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Decoding Charge Block Sequence Effects on Polyampholyte Behavior with Synthetic Protein Analogs
No full textPolyampholytes are polymers containing both positively and negatively charged
groups along their backbone. The presence of these charged residues enables the development of stimuli-responsive and multi-functional materials. Incorporating ionic groups into polymeric materials has been shown to improve thermal and mechanical properties, as well as provide anti-fouling and cryopreservation effects. Naturally occurring polyampholytes are found in intrinsically disordered proteins (IDPs), which constitute 25-30% of known functional eukaryotic proteins. IDPs play critical roles in disease due to their involvement in forming condensed phases in intracellular environments.
Recent studies by computational and experimental scientists indicate that
charge sequence significantly influences the phase separation behavior of IDPs. To utilize charge sequence as a design parameter for polyampholyte materials and to enhance our understanding of the electrostatic contributions to peptide phase behavior, it is essential to understand how charge sequence affects polyampholyte conformation.
This thesis experimentally investigates the effects of charge sequence on polyampholyte conformation and phase behavior. Using Fmoc-based solid-phase peptide synthesis, we construct sequence-specific polyampholyte peptides with varying charge blockiness, from alternating to diblock arrangements.
Experiments are conducted in dilute concentrations to better assess single-chain structure and dynamics. In the first part of this thesis, we characterize the conformation of L-chiral polyampholyte
peptides. In the second part, we synthesize atactic polypeptides by incorporating D-chiral residues to better isolate electrostatic interactions and reduce hydrogen bond interactions. We generally observe increased phase separation with increased blockiness.
Small angle scattering reveals that these polyampholytes exhibit random coil or self-avoiding walk conformations with minimal differences in size at small block lengths. We also observe microphase separations in mid-sized blocks of
the atactic system, consistent with theoretical predictions. Lastly, we characterize the dynamics of polyampholyte solutions. All experiments are compared to solutions with added NaCl, where electrostatic interactions are screened. Comparing our experimental
results with recent simulations and complexation experiments provides
further insights into the thermodynamic driving forces behind our observations
Non-Equilibrium, Ultra-Fast Heating Techniques for Material Synthesis, PFAS Mineralization and Upcycling.
No full textThe increasing demand for sustainable technologies and materials has led to a critical need for efficient, scalable, and environmentally friendly solutions for material synthesis, environmental remediation, and resource recovery. Among the innovative technologies addressing these challenges, Flash Joule Heating (FJH) has emerged as a versatile and transformative technique. This thesis explores the application of FJH in four significant areas: heteroatom-substituted graphene synthesis, the destruction of per- and polyfluoroalkyl substances (PFAS), the recovery of critical metals from lithium-ion batteries (LIBs) aided by waste PFAS and the synthesis of silicon carbide nanowires using waste glass.
Graphene, a two-dimensional carbon-based material, is renowned for its extraordinary properties, including high electrical and thermal conductivity, mechanical strength, and chemical versatility. These properties make graphene a highly sought-after material for applications in energy storage, electronics, and catalysis. The functionality of graphene can be further enhanced by heteroatom substitution, which involves incorporating non-carbon atoms, such as nitrogen, boron, sulfur, and fluorine, into its lattice structure. These heteroatoms modify the electronic and chemical properties of graphene, expanding its range of potential applications. Traditional methods for heteroatom substitution, such as chemical vapor deposition and solvothermal processes, are often time-consuming, resource-intensive, and difficult to scale. In contrast, FJH offers a rapid, energy-efficient, and scalable alternative for producing high-quality, heteroatom-substituted graphene. By subjecting pre-formed graphene or carbon precursors to rapid high-temperature heating in the presence of heteroatom-containing precursors, FJH enables precise control over doping levels and ensures structural integrity, making it a promising method for scalable graphene functionalization. This is covered in chapter one of this thesis.
In addition to its role in material synthesis, FJH provides a novel solution to a pressing environmental challenge: the destruction of PFAS, often referred to as "forever chemicals." PFAS are a class of synthetic organofluorine compounds widely used in industrial and consumer applications, including firefighting foams, non-stick coatings, and water-resistant materials. The strong carbon-fluorine bonds in PFAS make them highly resistant to degradation, leading to their accumulation in the environment and posing significant risks to human health and ecosystems. Current remediation techniques, such as adsorption onto granular activated carbon (GAC), capture PFAS but do not degrade them, leaving behind secondary waste. FJH addresses this limitation by degrading PFAS adsorbed onto GAC through high-temperature treatment, breaking the carbon-fluorine bonds and converting PFAS into benign byproducts. This process not only eliminates PFAS but also enables the upcycling of PFAS-contaminated GAC into valuable materials, such as graphene, demonstrating a sustainable approach to waste management. This is covered in chapter two of this thesis.
We then demonstrate that FJH offers a sustainable and efficient approach for resource recovery from spent lithium-ion batteries (LIBs), which play a crucial role in modern energy storage systems. LIBs contain valuable metals, such as lithium and cobalt, whose extraction and processing are energy-intensive and environmentally damaging. The growing demand for these metals, driven by the proliferation of electric vehicles and renewable energy technologies, has raised concerns about resource scarcity and the environmental impact of conventional recycling methods. FJH offers a rapid and solvent-free approach to metal recovery, facilitating the fluorination of lithium into lithium fluoride (LiF) and the reduction of cobalt into metallic form. These transformations occur within a few seconds, minimizing energy consumption and environmental impact while enabling the efficient separation of metals for reuse. FJH addresses the challenges associated with LIB recycling as well as waste PFAS degradation. This is covered in chapter three.
Finally, we demonstrate a flash process for upcycling waste glass into SiC nanowires within seconds. By introducing fluorine, iron oxide present in the waste glass is activated, catalyzing the formation of one-dimensional (1D) SiC nanowires. The resulting SiC nanowires exhibit superior performance in composite reinforcement compared to conventional SiC powders. Additionally, a life cycle assessment (LCA) and techno-economic analysis (TEA) reveal that our process significantly reduces environmental impact and production costs compared to conventional synthesis methods. This work highlights fluorine as a versatile and cost-effective agent for modulating nanomaterial growth kinetics and tailoring morphology, providing a sustainable and scalable approach for advanced material synthesis and is discussed in chapter 4 of this thesis.
This thesis underscores the versatility and scalability of FJH as a platform for material innovation, environmental remediation, and resource recovery. Through its application in heteroatom-doped graphene synthesis, PFAS destruction, metal recovery from LIBs, and synthesis of one-dimension materials, FJH demonstrates its potential to bridge the gap between fundamental research and practical solutions, advancing both sustainability and technological progres
Microneedle-Assisted Platforms for Diagnostic Testing in Dermal Interstitial Fluid
Get PDFProtein biomarkers are important for disease diagnosis, particularly for early detection and precise diagnosis. With the aid of rapid diagnostic tests (RDTs), such as immunoassays or biosensors, researchers, clinicians, and patients can collect and analyze a broad range of biomarkers with great accuracy and sensitivity. However, traditional tests rely on blood sampling and analysis, which can cause discomfort or pose the risk of infection, especially for underserved demographics. Recently, interstitial fluid (ISF) has gained interest as an alternative diagnostic biofluid due to its rich protein content. However, the utilization of ISF for diagnostics is hindered by the lack of efficient and non-invasive sampling methods. Most current techniques, such as microdialysis or suction blistering, are time-consuming and invasive in nature, limiting their practicality. Microneedles (MNs) present a promising alternative by offering minimally invasive access to ISF in the upper dermis of the skin, although previous research efforts have fallen short in creating a time-efficient method to extract ample amount. This paper advances ISF sampling and analysis through the development of MN-based sampling devices for increased quantity. The sampling devices are integrated into RDTs, enabling real-time analysis of protein biomarkers for disease diagnosis and immunity testing. Additionally, novel wicking MNs are fabricated to facilitate ISF extraction without the need for external pressure, enhancing the usability and accessibility. Through these aims, this research significantly contributes to the field of ISF-based disease diagnostics
Encapsulated Cell Therapy Platforms for Dynamic Production of Protein-Based Therapeutics in situ
No full textProtein-based therapeutics revolutionized medicine by adapting biology to create effective medical therapies. However, their relative instability makes them challenging to administer and their relative simplicity leaves them unadaptable in response to dynamic biologic contexts where their levels over time are governed only by dose, injection time and pharmacokinetics. Cell based therapeutics have emerged as the next paradigm shifting class of therapies. By taking advantage of and building upon intrinsic cell abilities to produce proteins and respond to their environments we can create therapeutics better suited to match the dynamic nature and complexity of biology and pathologies. Here, we endeavor to create a set of encapsulated cell therapy platforms, engineered to specifically address the context for which they were designed. Through cell engineering we created systems capable of externally controllable therapeutic production, stable therapeutic production and context dependent therapeutic production. By encapsulating these cells in different materials and form factors we built platforms for integration into biohybrid devices, for addressing short term disorders and for treating chronic diseases. Taken together, this research aims to demonstrate the versatility of encapsulated cell platforms and their promise as a therapeutic modality
Sharing and Isolation in Modern Kernel Memory Management
Get PDFKernel memory management sits at the critical intersection of performance and security in today's complex computing environments. In this thesis, we leverage the latest hardware-level support to tackle the interconnected challenges in kernel memory management by examining instruction address translation overheads, introducing new strategies for preserving memory contiguity, and proposing transparent sandboxing mechanisms for user-provided kernel code.
Modern processors suffer significant instruction address translation overhead in a variety of widely used workloads. We show how microarchitectural differences, especially TLB organization, affect these overheads and provide insights for reducing translation costs. Based on our findings, we recommend both kernel- and hardware-level approaches to automatically lower instruction address translation overhead. Next, we propose novel kernel-level heuristics to control memory fragmentation. These heuristics preserve and recover contiguity by protecting actively populating reservations, delaying a complete drain of contiguity in the buddy allocator, and proactively relocating constituents in inactive reservations. Our results demonstrate the subtle ways in which memory fragmen- tation persists in long-running systems and show that our approach virtually eliminates fragmentation-induced allocation failures. Finally, we address the challenge of isolating user-provided programs that execute in the kernel, while still allowing controlled access to shared kernel memory. Specifically, we propose a transparent sandboxing scheme for Linux eBPF that leverages the AArch64 Memory Tagging Extension, providing lightweight defense-in-depth for user-supplied code executed within the live kernel
Building an AI-Powered Archive for US Science Policy Documents
Get PDFThis project aims to develop an open-source, Django-based web application to support the automated processing of complex, born-digital documents—specifically PDFs released under Freedom of Information Act (FOIA) requests. The application will serve as a digital repository within the White House Scientist and Science Policy Dynamic Digital Archive, hosted by the Woodson Research Center. Leveraging advanced AI technologies, including optical and layout recognition and integration with Large Language Models (LLMs), the tool will streamline data extraction, analysis, and searchability of irregularly formatted documents, enhancing accessibility and research capabilities
Human Perception of AI Capabilities at Classifying Perturbed Roadway Signs
No full textArtificial Intelligence (AI) is crucial to numerous functions required for driving automation systems, including the computer vision techniques used to detect the roadway environment and make real-time decisions. However, the images used as inputs to the AI system may be maliciously perturbed, or manipulated, causing the AI system to make an incorrect classification. In this study, we examined humans’ perception of the AI’s computer vision capability of classifying various road sign images, including the original images, images with two different types of malicious attacks, and images that are scrambled randomly at the pixel level. Our results showed that participants rated the AI agent to be less capable than themselves of classifying the road signs. However, they overestimated the AI’s computer vision capability for correctly classifying images with malicious attacks that should cause the AI system to misclassify the image. These findings suggest that people lack an accurate understanding of the vulnerabilities of AI computer vision technologies and tend to overtrust AI in driving automation systems.Rice School of Social Sciences Open Access Publication Gran
Chiral Phonon induced Magnetization
No full textChiral phonons, lattice vibrations with angular momentum, offer a promising pathway for inducing and controlling magnetization in non-magnetic materials through spin-lattice coupling. Unlike traditional magnetic fields, chiral phonons create local angular momentum in the lattice, which can interact with electronic spin states to produce a net magnetization even in initially non-magnetic systems. This phenomenon enables the manipulation of magnetic properties in materials through selective excitation of chiral phonon modes, making it a powerful tool in spintronics and quantum information processing. Besides, the efficiency is much higher than any present magnetize method (GHz) since most of the phonon modes are at the frequency of THz, which is at least 2-3 orders higher. In this thesis, we report that coherent chiral phonons, driven by circularly polarized terahertz light pulses, can polarize the paramagnetic spins in CeF3 like a quasi-static magnetic field on the order of 1 Tesla. Through time-resolved Faraday rotation and Kerr ellipticity, we found the transient magnetization is only excited by pulses resonant with phonons, proportional to the angular momentum of the phonons, and growing with magnetic susceptibility at cryogenic temperatures, as expected from the spin-phonon coupling model. The time-dependent effective magnetic field quantitatively agrees with that calculated from phonon dynamics. Also, we used optical centrifuge to excited coherent chiral Raman active phonon and saw the same ultrafast magnetization. The strong spin-phonon coupling mode with ultralong lifetime is first observed by our magnetic field dependent Raman spectrum measurements. These results further confirm that chiral phonon is a general manipulation to break time reversal symmetry and generate ultrafast magnetization. Finally, we designed a magnetic heterostructure comprising ferromagnet Cr2Ge2Te6 and paramagnet CeF3 to prove that this chiral phonon-induced magnetization can influence nearby magnetic structures. By driving the coherent chiral phonons in CeF3 at cryogenic temperature, we observed that the chiral phonon-induced magnetization has a noticeable proximity effect on the upper ferromagnetic layer. Using time-resolved magnetic circular birefringence measurements, we monitored the dynamics of this transient ferromagnetic magnetization and demonstrated a strong correlation with the excited chiral phonon. Our analysis quantitatively reveals that the interlayer exchange interaction occurs on a timescale of less than 1 picosecond, indicating that phonon-mediated processes facilitate this ultrafast magnetization. These findings suggest that phonon-driven magnetization could offer a novel method for non-local control of magnetic order at terahertz frequencies, with potential applications in energy-efficient and high-speed spintronic devices
Asian American Community Study: Creating Identity in the Greater Houston Area
No full textIdentity is shaped by a wide range of sources, including (but not limited to) religion, national origin, cultural traditions, migration histories, and language. People’s unique histories and lived experiences mean that while there may be similar sources that are important to identity from person to person, the content of those sources differs for each individual. This may be particularly true for Asian residents, many of whom have moved to the United States from another county and experience the duality of being both “Asian” and “American.”The Greater Houston area’s size and diversity make it ideal for exploring the individual dimensions of and collective presentation of identity among Asian communities. More than 600,000 residents identify as Asian, with the seven most populous ethnicities being Asian Indian, Chinese/Taiwanese, Filipino, Japanese, Korean, Pakistani, and Vietnamese. Drawing on data from the Asian American Community Study (AACS), this brief looks across several Asian ethnicities to study residents’ views about their identity; which sources of identity are important to them; what makes up those sources; and the constellations, or groupings, of those sources. In doing so, this study helps to clearly describe complex identities within and between Asian communities in the Greater Houston area
Temporally Feathered Radiation Therapy under Uncertainty
No full textThis thesis focuses on radiation therapy planning through novel stochastic optimization models that account for biological heterogeneity and uncertainty in organ-at-risk (OAR) responses. Building on the temporally feathered radiation therapy (TFRT) strategy, we develop a personalized, biologically informed treatment framework that dynamically adjusts dose intensities and rest periods based on tissue-specific recovery potential. The model incorporates inter-organ variability using the linear-quadratic model within a dynamic normal tissue complication probability framework, allowing for tailored protection of sensitive tissues without compromising tumor coverage.
To address uncertainty in patient-specific parameters, we propose two stochastic models: a risk-neutral formulation that maximizes expected OAR recovery and a risk-averse approach minimizing worst-case toxicity using Conditional Value-at-Risk. A modified L-shaped algorithm is developed to solve the resulting nonconvex problems more efficiently than conventional solvers. Clinically, these models outperform standard intensity modulated radiation therapy, with the risk-averse version protecting critical structures such as the spinal cord and brainstem, and the risk-neutral version improving outcomes for the parotid and structures associated with speech and swallowing.
Because stochastic TFRT models can be computationally demanding as the number of scenarios increases, we introduce scenario generation techniques based on Wasserstein and fused Gromov-Wasserstein distances to approximate complex stochastic processes effectively. To solve the resulting models, we design block coordinate descent algorithms and demonstrate their performance across applications in stochastic TFRT, portfolio management, and capacity expansion