Dimorphism in Shell Shape and Strength in Two Species of Emydid Turtle

This study investigates the mechanical implications of shell shape differences between males and females of two North American turtle species: Chrysemys picta and Glyptemys insculpta. These species show patterns of sexual dimorphism that are common to many species of turtle. Females have wider and more highly domed shells, whereas males tend to have flatter, more streamlined shells. In addition, the males of many terrestrial species have concave plastra, most likely to accommodate the domed shells of the females while mating. The purpose of this study was to determine whether the known morphological differences in male and female turtle shells are also associated with differences in shell strength. Landmark coordinate data were collected from the shells of males and females of both species. These data were used to create digital models of each shell for finite-element (FE) analysis. FE models were generated by transforming a single base model of a turtle shell to match the shapes of each specimen examined in this study. All models were assigned the same material properties and restraints. Twelve load cases, each representing a predator’s bite at a different location on the carapace, were applied separately to the models. Subsequently, Von Mises stresses were extracted for each element of each model. Overall, the shells of females of both species exhibited significantly lower maximum and average stresses for a given load than those of their male counterparts. Male G. insculpta exhibited significant increases in stresses because of the concave shape of their plastra. We suggest that the mechanical implications of shell shape differences between males and females may have a large impact on many aspects of the biology of these turtle species.

Synthesis and Surface Modification of CdSe and CdS Quantum Dots Exhibiting High Quantum Yield

The thesis investigates the effect of surface treatment with various reducing and oxidizing agents on the quantum yield (QY) of CdSe and CdS quantum dots (QDs). The QDs, as synthesized by the organometallic method, contained defect sites on their surface that trapped photons and prevented their radiative recombination, therefore resulting in adecreased QY. To passivate these defect sites and enhance the QY, the QDs were treated with various reducing and oxidizing agents, including: sodium borohydride (NaBH4), calcium hydride (CaH2), hydrazine (N2H4), benzoyl peroxide (C14H10O4), and tert-butylhydroperoxide (C4H10O2). It was hypothesized that the reducing/oxidizing agents reduced the ligands on the QD surface, causing them to detach, thereby allowing oxygen from atmospheric air to bind to the exposed cadmium. This cadmium oxdide (CdO) layeraround the QD surface satisfied the defect sites and resulted in an increased QY. To correlate what effect the reducing and oxidizing agents were having on the optical properties of the QDs, we investigated these treatments on the following factors:chalcogenide (Se vs. S), ligand (oleylamine vs. OA), coordinating solvent (ODE vs.TOA), and dispersant solvent (chloroform vs. toluene) on the overall optical properties of the QDs. The QY of each sample was calculated before and after the various surface treatments from ultra-violet visible spectroscopy (UV-Vis) and fluorescence spectroscopy data to determine if the treatment was successful.From our results, we found that sodium borohydride was the most effective surface treatment, with 10 of the 12 treatments resulting in an increased QY. Hydrazine, on the other hand, was the least effective treatments, as it quenched the QD fluorescence in every case. From these observations, we hypothesize that the effectiveness of the QD surface treatments was dependent on reaction rate. More specifically, when the surface treatment reaction happened too quickly, we hypothesize that the QDs began to aggregate, resulting in a quenched fluorescence. Furthermore, we believe that the reactionrate is dependent on concentration of the reducing/oxidizing agents, solubility of the agents in each solvent, and reactivity of the agents with water. The quantum yield of the QDs can therefore be maximized by slowing the reaction rate of each surface treatment toa rate that allows for the proper passivation of defect sites.

Monitoring and Modeling of the Hydraulic and Sediment Processes at In-Stream Restoration Structures in the Vicinity of a Bridge Crossing

The long-term performance of infrastructure depends on reliable and sustainable designs. Many of Pennsylvania’s streams experience sediment transport problems that increase maintenance costs and lower structural integrity of bridge crossings. A stream restoration project is one common mitigation measure used to correct such problems at bridge crossings. Specifically, in an attempt to alleviate aggradation problems with the Old Route 15 Bridge crossing on White Deer Creek, in White Deer, PA, two in-stream structures (rock cross vanes) and several bank stabilization features were installed along with a complete channel redevelopment. The objectives of this research were to characterize the hydraulic and sediment transport processes occurring at the White Deer Creek site, and to investigate, through physical and mathematical modeling, the use of instream restoration structures. The goal is to be able to use the results of this study to prevent aggradation or other sediment related problems in the vicinity of bridges through improved design considerations. Monitoring and modeling indicate that the study site on White Deer Creek is currently unstable, experiencing general channel down-cutting, bank erosion, and several local areas of increased aggradation and degradation of the channel bed. An in-stream structure installed upstream of the Old Route 15 Bridge failed by sediment burial caused by the high sediment load that White Deer Creek is transporting as well as the backwater effects caused by the bridge crossing. The in-stream structure installed downstream of the Old Route 15 Bridge is beginning to fail because of the alignment of the structure with the approach direction of flow from upstream of the restoration structure.

Analysis and Prediction of Transient Opacity Spikes Using Dimensional Modeling

Dimensional modeling, GT-Power in particular, has been used for two related purposes-to quantify and understand the inaccuracies of transient engine flow estimates that cause transient smoke spikes and to improve empirical models of opacity or particulate matter used for engine calibration. It has been proposed by dimensional modeling that exhaust gas recirculation flow rate was significantly underestimated and volumetric efficiency was overestimated by the electronic control module during the turbocharger lag period of an electronically controlled heavy duty diesel engine. Factoring in cylinder-to-cylinder variation, it has been shown that the electronic control module estimated fuel-Oxygen ratio was lower than actual by up to 35% during the turbocharger lag period but within 2% of actual elsewhere, thus hindering fuel-Oxygen ratio limit-based smoke control. The dimensional modeling of transient flow was enabled with a new method of simulating transient data in which the manifold pressures and exhaust gas recirculation system flow resistance, characterized as a function of exhaust gas recirculation valve position at each measured transient data point, were replicated by quasi-static or transient simulation to predict engine flows. Dimensional modeling was also used to transform the engine operating parameter model input space to a more fundamental lower dimensional space so that a nearest neighbor approach could be used to predict smoke emissions. This new approach, intended for engine calibration and control modeling, was termed the "nonparametric reduced dimensionality" approach. It was used to predict federal test procedure cumulative particulate matter within 7% of measured value, based solely on steady-state training data. Very little correlation between the model inputs in the transformed space was observed as compared to the engine operating parameter space. This more uniform, smaller, shrunken model input space might explain how the nonparametric reduced dimensionality approach model could successfully predict federal test procedure emissions when roughly 40% of all transient points were classified as outliers as per the steady-state training data.