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Computational Design of Highly Stable Single-Atom Alloys
No full textMetal alloy catalysts are widely used in the chemical industry for their enhanced properties compared to monometallic catalysts. Adding a second metal alters the physicochemical characteristics of the host metal, improving catalytic performance and reducing susceptibility to chemical intermediates. Recently, single-atom alloys (SAAs) have stood out as single-site catalysts for their well-defined active sites. SAAs typically feature a small amount of active metal dispersed on a host metal surface, combining the benefits of traditional heterogeneous and single-atom catalysts. The performance and stability of SAAs depend on the dopant's ability to segregate to the surface without clustering, quantified by segregation energy (Eseg), aggregation energy (Eagg), and dopant diffusion (atom mobility).
This thesis focuses on investigating the thermodynamic and kinetic stability of SAAs under vacuum conditions (non-ligated systems) and in the presence of adsorbates (ligated systems). Research efforts have predominantly focused on select non-ligated SAAs and the impact of common reaction intermediates, such as CO and H, on their stability. First, we examined the segregation behavior across a wide range of SAA combinations, including platinum group metals as hosts and d8 (Ni, Pd, Pt)- and d9 (Ag, Au, Cu)-based SAAs, and facets under vacuum conditions and in the presence of commonly used ligands (amine and thiol groups) in colloidal nanoparticle (NP) synthesis using Density Functional Theory (DFT) and machine learning (ML). We developed models that predict Eseg in ligated and non-ligated SAAs. Second, we investigated the impact of ligands on the aggregation behavior. Furthermore, we developed a robust approach to identify cases where aggregates will form, based on the thermodynamic stability and surface strain features. Using ML techniques, we built a radial basis function kernel support vector regression model to predict the Eagg in both ligated and non-ligated systems. Third, we gained insight into the mobility of dopants in ligated and non-ligated Pt-based SAAs on (100) and (111) surfaces. Finally, we applied a genetic algorithm coupled with the bond-centric model to determine the most thermodynamically stable chemical ordering in multimetallic NPs composed of Ag, Au, Pd, and Pt. Overall, this thesis deepens our understanding of SAA and multimetallic NP stability and offers crucial insights that can accelerate catalyst design, essential for diverse industrial applications
Computational Modeling of Stability in Locomotion and the Effects of Vestibular Loss
No full textThe human body is unstable during walking and must be actively controlled. Four major strategies are used to maintain walking stability, including regulating foot placement, modulating lateral ankle roll, adjusting ankle push-off, and controlling trunk posture. These strategies use sensory feedback from the vestibular system, vision, and somatosensation. Deficits in the vestibular system are associated with decreased walking stability and increased fall risk. This dissertation used experimental and computational techniques to gain a greater understanding of how and why walking stability is impacted by vestibular impairment in people with vestibular hypofunction (PwVH) to suggest effective rehabilitation efforts. In Aim 1, I recruited healthy control participants (HCs) and PwVH to evaluate how these cohorts used the four strategies to maintain stability while walking with underfoot perturbations. HCs showed decreased stability following medial perturbations accompanied by decreased step width, increased ankle inversion, increased ankle push-off, and increased rightward trunk sway, with generally opposite changes for lateral perturbations. PwVH showed similar behavior; however, the response magnitudes were dependent on the side of the vestibular lesion and level of functional compensation. PwVH were more destabilized and had less effective trunk responses when perturbations caused acceleration toward the lesion. Additionally, poorly compensated PwVH were more unstable, showed exaggerated trunk and ankle responses, and walked slower. This aim highlighted the biomechanical differences associated with poor stability in PwVH. In Aim 2, I developed a computational model of human walking to understand why PwVH show poor gait stability and suggest mechanisms to improve stability. The model incorporated all stabilization strategies and responded to perturbations similarly to humans. Simulation results showed that exaggerated trunk sway can be attributed to vestibular loss, but poor stability is rather caused by PwVH walking more slowly. Normal trunk sway could be restored by reducing reliance on inaccurate vestibular input and increasing reliance on somatosensation. Stability could be improved by increasing step width. Together, these findings show how walking stability and stabilization strategies are affected in PwVH, why these differences arise, and how these impairments may be addressed through rehabilitation that modifies sensory reliance, walking speed, and the use of stabilization strategies
Fiber Optic Sensors Enabled by Femtosecond Laser for Energy and Biomedical Applications
No full textFiber optic sensors, as a flexible and adaptive sensor technology, have been widely used in nuclear power generation, infrastructure monitoring, fossil fuel exploration, disease diagnosis, drug screening, and environmental monitoring. Compared with traditional electronic sensors, fiber optic sensors offer advantages such as immunity to electromagnetic interference, high-temperature resistance, low cost, multiplexability, and biocompatibility.
Although commercial fiber optic sensors have been applied to measure parameters in the energy field, such as temperature and strain, they still have significant deficiencies as passive sensors in performing active measurements like liquid level and flow velocity, which require the sensors to be energized. Meanwhile, the application of fiber optic sensors in biomedical sector faces challenges such as high manufacturing costs, signal instability, and complex signal processing, limiting their broader application.
This thesis presents an integrated femtosecond laser fabrication approach to produce fiber optic sensors in the forms of multiplexed Fiber Bragg Gratings (FBGs), Rayleigh-enhanced distributed fiber sensors, and intrinsic Fabry-Perot interferometers (IFPIs). For energy applications, by optimizing the laser fabrication process, high-temperature and radiation-hardened fiber sensors are fabricated and tested for flow, liquid level, strain, and temperature measurement in multiple extreme environments. In the biomedical field, embedding FBG sensors into multilayer microfluidic chips enables low-cost, high-resolution temperature and flow monitoring. All these technologies and devices offer reliable and flexible sensing solutions for both energy and biomedical sectors, enabling capabilities that were not previously possible
Low-Cost Distributed Fiber Optic Interrogation Based on Optical Frequency Domain Reflectometry
No full textAs a highly flexible sensor technique suitable for wide-range real-time detection, with immunity to electromagnetic interference and adaptability to harsh environments, distributed fiber optic sensing has become indispensable in modern industrial fields, playing a key role in process control, fossil fuel management, and energy facility monitoring. Among these technologies, optical frequency domain reflectometry (OFDR) is distinguished by its high accuracy, sensitivity, and spatial resolution. However, its applicability is severely limited by the high cost of instrumentation.
This dissertation investigates the potential of using low-cost telecom distributed feedback (DFB) lasers to perform OFDR measurements. Through direct current modulation, a DFB laser with a 1-MHz linewidth is tuned across 1 nm to achieve a 1.2-mm spatial resolution, providing an affordable OFDR-based light detection and ranging (LiDAR) solution for diverse, complex environments.
The research further explores optical fibers with enhanced Rayleigh backscattering profiles to improve in-fiber OFDR sensing performance while reducing instrumentation costs. Utilizing ultrafast laser direct writing, the Rayleigh backscattering intensity of standard telecom fiber is enhanced by over 40 dB and the signal-to-noise ratio is improved significantly. This enhancement, combined with image processing, enables distributed strain sensing with a 4.8-cm gauge length and an error below 2.7 µε. The study also identifies limitations in the performance improvements associated with higher Rayleigh backscattering enhancements.
Additionally, the dissertation examines the effects of mode hopping in DFB lasers on the performance of low-cost OFDR systems. Numerical simulations reveal that mode hopping has minimal impact on free-space LiDAR measurements and only slightly affects distributed strain sensing, with errors of less than ±1 µε when 100 µε is applied.
These findings highlight the potential of using low-cost 1-nm DFB lasers in OFDR systems while maintaining reliable and accurate sensing performance. The findings detailed in this dissertation enable the development of high-performance optical sensors for a wide range of applications demanding high spatial resolution, which is unattainable with other measurement schemes such as microwave, ultrasonic, and electronic sensors, while offering a significant cost advantage over conventional OFDR instruments
Experiential Learning in Multiple Settings
No full textSituated in the context of declining college enrollment and low levels of student engagement, this narrative inquiry seeks to relate the lived experiences of students in three non-typical learning environments. The two research questions guiding this article are: How is engagement influenced by experiential/active learning, identity, and belongingness in non-typical settings? In what ways are student experiences similar and different across non-typical settings? The Narrative Inquiry methodology is used to provide a robust and authentic description of the places, people, and experiences in each setting. The findings support existing research on student engagement and highlight the interconnectedness of learning, identity, and belongingness. Comparisons between settings show the influence of institutional structures such as competency-based curriculum, hierarchical roles, and student-to-teacher ratio. Non-typical settings tend to have greater control over these factors than do typical settings and thus engagement in these settings is often higher than in formal classrooms
Creating Social Cohesion in a Rural Elementary School
No full textRural schools in high-poverty areas face significant challenges in fostering social cohesion
among students. This dissertation focuses on the implementation of a Social Emotional Learning
(SEL) curriculum in an elementary school. The initiative began with comprehensive teacher
training on the SEL curriculum and strategies for facilitating student engagement activities. Data
were collected to assess the effectiveness of the interventions, focusing on student achievement,
social-emotional skills, engagement levels, and the need for tier three support as identified by the
Multi-Tiered System of Supports (MTSS) screener. Feedback from both teachers and students was
instrumental in refining the approach.
Results indicated significant improvements in student self-esteem, empathy, selfregulation, social skills, and behavior. Teachers reported easier curriculum integration, increased
positivity, and improved student cooperation and accountability. Data showed little reduction in
the number of students requiring tier three support, with notable growth in students' ability to see
the good in others, avoid blame, stay neat and clean, and refrain from making excuses. Discipline
data also remained consistent with the previous year for this fourth-grade class, which is unusual
when compared with historical data from Springfield Clifford N. Pritts Elementary.
The goal of this Plan-Do-Study-Act (PDSA) cycle was to enhance student achievement
and social-emotional skills, thereby promoting social cohesion within the school. By continuing
to provide students with opportunities to learn and grow both academically and socially, the school may allow for continued growth in social cohesion and respect for the differences among us to all
for each student to reach their full potential in the school
Advanced autonomous vehicles analytics for predicting navigation performance.
No full textAutonomous Vehicles (AVs) stand as a monumental leap in modern transportation technology, offering the potential to enhance road safety and optimize transportation efficiency. However, their broad adoption is hindered by uncertainties associated with their sensors that allow for perceiving, interpreting, and interacting with their surroundings. Sensor uncertainties (SUs) arise from various sources, including sensor noise, varying environmental conditions, and inherent limitations in sensor design. SUs undermine the accuracy and reliability of AV navigation, posing substantial risks to AV performance and, by extension, to passenger safety. Considering the high stakes involved, including human lives, traffic flow optimization, and the structural integrity of transportation infrastructures, it is crucial for AVs to operate with minimal SUs. As driving on roads is dynamic with unpredictable elements, like sudden weather changes, AVs must be designed to handle both planned and unforeseen changes with unwavering precision. Failure to account for such uncertainties can cause unsafe driving and culminate in catastrophic outcomes, thereby deteriorating public confidence in autonomous driving technologies.
Common approaches to identifying and handling SUs in AVs involve data fusion and machine learning techniques. Despite their acceptable performance, these techniques are constrained by several critical limitations that hinder their applicability in complex real-world scenarios. For instance, conventional sensor fusion techniques often make overly simplistic assumptions, such as treating uncertainties as independent and normally distributed, which fail to capture the complex interdependencies and nonlinearities present in real sensor data. This simplification leads to suboptimal solutions, especially in challenging environments. Additionally, these techniques lack adaptive mechanisms to respond to changing environmental conditions, limiting their robustness. On the other hand, machine learning techniques, though capable of processing large volumes of data and uncovering hidden patterns, typically suffer from a lack of interpretability, often referred to as the “black-box” problem. This opacity inhibits a comprehensive understanding of the decision-making processes, complicating efforts to ensure transparency and accountability in AV operations. Furthermore, the extant literature is void in furnishing robust evaluative metrics and tools that could facilitate the systematic analysis of AV sensor performance, both before and after occurrence of incidents.
This thesis addresses these critical gaps by introducing an advanced AV analytics (AVA) framework and making the following contributions. Firstly, it introduces a novel ontology that represents and formalizes major concepts related to SUs in AV navigation. This ontology serves as a conceptual foundation for automated reasoning about navigation safety. Secondly, the thesis formulates a set of tailored performance metrics that provides a more nuanced evaluation of sensor reliability and accuracy under varying operational conditions. Thirdly, the AVA framework incorporates predictive models that not only quantify AV navigation sensor performance but also identify factors contributing to SUs. These models are unique in their multidimensional scope, encompassing environmental variables, and sensor specifications, and are of two types: online and offline. Online models focus on real-time evaluation of uncertainties for immediate decision-making, while offline models, also called forensic models, allow to analyze factors behind any unexpected behaviors. Finally, the thesis introduces a global path planner that integrates AVA’s analytical outputs to optimize AV route planning. Unlike commonly used route optimization criteria, such as shortest or fastest routes, this path planner incorporates sensor performance to identify safest routes by avoiding high-risk areas or conditions that could exacerbate SUs. These contributions are thoroughly validated using simulated and real data. The outcomes of the proposed research will help develop AV navigation solutions that are reliable and safe
Wireless strategies for infectious disease control: Contact tracing and hand hygiene monitoring.
No full textThe healthcare sector increasingly faces disruptions that demand advanced strategies, robust systems, and innovative solutions. One significant disruption has been the COVID-19 pandemic, which has claimed over 7 million lives globally. Its rapid spread and the emergence of new variants highlight the importance of preventive and screening measures such as vaccination and contact tracing (CT). CT identifies individuals exposed to infected persons, allowing timely interventions like isolation. A common method of digital contact tracing (DCT) involves using smartphones with Bluetooth to broadcast and register close contacts (phone-phone CT). However, these approaches suffer from low accuracy due to limited control over range. Also, most DCT efforts focus on direct contact, such as touching or talking, while neglecting indirect contact via contaminated surfaces or respiratory particles.
Another critical issue in healthcare is healthcare-associated infections (HAIs), which, according to the World Health Organization, are a leading cause of mortality in healthcare settings. One major contributor to HAIs is the failure of healthcare workers (doctors, nurses, etc.) to consistently adhere to hand hygiene protocols.
This factor also contributes to the transmission of infections like COVID-19 within hospitals.
Ensuring proper hand hygiene compliance (HHC) can significantly reduce the incidence of HAIs.
This dissertation addresses both of these challenges. First, it aims to enhance the accuracy of DCT while safeguarding user privacy. We use the deployment of Bluetooth-based IoT devices in public gathering spaces, such as restaurants, hospitals, and schools, to detect direct and indirect contacts. We create a simulation to study the improvements of this method over common phone-phone-based approaches and efficient strategies for placing beacons. Additionally, we extend this approach to support bidirectional tracing, identifying additional contacts arising from asymptomatic carriers. We observe that the proposed bidirectional CT outperforms existing DCT works in averting possible infections. To address the second challenge, we propose a deep learning-based system that utilizes WiFi channel state information (CSI) to monitor hand hygiene compliance. We observe that the proposed model outperforms existing time series models on an existing HHC dataset in accuracy and training time
In Between Sovereigns: The Political Economy of International Oil Concessions
No full textThis dissertation develops a novel theory of oil nationalization based on insights from new institutionalism in political science and economics, arguing that high enforcement costs inherent in international oil concession agreements contribute significantly to oil nationalization. The 20th century, characterized by fragile international oil governance, saw numerous instances where international oil companies (IOCs) struggled to enforce contracts, leading to host state opportunistic behavior and ultimately nationalization. While existing scholarship offers valuable insights into the political, economic, and ideological drivers of oil nationalization, it often overlooks a crucial structural factor: the lack of third-party enforcement in international oil concession agreements. This research utilizes a case study methodology, focusing on the Aramco concession agreement with the Saudi Arabian government. Through an analysis of archival data, diplomatic correspondence and personal memoirs, the research reveals the substantial enforcement costs paid by both Aramco and the United States’ government in their efforts to maintain the concession agreement. These costs, manifesting as financial compromises, diplomatic maneuvering, and even covert operations, highlight the fragility of self-enforcing contracts in the absence of a formal third-party enforcement mechanism. The dissertation argues that nationalization emerges as a consequence of the failure to adequately address these escalating enforcement costs. Building upon these findings, the dissertation suggests future research avenues exploring other historical nationalization cases, such as the Anglo-Iranian Oil Company (AIOC) in 1951, as well as contemporary examples such as Chinese national oil companies operating abroad. By investigating these diverse cases through the lens of enforcement costs, this research contributes to a deeper understanding of the complex dynamics shaping international resource governance
Visualizing Menstrual Traditions and Waste Disposal through Collaborative Filmmaking in Urban Indian Slums
No full textWhile many studies have explored menstrual stigma in India, there is limited research on the disposal of menstrual products. Harmful menstrual attitudes impact hygiene and sanitation practices, making the proper disposal of menstrual products a critical issue. Researchers implemented Collaborative Filmmaking, a participatory visual method, in three slums of Mumbai—Dharavi, Kandivali, and Kalwa — to explore the menstrual and sanitation experiences of 23 women and girls, aged 13-46. Participants filmed their own experiences, after which individual co-analysis interviews were conducted to review the films and identify key themes. Group screenings followed to discuss similarities and differences among the group, and community screenings were held to encourage wider dialogue. The study found that sanitary pads were the most common menstrual product, valued for comfort and accessibility. While there was interest in alternatives like menstrual cups, fear of insertion and limited availability deterred their use. Disposal challenges were linked to a lack of waste management systems, clean public toilets, and misinformation on proper disposal, jeopardizing women’s health and access to sanitation