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Bifacial Passive Radiative Cooling of Silicon Solar Cells Using PDMS for Increased Efficiency
No full textWith the rising levels of pollution and environmental degradation due to fossil fuel energy, there is an urgent need for clean and renewable energy. To compete with the vastly greater generation capacity of fossil fuels, the sources of renewable energy need to be more efficient at lower cost. There has been an increasing amount of work in improving the efficiency of solar cells by using several different materials and methods to generate power more effectively. However, all standard solar cells heat up in the course of operation under the sunlight. This heat absorption comes with significant reductions in the efficiency, reliability, and overall lifespan of the solar cells. In this thesis research, we present a relatively inexpensive, energy efficient and simple way to increase the efficiency of a solar cell by using a high emissivity material showing the effectiveness of radiative cooling on the simplest monocrystalline silicon solar cell. We found that coating a high emissivity material, such as PDMS, on both top and bottom of a solar cell allows for a large amount of thermal radiation to be removed to outer space, which acts as the ultimate heat sink. With in-field tests, we found an average temperature reduction of 6��C of a 1.06% performance increase. Considering the limited power conversion efficiency of a typical solar cell, we anticipate that energy efficient radiative coolers will be ubiquitous in the upcoming generations of solar cells
Advanced Autonomous Algorithms for Versatile Terrain Navigation and Multirobot Coverage Control
No full textThe primary objective of this research is to develop robust autonomous navigation and multirobot coverage control algorithms with broad applicability. To achieve this objective, this dissertation delves into three core topics: 1) terrain-aware path planning, 2) real-time stair detection, and 3) multi-robot coverage control. To address the challenges associated with autonomous navigation using vision-based terrain classification, a novel technique, the uncertainty rejection filter, is introduced. This filter, when combined with a neural networks-based terrain classification model, enhances the reliability of autonomous navigation by identifying uncertain regions and assigning appropriate traversal costs. Simulations and field tests demonstrate the effectiveness of this path-planning scheme. This dissertation also presents a real-time stair detection algorithm based on a decision boundary-aware model. Leveraging a support vector machine trained on RGB images, the algorithm outperforms existing models. Lastly, novel algorithms for multi-robot coverage control are proposed for both homogeneous and heterogeneous systems. A centralized approach incorporating agent dropout and reinsertion processes improves overall coverage, while a decentralized version achieves desired outcomes without a central computer. These algorithms exhibit improved coverage performance in diverse non-convex environments. Additionally, a user interface is introduced to enable users to define target areas for coverage by the proposed algorithms. Field experiments showcase the successful integration of the user interface with the coverage control algorithms
From Syntheses to Applications of Cyclized Conjugated Molecules and Macromolecules
No full textThis dissertation delves into conjugated molecules and macromolecules featuring cyclized constitutional structures, including conjugated ladder molecules, polymers, and macrocycles. These materials offer unique optical and electronic properties due to their extended ��delocalization and strong intermolecular coupling, stemming from their rigid structures. The dissertation explores how these structures can be integrated into conjugated molecules and polymers to address challenges such as instability and limited state delocalization, paving the way for practical applications.
The introduction provides an overview of conductive organic molecules and discusses conjugated macrocycles and ladder molecules, along with the processing of conjugated ladder polymers.
Chapter II presents the synthesis of conjugated ladder polymers with isotopic substitutions, such as deuterium and carbon-13 labels. Deuterium labeling enhances neutron scattering contrast, aiding structural analysis, while carbon-13 labeling assists in defect quantification. Two such polymers are synthesized.
Chapter III introduces the synthesis of a ladder-type structure in polyaniline-inspired polymers. A low-defect conjugated ladder polymer is synthesized, achieving high conductivity (7 mS cm^���1 ) through oxidation and acid doping. It demonstrates exceptional stability against acids and UV irradiation, surpassing commercial standards, and excels in electrochromic devices and supercapacitors.
Chapter IV describes a self-doping ladder-type cyclohexadiene-1,4-diiminium-based system, offering stability and homogeneity, with a conductivity of 1��10^���3 S cm^���1 , surpassing traditional p-type molecules.
Chapter V discusses the synthesis and iodine doping of conjugated macrocycles with different side chains, forming single-crystal structures when doped with iodine, with conductivity ranging from 2.6��10^���3 to 0.65 S cm^���1 . The chapter also explores crystal packing and doping mechanisms.
The dissertation concludes with an outlook on future research into conjugated ladder molecules and cyclic macrocycles, showcasing how cyclized structures in conjugated molecules and macromolecules address challenges in organic electronics, revealing their potential as next-generation electronic materials
Targeted Therapeutic Strategies Against Triple-Negative and Metaplastic Breast Cancers
No full textBreast cancer is the second most common cause of cancer mortality in women in the United States. As a heterogenous disease, breast cancer is clinically classified by expression of estrogen receptor (ER), progesterone receptor (PR), and overexpression/amplification of human epidermal growth factor receptor 2 (HER2). Triple-negative breast cancer (TNBC), i.e., those not expressing ER, PR, or HER2, represent ~10-20% of all breast cancer cases, has the worst prognosis in comparison to ER+ or HER2+ breast cancers.
Gene profiling analysis has identified TNBC subtypes with unique molecular signatures that may be therapeutically targeted. Among the six known TNBC molecular subtypes, the mesenchymal subtype comprises 30% of all TNBCs and has the worst disease-free survival compared to other TNBC subtypes. Metaplastic breast cancer (MpBC) is a highly aggressive, and metastatic breast cancer malignancy that typically falls under the mesenchymal TNBC subtype, accounting for <5% of all invasive breast cancers. Patients with MpBC are generally diagnosed at a later age with a more aggressive disease, likely contributing to their dismal median survival of 8 months after metastasis. There are no standardized therapeutic options for patients with MpBC.
MpBC is characterized by one or more cell populations that have undergone metaplastic differentiation, i.e. cells convert from glandular to non-glandular morphology. MpBCs are enriched with epithelial-to-mesenchymal (EMT) and cancer stem cell (CSC) markers, with hyperactivation of phosphoinositide-3-kinase (PI3K) pathway and enhanced nitric oxide (NO) production. Patients diagnosed with metaplastic TNBC have a significantly worse prognosis compared to non-metaplastic TNBC (non-MpTNBC). Thus, the development of effective therapies for women with MpBC and non-MpTNBC represents a significant unmet clinical need.
The focus of my dissertation has been on evaluating novel therapeutic strategies in the preclinical setting to treat MpBC and non-MpTNBC. Using human breast cancer cell lines and patient-derived xenograft (PDX) models, my preclinical studies focused on evaluating targeted therapies, such as utilizing monoclonal antibodies against EGFR to treat TNBC, as well as combining PI3K and nitric oxide synthase (NOS) inhibitors to treat MpBC. I found that combined PI3K and NOS inhibition reverses EMT, decreases CSC populations, rendering MpBC tumors more chemosensitive
Advanced Techniques of Image Dehazing and Object Detection in Hazy Environments
No full textIntelligent Transportation Systems (ITS) represent the future of smart city transportation, heavily reliant on computer vision for tasks such as object detection and recognition. However, real-world scenarios often introduce challenges such as haze, which can significantly impact the effectiveness of computer vision systems. This dissertation addresses these challenges through the proposal of innovative methods aimed at enhancing object detection in hazy environments.
Firstly, a novel Multiple Linear Regression Haze-removal model based on Dark Channel Prior (MLDCP) is introduced. This model achieves state-of-the-art dehazing performance by optimizing the estimation of transmission maps and atmospheric light. Additionally, pre-dehazing test images using MLDCP significantly enhance object detection accuracy by improving visibility. Secondly, a Synthetic Haze Generation Model is proposed to augment existing image datasets. By introducing synthetic haze, this model enables the training of object detectors robust to hazy conditions. This approach enhances the generalizability of object detection models, enabling them to perform effectively in diverse environmental conditions. Furthermore, depth estimation is addressed using a Dilated Fully Convolutional Neural Network. By incorporating dilated convolution, this model achieves depth estimation with reduced computational costs while maintaining high accuracy. This enhancement contributes to a more comprehensive understanding of the geometric relations within a scene, further improving object detection performance. Lastly, a Local Dehazing algorithm based on Dark Channel Prior is proposed. Leveraging object coordinates obtained from object detection tasks, this algorithm optimizes dehazing over specific regions, leading to improved detection accuracy on targeted objects.
Collectively, these contributions advance the field of computer vision for intelligent transportation systems and related applications in hazy environments. By addressing the challenges posed by haze, these methods offer promising solutions for improving the reliability and effectiveness of ITS in real-world settings
Dry Cell Radiation Field Characterization at the Texas A&M University Nuclear Engineering and Science Center
No full textThe Nuclear Engineering and Science Center at the Texas A&M Engineering Experiment Station routinely conducts experiments involving neutron and gamma irradiations with a 1-MW TRIGA research reactor. The Center is equipped with a dry irradiation cell, which can be used for experiments requiring a mixed neutron/gamma field. However, in recent years the dry irradiation cell has not been utilized and there exists no surviving information on the radiation environment inside the cell during operation. To resolve this, a full characterization of the neutron and gamma radiation environment inside the cell has been completed.
To fully characterize the neutron flux environment inside the dry irradiation cell, neutron activation analysis has been performed on two different configurations: one with shielding and one without shielding. The program STAYSL PNNL was employed to unfold the full neutron spectrum using the experimental results of the neutron activation analysis. Furthermore, thermoluminescent dosimetry has been utilized to calculate the absorbed gamma dose rates of silicon and tissue inside the cell for each configuration. Each technique has been utilized according to industry standard practices.
The results of this work have shown that adding shielding material to the dry irradiation cell window will have a significant impact on the quantity and energy of radiation which enters the cell. With no shielding present in the window, 50-kW reactor operation produces a neutron flux of 2.59E+09 and a gamma dose rate 50.84 krad/hr in silicon. When 8 inches of high-density polyethylene shielding and 0.8 inches of lead shielding are installed in the window, the neutron flux drops to 4.81E+07 while the gamma dose rate falls to 13.45 krad/hr in silicon
Colonial Calamities: The Politics of U.S. Disaster Relief and Economic Development in Puerto Rico, 1898-1979
No full textWhen Hurricanes Maria and Fiona battered Puerto Rico in 2017 and 2022, it reopened public conversations about Puerto Rico���s colonial status. With thousands out of electricity, displaced from their homes, and ruined economically, then- and only then- did U.S. policymakers debate the appropriate humanitarian response. In addition, the disaster relief aid overseen by the Federal Emergency Management Agency (FEMA) provided only temporary solutions but did nothing to address the ongoing structural issues of Puerto Rico. This type of response was not a new phenomenon. It was just the latest chapter in a century-long history of United States disaster relief operations that continually reinforced Puerto Rico���s colonial status.
This dissertation Colonial Calamities: The Politics of U.S. Disaster Relief in Puerto Rico, 1898-1979, is the first to explore the history of American disaster relief in Puerto Rico from the period 1898-1979. Disasters provide a window to reveal how structures of power determined who ���deserved��� aid and what conditions were attached to it. I argue that after disasters, the relief policies undertaken by U.S. government institutions were justifications to strengthen influence in the region and actually made Puerto Ricans more vulnerable to disasters. The American military, Red Cross, and colonial administrators prioritized short-term mitigation strategies that served U.S. strategic and economic interests. In this way the American colonial government constructed and maintained Puerto Rican vulnerability to disasters and the vagueness of status as subjects/citizens. American policymakers perpetuated the idea that if Puerto Ricans were poor and vulnerable to disasters, it could not have anything to do with the United States or colonialism. However, policymakers also thought that if Puerto Rican poverty and vulnerability to disasters was caused by Puerto Ricans themselves, then the U.S. and only the U.S. could help them. Together, these ideas inscribed Puerto Ricans as inferior, borderline citizens, while at the same time rejecting them as completely alien. To combat these positions and policies, Puerto Ricans developed local networks to promote solidarity and conduct disaster relief without relying on American agencies. This strategy proved important during the latter part of the twentieth century with the increasing effects of climate change causing more frequent disasters
The Joppa Environmental Health Project: Evaluating Local Air Quality and Respiratory Health in Dallas' Environmental Justice Neighborhoods
No full textDue to historical racial segregation, People of Color (POC) and low socioeconomic status groups experience disproportionate exposure to particulate matter (PM) air pollution. Fine PM (PM2.5) pollution is associated with several health effects, including cardiovascular and lung disease, respiratory mortality, and adverse birth outcomes. There is a need to characterize environmental and health disparities in susceptible populations like environmental justice (EJ) communities. In Dallas, Texas, EJ communities such as Joppa and ���Singleton��� are POC neighborhoods that are surrounded by multiple pollutant sources. Using a community-based participatory research (CBPR) approach, the Joppa Environmental Health Project (JEHP) was formed in 2020 with the primary objective to assess residents��� perceptions of air pollution and other health concerns through a community household health survey and to evaluate PM pollution through the installation of a local air monitoring network. To carry out these objectives, the following aims are addressed in this dissertation: 1) apply a CBPR approach to facilitate community engagement and action-oriented research on PM pollution and respiratory health in Joppa; 2) quantify daily and monthly PM2.5 concentrations in Joppa and ���Singleton��� using low-cost sensors, PurpleAir and SharedAirDFW; and 3) examine the association between daily PM2.5 exposure and airway inflammation in children residing in Joppa.
Strong community engagement activities and efforts from our steering committee yielded a high neighborhood survey response rate (51%). A high lifetime asthma rate (18%) was also observed among survey respondents. Monitor data demonstrated elevated levels of PM2.5 concentrations in Joppa. Last, active airway inflammation was observed in children who were not diagnosed with asthma. Overall, the application of this research facilitated robust community engagement and resident-led research to address critical neighborhood concerns. Subsequently, an asphalt batch plant in Joppa voluntarily relocated operations in June of 2023. The steering committee organized a new group known as Justice for Joppa/Justicia para Joppa to sustain momentum created by this work, targeting local zoning and land use policies. Future work from this research will involve the placement of a permanent medical clinic in Joppa where residents will have access to long-term health support (i.e., chronic disease diagnosis and management)
Wind Tunnel Data Quality Assessment and Improvement Through Integration of Uncertainty Analysis in Test Design
No full textThe practical application of uncertainty quantification in wind tunnel testing is not consistently or proactively applied. Although there is a solid methodology to quantify uncertainty, the resources required to implement this methodology at the pace of testing while adapting to the unique designs for each test are rarely available. This research combines the use of Monte Carlo simulations for uncertainty quantification with a decision-based integration of uncertainty estimates into the test design process and test execution. This implementation reduces the resources required to routinely quantify uncertainty to a practicable level and aims to proactively affect data quality by incorporating uncertainty estimates into early test design decisions. This methodology is used in the design, execution, and data analysis of a wind tunnel test at the Oran W. Nicks Low-Speed Wind Tunnel (LSWT) at Texas A&M University. The test analyzes the uncertainty in the aerodynamic coefficients and performance parameters of an aircraft test model. After quantifying the uncertainty of the aerodynamic coefficients, this research investigates a potential elemental error source in the measurement of static aerodynamic coefficients due to oscillating nonlinear aerodynamic loads. Notable results from this research include the demonstration of integrating uncertainty analysis with test design in a practical way, reduction of uncertainty intervals in aircraft performance parameters measured in the LSWT by an average of more than 90% through this integration, and experimental evidence of an elemental error source from oscillating nonlinear aerodynamic loads in the measurement of static aerodynamic coefficients
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