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Lean Premixed Hydrogen Flames: Turbulence, Chemistry, and Modelling
Get PDFLean turbulent premixed hydrogen/air flames have substantially increased flame speeds, a behaviour which is attributed to differential diffusion effects. In this thesis, the relationships between turbulence, chemistry, and modelling are studied through direct numerical simulation (DNS) and large eddy simulation (LES).
The effect of turbulence on lean hydrogen combustion is studied through DNS using detailed chemistry and detailed transport. Simulations are conducted at six Karlovitz numbers and four integral length scales. A general expression for the burning efficiency is proposed which depends on the conditional mean chemical source term and gradient of a progress variable. At a fixed Karlovitz number, the normalized turbulent flame speed and area both increase almost linearly with the integral length scale ratio. The effect on the mean source term profile is minimal, indicating that the increase in flame speed can solely be attributed to the increase in flame area. At a fixed integral length scale, both the flame speed and area first increase with Karlovitz number before decreasing. Neglecting Soret diffusion is shown to reduce the flame speed, area, and burning efficiency. At higher Karlovitz numbers, the diffusivity is enhanced due to penetration of turbulence into the reaction zone, significantly dampening differential diffusion effects.
The structure of lean hydrogen flames, namely the species mass fraction dependence on the local temperature, differs significantly from that of unity Lewis number fuels due to thermodiffusive instabilities. When subjected to turbulence, the conditional mean species mass fraction profiles are observed to transition from the laminar mixture-averaged flamelet solution to the unity Lewis number flamelet solution. We assess the impact of Soret diffusion and integral length scales on an effective Lewis number model. The results show that the turbulent flame structure can be mapped onto laminar flamelets via the use of effective Lewis numbers, which are expressed by an a priori Karlovitz number model. Although the flame structure is altered by Soret diffusion, there is still strong agreement with previously derived Karlovitz number models for effective Lewis numbers. To map the turbulent flames onto laminar flames with effective Lewis numbers, the relative impact of Soret diffusion needs to be proportionally reduced.
To assess the LES modelling of lean hydrogen flames, we simulate a low-swirl burner, an alternative means of clean energy generation. The LES modelling of these flows remains challenging because the transition of small-scale instabilities into large-scale turbulent structures cannot be modelled by conventional strategies. Traditional one-equation tabulated chemistry formulations require only a progress variable, and cannot capture differential diffusion and curvature effects. In this work, we study the effects of tabulating different conditional mean source terms. It is shown that tabulating the appropriate conditional mean source term leads to improvements in the flow field prediction, however, key features such as the main recirculation region are not reproduced. Then, a two-equation tabulated chemistry model which accounts for differential diffusion and curvature effects is tested. This model provides the best agreement with experimental results. The work is a first effort in evaluating the performance of the two-equation model in the LES framework.</p
Spatiotemporal Regulation of Nascent Protein Targeting
No full textProper protein targeting to the correct cellular compartments is essential for maintaining the functionality and organization of all cells. However, the mechanisms that ensure newly synthesized proteins are accurately and efficiently directed to their specific cellular destinations remain unclear. Moreover, how protein targeting is coordinated with protein folding and other cellular processes, both spatially and temporally, is largely unknown.
In my thesis, I first demonstrated the mechanism of a nascent protein transport pathway in prokaryotes, mediated by a conserved ATPase SecA. Using a combination of ribosome profiling methods, I revealed the essential roles of SecA in recognizing and resolving the widespread accumulation of large periplasmic loops of inner membrane proteins in the cytoplasm during their cotranslational translocation, and in the cotranslational transport of secretory proteins with highly hydrophobic signal sequences. I also uncovered a function of the chaperone trigger factor (TF) in temporally regulating SecA engagement on secretory proteins. These findings elucidate the principles of SecA-driven cotranslational protein translocation and reveal a hierarchical network of protein export pathways in bacteria (Chapter 2).
The second part of research focused on the more complex protein sorting systems of eukaryotes, where I comprehensively investigated the mitochondrial protein delivery from the cytosol using selective ribosome profiling in human cells. I found that the cotranslational protein targeting to mitochondria is initiated late during translation, directed by an N-terminal presequence and the exposure of a complex globular fold in the nascent protein. This pathway does not favor membrane proteins, but is predominantly used by large, multidomain and topologically complex proteins, whose import efficiency is enhanced when targeted cotranslationally. My results indicate that the cotranslational targeting of mitochondrial proteins is fundamentally different from that of the endoplasmic reticulum (ER) proteins, highlighting the diversity and specificity of protein targeting mechanisms across cellular systems (Chapter 3).</p
Modeling and Design of Synthetic Biochemical Circuits for Biological Phenotypes
Get PDFBiological behaviors arise from the dynamical interactions of biochemical networks. For example, the various immune responses to damage are manifestations of signaling networks between immune cell types. A central goal in systems and synthetic biology is to elucidate the design principles of these networks, or circuits, both in the sense of dissecting how function arises from structure in the natural context and in the sense of understanding the guidelines for optimal engineering of synthetic biological systems. The study of design principles in both senses is aided by mathematical modeling and simulation, which provide a self-consistent framework for evaluating the theoretical implications of biological hypotheses as well as a testbed for the development of novel circuits for desired biological phenotypes. This thesis pertains to two related challenges in this field, namely the scaling of computational design to larger circuits and the engineering of global phenotypes that emerge nonlinearly from local interactions.
The first section of this thesis presents a novel design platform for biological circuits, called CircuiTree, that uses a game-playing paradigm to overcome the combinatorial complexity of \textit{de novo} circuit design. This platform treats circuit design as a game of circuit assembly and traverses the tree of possible assemblies using Monte Carlo tree search (MCTS). Borrowed from artificial intelligence (AI) agents that have mastered complex games, MCTS is a reinforcement learning (RL)-based search algorithm that efficiently searches for the most effective design strategies and naturally discovers design principles in the form of network motifs, which appear as clusters of solutions in the search tree. Finally, when tasked with designing fault-tolerant oscillators with five components, CircuiTree finds a novel design strategy, which we call motif multiplexing, in which multiple sub-oscillators are interleaved so as to render the circuit highly resistant to deletions and knockdowns. This design principle, which may be responsible for the multiple oscillatory loops observed in eukaryotic circadian clocks, opens the possibility of engineering synthetic circuits at a larger scale and suggests that larger biological circuits contain yet-unknown design features that are not simply extensions of smaller circuits.
The second section describes a novel mechanosensitive property of the SynNotch synthetic chimeric receptor and uses a multicellular modeling framework to show how it can be used to control spatiotemporal patterning \textit{in vitro}. Modified from the endogenous juxtacrine receptor Notch, SynNotch binds to an arbitrary extracellular ligand and, in response, releases an arbitrary transcription factor, thus acting as a user-defined signal transducer. We show that, in mouse fibroblasts, a simple sender-receiver SynNotch circuit ceases to transduce a membrane-bound GFP signal at high cell densities in 2D culture. Because of this feature, a lawn of cells expressing a signal-relay circuit, which we call the transceiver circuit, can undergo spatially limited activation, where the signal propagates in a wave outward from a GFP-expressing sender cell until, due to cell division, the cell density crosses a threshold value and the signaling system shuts down. Using a multicellular lattice-based model combined with experiments, we demonstrate that perturbations of growth parameters can be used to control the size of activated spots. Finally, we achieve spatiotemporal patterns of activation by seeding the growth dish nonuniformly, creating a wave of activation at the millimeter scale that recapitulates the kinematic wave patterning phenomenon observed during vertebrate somitogenesis.
Together, this body of work represents an advance in the use of computational methods and mathematical modeling to guide the design and control of complex biological phenotypes. Advances in these methods promise to catalyze the development of more advanced cell-based therapies and engineered tissues.</p
Test and Evaluation of Autonomous Systems: Reactive Test Synthesis and Task-Relevant Evaluation of Perception
Get PDFAutonomous robotic systems have potential for profound impact on our society -- legged and wheeled robots for search and rescue missions, drones for wildfire management, self-driving cars for improving mobility, and robotic space missions for exploration and repair of spacecraft. The complexity of these systems implies that formal guarantees during the design phase alone is not sufficient; mainstream deployment of these systems requires principled frameworks for test and evaluation, and verification and validation. This thesis studies two such challenges to mainstream deployment of these systems.
First, we consider the problem of evaluating perception models in a manner relevant to the system-level specification and the downstream planner. Perception and planning modules are often designed under different computational and mathematical paradigms. This talk will focus on evaluating models for classification and detection tasks, and leverages confusion matrices which are popularly used in computer vision to evaluate object detection models to derive probabilistic guarantees at the system-level. However, not all perception errors are equally safety-critical, and traditional confusion matrices account for all objects equally. Thus, task-relevant metrics such as proposition labeled confusion matrices are introduced. These are constructed by identifying propositional formulas relevant to the downstream planning logic and the system-level specification, and result in less conservative system-level guarantees. Using this analysis, fundamental tradeoffs in perception models are reflected in the tradeoffs of probabilistic guarantees. This framework is illustrated on a car-pedestrian example in simulation, and the confusion matrices are constructed from state-of-the-art detection models evaluated on the nuScenes dataset.
Second, we consider the problem of automatically synthesizing tests for autonomous robotic systems. These systems reason over both discrete (e.g., navigate left or right around an obstacle) and continuous variables (e.g., continuous trajectories). This talk presents a flow-based approach for test environment synthesis which handles discrete variables and is also reactive to the system under test. Reactivity is important to account for uncertainties in system modeling, and to adapt to system behavior without knowledge of the system controller. These tests are synthesized from high-level specifications of desired behavior. Though the problem is shown to be NP-hard, a flow-based mixed-integer linear program formulation is used that scales well to medium-sized examples (e.g., >10,000 integer variables). The test environment can consist of static and reactive obstacles as well as dynamic test agents, whose strategies are synthesized to match the solution of the flow-based optimization. The overview of the approach is as follows. First, principles of automata theory are used to translate the high-level system and test objectives, and the non-deterministic abstraction of the system into a network flow optimization. The solution of this optimization is then parsed into GR(1) formulas in linear temporal logic. This GR(1) formula is used to synthesize reactive strategies of a dynamic test agent in a counterexample-guided fashion. We provide guarantees that the synthesized test strategy will realize the desired test behavior under the assumption of a well-designed system, the test strategy is reactive and least-restrictive,. This framework is illustrated on several simulation and hardware experiments with quadrupeds, showing promise towards a layered approach to test and evaluation.</p
Interferometric Precision Measurement with Macroscopic Silicon Optomechanics
Get PDFOptomechanical sensors provide our most sensitive measurements of spacetime, including observations of gravitational waves by laser interferometric detectors. However, even state of the art detectors like the Advanced Laser Interferometric Gravitational-Wave Observatory (LIGO) are still tens of orders of magnitude away from the measurement limits imposed by Heisenberg uncertainty. This thesis maps out the contours of mechanical and optical losses limiting next generation gravitational wave interferometers, and describes several experiments and analyses to improve those limitations. We review the theory of optomechanical force sensing to understand the influence of optical radiation pressure on the dynamics of mechanical oscillators. We analyze several modified Mach-Zehnder interferometers and show how radiation pressure can be a resource for quantum measurement, including by establishing a surprising optical spring effect in a cavity held on-resonance. The most developed proposal is for a phase-sensitive optomechanical amplifier to avoid the photodetection losses that may limit next-generation gravitational wave interferometers utilizing cryogenic silicon mirrors and ≈2000 nm infrared lasers. The amplifier calls for high quality mechanical oscillators made of single crystal silicon, which we fabricate. We describe our efforts to develop a testbed for cryogenic mechanical loss measurements of silicon oscillators and thin film coatings. And, we show how Bayesian inference can be used to improve our understanding of the physical mechanisms limiting a system’s mechanical loss. Finally, we describe the optical, mechanical, and electronic design of a prototype phase sensitive optomechanical amplifier. The prototype is useful for testing the control system required to implement the full amplifier, and we characterize the current control scheme and the scheme for near-term upgrades. Our latest measurements show a clear path to steadily improving the amplifier’s noise figure with well understood technology
Partial Synthetic Models of the FeMoco Nitrogenase Cluster with Bridging C-Based Ligands
No full textBiological N₂ reduction to NH₃ occurs in microorganisms using the enzyme nitrogenase. This complex system consists of several iron-sulfur clusters, where the active site contains a MFe₇S₉C cluster (M = Mo, V, Fe) known as FeM cofactor (FeMco). The cluster includes an unusual interstitial carbide ligand, which is rare in both inorganic chemistry and biology. In addition, the role of this motif within the enzyme is not well-understood, and studies on synthetic model complexes are limited due to the absence of any previously reported iron-sulfur cluster systems bearing a carbon-based ligand that bridges the Fe atoms. Thus, this thesis focuses on developing strategies to insert a bridging carbon-based ligand into an iron-sulfur cluster platform.
Chapter 1 provides a general introduction and overview of complex biologically relevant iron-sulfur clusters and their corresponding synthetic analogs, with focus on NiFe CO dehydrogenase (CODH), acetyl CoA synthase (ACS), [FeFe] hydrogenase, P-cluster, and M-cluster of nitrogenase.
Chapter 2 discusses the formation of a cluster with a μ₃-carbyne ligand resulting from the ring-opening of a bisaminocyclipropenylidene ligand. Electrochemical studies on this system and related species suggest that a chelating μ₃-carbyne leads to clusters with highly negative reduction potentials compared to μ₃-N or S ligands, suggesting that the interstitial carbide in FeMco may play a role in modulating the redox potential of the cluster to allow for the reduction of difficult substrates like N₂.
Chapter 3 focuses on the binding of CO to the cluster with a μ₃-carbyne fragment, resulting in a high level of CO activation at 1851 cm⁻¹ in the neutral cluster and 1782 cm⁻¹ in the reduced cluster, Computational studies suggest that the bridging carbyne stabilizes the intermediate spin state at the Fe sites, resulting in more electrons in orbitals that can backbond with CO and greater activation. This suggests that the carbide in FeMco might play a role in modulating the electronic structure at the Fe sites to allow for greater activation of substrates.
Chapter 4 highlights the synthesis of a cluster bearing a μ₄-carbide ligand using a previously reported terminal Mo carbide complex, with a bridging CO ligand that resembles the lo-CO form. The S = 1/2 spin state provides an opportunity to study the metal-carbon interaction by pulse EPR spectroscopy.
In Chapter 5, a cluster ligated by an anthracene-bridged bisphenoxide ligand is described. Upon reduction, the anthracene bridge moves closer to one Fe site and interacts with it in an η² manner. This species can catalyze the electrochemical reduction of proton to form H2, possibly through a protonated cluster intermediate. The studies demonstrate the ability of the cluster to catalyze a biologically relevant reaction, and possibility for future studies on protonated species that have only been proposed in reactions of synthetic iron-sulfur clusters.</p
Greater Than One Billion Optical Q Factor for On-Chip Microresonators
Get PDFThis thesis is focused on making ultra-high-Q optical microresonators on silicon chips based on design and constructing ultra-low-loss optical waveguides (with losses around 20 dB/km), their fabrication process development, and device applications in on-chip nonlinear optics, including frequency combs, low-noise microwave generation, and narrow-linewidth lasers.
First, using thermally grown oxide (thermal silica) and wedge microresonator structure, a record Q factor exceeding 1.1 billion is achieved. Then, the limitations of the Q-factor due to surface roughness scattering loss and OH absorption loss are investigated and identified. Absorption limited Q-factor of 8 billion mainly attributed to OH ions is measured. To further explore the potential of thick thermal silica as under cladding material, wedge resonator fabricated in 25-µm-thick thermal silica achieves a Q-factor of over 60 million, along with a sixfold improvement in thermal stability and a 5 billion absorption-limited Q-factor. Subsequently, low noise microwave signal generation is demonstrated using these devices in a fully optical packaged form, operating soliton microcomb to generate beatnote microwave signals. Noise limitations arising from dispersive waves induced by distinct transverse modes are identified. Additionally, a low-fundamental-linewidth microcavity Brillouin laser is demonstrated, benefiting from device high Q-factor. The noise limits stemming from thermal refractive fluctuation at low offset frequencies and laser output power at high offset frequencies are identified. To improve device integration level, an engineered reduction of interface scattering using TM mode enables a demonstration of 700 million Q factor in a fully-integrated high-aspect-ratio thin SiN platform fabricated in a CMOS foundry. To add one more thing, room temperature soliton microcomb generation is demonstrated for the first time in high-Q AlGaAs microresonators.</p
Neuropsychiatric Drug Biosensors in Organelles, Cells, Biofluids, & Behaving Animals
Get PDFBiology is distinguished in its several levels of spatial organization—from molecular to whole body—that give rise to coherent, goal-related behaviors. Cutting across these layers are definable circuits, each with its own dynamics. These systems should be studied in their natural context to preserve their structure and function. However, quantitative measurements typically demand invasive apparatuses or infrequent, ex vivo measurements. The advent of genetically encoded fluorescent biosensors solves this problem by detecting molecules in situ, read by a microscope or implantable optical probe. These biosensors typically are fusions of a conformational switch and a fluorescent protein. A naturally occurring protein that binds the target of interest typically provides an initial scaffold. However, several molecules, particularly various human-made drugs, do not have similar naturally occurring cognate conformational switches in nature robust enough for this approach.
This work develops and applies the first genetically encoded drug biosensors in cellular and behavioral assays addressing substance abuse disorders. We term these biosensors intensity-based drug-sensing fluorescent reporters or “iDrugSnFRs.” These biosensors are based on a choline-binding periplasmic binding protein (PBP), OpuBC, interrupting a circularly permuted green fluorescent protein (GFP). This work reports a method of optimizing this construct toward the detection of several classes of neural drugs, including nicotinic, SSRIs, ketamine family drugs, and opioids.
The opportunity for continuous monitoring is particularly prominent in brain-body-behavior relationships. For example, a core tenet of behavioral neuropharmacology is the existence of some stereotyped relationship between the time course of a drug and behavioral outcomes such as opioid use disorder. Interindividual variability in pharmacokinetics (PK) complicates the problem of optimized opioid dosing, especially outside the clinic. The problem of personalizing pharmacokinetics is severe in substance use disorders: the patient must receive opioid levels that relieve pain, minimize tolerance and other side effects, and remain within a therapeutic window to maximize adherence. That ideal window is a “moving target” due to tolerance, changes in metabolism, and stressors.
As an end-to-end study with a preclinical model, the final chapter reports the development of iOpioidSnFRs and their application to the continuous monitoring of fentanyl alongside a computer vision routine to quantify behavior. The fentanyl sensor, iFentanylSnFR2.0, was expressed in the ventral tegmental area of mice and reported [fentanyl] vs. time. This recording is the longest continuous measurement of the brain [drug] alongside behavior (4 hours). We found a stereotypic, repetitive motor pattern that tracked the entire fentanyl time course (2-3 hours) despite variable PK across individuals. This result challenges current models of cellular desensitization and acute tolerance timescales. In a separate experiment, we investigated if this stereotypical pattern impaired mice in a survival task where mice forage for water through a labyrinth maze. Like in the open arena, mice in the maze exhibited circling/stalling for approximately 3 h, to the complete exclusion of successful foraging. Critically, this paradigm offers a normative definition of a deficit, as mice should have a baseline level of successful foraging to survive. We introduce this task to the substance use disorder field as an additional metric for the deficits caused by opioid administration.
Finally, this work demonstrates the utility of iOpioidSnFRs in diagnostic tests owing to their suitable aqueous solubility, dynamic range, sensitivity, selectivity, kinetics, and stability after lyophilization. Plate reader assays using iFentanylSnFR2.0, iS-methadoneSnFR, iTapentadolSnFR, and iLevorphanolSnFR provided quantitation across the pharmacologically relevant concentration ranges. These biosensors were also used in a simulated field test using readily available parts: dark box, blue LED strips, band pass filter, and a cellphone camera. This test could be used to determine the presence of a health hazard in the environment (e.g., fentanyl) or determine the exposure level in a person. These results encourage diagnostic and continuous monitoring approaches to personalizing opioid regimens.</p
Unveiling the Structure of the Circumgalactic Medium of High-Redshift Galaxies via Emission and Absorption Lines
Get PDFThe circumgalactic medium (CGM), namely the gaseous matter beyond the stars and interstellar medium of a galaxy and within the virial radius of its dark matter halo, plays a pivotal role in governing crucial aspects of galaxy evolution. This thesis focuses on investigating the multiphase, clumpy structure of the CGM through the application of state-of-the-art numerical simulations, the development of novel semi-analytic models, and comprehensive analyses involving comparison with high-resolution, spatially resolved observational data obtained from the world's largest ground-based telescopes.
In this thesis, Chapter 2 represents a theoretical investigation into the fate of cool clouds within a hot ambient medium, which offers new insights into predicting the destiny of cool clouds based on observed CGM properties. Chapters 3 and 4 detail endeavors to model spatially resolved Lyα emission spectra obtained from SSA22 Lyα Blob 1 and 2 to constrain the cool gas properties, employing a multiphase, clumpy radiative transfer (RT) model for the CGM. Chapter 5 offers a theoretical exploration on extracting and interpreting physical parameters of cool gas in the CGM from Lyα spectra using physically realistic RT models. Chapter 6 introduces a novel method to self-consistently reproduce the spatially extended Lyα emission from the CGM of twelve extreme emission line galaxies at z ~ 2, marking the first successful attempt to model spatially varying Lyα emission within a physically realistic CGM framework. Chapter 7 introduces ALPACA, a new semi-analytic model for simulating low-ionization state (LIS) metal absorption lines in the clumpy CGM. Applying ALPACA to model CIIλ1334 absorption line profiles in star-forming galaxies at 2 < z < 3, the study reveals the intricate physical and kinematic structure of the CGM, and it underscores the necessity of integrating emission and absorption line modeling to effectively break the intrinsic degeneracy of complex CGM models. Concluding the thesis, Chapter 8 offers a brief summary and outlines potential applications of the newly developed CGM models in the James Webb Space Telescope (JWST) era.</p
Characterizing the Molecular Structure of Preceramic Polysiloxanes for Freeze Casting of Silicon Oxycarbide Ceramics
Get PDFPreceramic polymers are frequently used as a lower energy intensive precursor for creating ceramics, as they can be transformed into robust ceramics at lower temperatures than is required by traditional processing routes. Additionally, preceramic polymers can be used to produce structures with microstructural variability, such as porosity. Polysiloxanes are one type of preceramic polymer that have been used to create silicon oxycarbide materials. Previous research has utilized polysiloxanes in freeze casting to create porous ceramics, specifically investigating development of different pore morphologies and pyrolysis profiles. However, there has been little exploration into the differing molecular structures of various polysiloxanes impact their behavior through the freeze casting process. Investigating the molecular structure of commonly used proprietary polysiloxane Wacker SILRES® MK has provided some insight into molecular structural changes during the freeze-casting process. These can be used to improve freeze-casting microstructure from another proprietary polysiloxane, Wacker SILRES® H44. MK and H44 were characterized in powder, solution, and post pyrolysis stages of the freeze casting process. Techniques including FTIR-ATR spectroscopy, Raman spectroscopy, NMR spectroscopy, DSC, and SEM imaging were used to determine how to improve the robustness of freeze cast structures made with H44. MK was determined to be a polymethylethoxysiloxane, and H44 to be a polymethylphenylsiloxane. The high energy and high steric strain phenyl groups in H44 require additional energy to facilitate crosslinking during the freezing process for H44. Both MK and H44 converted to silicon oxycarbide upon pyrolysis. Adding crosslinker improved the desired porous microstructure and robustnesss of freeze-cast structures made with H44, as evidenced by SEM imaging. Future exploration into other preceramic polymers should consider the impact of high energy functional groups upon the processing methods to create desired microstructures.</p