12023 research outputs found
Sort by
Lithium Niobate Nanophotonic Circuits for Information Processing
No full textIn today's world, electronic information processors are ubiquitous. This dissertation explores an alternative paradigm of processing information using nanophotonics. We develop and investigate nanophotonic lithium niobate circuits leveraging strong χ⁽²⁾ nonlinearity for information processing. We demonstrate promising performance of nanophotonic circuits as building blocks of unconventional computing architectures that exploit the rich classical and quantum dynamics inherent to optics. Additionally, we introduce a new class of ultrafast nanophotonic sources, enabling novel opportunities for information processing. Ultimately, this dissertation puts forth the building blocks of next generation ultrafast photonic information processors in lithium niobate nanophotonics which may lead to photonic advantage
Explications of a Changing Climate
Get PDFClimate models encode our collective knowledge about the climate system and are among the best tools available for estimating past and future climate change. However, in response to greenhouse gas forcing, climate models exhibit a large intermodel spread in various aspects of the climate system, adding considerable uncertainty to future climate projections. This dissertation introduces a series of conceptual models and frameworks to understand the behavior of climate models under greenhouse gas forcing and, consequently, Earth's changing climate.
A simple statistical model is used to explain and constrain the intermodel spread in Arctic sea ice projections across climate models. The probability of encountering seasonally ice-free conditions in the twenty-first century is also explored by systematically constraining components of the statistical model with observations.
A conceptual framework is introduced to understand controls on the strength and structure of the Atlantic meridional overturning circulation (AMOC) in climate models. This framework is used to explain why climate models suggest the present-day and future AMOC strength are related. This framework, in conjunction with observations, implies modest twenty-first-century AMOC weakening.
A simple energy budget framework is used to examine precipitation over a wide range of climates simulated by climate models. It is shown that in extremely hot climates, global-mean precipitation decreases despite increasing surface temperatures because of increased atmospheric shortwave absorption from water vapor, which limits energy available for surface evaporation. These results have large implications for understanding weathering rates in past climates as well as Earth's climate during the Hadean and Archaean eons.
Finally, a framework is introduced to reconcile two different approaches for quantifying the effect of climate feedbacks on surface temperature change. The framework is used to examine the influence of clouds on Arctic amplification in a climate model and an energy balance model. This work introduces an important non-local mechanism for Arctic amplification and shows that constraining the mid-latitude cloud feedback will greatly reduce the intermodel spread in Arctic warming.
This dissertation advances our understanding of various aspects of Earth's changing climate and provides a series of conceptual frameworks that can be used to further constrain the behaviour of climate models in response to external forcing.</p
Development of Photoinduced Copper-Catalyzed Amination of Alkyl Electrophiles: Synthesis and Mechanism
Get PDFThe formation of carbon-nitrogen (C–N) bonds is crucial in organic chemistry due to the importance of nitrogen-containing functional groups. While traditional nucleophilic substitution reactions, such as SN1, SN2, and SNAr, are limited in scope and efficiency, transition metal-catalyzed versions of these reactions, particularly involving copper, offer a more versatile approach by activating electrophiles and facilitating C–N bond formation via oxidative addition and reductive elimination.
Copper-catalyzed C–N couplings have been extensively developed but are primarily effective for aryl electrophiles rather than alkyl electrophiles due to the need for thermal activation, which often leads to undesired side reactions in alkyl electrophiles. The development of photoinduced copper-catalyzed reactions by Fu and Peters addresses these challenges, enabling the activation of alkyl electrophiles without thermal activation.
Over the past decade, the Fu group has focused on expanding the scope of this novel approach. The research detailed in this thesis focuses on developing photoinduced copper-catalyzed C–N coupling reactions for more challenging substrates, such as sterically hindered alkyl electrophiles and amines.
Chapter 2 discusses the photoinduced, enantio-convergent coupling of racemic tertiary alkyl electrophiles with aniline nucleophiles, catalyzed by bisphosphine-copper complexes. The mechanism of this reaction was elucidated using various tools, identifying key copper-based intermediates, including a chiral copper(II)–anilido complex that couples with a tertiary organic radical to form the C–N bond with good enantioselectivity.
Chapter 3 presents the photoinduced, copper-catalyzed coupling of secondary alkyl amines with secondary or tertiary alkyl bromides to synthesize N-tertiary alkyl amines under mild conditions. This novel reaction provides unique stereoselectivity and compatibility with strained electrophiles, contributing valuable methodologies to the synthesis of bioisosteres and other complex amine structures.
Overall, this work broadens the understanding and application of photoinduced copper-catalyzed reactions, offering new pathways for the synthesis of sterically hindered amines.</p
Image Charge Effects Near Solid Surfaces
Get PDFIon–surface interactions underpin fundamental biological and technological processes and hold the key to advancing the performance of modern electrochemical devices, such as electric double-layer capacitors (EDLCs). As such, a comprehensive understanding of the mechanistic details governing these interactions and their effects on the electrical double layer structure and charge transport is crucial. However, accurately modeling ion–surface interactions in theory and simulations remains challenging due to the complexities and computational cost associated with properly treating dielectric discontinuities at ion–surface interfaces. This thesis leverages the efficient method of image charges in coarse-grained molecular dynamics simulations to capture the correct behavior at the ion–surface interface and unravel anomalous phenomena in various charged soft matter systems with conductive metal surfaces. Specifically, we construct a molecular model to demonstrate a spontaneous symmetry breaking transition in room-temperature ionic liquid EDLCs that provides a molecular mechanism for a hysteresis in the capacitance behavior observed experimentally. We also introduce a physically motivated soft-core model, the Gaussian core model with smeared electrostatics (GCMe), which addresses the limitations of traditional hard-core force fields in representing bulky organic ions and their spread charges, while also being orders of magnitude faster. Using GCMe, we then characterize the effects of the polyelectrolyte chain length, electrolyte polarizability, and electrode material on the energy storage of polymerized ionic liquid EDLCs, and the ion adsorption behavior and charging/discharging dynamics in polyelectrolyte EDLCs. Finally, we present MDCraft, an open-source Python assistant designed to streamline computational research workflows by providing tools for simulation setup, data analysis, and visualization. This comprehensive study not only enhances the understanding of ion-surface interactions but also offers practical insights and tools for advancing the design and optimization of systems involving charged species near surfaces, such as next-generation electrochemical energy storage devices
Direct Visualization of Cellular Protein Complexes in situ by Fluorescence-Guided Cryo-FIB-SEM and Cryo-ET
Get PDFCryogenic electron tomography (cryo-ET) is a technique that can reconstruct three-dimensional volumes of large protein complexes in situ at sub-nanometer resolution. In addition to imaging proteins extracted from cells, cryo-ET also allows direct visualization of macromolecular complexes in their native environment. To reveal molecular details buried deeply inside thick eukaryotic cells, cryogenic focused ion beam milling with scanning electron microscopy (cryo-FIB-SEM) has been established as the leading approach for preparing thin sections of cells suitable for cryo-ET. Recent advances in cryo-FIB-SEM systems integrate fluorescence microscopy (cryo-FM-FIB-SEM) to help direct the milling to specific labeled regions of interest. This method has had success localizing large organelles and protein aggregates. Unfortunately, it is difficult to localize small and rare targets along the optical axis of the cryo-FIB. This thesis work pioneered a customized integrated tri-coincident imaging system (ENZEL) that allows for simultaneous fluorescence imaging and cryo-FIB milling. This novel method allows precise targeting of small and rare structures with a high success rate compared to other systems. To demonstrate the imaging workflow, we applied this approach to visualize the microtubule organizing center (MTOC), a crucial organelle responsible for cell division and cellular transport in mammalian cells. It presents as a single fluorescent punctum expanding approximately 1 µm in diameter in live cells, making it a challenging target for cryo-FM-FIB-SEM. Our cryo-tomograms resolved the molecular architecture of the MTOC and revealed molecular details at the microtubule nucleation sites. We then used the ENZEL to explore more complicated biological systems. Here we chose the NLRP3 inflammasome, a master mediator of innate immunity colocalized with the MTOC. We captured the first in-situ image of the NLRP3 inflammasome and showed new mechanistic insights that this complex forms a condensate at the MTOC, halting cell division and inducing drastic organelle changes
Computational Approaches to Problems in Energy and Sustainability
Get PDFThe rapid development of modern society has been met by a fierce and overwhelming increase in fossil fuel utilization and the mass production of nonrenewable/recyclable materials. The escalating usage of fossil fuels results in rising greenhouse gas (GHG) emissions, while mass production of non-recyclable materials has led to unimaginable amounts of waste, which ultimately ends up in landfills or in the ocean. If we seek a sustainable future, it is imperative that we develop methods that can harness “green” electrons to generate power, particularly synthetic routes that selectively generate renewable materials via these electrons.
In this thesis, we leverage theoretical methods to investigate several platforms for the conversion of GHGs to value-added products such as methanol, ethylene, methylacetic acid, styrene, etc. To generate these products, we use heterogeneous and homogeneous catalysts, with and without the assistance of an applied potential. The overarching goal of these methods is to remediate carbon and nitrogen cycles, such that generation of harmful carbon and nitrogen-based products is immediately followed by conversion of said products back to useful reactant species.
In summation, this thesis provides several catalytic platforms for the selective and efficient production of useful fuels and feedstocks from harmful GHGs.</p
Space Legos: A Concept for In-Space Assembly of Large Structures with a Stationary Robot
Get PDFHuman nature is inherently driven by the desire to build; advancing from primitive shelters to skyscrapers, and extending this relentless pursuit of progress to space through technological innovations. As space missions require larger and more complex structures, traditional deployable systems face challenges due to constraints on launch mass, volume, and complex deployment mechanisms. In-space assembly (ISA) offers a promising solution for constructing large structures, such as telescopes and satellites, directly in space.
This thesis introduces a novel ISA concept with a centralized `truss builder' for autonomous assembly of polygonal-ring structures, using simple, repetitive operations and focusing on scalable mesh reflectors for communication and imaging. Utilizing the standard AstroMesh architecture, a rapid generalized design method is developed. Through the analysis of reflector geometry, optimized cable prestress, structural design, and a high-fidelity finite element model, analytical scaling laws are derived for mass, stowed envelope, and natural frequency based on aperture diameter. A semi-analytical homogenization model is introduced to efficiently predict fundamental natural frequencies. Stowed volume is a key limitation for large deployable reflectors, approaching current and future launch capacity limits, while the proposed ISA reflectors face no such constraints for apertures up to 200 meters.
A two-dimensional finite element model simulates the assembly kinematics of large ring-like structures with the proposed ISA concept, enhancing understanding of the process and evaluating key design aspects of a stationary robot assembling scalable ring-like trusses. The model provides insights for optimizing autonomous assembly systems and underscores the need for advanced numerical simulations to ensure smooth assembly and stability during ISA, especially as structures scale.
Lab-scale prototype testing validates the ISA concept, with results aligning qualitatively with simulations. Both experiments and simulations reveal a range of viable solutions, demonstrating flexibility for future mission designs. This research offers crucial insights into the design and scaling of mesh reflectors, setting the stage for comparing ISA with traditional deployable systems. The proposed ISA concept presents a practical solution for building high-precision, large-scale structures in space, advancing the field of space construction and supporting future extended space missions.</p
In Situ Signal Amplification for Spatial Transcriptomics Using Programmable DNA Assemblies
Get PDFSequential Fluorescent In Situ Hybridization (seqFISH) has been an invaluable tool in imaging-based spatial transcriptomics, aiding researchers in elucidating spatially-resolved, gene expression patterns in intact tissues and cell culture models. However, methods that rely on smFISH, such as seqFISH, suffer from poor signal-to-noise ratio in certain tissue types or target RNA, require many fluorescently labeled RNA targeting probes which prohibits imaging of small RNA species, and exhibit poor sample throughput due to the need of high magnification objective or long exposure times. Herein, we develop solutions to these limitations by developing and utilizing a robust signal amplification strategy. While various amplification technologies exist, their limitations often hinder broad applicability. Moreover, we desire an amplification platform that is amenable to the denaturing wash conditions used in seqFISH. We will begin Chapter I by discussing the background, technical challenges, and utility of various in situ signal amplification technologies. Chapter II details the exploration and technical limitations of rolling circle amplification (RCA) and branched DNA (bDNA) assembly utilizing ssDNA padlock amplifier strands. Chapter III discusses the design and development of a novel amplification strategy called Signal amPlicAtion by Recursive Crosslinking (SPARC), which builds upon the knowledge gained from Chapter II. We highlight SPARC as a unique photochemical signal amplification method that iteratively deposits amplifier strands near the primary probe target for linear signal amplification. Then, the deposited amplifier strands act as a scaffold for branched DNA assembly, leading to an exponential signal amplification. Through each deposition and assembly step, amplifier strands are photo-crosslinked to the extracellular matrix, forming highly stable DNA nanostructures that can withstand harsh denaturing wash conditions. We demonstrate the utility of SPARC in amplifying signal of both single-molecule transcripts and proteins
Engineering and Computational Tools for Salivary Biomedicine
Get PDFSaliva is emerging as a powerful biofluid for noninvasive diagnostics, offering a window into human health through its diverse biomolecular composition. This dissertation advances the field of salivary biomedicine by addressing critical challenges in saliva collection, processing, and analysis. First, a comparative analysis of five saliva collection devices highlighted key usability factors, informing the development of SalivaStraw--a novel device designed to improve collection efficiency and minimize leakage. Next, colosseum, a low-cost, open-source fraction collector, was designed and developed to facilitate scalable saliva processing and improve biomarker isolation. Finally, a computational framework leveraging spline regression was applied to longitudinal salivary transcriptomic data, enabling the identification of temporally regulated genes and underscoring saliva’s potential for dynamic health monitoring. Collectively, this work contributes new tools and methodologies that strengthen the foundation of saliva-based diagnostics, broadening its applications in precision medicine and beyond
Reduced Order Modeling of Near-Wall and Roughness Sublayer Turbulence Using Resolvent Analysis
Get PDFModeling near-wall and roughness sublayer turbulence using physics-based methods remains a topic of paramount importance, since most engineering-relevant flows are turbulent and most surfaces are not smooth. While today there exists a wide range of empirical, data-driven modeling approaches for turbulence, these methods are limited because fully resolved turbulence data remains expensive to generate and burdensome to store and analyze. Therefore, the ability to predict out-of-sample is important, and since data-driven methods struggle to extrapolate, developing physics-based approximations that give useful, inexpensive predictions remains necessary. Yet the complexity of near-wall turbulence makes developing theoretical models difficult. This thesis tackles two main challenges. First, methods for reduced order modeling of the sensitivity of turbulence to multiscale, engineering-relevant roughness geometries are developed. In particular, a physics-based method for incorporating a drag-scaled, Reynolds-decomposed volume penalization into resolvent analysis yields a linear reduced order model that gives computationally inexpensive estimates for roughness sublayer fluctuations and dispersive stresses given a surface geometry and the mean flow profile in a rough wall channel flow. Then, an iterative method is developed to predict the mean flow profile, equivalent sand grain roughness, and Hama roughness function that utilizes the discovered relationship between the fluctuations and the mean flow. That model yields a closed-loop system for predicting roughness sublayer turbulence and the mean response given only a scan of the roughness geometry and a bulk Reynolds number in a rough wall channel flow. Second, a methodology for generating spatiotemporal representations of near-wall turbulence with very few degrees of freedom is developed. It utilizes a coarse-graining approach to reduce the number of modes required to describe a turbulent flow, selection criteria for picking descriptive modes, and Reynolds number scaling to provide predictions for an out-of-sample, higher Reynolds number flow. A spatiotemporal representation is generated, and results from Piomelli et al. that incorporate the modal representation into the wall layer of a wall modeled large eddy simulation are presented. Overall, this thesis contributes new reduced order modeling approaches that make use of physics-based insights to tackle outstanding problems in the prediction of near-wall and roughness sublayer turbulence