Aqueous electrolyte solutions at temperature and pressure conditions near the critical point of water are difficult to describe using traditional equations of state based upon the excess Gibbs energy. Models based upon the residual Helmholtz energy have proven more effective. Anderko and Pitzer1 developed a residual Helmholtz energy model (AP) for aqueous electrolyte solutions in which the electrolyte is assumed to be fully associated. The model has been effectively used in describing densities and vapor-liquid equilibria for simple electrolyte systems. The model is less effective for describing enthalpic properties such as heats of dilution. Oscarson and coworkers2, 3 modified the AP model for NaCl solutions by adding a term accounting for the change in Helmholtz energy as a result of aqueous dissociation reactions. This new model, called the RI model, is more accurate than the AP model at conditions where the NaCl dissociates more fully into ions. Liu et. al4, 5 modified the RI model by adding a term to describe interactions between ions in solution and by regressing new model parameter values. This new model, called the RIII model, is more accurate than both the AP model and the RI model and may be used to predict species concentrations in solution as a result of aqueous phase reactions. The RIII model has substantial thermodynamic inconsistencies, however, and is poorly suited for describing mixed solute solutions. This dissertation presents the RIV model which is an electrolyte solution model for solutions in the ranges of 350 °C to 400 °C and 18 MPa to 40 MPa. The RIV model has been applied to aqueous NaCl solutions and aqueous LiCl solutions. The RIV model is a modification of the AP model and includes aqueous phase reactions implicitly through fundamental species interactions. The RIV model is thermodynamically consistent. It is capable of describing densities and heats of dilution. Density predictions from the RIV model are less accurate than the AP model predictions (6.66 % error vs. 3.51 % error) but are reasonable. The heats of dilution predictions from the RIV model are much more accurate than those from the AP model (25.16 % error vs. 78.78 % error). Predictions of the ionic species concentration from the RIV model are likely to be poor as indicated by the poor agreement between experimental values and calculated values of equilibrium constants valid at infinite dilution. In order to provide the necessary data from which to regress the parameters of the RIV model, experimental heat of dilution values were determined using flow calorimetry techniques. These values are also reported in this dissertation.

A database containing Henry's law constants, infinite dilution activity coefficients and solubility data of industrially important chemicals in aqueous systems has been compiled. These properties are important in predicting the fate and transport of chemicals in the environment. The structure of this database is compatible with the existing DIPPR® 801 database and DIADEM interface, and the compounds included are a subset of the compounds found in the DIPPR® 801 database. Thermodynamic relationships, chemical family trends, and predicted values were carefully considered when designating recommended values. Henry's law constants and infinite dilution activity coefficients were measured for toluene, 1-butanol, anisole, 1,2-difluorobenzene, 4-bromotoluene, 1,2,3-trichlorobenzene, and 2,4-dichlorotoluene in water using the inert gas stripping method at ambient pressure (approximately 12.5 psia) and at temperatures between 8°C and 50°C. Fugacity ratios, required to determine infinite dilution activity coefficients for the solid solutes, were calculated from literature values for the heat of fusion and the liquid and solid heat capacities. Chemicals were chosen based on missing or conflicting data from the literature. A first-order temperature-dependent group contribution method was developed to predict Henry's law constants of hydrocarbons, alcohols, ketones, and formats where none of the functional groups are attached directly to a benzene ring. Efforts to expand this method to include ester and ether groups were unsuccessful. Second-order groups were developed at a reference condition of 298.15 K and 100 kPa. A second-order temperature-dependent group contribution method was then developed for hydrocarbons, ketones, esters, ethers, and alcohols. These methods were compared to existing literature prediction methods.

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Actinomyces species play an important role in the pathogenesis of oral diseases and infections. Susceptibility testing is not always routinely performed, and one may oversee a shift in resistance patterns. The aim of the study was to analyze the antimicrobial susceptibility of 100 well-identified clinical oral isolates of Actinomyces spp. against eight selected antimicrobial agents using the agar dilution (AD) and E-Test (ET) methods. We observed no to low resistance against penicillin, ampicillin-sulbactam, meropenem, clindamycin, linezolid and tigecycline (0–2% ET, 0% AD) but high levels of resistance to moxifloxacin (93% ET, 87% AD) and daptomycin (83% ET, 95% AD). The essential agreement of the two methods was very good for benzylpenicillin (EA 95%) and meropenem (EA 92%). The ET method was reliable for correctly categorizing susceptibility, in comparison with the reference method agar dilution, except for daptomycin (categorical agreement 87%). Penicillin is still the first-choice antibiotic for therapy of diseases caused by Actinomyces spp.

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<p>Simulation plays a key role in nuclear reactor safety analysis and being able to assess the accuracy of results obtained by simulation increases their credibility. This thesis examines the propogation of nuclear data uncertainties through lattice level physics calcualtions. These input uncertainties are in the form of covariance matrices, which dictate the variance and covariance of specified nuclear data to one another. These covariances are available within certain nuclear data libraries, however they are generally only available at infinite dilution for a fixed temperature. The overall goal of this research is to examine the importance of various applications of covariance and their associated nuclear data libraries, and most importantanly to examine the effects of dilution and self-shielding on the results. One source of nuclear data and covariances are the TENDL libraries which are based on a reference ENDF data library and are in continuous energy. Each TENDL library was created by randomly perturbing the reference nuclear data at its most fundamental level according to its covariance. These perturbed nuclear data libraries in TENDL format were obtained and NJOY was used to produce cross sections in 69 groups for which the covariance was calculated at multiple temperatures and dilutions. Temperature was found to have little effect but covarances evaluated at various dilutions did differ significantly. Comparisons of the covariances calculated from TENDL with those in SCALE and ENDF/B-VII also revealed significant differences. The multigroup covariance library produced at this stage was then used in subsequent analyses, along with multigroup covariance libraries available elsewhere, in order to see the differences that arise from covariance library sources. Monte Carlo analysis of a PWR pin cell was performed using the newly created covariance library, a specified reference set of nuclear data, and the lattice physics transport solver DRAGON. The Monte Carlo analysis was then repeated by systematically changing the input covariance matrix (for example using an alternative matrix like that included with the TSUNAMI package) or alternate input reference nuclear data. The uncertainty in k-infinite and the homogenized two group cross sections was assessed for each set of covariance data. It was found that the source of covariance data as well as dilution had a significant effect on the predicted uncertainty in the homogenized cell properties, but the dilution did not significanty affect the predicted uncertainty in k-infinite.</p> / Master of Applied Science (MASc)

In recent years there has been an increased interest in inquiry-based learning, also known as experiential learning or problem-based learning, as a more appropriate model of teaching science. The purpose of this study was to incorporate inquiry-based learning in a college sophomore-level General Microbiology Laboratory. The goal of this laboratory course is to introduce students to basic techniques and procedures necessary for the study of microorganisms. Laboratory sections were randomly assigned to an experimental group or a control/reference group. The experimental group was taught the concept of serial dilutions using an inquiry-based learning approach whereas the control group was taught using traditional teaching methods. Analysis of the data generated from the students' involvement in the investigation during the fall semester indicated that the experimental group had a slightly greater improvement in their knowledge of serial dilution. The study continued in the spring semester and involved close to 300 students. During the spring semester both the experimental and the control groups had similar attitudes about their learning experience as evaluated by a Lickert Scale survey. However, a statistical analysis of the quiz scores of the students with values within the interquartiles indicated the experimental classes' quiz scores were significantly higher on quiz 2 taken at the midpoint in the study. Thus an inquiry-based learning approach was found to be beneficial to the middle 50% of the class. / Master of Science

Group (pooled) testing, i.e., testing multiple subjects simultaneously with a single test, is essential for classifying a large population of subjects as positive or negative for a binary characteristic (e.g., presence of a disease, genetic disorder, or a product defect). While group testing is used in various contexts (e.g., screening donated blood or for sexually transmitted diseases), a lack of understanding of how an optimal grouping scheme should be designed to maximize classification accuracy under a budget constraint hampers screening efforts. We study Dorfman and Array group testing designs under subject-specific risk characteristics, operational constraints, and imperfect tests, considering classification accuracy-, efficiency-, robustness-, and equity-based objectives, and characterize important structural properties of optimal testing designs. These properties provide us with key insights and allow us to model the testing design problems as network flow problems, develop efficient algorithms, and derive insights on equity and robustness versus accuracy trade-off. One of our models reduces to a constrained shortest path problem, for a special case of which we develop a polynomial-time algorithm. We also show that determining an optimal risk-based Dorfman testing scheme that minimizes the expected number of tests is tractable, resolving an open conjecture. Our case studies, on chlamydia screening and screening of donated blood, demonstrate the value of optimal risk-based testing designs, which are shown to be less expensive, more accurate, more equitable, and more robust than current screening practices. / PHD