137091 research outputs found
Sort by
Designing the Structure of Inorganic Clusters Within Vapor Phase Infiltrated Polymer Membranes to Control Properties
No full textVapor phase infiltration (VPI) creates new pathways for synthesizing organic-inorganic hybrid materials with tailored properties, particularly for applications such as chemical separation membranes. The chemistries and processes employed in VPI dictate how inorganic species are integrated into polymer matrices, ultimately influencing the properties of the resulting hybrid materials. This technique incorporates inorganic species at the molecular level while preserving the macroscale structure of the polymer, enabling post-fabrication modifications to materials. Despite its potential, a fundamental understanding of how the structure of infiltrated inorganic species influences material properties remains limited. This thesis aims to bridge this gap by exploring the intricate relationships between processing conditions, precursor chemistry, and the resulting hybrid structures.
This thesis will focus on the fundamental inorganic structure in infiltrated materials and its impact on material properties, particularly membrane performance. Initially, this study examines how precursor chemistry affects infiltration parameters, including mass uptake kinetics, binding energy, inorganic loading, and the properties of hybrid membrane materials. Subsequently, advanced spectroscopic techniques are employed to elucidate cluster structure, connectivity, and size, highlighting the role of these characteristics in enhancing hybrid membrane performance. By gaining new insights into the physicochemical structure of the inorganic components within these infiltrated hybrid materials, this research bridges the gap between process-structure and structure-property relationships, ultimately advancing the understanding of vapor phase infiltrated materials. The findings aim to optimize VPI technology for various applications, with a particular emphasis on infiltrated PIM-1 membranes for organic chemical separations.Ph.D.Materials Science and Engineerin
An Approach for Rapic, Uncertainty-aware Damage Diagnosis of Rotating Machinery
No full textRotating machinery plays a critical role in the aerospace industry, supporting systems from aircraft propulsion to manufacturing equipment. Faults in such machinery, if left unaddressed, can lead to costly repairs or even catastrophic failure. Recent initiatives—such as Industry 4.0, digital engineering, and the Internet of Things (IoT)—have accelerated the development of sensing and computing technologies that enable high-frequency condition monitoring of vibration signals. By analyzing vibration response data at specific frequencies, parameters associated with common faults (e.g., imbalance, misalignment, cracks, bearing defects) can be inferred. These inferred parameters can then inform maintenance and control strategies aimed at preventing failure in safety-critical systems, minimizing downtime, and maximizing overall performance.
Model-based methods for damage diagnosis leverage vibration measurements and physics-based models of rotating machinery to solve an inverse problem to infer fault parameters of interest. Bayesian model calibration is a promising approach to solve the inverse problem while accounting for uncertainties arising from sensor noise and model discrepancies. However, to make Bayesian calibration feasible for real-time, online diagnosis, surrogate models are employed to approximate the potentially expensive rotordynamics simulations. While this surrogate-accelerated approach enables rapid inference, several technical challenges arise in its implementation for effective online damage diagnosis.
The first challenge is the need for a scalable, probabilistic surrogate modeling method for rotordynamics simulations. To address this, the thesis proposes a deep ensemble neural network approach for constructing surrogate models. The second challenge involves accurately quantifying and propagating surrogate model uncertainty through Bayesian inference during damage diagnosis. This is addressed by leveraging techniques from the uncertainty quantification (UQ) literature for regression models. The final challenge is the development of a Bayesian inference method suitable for rapid, online use. To this end, the thesis proposes an Extended Kalman Filter–based method that continuously assimilates vibration data to enable real-time fault diagnosis. Each proposed method is validated individually using a simulated case study involving imbalance diagnosis of a six-stage compressor. Finally, the complete framework is demonstrated on a physical test rig, showcasing its effectiveness in diagnosing imbalance faults in real hardware
Tailored Computing: Cross-Layer System, Architecture, and Silicon Co-Design for Physical Intelligence
No full textPhysical intelligence -- where embodied agents perceive, reason, plan, and act in the physical world -- is emerging as the next transformative computing platform, offering significant potential impact across robotics, healthcare, manufacturing, and scientific automation. Yet today’s physical intelligence systems suffer from prohibitive latency, excessive energy consumption, and fragile reliability when deployed on resource- and power-constrained platforms operating in dynamic, uncertain environments. The fundamental mismatch between advanced algorithmic intelligence and the underlying computational substrate limits scalability, robustness, and real-world deployment.
This dissertation addresses this gap through cross-layer system-architecture-silicon co-design for physical intelligence, integrating system runtimes, heterogeneous compute substrates, and solid-state silicon. These works develop tailored computing architectures that unify high-level cognitive reasoning with low-level perceptual actuation, realized through algorithm-software co-design, technology- and integration-driven cognitive architectures, and programmable System-on-Chip (SoC) tapeouts. These contributions collectively deliver substantial improvements in real-time responsiveness, multi-agent scalability, and energy efficiency, validated through hardware simulations, FPGA prototypes, GPU-resident heterogeneous systems, and fabricated SoC silicon.
The research advances the field through three synergistic thrusts:
First, to enable efficient and scalable high-level cognitive reasoning, this work introduces the end-to-end system-architecture-silicon co-design frameworks for neuro-symbolic AI. It presents the CogSys and REASON architectures featuring unified intermediate representations, flexible microarchitectures, and scalable dataflow to accelerate neuro-symbolic workloads. These innovations culminate in the design and fabrication of the programmable heterogeneous SoC for neuro-symbolic cognition. The resulting silicon prototype demonstrates how memory-centric datapaths and software-defined power management can deliver real-time reasoning with orders-of-magnitude energy-efficiency improvements over commodity hardware.
Second, to advance efficient and scalable low-level perceptual autonomy, this dissertation bridges the divide between deliberative planning and reactive control. It introduces ReCA, an integrated architecture for cooperative embodied agents that unifies fast and slow thinking loops, and MulBERRY, an energy-aware framework for autonomous swarms. These systems optimize the “sense-plan-act” loop via hierarchical coordination and hardware acceleration for kernels such as SLAM and path planning, enabling robust and scalable multi-agent deployment in dynamic environments.
Third, to achieve energy- and safety-aware autonomous operation, this work develops an end-to-end cross-layer reliability modeling infrastructure that exposes inherent robustness variations within autonomous systems. Building on these insights, it proposes a vulnerability-adaptive protection paradigm that dynamically allocates protection budgets proportional to kernel robustness. This strategy achieves high functional safety coverage with minimal overhead, an essential requirement for embodied agents operating in unpredictable, resource-constrained settings.
Bridging computer architecture, systems, and silicon, this dissertation advances the computational foundations needed for physical intelligence -- enabling embodied agents that can think and act efficiently, adaptively, and reliably in the real world
Infrared Spectroscopy at Three and Six Microns of Water Ices Mixtures of Astrochemical Interest
No full textWater ice exists in many cold regions of the solar system, from our own Earth’s poles to comets far past the orbit of Pluto. On the moon, stable ice can exist for billions of years in the cold permanently shadowed polar regions, but water has also been detected in sunlit areas, furthering debate over its origins and morphology. Similarly ancient ice exists elsewhere: comets may be the least modified reservoirs of ice and dust from the early solar system. Wherever ice is found, its formation mechanism and heating history can greatly influence its structure. The presence and composition of minerals can also shape ice’s structure, and trapped volatile species can reveal even more about the ice’s provenance. The underlying structure of water ice can be determined remotely or in the laboratory through infrared absorption observations, especially at the 3 µm (3300 cm-1) O-H stretch and the 6 µm (1600 cm-1) H-O-H bend.
This thesis examines the role of formation method and mineral composition on water ice structure as revealed by its infrared features. Water ice formed through liquid aerosol injection and vapor deposition appears different at cryogenic temperatures between 91-165 K. The presence of 5 wt% mineral is sufficient to modify the resulting ice structure from mineral-free water, and spectral differences depend on mineral composition. At 50 wt% mineral, ice appears more amorphous with greater olivine weight percentage. Water sublimation from mineral-containing ice is complete by 200 K, demonstrating that adsorption is insufficient to retain water in sunlit lunar regions. Finally, implications of the isotopic composition of noble gases trapped in cometary ice are explored in a mission concept for comet surface sample return
Biomaterial-Based Mesenchymal Stromal Cell Manufacturing and Transport Methods to Expand Access to Therapeutic Cell Products
No full textMesenchymal Stromal Cells (MSCs) are a therapeutic cell type of interest for the treatment of various inflammatory injuries and diseases. MSCs have been used in research for many years, but robust manufacturing methods are necessary to ensure quality and consistency of cell products for clinical use. Hundreds of ongoing clinical trials are investing their therapeutic benefit to inflammatory conditions and injury from arthritis to stroke. Typical doses of MSCs require hundreds of millions of cells per patient, necessitating large scale culture processes that can produce billions or trillions of high-quality therapeutic cells. Furthermore, large scale manufacturing presents challenges related to culture platforms, high costs, and cryopreservation-related transportation challenges.
The overarching goal of this thesis was to develop biomaterials-based solutions to address challenges facing MSC manufacturing in order to enable scalable, reduced-cost, high quality manufacturing of MSCs as cell therapies. First, we explored a hydrogel microcarrier platform to enhance growth and modulate gene expression in small scale cultures and large scale bioreactors. Next, we investigated the integration of heparin into microcarriers to support MSC expansion in low serum conditions. Lastly, we developed a hyaluronic acid support gel that enables the transport of viable and functional MSCs at ambient temperature for up to 72 hours. Overall, this work provides biomaterial platforms that can be used to address multiple arms of MSC manufacturing challenges, from scale-up to transport.Ph.D.Biomedical Engineerin
Biophysical Scaffolding in the Evolution of Complexity
No full textPressure to become larger is thought to be a driver of the evolution of multicellular organisms. Large size can help an organism avoid predation, resist stress, and use resources more efficiently. However, though size can solve many problems, it also creates new ones, and it is not clear how nascent multicellular organisms overcome them, since they are simple clumps of cells that lack the group-level adaptations of established organisms.
One problem with large size involves nutrient limitation: nutrients usually cannot penetrate more than a few tens of microns into a group of cells, meaning that cells on the inside of a large group will be starved, and growth will be limited. However, our model organism for early multicellularity, snowflake yeast, defies this diffusion/uptake limit. Over 1,000 days of selection for large size, these yeast evolved to grow exponentially to millimeter sizes, far larger than previously-demonstrated limits. Snowflake yeast does not have cilia to move fluid around, nor does it have complex multicellular adaptations like a circulatory system. Instead, the organism's metabolism drives a rapid, long-range buoyant flow that enables nutrient-rich fluid to move throughout the cluster of cells.
In this thesis, I examine the phenomenon of metabolic flow and the characteristics that make it possible. I argue that it is not unique to snowflake yeast clusters placed in perfectly still media with plentiful nutrients, but is possible across a wider range of environmental and organismal characteristics. I show that metabolically-driven flow remains effective in an environment with substantial external flows, widening the range of possible environments. I also examine the organismal characteristics that make flow possible for snowflake yeast, including permeability, toughness, and nutrient uptake rates. I suggest that emergent phenomena can circumvent the need for nascent multicellular organisms to evolve a morphologically complex body, including features like a circulatory system, in order to solve problems of large size. Instead, large size can evolve first, and the existing form and physics of the group can scaffold the subsequent evolution of development of the body plan
Hardware Accelerator Generation Framework for Cryptographic Primitives
No full textCryptographic primitives, consisting of repetitive operations with different inputs, are typically implemented using straight-line C code due to traditional execution on CPUs. Computing these primitives is necessary for secure communication; thus, dedicated hardware accelerators are required in resource and latency-constrained environments. High-Level Synthesis (HLS) generates hardware from high-level implementations in languages like C, enabling the rapid prototyping and evaluation of designs, leading to its prominent use in developing dedicated hardware accelerators. However, directly synthesizing the straight-line C implementations of cryptographic primitives can lead to large hardware designs with excessive resource usage or suboptimal performance.
This thesis introduces a tool Cryptonite that automatically generates efficient, synthesizable, and correct-by-design hardware accelerators for cryptographic primitives directly from straight-line C code. Cryptonite first identifies high-level hardware constructs through verified rewriting, emphasizing resource reuse. The second stage automatically explores latency-oriented implementations of the compact design. This enables the flexible scaling of a particular accelerator to meet the hardware requirements. This thesis demonstrates Cryptonite's effectiveness using implementations from the Fiat Cryptography project, a library of verified and auto-generated cryptographic primitives for elliptic-curve cryptography.M.S.Electrical and Computer Engineerin
Mechanisms of Palindrome-Mediated Chromosomal Instability
No full textPalindromes are highly unstable DNA sequences that induce chromosomal rearrangements and underlie cancer and hereditary diseases such as Emanuel Syndrome. Despite being a significant source of genomic instability, the mechanisms governing palindrome fragility in eukaryotic cells are not fully defined. In this thesis, I make significant contributions to our understanding of palindrome-mediated chromosomal instability by defining structural and genetic determinants of palindrome fragility. I discovered that palindromic sequences with nonpalindromic spacers of 8 bp or less form stable hairpins which can block replication, and these hairpins are cleaved by the Mre11/Rad50/Xrs2/Sae2 complex. In contrast, palindromic sequences with spacers longer than 8 bp are not cleaved by MRX/Sae2 and do not block replication fork progression. I show that these effects are mediated by single stranded DNA binding protein RPA, which is likely facilitating hairpin unwinding. I also describe a novel pathway of palindrome-mediated chromosomal instability involving Mph1, Rad51, Rad54, Smc5, and Smc6. Deletion or mutation of any of these genes results in a ~3-fold reduction in palindrome-mediated breaks. I propose that Mph1, Rad51, and Rad54 mediate fork remodelling at stalled replication forks, generating cruciform structures, and that the Smc5/6 holo-complex stabilizes these structures and facilitates cleavage by Mus81/Mms4 and other resolvases. Last, I described the Lobachev Lab’s progress in creating a novel, antibiotic resistance-based gross chromosomal (GCR) and gene amplification assay in human cells. The human cell GCR assay will enable foundational work regarding palindrome instability performed in model organisms to be extended into the realm of clinical application and enable testing of human specific factors. Defining the pathways and mechanisms that govern palindrome instability is essential for understanding how pathogenic genomic rearrangements occur.Ph.D.Biolog
