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Tailoring the Structures of Magnetic Nanoparticles: A Strategic Approach for Discovering Unique Magnetic Properties and Exploring Potential Applications
No full textMagnetic nanoparticles have gained remarkable attention in recent decades due to their significant potential in advanced biomedical technologies, such as biosensing, magnetic resonance imaging, drug delivery, and magnetic hyperthermia. Additionally, magnetic nanoparticles have demonstrated their potential uses in diverse fields, including energy, environmental remediation, magnetic recording, and electronic devices. At the nanoscale, magnetic particles can exhibit either ferro/ferrimagnetic or superparamagnetic properties, both of which are useful depending on the targeted applications. Tailoring the structures of magnetic nanoparticles is a primary strategy to broadly understand the behavior of nanomagnetism and optimize nanoparticle configurations for specific applications. This dissertation particularly focuses on applying chemical synthesis to control the nanostructures (size, shape, crystallinity) of magnetic nanoparticles (iron oxide, nickelzinc ferrites) to widely and finely tune their magnetic properties. Several key lessons have been learned, especially regarding nanoparticles in secondary structures, either polycrystalline or supercluster particles, formed by the agglomeration or clustering of numerous constituent nanocrystals. In these systems, magnetic properties are influenced by both the size of the particles and the size, shape, and composition of the constituent crystals. In these nanoparticle configurations, the latter aspects play a more dominant role in determining the magnetic properties. By controlling the structures of the constituent crystals, the magnetic properties of nanoparticles can be: (1) widely tuned from ferromagnetic to superparamagnetic states, (2) finely adjusted in the magnitude of magnetization at similar particle sizes, or (3) well preserved across a broad range of sizes. This dissertation places special emphasis on magnetic hyperthermia applications and the behaviors of large-size superparamagnetic nanoparticles, which hold promise for biomedical applications
Thermal Behavior and Gas Emissions of Biomass and Industrial Wastes as Alternative Fuels in Cement Production: A TGA-DSC and TGA-MS Approach
No full textThe cement industry contributes approximately 7% of global anthropogenic CO<sub>2</sub> emissions, primarily through energy-intensive clinker production. This study evaluates the thermal behavior and gas emissions of seven waste materials (sawdust, pecan nutshell, wind blade waste, industrial hose waste, tire-derived fuel, plastic waste, and automotive shredder residue) as alternative fuels for cement manufacturing, motivated by the limited information available regarding their performance and environmental impact, with bituminous coal used as a reference. Thermogravimetric analysis and differential scanning calorimetry (TGA-DSC) were used to quantify mass loss and energy changes, while TGA coupled with mass spectrometry (TGA-MS) was used to identify volatile compounds released during thermal degradation. Both TGA-DSC and TGA-MS were conducted under oxidative conditions. The analysis revealed that these waste materials can generate up to 70% of coal&rsquo;s energy, with combustion primarily occurring between 200 &deg;C and 600 &deg;C. The thermal profiles demonstrated that these materials can effectively replace fossil fuels without releasing harmful toxic gases like HCl, dioxins, or furans. Combustion predominantly emitted CO<sub>2</sub> and H<sub>2</sub>O, with only trace volatile organic compounds such as C<sub>3</sub>H<sub>3</sub> and COOH. The findings highlight the potential of alternative fuels to provide substantial energy for cement production while addressing waste management challenges and reducing the industry&rsquo;s environmental impact through innovative resource valorization
Sharing Stories From 1977: S.T.O.P the Equal Rights Amendment ("Stop Taking Our Privileges")
No full textThe 1977 National Women's Conference in Houston marked a pivotal moment in the fight for gender equality, bringing together 20,000 women to draft a National Plan of Action addressing reproductive rights, equal pay, LGBTQ+ protections, and racial equity. This federally funded event was a landmark in American history, yet it also ignited fierce opposition. Conservative women, led by figures like Phyllis Schlafly and Edith "Peggy" Brandon, mobilized against the Equal Rights Amendment (ERA), abortion rights, and what they saw as threats to traditional family values. Their counter-conference drew 15,000 attendees, highlighting the deep ideological divide surrounding women's rights. This research examines the anti-ERA movement's motivations, particularly through the activism of Peggy Brandon, who played a crucial role in lobbying against the ERA in Texas and beyond. Opponents feared the amendment would erode gender roles, impact custody and alimony laws, mandate women's military service, and dismantle protective labor regulations. These anxieties, rooted in the 1970s, persist in modern Texas politics, particularly in debates over reproductive rights. Today, Texas continues to pass restrictive laws limiting abortion access, culminating in some of the nation's strictest post-Dobbs legislation. The consequences include increased maternal mortality rates and the exodus of medical professionals from the state. This study contextualizes how historical opposition to the ERA informs contemporary policies, revealing an enduring struggle over gender, autonomy, and state intervention in women's lives. By analyzing the past, we better understand the political landscape shaping women's rights today.History, Department ofHonors Colleg
Unanticipated Peripheral Nerve Stimulation in MRI: Implications for Patients with Implantable Devices
No full textThis dissertation presents computational investigations into the effects of metallic implants and implanted electrodes on peripheral nerve stimulation (PNS) during magnetic resonance imaging (MRI). By integrating anatomical electromagnetic simulations with thermal and neurophysiological modeling, the work aims to assess how implant-related modifications to the local electromagnetic environment influence neural activation thresholds under MRI exposure. The first study examines the impact of orthopedic plates implanted in clinically relevant anatomical regions on gradient-induced PNS thresholds. Results demonstrate that metallic implants can significantly reduce activation thresholds—by up to 80%—bringing some values close to the safety limits defined in IEC 60601-2-33. The second study extends this analysis by evaluating how the length of orthopedic plates affects both gradient-induced electric field distribution and RF-induced tissue heating. It reveals that longer plates enhance electric field gradients near their edges, lowering nerve activation thresholds, while also exhibiting higher local RF heating, which further modulates nerve excitability. The third study focuses on unintended vagus nerve stimulation in the presence of cuff electrodes, where combined gradient and RF fields are shown to reduce activation thresholds, particularly during short-pulse gradient waveforms. Together, these studies highlight critical interactions between implants, MRI gradient and RF fields, revealing potential safety concerns for patients with orthopedic implants or implantable neurostimulators. The findings show the importance of incorporating neurophysiological modeling into MRI safety assessments for individuals with implants
Development of Advanced Machine Learning Models for Predicting CO2 Solubility in Brine
No full textThis study explores the application of advanced machine learning (ML) models to predict CO<sub>2</sub> solubility in NaCl brine, a critical parameter for effective carbon capture, utilization, and storage (CCUS). Using a comprehensive database of 1404 experimental data points spanning temperature (&minus;10 to 450 &deg;C), pressure (0.098 to 140 MPa), and salinity (0.017 to 6.5 mol/kg), the research evaluates the predictive capabilities of five ML algorithms: Decision Tree, Random Forest, XGBoost, Multilayer Perceptron, and Support Vector Regression with a radial basis function kernel. Among these, XGBoost demonstrated the highest overall accuracy, achieving an R<sup>2</sup> value of 0.9926, with low root mean square error (RMSE) and mean absolute error (MAE) of 0.0655 and 0.0191, respectively. A feature importance analysis revealed that pressure has the most impactful effect and positively correlates with CO<sub>2</sub> solubility, while temperature generally exhibits a negative effect. A higher accuracy was found when the developed model was compared with one well-established empirical model and one ML-based model from the literature. The results underscore the potential of ML models to significantly enhance prediction accuracy over a wide data range, reduce computational costs, and improve the efficiency of CCUS operations. This work demonstrates the robustness and adaptability of ML approaches for modeling complex subsurface conditions, paving the way for optimized carbon sequestration strategies
Sources and Photochemical Behavior of Atmospheric Pollutants (Nitrophenols, Non-Methane Hydrocarbons, and Mercury) in the Houston, Texas, Area
No full textThis thesis investigated the sources and photochemical behavior of Nitrophenols (NPs), Non-Methane Hydrocarbons (NMHCs), and Gaseous Elemental Mercury (GEM) in Houston. In the first task, sources and photochemical behavior of NPs were studied during the Study of Houston Atmospheric Radical Precursors (SHARP) field campaign at the Moody Tower (MT) site from May 15 to June 30, 2009, in Houston. Seven sources (industrial NPs, secondary formation, phenol sources, acetonitrile source, natural gas/crude oil, traffic, and petrochemical industries/oil refineries) of NPs were identified using the positive matrix factorization (PMF) model. The formation of secondary NPs (2-nitrophenol and 2,4-dinitrophenol) were simulated using Atmospheric Chemistry (AtChem2) Master Chemical Mechanism (MCM) v3.2 box model and yields a nitrous acid (HONO) formation rate of 7.5 ± 2.5 ppt/h. Combining receptor modeling with photochemical box modeling can help to adopt effective NPs control strategies. In the second task, a twenty-year (2004–2023) trend analysis of NMHC data at the Lake Jackson site maintained by the TCEQ (Texas Commission on Environmental Quality), to explore potential changes of NMHC emissions and in the atmospheric oxidation capacity using Propylene Equivalent (Propy-Equiv) concentration by analyzing background marine air. One important finding is that the rising temperature (1.58 ± 0.14 °C) likely led to increased isoprene emissions (20 ± 1.6%) and consequently increased isoprene Propy-Equiv concentrations (0.45 ppbC/year) over the past 20 years. In the third task, one year of continuous measurements of GEM were performed at MT (May 2023 to April 2024) to identify emission sources in Houston. Source apportionment analysis included observations of methane (CH4), carbon dioxide (CO2), and their respective isotopic ratios as well as of carbon monoxide (CO) and ozone (O3) routinely collected at MT. CO2 is released due to oxidation processes, whereas CH4 is produced due to anerobic processes. Due to the limited number of tracers, PMF analysis alone was insufficient to resolve anthropogenic sources. Bivariate polar plots and Hybrid Single Particle Lagrangian Integrated Trajectory Model (HYSPLIT-5.0) were further utilized and able to identify landfills as one of the most dominant anthropogenic Hg sources in Houston for the first time. This work provides a promising method to characterize GEM emission sources using i-CO2 and i-CH4
Percussion-Based Single and Multi-Bolt Looseness Detection and Composite Plate Delamination Detection Using Machine Learning
No full textBolted Flanges are commonly used in the energy industry. Bolt looseness is a common cause of leaks. Pipeline leakage monitoring is essential for ensuring safety and operational efficiency. This study uses a percussion-based method to apply machine learning in the detection of bolt looseness in flanged pipeline connections. A weld neck flange with eight bolts is examined under two test cases. In the first case, the objective is to identify a single loose bolt among eight, where seven bolts are torqued to 210 ft-lbs, and one remains loose. The second case investigates the ability to determine the number of loose bolts from four possible conditions: 0, 2, 4, or 8 loose bolts. A hammer is used to strike between two bolts, and the resulting audio is recorded using an iPhone and applied to machine learning algorithms. Various machine learning techniques are employed, including the shallow learning method Support Vector Machine (SVM), the deep learning method Recurrent Neural Network (RNN), and the clustering method Spectral Clustering. With the use of machine learning becoming more common, so does the need to educate students in applying machine learning to real-world applications. Composite plates made of carbon fiber and aluminum with a sandwiched layer of closed-cell foam and epoxy are used to simulate carbon fiber delamination for the purpose of educating students in machine learning topics. A grid system is implemented, where both healthy and delaminated sections are manufactured to evaluate machine learning models for identifying delaminated regions. This study demonstrates the potential of machine learning for structural health monitoring and predictive maintenance in industrial applications
Time Series Transformer-Based Modeling of Pavement Skid and Texture Deterioration
No full textThis study investigates the deterioration of skid resistance and surface macrotexture following preventive maintenance using micro-milling techniques. Field data were collected from 31 asphalt pavement sections located across four climatic zones in Texas. The data encompasses a variety of surface types, milling depths, operational speeds, and drum configurations. A standardized data collection protocol was followed, with measurements taken before milling, immediately after treatment, and at 3, 6, 12, and 18 months post-treatment. Skid number and Mean Profile Depth (MPD) were used to evaluate surface friction and texture characteristics. The dataset was reformatted into a time-series structure with 930 observations, including contextual variables such as climatic zone, treatment parameters, and baseline surface condition. A comparative modeling framework was applied to predict the deterioration trends of both skid resistance and macrotexture over time. Eight regression models, including linear, tree-based, and ensemble methods, were evaluated alongside a time series Transformer model. The results show that the Transformer model achieved the highest prediction accuracy for skid resistance (<i>R</i><sup>2</sup> = 0.981), while Random Forest performed best for macrotexture prediction (<i>R</i><sup>2</sup> = 0.838). The findings indicate that the degradation of surface characteristics after preventive maintenance is non-linear and influenced by a combination of environmental and operational factors. This study demonstrates the effectiveness of data-driven modeling in supporting transportation agencies with pavement performance forecasting and maintenance planning
The Effect of Pupil Size on Visual Performance in Multifocal Contact Lenses
No full textPurpose: Multifocal contact lenses have been shown to slow myopia progression in children. While multifocal lenses have little effect on high-contrast visual acuity, which is the standard for assessing vision in clinical settings, they reduce low-contrast visual acuity and contrast sensitivity. The purpose of this thesis is to determine the effect of pupil size on visual performance when wearing multifocal versus spherical soft contact lenses. Methods: Twenty-four myopic, nonpresbyopic adults were fitted with a single vision (Biofinity) and a center-distance multifocal (Biofinity D +2.50 add) contact lens, in random order. The pupil was dilated, and visual acuity (high- and low-contrast) and contrast sensitivity were measured through a 3mm and 6mm aperture. Data were analyzed using repeated-measures analyses of variance (RM-ANOVA), with Benjamini-Hochberg adjusted post-hoc t-tests, when appropriate. Results: Visual acuity differed by contrast level and depended on lens design and pupil size (p = 0.004). With both pupil sizes, high-contrast acuity with the multifocal lens was 2 letters worse than with the single vision lens (p < 0.001). Lens design effects on low-contrast acuity were pupil size dependent (p < 0.001). Compared to single vision lenses, low-contrast acuity with multifocal lenses was about 3 letters worse for a 3mm pupil (mean ± SEM = 0.07 ± 0.02 logMAR; p = 0.007) and about 2 lines worse with a 6mm pupil (0.19 ± 0.02 logMAR; p < 0.001). While wearing the multifocal lens, low-contrast acuity decreased by nearly 2 lines when pupil size increased from 3mm to 6mm (0.17 ± 0.02 logMAR; p < 0.001). The effect of lens design on the area under the log contrast sensitivity function (AULCSF) was pupil-size dependent (p = 0.02). The greatest reduction in AULCSF was at the larger 6mm pupil size with the multifocal lens causing a 17% decrease compared to the single vision lens. (p < 0.001). Conclusions: Multifocal contact lenses perform similarly to single vision lenses under high contrast and smaller pupil conditions; however, visual performance reductions increase with lower contrast and a larger pupil size
Advances in Lithographically Patterned Nanostructures for Applications in Plasmonic Biosensing
No full textThere has been extensive research interest in metallic nanostructures because of their intriguing optical properties which can be employed for numerous applications including biosensing, catalysis, photovoltaics, and imaging. These properties originate from the phenomenon called localized surface plasmon resonance (LSPR) which describes the interaction of free electrons in the metal with electromagnetic waves. Our group has recently been working on the fabrication and applications of arrayed nanostructures such as arrayed gold nanodisks. These arrayed nanostructures possess many remarkable properties such as enlarged surface area and tunable optical and plasmonic properties. However, arrayed gold nanodisks suffer from many limitations such as low surface sensitivity and a red resonance peak beyond the detectable range of silicon photodetectors. To tackle such issues, we fabricated a nanoporous gold disk array containing pores and ligaments in the disks, which redistributes the hotspots throughout the disks. This redistribution of the hotspots makes the surface very sensitive and we explored the catalytic and photothermal properties of the nanoporous gold disk array. Next, we fabricated another class of arrayed nanostructures called alloy nanodisk array which contained both gold and silver. We observed the symmetry-breaking properties of these arrayed nanostructures for the first time as the LSPR peak split into a high and low energy mode due to the plasmonic hybridization. The high-energy mode can be precisely engineered at ~540 nm where the resonance was employed for color change detection. The index sensitivity of ANA is the highest among existing plasmonic arrays (particles or holes) within a similar resonance wavelength region. We demonstrated colorimetric detection of sub-nanomolar and sub-monolayer biotin-streptavidin surface binding with the alloy nanodisk array, a smartphone camera, and a white light lamp. However, simulations showed us that arrayed nanodisks on glass were still limited in their surface sensitivities because most of the electric field responsible for surface activity was buried inside the glass substrate. To address this drawback, we introduced another fabrication technique called arrayed gold nanodisks on an invisible substrate where we used chemical wet etching to remove the glass surface underneath the nanodisks. We demonstrated the engineering capability of making the glass pillars underneath the nanodisks so thin that the effects of the glass surface were eliminated. Furthermore, the LSPR peak blue shifted because of the reduced average refractive index while the surface sensitivity improved 2.8 times allowing the detection of cancer exosomes and protein interactions in the femtomolar range. There has been extensive research interest in metallic nanostructures because of their intriguing optical properties which can be employed for numerous applications including biosensing, catalysis, photovoltaics, and imaging. These properties originate from the phenomenon called localized surface plasmon resonance (LSPR) which describes the interaction of free electrons in the metal with electromagnetic waves. Our group has recently been working on the fabrication and applications of arrayed nanostructures such as arrayed gold nanodisks. These arrayed nanostructures possess many remarkable properties such as enlarged surface area and tunable optical and plasmonic properties. However, arrayed gold nanodisks suffer from many limitations such as low surface sensitivity and a red resonance peak beyond the detectable range of silicon photodetectors. To tackle such issues, we fabricated a nanoporous gold disk array containing pores and ligaments in the disks, which redistributes the hotspots throughout the disks. This redistribution of the hotspots makes the surface very sensitive and we explored the catalytic and photothermal properties of the nanoporous gold disk array. Next, we fabricated another class of arrayed nanostructures called alloy nanodisk array which contained both gold and silver. We observed the symmetry-breaking properties of these arrayed nanostructures for the first time as the LSPR peak split into a high and low energy mode due to the plasmonic hybridization. The high-energy mode can be precisely engineered at ~540 nm where the resonance was employed for color change detection. The index sensitivity of ANA is the highest among existing plasmonic arrays (particles or holes) within a similar resonance wavelength region. We demonstrated colorimetric detection of sub-nanomolar and sub-monolayer biotin-streptavidin surface binding with the alloy nanodisk array, a smartphone camera, and a white light lamp. However, simulations showed us that arrayed nanodisks on glass were still limited in their surface sensitivities because most of the electric field responsible for surface activity was buried inside the glass substrate. To address this drawback, we introduced another fabrication technique called arrayed gold nanodisks on an invisible substrate where we used chemical wet etching to remove the glass surface underneath the nanodisks. We demonstrated the engineering capability of making the glass pillars underneath the nanodisks so thin that the effects of the glass surface were eliminated. Furthermore, the LSPR peak blue shifted because of the reduced average refractive index while the surface sensitivity improved 2.8 times allowing the detection of cancer exosomes and protein interactions in the femtomolar range