Femtosecond Laser Figuring and Polishing for Freeform Optics Fabrication

Freeform optics revolutionizes imaging systems by enabling compact designs and high-performance capabilities, which are crucial for applications such as augmented and virtual reality, medical diagnostics, biosensing, space exploration, remote sensing, and EUV lithography. However, fabricating freeform optical components presents significant challenges: conventional chemical and mechanical methods generate chemical waste and introduce mid-spatial-frequency errors that degrade imaging contrast. This research seeks to overcome these limitations by establishing ultrafast-laser-based figuring and finishing methods. These methods offer a fast, non-contact, flexible, and precise green solution, paving the way for sustainable, high-quality freeform optics fabrication. We expanded a laser pulse propagation model to simulate the ultrafast-laser-glass interaction processes, including plasma generation, temperature evolution, and material removal. Through interactive modeling and experimental validation, we determined optimum laser processing parameters to precisely control ablation and thermal effects. This approach enables the fabrication of optical-quality surfaces with nanometer precision and sub-nanometer surface finishing, critically without detectable subsurface damage. Key demonstrations include the fabrication of staircase structures and the correction of mid-spatial-frequency-like errors, achieving optical-quality surfaces. Furthermore, we developed a predictive method for generating periodic nanostructures and successfully fabricated microprism structures and arrays. These advancements demonstrate the transformative potential of the proposed figuring and finishing methods, systems, and processes for fabricating complex freeform optics with unprecedented precision, flexibility, and quality

Application of Science and Technology Studies to Global Medical Device Donation Policy

In low and middle income countries, the World Health Organization estimates that nearly 80% of medical devices and equipment are procured through donation (WHO 2000). While these donations have the potential to play a critical role in the recipient communities’ health, the donated devices sometimes fall short of their potential due to improper use or lack of support—despite the existence of a variety of guidelines and regulations for medical equipment donations. While conducting volunteer biomedical equipment technician work in Guatemala and Uganda, I came to notice the disparity between the guidelines and the reality on the ground. This study aims to identify and address the underlying processes and attitudes fueling this disparity using critical frameworks from the discipline of Science and Technology Studies: critical innovation studies, maintenance studies, feminist technoscience, and discard studies. This is a multi-sited mixed-method study incorporating nearly three cumulative months of fieldwork in various healthcare facilities throughout Guatemala and Uganda. Interviews with a variety of stakeholders in Uganda and the US were iteratively coded using qualitative analysis software (MAXQDA). Analysis was based in grounded theory to bring forward the lived experiences of all stakeholders. In order to improve existing donation policies and guidelines, a number of tenets of “useful donations” are identified. These include continuous and multidirectional dialogue, ethics of empowerment and care, and emphasis on long-term system-level change. By situating the analysis in the specific contexts and struggles of my research sites, the conclusions take into account the existing infrastructural and maintenance systems. Despite the study design’s specificity, the conclusions are applied to the larger context of organizational and governmental policy for donations, community health, engineering, and advocacy

Personalized Nutrition Recommendations for Arab Communities: Transforming Diet and Health through Machine Learning

A lack of cultural understanding in traditional nutrition approaches prevents proper adherence to healthy dietary patterns among individuals. The Arab population faces unique challenges due to cultural dietary customs and lifestyle changes that have made obesity and diet illnesses major global public health challenges. The rapid shift to high-calorie but undernourished foods and less physical activity has resulted in alarming rate increases of obesity, cardiovascular diseases, and type 2 diabetes. The lack of cultural understanding in traditional nutrition approaches prevents proper adherence to healthy dietary patterns among individuals. This study focuses to fills the existing gap by applying Artificial Intelligence (AI) and Machine Learning (ML) methods to establish personal dietary recommendations for Arab populations. This research uses advanced AI methodologies to evaluate dietary behaviors while combining cultural food tastes and health status information into personalized dietary recommendations. The dietary recommendation process utilizes four machine learning models comprising SVM and Logistic Regression together with Random Forest and Gradient Boosting. The system creates customized dietary plans by evaluating several aspects including BMI values and health issues and dietary limitations together with caloric consumption information and cultural food choices. Furthermore, the research shows that Artificial Intelligence (AI) implements dietary interventions better than traditional approaches because it produces targeted recommendations which reflect individual culture. The evaluation of the model’s performance measured its accuracy together with precision and recall which yielded encouraging outcomes towards promoting healthier eating habits. To provide fair and ethical recommendations, the implementation requires solutions for data biases, solutions for restricted access to regional dietary data, and transparent algorithms. The study advances AI-driven personalized nutrition research through its framework that integrates AI techniques into culturally appropriate dietary interventions. Future research needs to work on extending data sources while integrating immediate feedback from users alongside advanced AI model development to improve forecasting precision. The implementation of AI-driven dietary systems has the potential to transform public health policies and improve nutritional outcomes for diverse populations

The Cutting Edge: A Virtual Reality Simulation For Surgeons to Learn and Understand Carpal Tunnel Surgery

As virtual reality (VR) technology becomes increasingly popular, its applications in healthcare have gained significant traction. Many current VR training tools for surgeons lack aesthetic and functional depth; they lack an understanding of the tools needed in the operating room and have limited user experience design and interactivity. In addition, there is an opportunity for specialization with immersive training models tailored to specific procedures like carpal tunnel surgery, making it difficult for surgeons to visualize complex techniques before practicing on real patients in a realistic and guided environment. This project investigates the intersection of VR experiences and their potential to shape the future of surgical training, specifically focusing on carpal tunnel surgery, by developing an immersive experience using tools such as Figma and Cinema 4D, aiming to understand the future of user experience design for surgeons within a virtual environment. With design tools like Figma and animation software, as well as 3D rendering and animation tools such as Cinema 4D and Adobe After Effects, we can create an immersive prototype with custom UI screens, intuitive UI icons, and seamless interactions. To enhance their confidence and precision, the goal is to envision a VR environment where surgeons can gain a clearer, more engaging understanding of carpal tunnel surgery techniques before entering the operating room

Product, Process, and Behavior Influences on Value Retention Processes: Toward a Circular Economy in Consumer Electronics

Consumer electronic products (CEP) manufacturing and retail contribute markedly to global industrial activity; worldwide estimates suggest revenue growth from $1 trillion in 2020 to$1.5 trillion by 2026. Consequently, the production, distribution, use, and end-of-life (EOL) disposition of these products are responsible for considerable social and environmental impacts, including 35 million metric tonnes of waste to landfill per year, 793 million metric tonnes (MMT)—and growing—of carbon dioxide-equivalent (CO2e) greenhouse gas (GHG) emissions per year, and innumerable adverse effects on human health and development. Driving these impacts are the inherent characteristics of CEPs themselves, which increasingly require critical materials in design, high energy consumption in manufacturing, and complex treatment of hazardous waste streams at EOL. In the age of information, however, access to CEPs is increasingly essential to technological and socioeconomic development, particularly in emerging Global South economies. Serving these growing consumer needs while mitigating environmental and human health impacts is a considerable challenge. The circular economy is broadly emerging as a means to address these challenges. In particular, value retention processes (VRPs) including remanufacturing, refurbishing, repair, and direct reuse are gaining market share and acceptance as practical applications of circular economy principles. In some industry sectors—e.g., automotive, aerospace, and commercial machinery—VRPs have been found to be more economically efficient than and environmentally preferable to incumbent linear business models, offering a means to decouple economic advancement from increasing environmental impact. Accelerating demand for CEPs and accordingly growing materials, energy, manufacturing byproducts, and EOL waste problems highlight the necessity of such decoupling to sustainable development in this sector as well. To that end, this research provides a framework for quantifying the market potential for and possible impacts of a shift to VRPs in consumer products industries at large, using the CEP sector as a high-impact case study. To this end, Chapter 2 conducts product-level material flow analysis (MFA) for five key CEP types to assess the material and behavioral feasibility of VRP models in the CEP sector across developmental strata, highlighting the United States of America (US) and the Republic of Ghana (GH) as Global North and South case studies, respectively. Chapter 3 then assesses the relative environmental performance of possible VRP business models at the product level, using survey data on specific rates and modes of failures to inform new VRP life cycle assessment (LCA) models for two case study products at opposite ends of the CEP spectrum: a smartphone and a domestic refrigerator. Finally, Chapter 4 assess the economic viability of VRP business models in the CEP sector through the user lens, proposing a new model for multigenerational multicriteria decision analysis (MCDA) between new and VRP CEPs. We evaluate this model using best available market, survey, and product specification data, and investigate how product attributes and consumer preferences influence VRP purchasing decisions in CEP markets. Outcomes of this research are twofold. First, Chapter 2 and 3 case study modeling results suggest that VRPs are indeed materially feasible and environmentally preferable across the CEP sector, even under current market, technology, and behavioral conditions. Similarly, Chapter 4 results illustrate that circular shifts are a plausible economic reality, and sensitivity analyses highlight strategies to optimize the benefits of and alleviate barriers to such shifts. Second, the underlying modeling frameworks themselves provide a foundation for quantitative analysis of how technology, market, and policy factors affect the viability and preferability of VRPs across contexts. This architecture is thus adaptable across product types, market circumstances, and developmental strata, supporting broader global analysis of circular opportunity, enabling conditions, and possible benefits

Structure/Property/Processing of a 3D Printed Self-healing Polymer Blend based on a Thermoplastic Healing Agent

Self-healing polymers can regain mechanical performance following damage, offering increased material durability and sustainability. This thesis establishes structure/property/processing relationships for a 3D printable extrinsically self-healing polymer based on a UV polymerizable thermosetting resin system blended with a low temperature thermoplastic healing agent. This work serves as the first example of a vat polymerization 3D printed soft, low Tg low-melt thermoplastic extrinsically self-healing polymer blend. This development enables high resolution fabrication of complex geometries with self-healing functionality. This strategy of imbuing self-healing properties onto vat polymerization resins will enable functionality in many application spaces including aerospace, biomedical, soft robotics, coatings, and military. Successful 3D printing of this type of material was found to have several requirements. The thermoplastic healing agent must first be miscible in the liquid resin system. This enables the light to be able to penetrate through the resin to initiate polymerization. This solubilizing of polymer unfortunately results in an increase of resin viscosity which results in difficulties in 3D printing, as lower resin viscosity are required for vat polymerization techniques. As the thermoset resin undergoes polymerization, the thermoplastic phase separates and subsequently crystallizes, resulting in a two-phase system. Upon heating above the thermoplastic’s melting temperature, it can flow into damaged regions; upon cooling, it recrystallizes to bond the fractured interfaces. Throughout this process, the thermoset phase preserves the overall geometric integrity of the structure. This work explores the intersection of materials chemistry and additive manufacturing by investigating how both the concentration and molecular weight of a thermoplastic healing agent influence 3D printability, as well as the resulting mechanical and self-healing properties of the printed material. In addition, the study explores how key 3D printing process parameters, specifically, print temperature and layer height, affect resin 3D printability and final material performance. This work lays the foundation for uncovering a deeper understanding of polymer behavior, paving the way for the design of highly functional, self-healing printed materials for a wide array of advanced applications

Resonating Patterns: Adaptive Resonance Theory and Self-Organizing Maps

Adaptive Resonance Theory (ART) represents a powerful neural network architecture designed to address the stability-plasticity dilemma. Its primary objective is to enable rapid learning without compromising retention. ART embodies characteristics of self-organization and self-stabilization, distinguishing it from traditional neural networks. Unlike error-based learning prevalent in conventional models, ART employs competitive learning mechanisms. One of the distinguishing features of ART is its involvement in hypothesis testing, a departure from the approach of deep learning. This capability allows ART to learn dynamically in real-time environments. Over time, various ART models have emerged that cater to different types of data, such as binary and analog. This research proposal aims to explore the artlib library in Python, implementing various ART models on different datasets to analyze and compare their performance with more conventional algorithms like Self-Organizing Maps and K-Means clustering. This study seeks to highlight their efficacy, practical implementation, and differences between these frameworks

A Computational Framework for Investigating Novel Bacterial Conjugation Inhibitors

Antimicrobial resistance (AMR) is a major global threat, contributing to an estimated 700,000 deaths annually. One of the main ways that AMR genes spread among bacteria is through conjugation. Therefore, it is crucial to develop conjugation inhibitors (COINs) to combat AMR. Shared characteristics between known COIN molecules are a hydrophobic tail, unsaturations, and a polar head. Considering these characteristics and accounting for the efficacy of 2-hexadecynoic acid and Tanzawaic Acids (TZAs) low toxicity, we designed a new set of TZA analogs. The predicted protein target of previously identified COINs is TrwD, an ATPase in the type IV secretion system (T4SS). This multi-protein complex plays a crucial role in bacterial conjugation. In this work, we set up a computational pipeline to screen our TZA analogs. This pipeline involves predicting the structure of the experimentally uncharacterized protein TrwD structure using homology-based protein structure prediction via Protein Homology/analogy Recognition Engine V 2.0 (Phyre2), and identifying binding pockets and potential binding poses are identified using a combined SwissDock and MELD (Modeling Employing Limited Data) accelerated molecular dynamics (MD) simulations (MELDxMD) approach. Preliminary results with ATP, the natural ligand for TrwD (the predicted target of COINs), demonstrate the effectiveness of our pipeline. SwissDock identified over 30 potential binding poses, which were grouped into five distinct clusters. The system was then optimized in AMBER under unbiased conditions to further refine the ligand binding predictions. These results validate the pipeline’s capacity to predict and refine binding poses. However, integrating the system into MELDxMD has proven challenging due to the use of a ligand and the GAFF force field. Despite efforts, difficulties arose in refining the binding poses and conducting detailed competitive simulations. Additional work is needed to fully integrate the system into MELDxMD, refine the binding poses, and evaluate the relative binding affinities of the novel COIN molecules. This study provides a robust framework for assessing COIN binding affinities and identifying promising candidates for synthesis and biological evaluation. Ultimately, this pipeline may aid in the development of novel therapeutics to combat AMR by inhibiting bacterial conjugation and curbing the spread of resistance genes

Engineering Microphysiological Systems to Investigate Cellular Responses to Biophysical Cues in Human Tissue Microenvironments

Cells constantly sense and respond to biophysical cues in their microenvironment. Understanding how they interpret these mechanical signals is essential for advancing tissue modeling, studying disease mechanisms, and developing more predictive in vitro platforms. This dissertation presents a series of engineered microphysiological systems (MPS) designed to investigate how mechanical stimuli shape cellular behavior in distinct human tissue microenvironments. In Aim 1, I developed a reconfigurable microfluidic platform that mimics key features of human vascular barriers. This system supports the integration of porous membranes, 3D hydrogels, and flow channels, enabling precise control over both mechanical and biochemical cues. The modularity of the platform allows for flexible adaptation to different tissue types and experimental conditions. Aim 2 introduces a microfluidic approach to engineer collagen fiber alignment interfaces by modulating extensional strain during gel injection. This method enables the creation of localized discontinuities in extracellular matrix (ECM) alignment, allowing investigation of how fiber heterogeneity and interfaces impact directed cell migration. In Aim 3, I examine topographical mechanical memory in mesenchymal stem cells. Using patterned ridge–flat substrates, I show that cells retain migratory behaviors influenced by prior topographical exposure, even after the mechanical cue is removed, suggesting long-lasting cellular memory effects. Finally, Aim 4 explores the independent and synergistic effects of shear stress and topographical cues on endothelial morphology and function. While both cues promote cell alignment, their effects on gene expression are not fully interchangeable, acting independently for some genes and synergistically for others. These findings highlight the complexity of mechanical regulation in vascular biology. Together, these platforms provide versatile tools to probe cellular mechanobiology and significantly enhance the physiological relevance of in vitro models for biomedical research, disease modeling, and therapeutic development