical chemistry. We present here, a methodology for the characterization of individual NPs by obtaining molecular information from single massive cluster impacts. The clusters used in this secondary ion mass spectrometry (SIMS) technique are Au4004+ and C60+. The ionized ejecta from each impact are recorded individually which allows to identify ions emitted from a surface volume of ~10 nm in diameter and 5-10 nm in depth. The mode of analyzing ejecta individually from each single cluster impact gives insight into surface homogeneity, in our case NPs and their immediate surroundings. We show that when the NPs (50 nm Al) are larger than the size of the volume perturbed by the projectile, the secondary ion emission (SI) resembles that of a bulk surface. However, when the NP (5 nm Ag) is of the size range of the volume perturbed by projectile the SI emission is different from that of a bulk surface. As part of this sub-assay volume study, the influence of neighboring NP on the SI emission was examined by using a mixture of different types of NPs (5 nm Au and 5 nm Ag). The methodology of using cluster SIMS via a sequence of stochastic single impacts yield information on the surface coverage of the NPs, as well as the influence of the chemical environment on the type of SI emission. We also present a case of soft landing NPs for laser desorption ionization mass spectrometry. NPs enhance the SI emission in a manner that maintains the integrity of the spatial distribution of molecular species. The results indicate that the application can be extended to imaging mass spectrometry.

Threat management of water distribution systems is essential for protecting consumers. In a contamination event, different strategies may be implemented to protect public health, including flushing the system through opening hydrants or isolating the contaminant by manipulating valves. To select the most effective options for responding to a contamination threat, the location and loading profile of the source of the contaminant should be considered. These characteristics can be identified by utilizing water quality data from sensors that have been strategically placed in a water distribution system. A simulation-optimization approach is described here to solve the inverse problem of source characterization, by coupling an evolutionary computation-based search with a water distribution system model. The solution of this problem may reveal, however, that a set of non-unique sources exists, where sources with significantly different locations and loading patterns produce similar concentration profiles at sensors. The problem of non-uniqueness should be addressed to prevent the misidentification of a contaminant source and improve response planning. This paper aims to address the problem of non-uniqueness through the use of Niched Co-Evolution Strategies (NCES). NCES is an evolutionary algorithm designed to identify a specified number of alternative solutions that are maximally different in their decision vectors, which are source characteristics for the water distribution problem. NCES is applied to determine the extent of non-uniqueness in source characterization for a virtual city, Mesopolis, with a population of approximately 150,000 residents. Results indicate that NCES successfully identifies non-uniqueness in source characterization and provides alternative sources of contamination. The solutions found by NCES assist in making decisions about response actions. Once alternative sources are identified, each source can be modeled to determine where the vulnerable areas of the system are, indicating the areas where response actions should be implemented.

Ni-Mn-Ga is a ferromagnetic shape memory alloy that can be used for future sensors and actuators. It has been shown that magnetic field can induce phase transformation and consequently large strain in stoichiometric Ni2MnGa. Since then considerable progress has been made in understanding the underlying science of shape memory and ferromagnetic shape memory in bulk materials. Ni-Mn-Ga thin films, however is a relatively under explored area. Ferromagnetic shape memory alloy thin films are conceived as the future MEMS sensor and actuator materials. With a 9.5 percent strain rate reported from magnetic reorientation, Ni-Mn-Ga thin films hold great promise as actuator materials. Thin films come with a number of advantages and challenges as compared to their bulk counterparts. While properties like mechanical strength, uniformity are much better in thin film form, high stress and constraint from the substrate pose a significant challenge for reorientation and shape memory behavior. In either case, it is very important to understand their behavior and examine their properties. This thesis is an effort to contribute to the literature of Ni-Mn-Ga thin films as ferromagnetic shape memory alloys. The focus of this project is to develop a recipe for fabricating NiMnGa thin films with desired composition and microstructure and hence unique properties for future MEMS actuator materials and characterize their properties to aid better understanding of their behavior. In this project NiMnGa thin films have been fabricated using magnetron sputtering on a variety of substrates. Magnetron sputtering technique allows us to tailor the composition of films which is crucial for controlling the phase transformation properties of NiMnGa films. The composition is tailored by varying several deposition parameters. Microstructure of the films has been investigated by X-ray diffraction (XRD) and transmission electron microscopy (TEM) techniques. Mechanical properties of as-deposited films have been probed using nano-indentation technique. The chemistry of sputtered films is determined quantitatively by wavelength dispersive X-ray spectroscopy (WDS). Phase transformation is studied by using a combination of differential scanning calorimetry (DSC), in-situ heating in TEM and in-situ XRD instruments. Magnetic properties of films are examined using superconducting quantum interface device (SQUID).

Zeolite beta has been synthesized using hydrothermal methods. In order to synthesize zeolite beta an aqueous gel having a molar batch composition of 2.2 Na2O&amp / #8729 / Al2O3&amp / #8729 / x SiO2&amp / #8729 / 4.6 (TEA)2O&amp / #8729 / 444 H2O was utilized. The synthesis parameters were SiO2/Al2O3 ratio (20 &amp / #8804 / x &amp / #8804 / 50) and crystallization time (6 &amp / #8804 / t &amp / #8804 / 16 days). Pure zeolite beta was crystallized from the experiments which were performed with the batch composition having SiO2/Al2O3 of 20 and 30 in 6 to 16 days period. For SiO2/Al2O3 of 20 and 30, the highest yield was obtained for 12 days. Therefore, the rest of the experiments, in which SiO2/Al2O3 was 40 and 50, were carried out keeping the synthesis time constant (12 days). Pure zeolite beta was also synthesized for SiO2/Al2O3 of 40 and 50. The highest yield and the most crystalline zeolite beta sample were obtained from the experiment performed at SiO2/Al2O3 of 50 with a synthesis time of 12 days. The morphology and crystal size of the zeolite beta samples were identified by using scanning electron microscope (SEM). It was observed that, zeolite beta samples had spheroidal morphology with the crystal size of about 0.5 &amp / #956 / m. According to the thermogravimetric analyses (TGA), it was found that template molecules and moisture constituted nearly 18 % by weight of the zeolite beta samples. The surface area of the calcined zeolite beta sample was determined by N2 adsorption and was found to be 488 m2/g. Gravimetric sorption analyses yield that, the limiting sorption capacity of Na-Beta for methanol, ethanol, isopropanol and n-butanol at 0&deg / C was about the same with a value of 0.25 cm3/g. For o-xylene, m-xylene and p-xylene that value was 0.21 cm3/g, 0.22 cm3/g and 0.24 cm3/g, respectively.

In this thesis, first we replace the condition X ¡Ø y in Huang and Su (2000) by X ¡Ù y and give necessary and sufficient conditions such that there exists a random variable X satisfying that E(g(X)| X ¡Ø y)=h(y) f(y )/ F(y), " y &#x00CE; CX, where CX is the support of X.Next, we investigate necessary and sufficient conditions such that h(y)=E(g(X) | X ¡Ø y ), for a given function h and extend these results to bivariate case.

This thesis presents the development of a novel atomic force microscope (AFM) based nanofabrication technique termed as thermochemical nanolithography (TCNL). TCNL uses a resistively heated AFM cantilever to thermally activate chemical reactions on a surface with nanometer resolution. This technique can be used for fabrication of functional nanostructures that are appealing for various applications in nanofluidics, nanoelectronics, nanophotonics, and biosensing devices. This thesis research is focused on three main objectives. The first objective is to study the fundamentals of TCNL writing aspects. We have conducted a systematic study of the heat transfer mechanism using finite element analysis modeling, Raman spectroscopy, and local glass transition measurement. In addition, based on thermal kinetics analysis, we have identified several key factors to achieve high resolution fabrication of nanostructures during the TCNL writing process. The second objective is to demonstrate the use of TCNL on a variety of systems and thermochemical reactions. We show that TCNL can be employed to (1) modify the wettability of a polymer surface at the nanoscale, (2) fabricate nanoscale templates on polymer films for assembling nano-objects, such as proteins and DNA, (3) fabricate conjugated polymer semiconducting nanowires, and (4) reduce graphene oxide with nanometer resolution. The last objective is to characterize the TCNL nanostructures using AFM based methods, such as friction force microscopy, phase imaging, electric force microscopy, and conductive AFM. We show that they are useful for in situ characterization of nanostructures, which is particularly challenging for conventional macroscopic analytical tools, such as Raman spectroscopy, IR spectroscopy, and fluorescence microscopy.

An advancement of the two stage growth recipe for the fabrication of CIGS solar cells was developed. The developed advancement was inconsistent in producing samples of similar stoichiometry. This was a huge barrier for up scaling the process as the behavior of devices would be different due to variation in stoichiometry. Samples with reproducible stoichiometry were obtained once the heating rate of elements, selenium in particular was better understood. This is mainly attributed to the exponential increase of selenium flux after its evaporation temperature. Monitoring the selenium flux was vital in getting constant selenium fluxes. Few changes to the growth recipe were induced to optimize the amount of selenium being used. Depositions were done using constant selenium to metal flux ratio of 5. Elemental tradeoffs were observed as a result of the growth recipe change. These tradeoffs are in favor of the two stage growth recipe. The solar cells were fabricated on a soda lime glass substrate with a molybdenum back contact. Improper sample cleaning and storage were found to affect the deposition outcome of the molybdenum back contact. This also had a cascading effect on the absorber layer. Residual precipitates during deposition of CdS were avoided by increasing the spinner speed which increased the reaction rate. This is attributed to the growth of CdS either by cluster-by-cluster growth or by ion-by-ion growth. SEM, EDS were some important tools used to characterize the devices. EDS in particular, was used extensively at different stages throughout the growth process to ensure that we were heading in the right direction. Current-voltage (I-V) measurements were done to study the solar cell performance under light and dark.

East Texas field, a giant U.S. oil-field, produced 5.42 billion stock-tank barrels from discovery in 1930 through mid-2007. The lower part of the siliciclastic Upper Cretaceous Woodbine Group is reservoir rock, and almost all production comes from the upper unit, the operator-termed Main sand. The field could produce 70 million stock-tank barrels (MMSTB) using current strategies, whereas 410 MMSTB of remaining reserves from the Stringer zone (lower unit), along with bypassed pay in both units and unswept oil, is possible. These favorable statistics have increased interest in reservoir characterization of the Woodbine, especially the Stringer zone. This study delineates sandstone geometry and interprets reservoir facies and heterogeneity of the Stringer zone and Main sand in northeast East Texas field. Additional objectives are to define key chronostratigraphic surfaces, such as flooding surfaces and unconformities, and to establish a realistic depositional model for the reservoir succession. To achieve these objectives, well log analysis, core description, and net-sandstone mapping of the Stringer zone and Main sand were conducted. According to sequence-stratigraphic and depositional-system analysis, the Woodbine Group is divided into two genetically unrelated units: (1) the highstand deltaic Stringer zone and (2) the lowstand incised-valley-fill Main sand. Principal reservoir units are Stringer 1 and Stringer 2 sands within the Stringer zone and the Main sand. Stringer 2, best developed in the southwest study area, is the most promising reservoir unit for new production. Well deepening and water-flooding in this more continuous and thicker sand are proposed to increase production in East Texas field. / text

Detection of the emission from the secondary component in a binary system can be extremely challenging, but equally rewarding. In the case of intermediate to high-mass binaries, detection of close companions can inform formation theories. In the extreme low mass-ratio case, where the secondary component is in fact a planet, detection of the emission in high resolution spectroscopy can be used to determine the true planet mass. In this thesis, we describe a technique to detect the thermal emission from the secondary component of a low mass-ratio binary system. We apply this technique to archived observations of early B-type stars using VLT/CRIRES, and simulate future observations of planetary systems with IGRINS, a near-infrared spectrograph being built now. / text

Paleokarst reservoirs consist of complex cave networks, which are formed by various mechanisms and associated collapsed cave facies. Traditionally, cave structures are defined using variogram-based methods in flow models and this description does not precisely represent the reservoir geology. Algorithms based on multiple-point statistics (MPS) are widely used in modeling complex geologic structures. Statistics required for these algorithms are inferred from gridded training images. However, structures like modern cave networks are represented by point data sets. Thus, it is not practical to apply rigid and gridded templates and training images for the simulation of such features. Therefore, a quantitative algorithm to characterize and model paleokarst reservoirs based on physical and geological attributes is needed. In this study, a unique non-gridded MPS analysis and pattern simulation algorithms are developed to infer statistics from modern cave networks and simulate distribution of cave structures in paleokarst reservoirs. Non-gridded MPS technique is practical by eliminating use of grids and gridding procedure, which is challenging to apply on cave network due to its complex structure. Statistics are calculated using commonly available cave networks, which are only represented by central line coordinates sampled along the accessible cave passages. Once the statistics are calibrated, a cave network is simulated by using a pattern simulation algorithm in which the simulation is conditioned to sparse data in the form of locations with cave facies or coordinates of cave structures. To get an accurate model for the spatial extent of the cave facies, an algorithm is also developed to simulate cave zone thickness while simulating the network. The proposed techniques are first implemented to represent connectivity statistics for synthetic data sets, which are used as point-set training images and are analogous to the data typically available for a cave network. Once the applicability of the algorithms is verified, non-gridded MPS analysis and pattern simulation are conducted for the Wind Cave located in South Dakota. The developed algorithms successfully characterize and model cave networks that can only be described by point sets. Subsequently, a cave network system is simulated for the Yates Field in West Texas which is a paleokarst reservoir. Well locations with cave facies and identified cave zone thickness values are used for conditioning the pattern simulation that utilizes the MP-histograms calibrated for Wind Cave. Then, the simulated cave network is implemented into flow simulation models to understand the effects of cave structures on fluid flow. Calibration of flow model against the primary production data is attempted to demonstrate that the pattern simulation algorithm yields detailed description of spatial distribution of cave facies. Moreover, impact of accurately representing network connectivity on flow responses is explored by a water injection case. Fluid flow responses are compared for models with cave networks that are constructed by non-gridded MPS and a traditional modeling workflow using sequential indicator simulation. Applications on the Yates Field show that the cave network and corresponding cave facies are successfully modeled by using the non-gridded MPS. Detailed description of cave facies in the reservoir yields accurate flow simulation results and better future predictions. / text