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Structured MXene-Polymer Composites from Pickering Emulsion Templating
No full textStructured polymer composites have gained increasing attention due to their superior property enhancement (e.g., thermal, electrical conductivity) compared to their homogeneous counterparts, by designing the internal filler structures in the polymer matrix. The fabrication and design of structured polymer composites are still challenging and the common methods (e.g., solution casting, melt blending) have limited control over the internal filler structures. Pickering emulsion templating, in contrast, is an attractive approach to creating structured composites due to their well-defined interfaces, manageable structure and dimensions, and ease of scale-up. Among the common Pickering particles, MXenes are of great interest as they have the ability to not only stabilize emulsions but also introduce functional properties into structures, such as high electrical conductivity, high EMI shielding, and rapid radio frequency (RF) heating.
In this work, we focus on the development of MXene Pickering emulsions in diverse fluidfluid systems (e.g., oil-water and oil-oil) and their use as templates for fabrication of functional structured MXene-polymer composites. Pickering emulsions drive nanosheets to the fluid-fluid interfaces and subsequent localized polymerization creates diverse structured polymer composites (e.g., capsules, armored particles, and porous monoliths). The ability to access both aqueous and nonaqueous emulsion systems largely expands the possible polymer compositions. The MXene nanosheets are organized in these composites instead of being randomly distributed throughout. For instance, polymerization of the emulsion interfaces gives polymer shells with nanosheets embedded, polymerization of the dispersed phase gives polymer particles armored with nanosheets, and polymerization of the continuous phase gives porous monoliths with polymer struct and nanosheets coated pores. The incorporation of MXenes imparts functional properties into their structures for additional applications. For example, the MXene armored particles can be used as feedstock to fabricate segregated films for efficient EMI shielding applications at low MXene loadings due to the templated network within the polymers. MXene-polymer capsules and porous monoliths show excellent RF heating performance due to the highly locally conductive regions in these structures. The research work in this dissertation provides a simple platform to produce diverse structured MXene-polymer composites with well-controlled filler distribution, versatile compositions, and functional properties for potential advanced applications
Architecture and Facies of the Sullivan Peak Member Within the Lower Permian Wolfcamp-A Equivalent Skinner Ranch Formation on the Southern Margin of the Delaware Basin, from Outcrop in the Glass Mountains in West Texas
No full textStrata of the Sullivan Peak Member within the Lower Permian Skinner Ranch Formation are well exposed along the Lenox Hills of the Glass Mountains in Brewster County, West Texas. These outcrops provide a unique opportunity to describe equivalent strata of the prolific Wolfcamp-A Formation along the southern margin of the Delaware Basin. The Sullivan Peak Member is exposed along a 1.5 mi wide by 100 ft vertical exposure of carbonate-rich, coarse-grained, and conglomeratic strata interbedded with mudstone. Three coarse-grained facies and one mudstone facies were characterized from an outcrop aerial photogrammetry model, hand samples, and thin section petrography. These include: 1) a Normally-Graded Carbonate-Clast Conglomerate facies (CCC), 2) a Normally-Graded Skeletal Grainstone facies (NmGs), 3) a Massive Skeletal Grainstone-Packstone facies (MaGs), and 4) a Mudstone facies (Mdst). Three Facies Associations (FA-1, FA-2, FA-3) w ere defined and correlated using the facies scheme and the photogrammetry model. Three Lowstand Systems Tracts (LST���s) w ere delineated within the Sullivan Peak Member (LST-1, LST-2, and LST-3)
The Economic and Financial Potential of Vineyards in the Texas Hill Country
No full textThere has been a substantial increase in the population of the Texas Hill Country over the last
decade (Hill Country Alliance 2008). Currently, the population in the Texas Hill Country is 3.1
million and is projected to increase to 4.3 million by the year 2030 (Hill Country Alliance 2008).
With respect to agricultural production activities in the area, a major downside of the population influx occurring is that property values have increased at a rapid rate in association with the resulting high demand (American Society of Farm Managers and Rural Appraisers 2019, p. 54). With the rapid influx of people into this region of the state expected to continue, many rural property owners are looking for sources of income that could help offset the increase in property taxes associated with the steadily increasing land prices. In this regard, the potential that vineyards have in the Texas Hill Country, due to their proximity to wineries, presents an interesting question, ���Are vineyards economically and financially feasible for current and potential vineyard operators as well as investors?���
While the concept of growing wine grapes to offset the cost of land and associated property taxes is intriguing, there are several questions that present themselves. Potential producers, and investors, do not have all the necessary information needed to make an informed decision as to if growing grapes is a sound financial endeavor. This thesis is an investigation of a series of scenarios that consider the relative effects of several factors on the potential profitability of a hypothetical Texas Hill Country vineyard operation. Evaluation of these projections allows economic and financial evaluation of the potential of self-sustaining vineyards in the Texas Hill Country, providing information to producers and investors interested in growing grapes in the Texas Hill Country.
This thesis includes a detailed documentation of the assumptions and parameters for three
vineyards of varied sizes as well as the economic and financial results for each size for the
specified scenarios. High land prices contribute to substantial initial capital investment
requirements which places an immediate financial strain on the vineyard from day one. Based
on the established parameters for this thesis, the potential for vineyards in the Texas Hill Country to be economically and financially attractive to potential investors and producers is unlikely. The limitations of this thesis and suggestions for future research are identified, with intentions of identifying pathways for further evaluating the potential of vineyard investments and the value of vineyards in complementing winery operations
Unfolding the Complexity of Soil Chemical Process and Remote Sensing for the Detection and Monitoring of Oil Contaminants Using AI Techniques
No full textThe rapid acceleration of global economic development has significantly increased energy demands, leading to severe environmental consequences, particularly soil contamination due to oil pollutants. This contamination not only alters soil's physical and chemical properties but also jeopardizes its ecological balance and human health. In response to these challenges, our research embarks on a comprehensive exploration of soil contamination by oil pollutants, emphasizing the need for a deep understanding of these contaminants within their ecosystems. We investigate the effectiveness of various remediation strategies, considering the intricate dynamics of soil ecosystems. Our study aims to contribute significantly to environmental science by identifying pollutants and deploying tailored remediation techniques that harmonize with soil ecosystem complexities.
First, our research utilizes an AI-assisted systematic review to understand the remediation of soils contaminated with Polycyclic Aromatic Hydrocarbons (PAHs) and heavy metals. By employing literature databases, text mining, and interactive data mining tools, we aim to offer a holistic view of soil contamination. Results indicate a prevalence of combined treatment techniques, with biological-biological approaches being most common, highlighting the challenges and potential strategies for effective remediation.
Secondly, our efforts are directed toward transforming soil remediation techniques with the introduction of Advanced Fenton-Photo Systems, complemented by the integration of deep-learning neural networks aimed at refining petrochemical degradation processes. The empirical evidence from our research indicates remarkable oxidation rates of Total Petroleum Hydrocarbons (TPHs) and Polycyclic Aromatic Hydrocarbons (PAHs), with degradation rates reaching up to 99% in mere minutes. This highlights a substantial leap forward in the efficiency of removing contaminants from soil.
In our third objective, we harness Artificial Intelligence (AI) to enhance the capabilities of remote sensing in accurately predicting oil contamination within the Al-Burgan oil field. Our findings, derived from the application of advanced neural network models and Sentinel-2 satellite data, have significantly improved oil contamination detection, achieving flawless accuracy in certain scenarios. This approach not only refines the detection and quantification of oil contamination but also showcases the transformative potential of AI in elevating environmental surveillance and monitoring practices.
Lastly, the research is aimed at advancing the monitoring and prediction of vegetation coverage in arid ecosystems, with a specific focus on Kuwait's Burgan field, through the application of AI-integrated remote sensing. Our analysis has unveiled notable vegetation recovery following remediation efforts, highlighted by interannual and seasonal changes in vegetation cover. The utilization of Soil-Adjusted Vegetation Index (SAVI) and Enhanced Vegetation Index (EVI), processed through neural networks, has provided deep insights into vegetative dynamics. This underscores the effectiveness of remote sensing in ecological assessments, driven by the development of innovative vegetation indices and the employment of advanced remote sensing techniques. These efforts are pivotal in offering profound insights into the health and dynamics of vegetation, underlining the critical role of ecological monitoring and management.
This research will result in improved strategies for environmental management and remediation, offering novel insights and methodologies that facilitate the sustainable management of contaminated soils. Through a multifaceted approach that integrates advanced technological solutions and ecological understanding, our study contributes to the advancement of environmental restoration efforts, ensuring healthier ecosystems for future generations
Family Involvement in Literacy-Infused Science Learning for Texas Rural Youth and Communities
No full textWhile much research has studied children���s informal science learning with their families at exhibitions, museums and after-school programs such as family science nights, limited research has explored family science learning using take-home science activity kits. To better understand how home science activity kits are developed and implemented to support children���s informal science learning, a systematic review was first conducted. In the second study, I described the development of Family Involvement in Science (FIS) activity kits, one of the innovative components of a larger federal funded research grant in Texas. Seven rural fifth-grade Hispanic students and their families��� home science learning experiences were compared with seven non-rural counterparts using a comprehensive and reliable multi-dimensional and multi-categorical observation instrument called Family Involvement in Science Observation Protocol (FISOP). Students��� and family members��� time allocation participating in scientific practices, including use of strategies, activity structures, communication modes, language of content, and language use were observed and analyzed using the chi-square tests of homogeneity. Statistically significant differences were found in nine out of ten sub-domains between rural and non-rural families. In the third study, family was viewed as a socio-cultural learning environment that empowered parent-child science conversations as they actively engaged in FIS activities. Parent-child science talks from nine rural Hispanic families were captured through two pairs of GoVision camera goggles, and the videos were collected, screened, transcribed, and coded using two qualitative coding schemes: literacy-infused science strategies and scientific behaviors. The findings provided initial evidence that FIS encouraged Hispanic rural children and their parents to use scientific inquiry-based approaches and strategies in scientific conversations and activities
Relaxation Dynamics and Thermal Properties of Polyelectrolyte Complexes and Multilayers
No full textPolyelectrolyte complexes and multilayers (PECs and PEMs) are prepared by mixing two oppositely charged polyelectrolytes and by layer-by-layer assembly respectively. PECs and PEMs find applications in drug delivery systems, humidity sensors, electrochemistry, separation membranes, and many more. The key to these applications is understanding the dynamics of polyelectrolyte (PE) chains in PECs and PEMs. The dynamics of PE chain in PECs and PEMs is influenced by temperature, hydration, pH, salt, solvent quality, PE type and, many more. This dissertation discusses two studies on the impact of temperature, water, pH, and, salt on the dynamics of PE chain in PECs and PEMs. Additionally, we also study the influence of salt type on glass transition temperature of PECs.
In the first study, we discuss the impact of salt type on glass transition temperature (Tg) of PECs composed of PSS and poly(diallyldimethylammonium chloride) (PDADMA). We specifically examine the effect of anion type (NaCl, NaBr, NaNO��� and NaI) on Tg. First, we evaluate the effect of salt type on doping of PECs using nuclear magnetic resonance (NMR) and neutron activation analysis (NAA). Next, we evaluate the Tg at different hydration levels using modulated differential scanning calorimetry (MDSC). Put together, these studies give insight into how different parameters such as water, pH, salt and salt type influence the dynamics and relaxation of PE chain in PECs and PEMs.
In the second study, we discuss the effect of temperature, water and pH on relaxation times solid PECs composed of poly(acrylic acid) (PAA) and poly(allylamine hydrochloride) (PAH). It has been previously shown that water, pH and salt concentration impact the glass transition temperature of hydrated PECs. The effect of salt on relaxation times in PECs has been well documented. However, knowledge is lacking on the effect of water and pH on relaxation times in hydrated PECs. This is accomplished by performing time-temperature, time-temperature-water and time-temperature-water-pH superpositions.
In the third study, we discuss the impact of salt concentration on diffusion coefficient of PE in PECs using fluorescent recovery after photobleaching (FRAP) in PECs composed of poly(styrene sulphonate) (PSS) and poly(vinylbenzyl trimethylammonium chloride) (PVBTMA). We first map out the phase diagram at room temperature using optical microscopy, UV-Vis spectroscopy and conductivity measurements. Next, we study the diffusion of counterions using a home-built setup. Finally, we study the lateral diffusion of fluorescently tagged PVBTMA using confocal microscopy.
In the fourth study we discuss the impact of solvent on the lateral diffusion of PVBTMA in PSS-PVBTMA PEMs. First, we study the effect of varying weight % of ethanol and urea on the growth of PEMs. Next, we discuss the effect of varying weight % of ethanol and urea on the strength of interaction between the PEs using Isothermal calorimetry (ITC)
Improving the Sensitivity of the Search for New Resonances in the X->HH->bbWW Channel at the LHC with the CMS Detector
No full textThe Standard Model (SM) of particle physics successfully describes fundamental particles and three out of four fundamental interactions. However, it fails to incorporate the fourth fundamental interaction, gravity, and cannot explain observed phenomena like matter-antimatter asymmetry. These limitations suggest new physics beyond the SM, possibly probed via Higgs boson pair (HH) production at the CERN Large Hadron Collider (LHC). This dissertation presents the results of a search for new heavy resonances (denoted as ���X���) with spin-0 and spin-2 decaying into HH at the LHC in the bbW+W��� channel, specifically: X ��� HH ��� bbW+W��� ��� bbl+��l�����. It uses 137.6 fb���1 proton���proton collision data at a center-of-mass energy of 13 TeV recorded by the Compact Muon Solenoid (CMS) detector from 2016 to 2018. The presence of two escaping neutrinos in the final state leads to an unconstrained kinematic system, making direct reconstruction of the heavy resonance mass impossible. To address this challenge, the search employs a new technique, called the Heavy Mass Estimator (HME), which estimates the heavy resonance mass in a probabilistic approach. Additionally, compared to an earlier CMS search using only 2016 data, this search extends event selection criteria to enhance signal acceptance and adopts an advanced machine learning architecture. These two enhancements alongside the HME technique significantly increase sensitivity, achieving 2 to 5 times greater sensitivity compared to the 2016 search, assuming the same amount of data. No statistically significant evidence for new heavy resonances is found within the data. Upper limits at a 95% confidence level are set on the production cross sections of new resonances decaying into HH. These limits vary from 7.149 pb to 0.030 pb for spin-0 resonances and from 5.569 pb to 0.023 pb for spin-2 resonances, in the mass range from 250 GeV to 900 GeV
Economic Indicators of the College Station - Bryan MSA, February 2025
No full textThe Business-Cycle Index increased 0.4% from November 2024 to December 2024. The local unemployment rate remained unchanged at 3.3% in December 2024 compared to November 2024. Local nonfarm employment increased by 0.3% from November 2024 to December 2024. Inflation-adjusted taxable sales increased by 0.2% from November 2024 to December 2024. The median sales price for single family homes in Brazos County saw a year over year increase of 9.9% since January 2024
Optimal Mass Screening and Quarantine Policies in Heterogeneous Populations Under Limited Budget and Resources
No full textMass screening of populations is an indispensable public health tool that is extensively utilized in a variety of settings (e.g., screening blood transfusion, gastric cancer, sexually transmitted diseases (STDs)). The main objective is to efficiently screen a large population to accurately classify them as positive or negative for a certain binary characteristic (e.g., presence of an infectious agent). Owing to the advent of the COVID-19 pandemic, the topic of mass screening has gained considerable attention as it is a crucial aspect in effectively mitigating the spread of infectious diseases. The objective of mass screening is to maximize the overall classification accuracy under limited budget and testing resources.
We study the problem through the development of optimization-based frameworks that account for various factors, including population heterogeneity, imperfect assays, budget constraints, diverse testing schemes (individual and/or Dorfman group testing), the presence of multiple competing assays, and different testing approaches (proactive and/or reactive). These comprehensive considerations give rise to distinct optimization models. By analyzing the resulting optimization problems, we take advantage of the structure of the problem and identify efficient solution schemes.
Using real-world data, we conduct geographic-based nationwide case studies on COVID-19 screening in the United States. Our results reveal that the identified screening strategies substantially outperform conventional practices by significantly lowering misclassifications. Moreover, our results provide valuable managerial insights with regard to the distribution of testing schemes, assays, and budget across different geographic regions. Such insights can inform policy-makers with tailored and implementable data-driven recommendations.
Since screening can identify infected individuals and assess the associated risk levels, these testing efforts can significantly influence quarantine policies aimed at isolating positive cases. Consequently, our research also delves into the development of risk-based quarantine strategies. Our model takes into account the trade-off between healthcare benefits and the economic implications of quarantine measures. We show our resulting formulation can be cast as a more tractable network flow problem solvable in polynomial-time. We then proceed to calibrate our model using real-life COVID-19 and census data for the state of Minnesota. Our optimal risk-based quarantine policies exhibit substantial reductions in disease spread while maintaining favorable economic outputs
Planning Under Uncertainty with Unreliable Robotic Actuators
No full textWe focus on a critical aspect of autonomous robotics: the challenge of decision-making under the uncertainty of inevitable actuator degradation. Through the lens of physically embodied decision-makers, this research explores the complexity in modeling and planning for robotic actuator deterioration and failure. By an analogy to biological aging, we explore the necessity for agents to anticipate and plan for their own senescence, thus embracing their finite lifespan to maximize their utility. This shift towards acknowledging and planning for actuator frailty is particularly crucial for robotic explorers on interplanetary and interstellar missions, where autonomous, resilient decision-making is paramount.
Central to our approach is the introduction of Fallible Actuator Markov Decision Processes (FA-MDPs), an extension of the traditional MDP framework that incorporates actuator reliability into planning. This allows for the anticipation of failures, enabling strategic actuator usage and rapid adaptation post-failure. Our methodology leverages the inherent structure of FA-MDPs to decompose the problem into manageable sub-problems which increases solver efficiency. Furthermore, we explore the concept of actuator dominance and introduce virtual actuators to model k-shot and degrading actuators, thereby extending our failure model and improving planning performance.
The contributions of this thesis include: (1) A novel framework for incorporating actuator reliability into planning, enabling proactive planning for actuator failures. (2) An improved solution methodology for FA-MDPs through problem decomposition and a value function lattice, demonstrating superior performance over naive solvers. (3) An analysis of actuator relationships to further enhance planning performance and address actuator degradation. This work represents a step towards the development of long-lived autonomous robots capable of navigating the uncertainties of dynamic environments and their own inevitable deterioration