Dissertation Defense Announcements

Coral reef ecosystems are supported by diverse mutualisms formed between cnidarians such as corals, sea anemones, and jellyfish and dinoflagellate algae in the family Symbiodiniaceae. These dynamic symbiotic relationships rely on the successful establishment of algal endosymbionts, often from the surrounding seawater, within cnidarian host tissues. Due to the current limitations in cellular and molecular tools in the field of cnidarian-Symbiodiniaceae symbiosis, the mechanisms of symbiosis establishment including infection, proliferation, and maintenance are poorly understood. The aim of this thesis is to uncover the cellular processes essential to cnidarian-algal symbiosis by developing in vitro and in hospite assays across Symbiodiniaceae and cnidarian species. In chapter two, the trophic flexibility of Symbiodiniaceae was explored. Symbiodiniaceae Breviolum minutum grown in vitro with organic nutrients showed stable growth and photosynthetic function when compared to limited nutrient conditions; this suggests that the oligotrophic waters of coral reef ecosystems may drive free-living Symbiodiniaceae into symbiosis with cnidarians. Next in chapter three, a new protocol for single-cell dissociation of cnidarian hosts is introduced and used to determine the localization of the first Symbiodiniaceae photosynthesis mutant ora1. Here, ora1 was found to retain its ability to form symbiosis in cnidarians, indicating that photosynthesis is not required for symbiosis establishment. Finally, the newly generated Symbiodiniaceae green mutant, gr02, is introduced and co-inoculated with the brown wild type B. minutum in the sea anemone Aiptasia to uncover the cellular events contributing to symbiont proliferation. For the first time, two algal genotypes (gr02 and wild type B. minutum) were observed co-localized in a single host cell via dissociation and microscopy but were rare in frequency. These results suggest that algal cell division and primary infections drive the proliferation of symbionts in hospite. Furthermore, the co-inoculation of gr02 with other species of Symbiodiniaceae in three cnidarian hosts (coral, sea anemone, and jellyfish) reveals intracellular localization and possible interaction between symbionts in host tissues. Together, this work lays the foundation for future cellular biological research using Symbiodiniaceae mutants to answer pressing questions surrounding cnidarian-Symbiodiniaceae symbiosis.

Silicon carbide (SiC) has excellent thermomechanical properties, and it is one of the most promising candidates for many demanding high temperature applications in military, aerospace, space mirrors, nuclear energy stations, filtering, and furnace. Manufacturing of SiC product is difficult due to its thermochemical and mechanical stabilities. The additive manufacturing (AM) of SiC has drawn a lot of attention in recent years due to these excellent materials properties and diverse applications. Previous studies from our lab have shown that the creation of a silica gel layer on the surface of SiC using NaOH solution activated the surface and allowed 3D printing of SiC using water based binder in a powder bed binder jet printer. The dried silica gel layer binds adjacent SiC particles upon hydration during 3D printing at room temperature. The 3D printed green parts require a secondary surface activation by impregnating in NaOH solution and thermal treatment to enhance density and strength. The secondary surface activation technique creates an additional silica layer on the surface of SiC at room temperature which can lead to the growth of silica nanowire inside the pore of 3D printed SiC parts upon heat treatment. The hypotheses underlying this approach are twofold: (i) maximum growth of the silica nanowires will facilitate densification and mechanical properties, and (ii) The silica gel layer can mediate a strong bond between SiC and silicate minerals such as mullite. This thesis has three main objectives. First to understand the effect of processing parameters including concentration of NaOH, thermal treatment temperature and dwelling time on silica nanowires growth and subsequent density and mechanical properties, second, to develop and validate a mathematical model for silica nanowires’ growth and ceramic strengthening, and the third objective is to examine the role of the silica gel layer and thermal treatment parameters on in situ mineralization of mullite bonding agent for SiC composite. This thesis is structured into two parts: (i) experimentally optimizing the processing parameters for silica (SiO2) nanowire growth inside the pore of 3D printed SiC discs based on quantitative SEM analysis and development of a mathematical growth model for silica nanowire growth, and (ii) creating in situ synthesized liquid mullite as a secondary binder phase for the densification and strengthening of 3D SiC manufactured using powder metallurgy technique.

We found that the silica nanowire growth rate and number density depend on the processing parameters such as NaOH concentration, sintering temperature, and time. Therefore, the goal of the first part of this dissertation is to investigate the effect of the processing parameters on nanowire growth and number density. Utilizing quantitative SEM image analysis and a silica nanowire growth model, the focus is on optimizing processing parameters to achieve maximum nanowire growth and density, ultimately enhancing the densification and mechanical properties of 3D printed SiC components. The silica nanowire was grown inside the pore of 3D printed SiC disc through the vapor solid (VS) noncatalytic mechanism. In this process silica (SiO2) vapor condensed directly onto the SiC particle surface, leading to the nucleation and growth of one-dimensional silica nanostructures. Differential scanning calorimetry (DSC) and thermogravimetric (TG) analysis were performed on SiC disc prepared via the powder metallurgy technique using NaOH solutions at varying concentrations (5%, 10%, and 20%). The thermal analysis helped us determine the nucleation temperature of silica droplets at 525 °C and crystallization temperature at 800 °C. The effect of NaOH concentrations analysis showed that the nanowire number density (mm-2) as well as the width and length of the nanowires increased according to the concentration of the NaOH used to pretreat 3D printed SiC in the order 20% > 10% > 5%. The optimal combination of NaOH concentration and heat treatment parameters identified for the highest nanowire number density and nanowire growth involved impregnating with 10% NaOH and heat treating at 550 °C for 6 hours and 1100 °C for 4 hours. The resulting sample exhibited a nanowire number density of 55431 ± 9232 mm-2, majority of the nanowires were in the width range of 0.3 µm – 0.6 µm, and length of 25.6 ± 3.3 µm as determined through quantitative SEM image analysis. The compressive strength, density and porosity were found to be 9.86 ± 1.4 MPa, 2.27 gcm-3, and 38.32%, respectively. Subsequently, a mathematical nanowire growth model was developed in order to investigate the growth mechanism and understand the effect of reaction kinetics on the nanowire growth. The model accounted for the reaction kinetics controlling the formation of silica molecule and its subsequent deposition on nanowire top surface contributing to the growth of the nanowire. The change in nanowire length relation with respect to different processing parameters obtained from the model showed a good agreement with the experimental data.

The silica gel layer on the surface activated SiC particles transforms into cristobalite (SiO2) upon heat treatment which serves as a binding agent that holds the SiC particles together. However, cristobalite has relatively poor mechanical strength and thermal properties compared to SiC. Therefore, in the second part of this dissertation, an in-situ mullite binding agent was formed which has superior thermomechanical properties compared to SiO2. Additionally, it has thermomechanical and chemical properties comparable to those of SiC. We have reported on using coal fly ash as a source of alumina (Al2O3) that reacts in situ with the silica (SiO2), oxidation product of SiC. The instantaneous mullite formation on the surface of SiC facilitated due to presence of minor concentrations of metal oxides in coal fly ash, resulted in a strong bonding zone between the two phases at relatively low temperature. In this work, SiC was mixed with coal fly ash at weight ratios of 90SiC/10ash, 85SiC/15ash, 80SiC/20ash, and 75SiC/25ash and sintered at 1400 °C. Measurements of mechanical properties showed that the 85SiC/15ash composition had the highest mechanical strength among samples. XRD analysis showed the phase composition of thermally treated 85SiC/15ash to be 81.8 wt% SiC, 11.4 wt% mullite, and 6.8 wt% cristobalite. SEM-EDX revealed a concentration gradient of Al in the cristobalite which enhanced formation of functionally graded bonding zones between phases and resulted in SiC-mullite composite with high thermomechanical properties. The compressive strength, nanoindentation elastic modulus, and Vickers hardness were 434 ± 20 MPa, 370.9 ± 22.6 GPa, and 11.5 ± 1.2 GPa respectively. The thermal shock resistance test showed high dimensional and mechanical stabilities after quenching in liquid nitrogen (−196 °C) from 1400 °C. The SiC-mullite composite showed low thermal expansion co-efficient from 3.17 x 10-7 /K to 5.615 x 10-6 /K when the sample was heated from 182 K to 354 K. The specific heat capacity, thermal diffusivity, and thermal conductivity were 7.83 ± 0.0014 J/g.K, 1.04 ± 0.013 mm2/s, and 17 W/m.K at 100 °C, respectively. The SiC-mullite composite exhibited moderate electrical conductivity of 3.48 x 10-2 S/m at 1000 °C. The resulting SiC-mullite composite is suitable for high temperature applications such as diesel motor parts, gas turbines, industrial heat exchangers, fusion reactor parts, high-temperature energy exchanger systems, and hot gas filters due to its high mechanical strength and thermal shock-resistance. This work demonstrated the potential of utilizing an in-situ mullite bonding agent instead of silica layer in additive manufacturing of SiC in the powder bed binder jet process for achieving a dense SiC parts with high thermomechanical properties.

An in-depth analysis of a distinctive electric machine topology characterized by a doubly salient structure and integrated permanent magnets within the stator is presented in this dissertation. The machine demonstrates high power density (up to 50 kW/L) with capabilities such as a rated torque of 95 Nm at 12,500 rpm and a maximum speed of 37,500 rpm. An analytical model using lumped parameter magnetic equivalent circuits (LPMEC) is developed, examining spatial harmonics and validating results through finite element analysis. A high-fidelity model-based motor drive system employs a field-oriented control approach and introduces a complex vector current (CVC) regulation strategy, enhancing stability compared to classical methods. Comparative analyses highlight the robustness of CVC regulation. Experimental tests have been conducted to validate the analytical outcomes and proposed control methodologies employing an open frame laboratory prototype (OFLP) of the proposed machine and SiC based traction inverter.

Novice programmers are known for holding incomplete and inconsistent mental models. A mental model stores knowledge that reflects a person's belief system, helps determine actions, and facilitates learning. Mental model correctness and consistency are two criteria that make a mental model useful. Though the literature on mental models is rich with more than two decades of research, novice programmers' mental model is understudied in the CS education research community. Guided by the mental model theories from psychology and cognitive science, I investigated novice programmers' mental models of arrays before and after CS1 course instruction. Furthermore, I explored the gap that might exist between students with varying levels of prior programming experience. To that end, by following the theories of mental models, I defined the mental models for Java arrays, including assertions of the array's parts and state changes. I further decomposed the array's parts and state changes into four sub-components each (parts: name, index, type, elements}; state changes: declaration, instantiation, assigning literals, assignment). To elicit the mental model assertions of novice programmers from large CS1 classrooms, I adopted a multiple choice-based questionnaire approach (the Mental Model Test) covering each array's component. I collected responses from novice programmers as they entered a CS1 course and transitioned into a CS2 course. I analyzed participants' mental model assertions based on their correctness and consistency.

The results show that participants' mental model correctness and consistency improved after formal classroom instruction. Moreover, even though improved, I found evidence that the mental model components of the array's state changes were less accurate and consistent than the parts. In addition, participants with prior programming experience had significantly lower mental model correctness and consistency than those with prior programming experience before classroom instruction on arrays. The mental model test highlighted several novice programmers' misconceptions. Over half of our participants held at least one misconception before and after learning arrays in classrooms. Novice programmers mostly held misconceptions about the arrays’ declarations (state change) as incoming CS1 students and when transitioning into CS2. After classroom instruction, the number of students holding misconceptions about the parts components decreased. However, for the state changes components, in most cases, the number of students holding misconceptions remained almost the same even after classroom instruction. I close my dissertation by summarizing the overall findings while investigating novice programmers' mental models in their different learning trajectories. Lastly, I discuss the implications of my research in designing instructional materials for CS educators on possible solutions to mitigate the mental model gap of novice programmers.