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The Planet-Disk Connection: from Protoplanetary Disks to Planetary Atmospheres
Get PDFWhen gas giant planets form, they influence the structure of the surrounding gas disk, which in turn shapes the final compositions of their gas envelopes. My thesis work combines two distinct techniques in order to better understand planet formation and evolution. As a planet accretes its atmosphere, information about its formation history is encoded in its composition (metallicity and C/O ratio). Taking advantage of equilibrium chemistry expectations of carbon bearing molecules for cool (T<~1000K) planets, in Chapter 2 we probe the atmospheric metallicities of this population of planets using Spitzer secondary eclipses. Expanding this sample set to all short-period gas giant planets with Spitzer thermal emission detections in Chapter 3, we can further explore which system parameters had the most impact on the infrared spectral slopes of these objects. In parallel with these projects, I also carried out a search for planets in protoplanetary disks using direct imaging in Chapter 4. As these planets accrete gas, they also carve out gaps in the protoplanetary disk, leaving hints as to where in the disk they formed. We conducted a multi-year direct imaging survey of more than 40 stars hosting protoplanetary disks in order to detect embedded gas giant planets and better constrain planet-disk interactions. These two approaches represent two distinct, yet complementary, methods of studying the formation histories of giant planets
Design and Application of Novel Membrane Materials
Get PDFMembrane technology is uniquely suited to meet the growing need for more sustainable processes due to membranes’ tailorable selectivity and energy efficiency. Efforts to further improve membrane performance and modify them for new applications have found success in academic studies with a versatile class of membranes known as mixed-matrix membranes (MMM). Mixed-matrix membranes combine the strength and controlled morphology of semicrystalline polymeric membranes with superior functionality of a separate material dispersed in the polymer matrix. The strength and toughness of the resulting membranes depends on polymer morphology, including degree of crystallinity and pore structure. Control of the membrane morphology is achieved by kinetically trapping a partially phase separated state, for example, using Nonsolvent Induced Phase Separation (NIPS) to drive liquid-liquid and solid-liquid demixing. However, the processes used to control the polymer morphology are influenced by the functional particles and can result in novel morphologies. In Chapter 2, we used a promising strategy for stably incorporating functional polymeric particles in a structural polymer matrix to investigate the role of the particles during NIPS. The interplay of functional polymeric particle loading and nonsolvent induced phase separation are examined using x-ray diffraction (to deduce the crystal morph adopted by polyvinylidene difluoride, PVDF) and scanning electron microscopy (to observe membrane morphology and the size and distribution of functional particles). We found that the interaction between nonsolvent and functional particles enables a shift in crystal phase usually not attainable with our solvent.
In addition to studying the fundamentals underlying MMM formation, we investigated two applications for novel membrane materials: purification of therapeutic antibodies and size-selective particle capture. Purification of proteins for medical use requires several chromatographic steps in order to produce solutions of sufficient purity. For many years, the gold standard in the field was resin-based packed bed chromatography; however, more recently membrane chromatography has gained prevalence due to its faster processing time, lower cost, and low operating pressure. With these advantages come the drawbacks of low binding capacity and a sensitivity to the concentration of salt ions in the solution. To address these two drawbacks, we investigated the chromatographic abilities of a modified MMM, in Chapter 3, and a novel membrane material comprising an MMM-ceramic composite, in Chapter 4. We discovered that the performance of the modified MMM is dependent on crosslinker chemistry and crosslink density. Upon optimization, the modified membrane demonstrated a binding capacity consistent with the upper range of available literature values as well as reduced sensitivity to salt. In addition, the development of the novel MMM-ceramic composite enables the use of a broader range of polymer matrix compositions for membrane chromatography.
Capture of pathogens from complex fluids, such as blood, has received substantial attention due to rising rates of sepsis and antibiotic resistance. In Chapter 5, we pursued the capture of pathogens from model fluids using the size-based separation capabilities of dendritic ceramic membranes. We found that interactions between the ceramic surface and the suspended particles played a significant role in membrane performance.</p
On the Variational Principles of Linear and Nonlinear Resolvent Analysis
Get PDFDespite decades of research, the accurate and efficient modeling of turbulent flows remains a challenge. However, one promising avenue of research has been the resolvent analysis framework pioneered by McKeon and Sharma (2010) which interprets the nonlinearity of the Navier-Stokes equations (NSE) as an intrinsic forcing to the linear dynamics. This thesis contributes to the advancement of both the linear and nonlinear aspects of resolvent analysis (RA) based modeling of wall bounded turbulent flows. On the linear front, we suggest an alternative definition of the resolvent basis based on the calculus of variations. The proposed formulation circumvents the reliance on the inversion of the linear operator and is inherently compatible with any arbitrary choice of norm. This definition, which defines resolvent modes as stationary points of an operator norm, allows for more tractable analytical manipulation and leads to a straightforward approach to approximate the resolvent (response) modes of complex flows as expansions in any arbitrary basis. The proposed method avoids matrix inversions and requires only the spectral decomposition of a matrix of significantly reduced size as compared to the original system, thus having the potential to open up RA to the investigation of larger domains and more complex flow configurations. These analytical and numerical advantages are illustrated through a series of examples in one and two dimensions. The nonlinear aspects of RA are addressed in the context of Taylor vortex flow. Highly truncated and fully nonlinear solutions are computed by treating the nonlinearity not as an inherent part of the governing equations but rather as a triadic constraint which must be satisfied by the model solution. Our results show that as the Reynolds number increases, the flow undergoes a fundamental transition from a classical weakly nonlinear regime, where the forcing cascade is strictly down scale, to a fully nonlinear regime characterized by the emergence of an inverse (up scale) forcing cascade. It is shown analytically that this is a direct consequence of the structure of the quadratic nonlinearity of the NSE formulated in Fourier space. Finally, we suggest an algorithm based on the energy conserving nature of the nonlinearity of the NSE to reconstruct the phase information, and thus higher order statistics, from knowledge of solely the velocity spectrum. We demonstrate the potential of the proposed algorithm through a series of examples and discuss the challenges and potential applications to the study and simulation of turbulent flows.</p
Cotranslational Pulling Forces Alter Outcomes of Protein Synthesis
Get PDFAs nascent proteins are synthesized by the ribosome, interactions between the nascent protein and its environment can create pulling forces that are transmitted to the ribosome's catalytic center. These forces can affect the rate and outcomes of translation. We use atomistic and coarse-grained simulation to characterize the origins of pulling forces, the propagation of force to catalytic center of the ribosome, and the effects of force on synthetic outcomes. We uncover a novel form of pulling force-mediated regulation in which the forces generated by the integration of a transmembrane helix induce frameshifting in a viral polyprotein. Computational force measurements of hundreds of mutant viral sequences in combination with deep mutational scanning experiments reveal the structural and sequence-level features that enable this powerful regulatory mechanism. Force measurements are also used to provide a molecular picture for complex pulling force experiments on multispanning membrane proteins. In particular, we identify signatures of cotranslational helix packing interactions and the translocation of surface helices. To understand how forces are propagated through the nascent protein in the ribosomal exit tunnel, we ran and analyzed hundreds of microseconds of atomistic molecular dynamics with an applied pulling force on the nascent protein. The simulations reveal how the secondary structure of nascent proteins and their interactions with the ribosome control force propagation. The inhibition of force transduction by nascent protein-ribosome interactions explains how amino acids tens of angstroms away from the catalytic center of the ribosome can still influence the force-induced restart of stalled ribosomes.</p
Towards Accurate and Automated Detection and Quantification of Localized Methane Point Sources on a Global Scale
Get PDFMethane (CH4) is the second most important anthropogenic greenhouse gas with a significant impact on radiative forcing, tropospheric air quality, and stratospheric water vapor. Because methane has a much shorter lifetime compared to carbon dioxide (CO2), reduction in methane emission is deemed a key target for climate mitigation strategies in upcoming decades. One crucial step in emission reduction is determining the location and emission rate of localized methane sources. Remote-sensing instruments using absorption spectroscopy have emerged as one promising solution for measuring atmospheric CH4 concentration over large geographical areas. However, the identification and quantification of local point sources based on the observed methane column enhancement distribution has proven challenging due to uncertainties in the knowledge of local wind speed and retrieval errors arising from surface spectral interferences and instrument noise. In this thesis, it is shown how plume morphology based on a 2-D image of methane column enhancement can be used to quantify the source emission rate directly without relying on any ancillary data such as local wind speed measurements. Large eddies simulations (LES) are utilized to create realistic synthetic plume observations under various atmospheric conditions. Using this data, a deep learning model named MethaNet is trained to predict emission rates directly from 2-D methane plume images. The model achieves a level of performance for quantifying methane emission rates that is state-of-the-art for a method that does not rely on wind speed information. Obtaining methane column measurements with low precision error and bias is a key step for separating real plume enhancements from artefacts and enhancing the quantification performance. Here an instrument tradeoff analysis is presented to assess the effect of changing instrument specifications and retrieval parameters. It is shown how the retrieval errors can be mitigated with optimal spectral resolutions and a larger polynomial degree to approximate surface albedo variations in the retrieval process. The results in this thesis contribute towards building an enhanced monitoring system that can measure CH4 enhancement fields and determine methane sources accurately and efficiently at scale.</p
Near-Horizon Black Hole Physics
Get PDFThis thesis studies the near-horizon black hole physics in depth from three perspectives.
An important tool for studying perturbations of black hole spacetime is the linearized Einstein equations (LEE). In the Kerr spacetime, the variables in LEE do not separate, which poses a lot of difficulties to obtaining analytical solutions. By taking the near-horizon limit of extremal Kerr black holes, additional symmetries emerge to make the LEE separable. This is achieved by decomposing the metric perturbations using some basis functions adapted to the symmetry. I further show that in two string-inspired low-energy effective theories of gravity, LEE can be directly solved and analytical black hole solutions can be found.
Naively, the near-horizon perturbations of an extremal black hole may destroy the horizon and make the singularity expose itself. This is a direct challenge of the weak cosmic censorship conjecture (WCCC). Based on Wald’s gendanken experiments to destroy black holes, I examine the WCCC for the extremal charged black hole in possible generalizations of Einstein-Maxwell theory due to the higher-order corrections, up to fourth-derivative terms. It turns out that, provided the null energy condition for the falling matter, the WCCC is preserved for all possible generalizations. I further find that for BTZ black holes, i.e. solutions to (2+1)-Einstein gravity with asymptotically AdS3 boundary, WCCC is always preserved. Through the AdS/CFT correspondence, this establishes the connections between black hole thermodynamics and WCCC.
From considerations of quantum gravity and quantum information, it has been conjectured that space-time geometry near the horizon can be modified, even at scales larger than the Planck scale. The resulting spacetime is commonly referred to as the exotic compact object (ECO). A viable method to look for the near- horizon quantum structures is searching for gravitational wave echoes in the GW signals. After discussing the stability issues associated with the ECOs, I build up the phenomenology for gravitational echoes. I also introduce a new framework to deal with the near-horizon boundaries by considering the tidal response of the ECO as experienced by zero-angular-momentum fiducial observers. It is then straightforward to apply the boundary condition to computing gravitational-wave echoes from exotic compact objects.</p
Predicting the Strength of Planetary Surfaces
Get PDFOur curiosity and spirit for exploration has fueled advancements towards visiting Earth's neighbors in the solar system. Environments outside of Earth are extreme, however, and it is far from guaranteed that landing and operating on the surface of these bodies is an easy task. Conditions such as reduced gravity, extreme temperatures and sparse atmospheres play a role in the compressive, shear, and tensile strength of a surface. These environmental factors make experiments that work to inform design decisions for spacecraft-surface interaction difficult and expensive. In order to better ensure successful mission operations in the future, this thesis focuses on the development of a platform of numerical modeling for planetary surface interaction.
Dry regolith and water ice are two surface materials that are pervasive in the solar system. For each, the mechanical properties are heavily reliant on features at the microscale that are insufficiently modeled. The first part of the thesis will focus on crushable dry regolith. There will be two chapters on this topic, the first of which discusses the development of the modeling capability to capture both the highly irregular particle shapes and the brittle nature of regolith. The second chapter on regolith will focus on the validation of this method on a crushable sand sample experiment. This model demonstrates excellent predictive capability for the constituitive relationship, the evolution of particle sizes, and the evolution of particle shape in the sample. Further, evidence from the forces between the particles shows that despite larger particles being weaker on average, many survive due to two reasons. One, the surviving particles are generally on the stronger side of the particle strength distribution, and second, that larger particles have a higher coordination number producing a more isotropic stress state in the particle.
Unlike dry regolith, distinct neighboring water ice particles will sinter together over time at varying rates depending on their environment. This leads to a large amount of the water ice surfaces that are of interest to future missions, having a highly varied and many times unknown levels of strength. The contact interaction between water ice particles at the microscale will be handled the same as regolith, however a modification was added to account for sintering. The strong cohesiveness sintering generates is modeled by placing massless bonds where sinters would form. The cross-sectional area of the bond represents the amount of sintering that has taken place and can be thought of as a representation of the neck geometry that early stage sintering is described as. The bonds used are linear elastic and breakable in order to capture the crushable nature of porous ice. Three chapters are dedicated to ice modeling. First, the model development will be shown with verification examples for its use. Second, the model will be used to predict cone penetration tests on ice that were previously conducted in experiment. Comparisons show that the model can produce similar stresses and qualitative features observed in the experiment. A sensitivity analysis is conducted and shows that the most important controlling parameters are the ice’s critical strength and the sinter's neck thickness. The relation of the bond characteristics to the sintering process is discussed. In the third chapter on water ice, the landing of a footpad on the surface of Enceladus is modeled. The model predicts that a lack of sintering could result in catastrophic sinkage, however even moderate sintering provides enough strength to support a lander. Also the model predicts landing on inclined surfaces and shows that landing could be possible at angles as high as 20 degrees.</p
Compilation and Inference with Chemical Reaction Networks
Get PDFThe successful advancement and deployment of technologies in the field of synthetic biology will require sophisticated computational infrastructure coupled with new theoretical ideas in order to more effectively engineer and reverse engineer biochemical networks. This thesis argues that the field of machine learning can inform the development of these underlying principles and techniques. First, software for compiling diverse chemical reaction network models of biological circuits from simple specifications is described. Second, three chemical reaction network implementations of a powerful machine learning model called a Boltzmann machine are analyzed and compared. Third, the class of detailed balanced chemical reaction networks are proven to be capable of probabilistic inference and, when coupled to a driven chemical system, autonomous learning. Finally, the use of machine learning to interpret and understand biological systems is explored in an experimental case study modeling E. coli cell extract metabolism.</p
The Effects of Confinement in Active Matter: the Casimir Effect, Partitioning, and Hindered Diffusion
Get PDFActive matter describes a class of materials for which constituent "particles" convert chemical energy into mechanical motion leading to self-propulsion (swimming). The origins of this swimming motion for both biological and synthetic constituents is a thriving area of research. However, here we focus on the physical properties and mechanics of the active matter systems. We model active particles using the active Brownian particle (ABP) model that is the simplest model that captures the essential physics, where a particle translates with a swim speed U0 in a direction q for a characteristic reorientation time τR; the average length they move between each reorientation is called the run, or persistence, length ℓ = U0τR. Owing to this persistent swimming, the ABPs distribute non-homogenously near surfaces, accumulating at no-flux boundaries leading to a concentration boundary layer near solid surfaces. Active particles often have an effective size—their run length—which can be much larger than their geometric size such that they experience confinement in geometries whose size is on the order of the run length. Active systems are inherently far from equilibrium, and we cannot appeal to properties of equilibrium thermodynamic such as the chemical potential to predict the partitioning. Fortunately, active particles are still subject to the laws of mechanics, and in this work, we present a simple macroscopic balance that allows one to predict behavior without detailed calculations. We predict the attractive force between two parallel plates in a reservoir (also called the Casimir effect) and find that the average concentration between the plates equals that in the bulk reservoir independent of the degree of confinement (ratio of run length to the spacing between the plates). We then examine the confinement effects in a channel geometry, where the behavior is fundamentally different, and the average concentration grows linearly with the degree of confinement. The understanding of these fundamental geometries motivated us to look into more complex geometries such as porous media. Based on dimensional analysis and our predictive model, we explain the transient behavior and steady-state partitioning of active particles between a fluid reservoir and a porous medium. Lastly, we discuss the hindered diffusion in periodic porous media and how the diffusion depends not only on the porosity of the medium but also on the degree of confinement. We believe that utilizing the insights in effects of confinement for these fundamental geometries and the porous media will be valuable in designing optimal structures for enhancing or isolating active particles.</p
The Nuanced Effects of Redox-Active Metabolites on Bacterial Physiology and Antibiotic Susceptibility
Get PDFThe production of secondary metabolites is widespread throughout the tree of life. Bacteria, including many relevant opportunistic pathogens, can make redox-active secondary metabolites, both in the environment and while causing infections. Yet, their physiological consequences for the microbial communities exposed to them are much less understood. This thesis investigates the multifaceted and nuanced effects that such metabolites can have on their producers and other bacteria found in the producer's vicinity, focusing on the role these molecules play as modulators of antibiotic susceptibility. I start by presenting a literature review addressing the link between secondary metabolite production and resilience to clinical antibiotics in diverse opportunistic and enteric bacterial pathogens.
Next, using Pseudomonas aeruginosa (a widespread opportunistic pathogen) and its endogenously produced metabolite called pyocyanin, I explore the nuanced effects of the metabolite's production throughout the producer's lifecycle. Pyocyanin is part of a class of redox-active molecules made by P. aeruginosa called phenazines. I show that the production of pyocyanin, due to its self-poisoning effects, is a "double-edged sword," where the ultimate consequences for the producer are directly dependent on the physiological and environmental conditions. Carbon source limitation plays a major role in the self-poisoning effect of pyocyanin, a process responsible for killing a subpopulation of cells that, through extracellular DNA release, seems critical for proper biofilm development.
Despite pyocyanin's toxicity, P. aeruginosa is remarkably tolerant to its harmful effects. For this reason, I then explore how P. aeruginosa handles the stress caused by the metabolite. I present results using a functional genomics approach (transposon-sequencing) to screen for genes involved in P. aeruginosa tolerance to pyocyanin. Defenses involved in pyocyanin tolerance are similar to ones involved in tolerance to clinical antibiotics. These shared mechanisms lead to testing the hypothesis that defenses induced by the production of or exposure to "natural antibiotics" (such as pyocyanin) may affect the efficacy of treatments with clinical antibiotics. Supporting this hypothesis, exposure to pyocyanin significantly induces tolerance and resistance to certain clinical drugs, both in P. aeruginosa and other opportunistic pathogens within the Burkholderia cepacia complex (Bcc). Pyocyanin and the drugs affected, such as fluoroquinolones, share molecular structure similarities, which is likely responsible for the shared protection.
Finally, based on these results, I explore the broader role of redox-active metabolites as modulators of antibiotic resilience in opportunistic pathogens. I show that pyocyanin, another phenazine called phenazine-1-carboxylic acid, and a non-phenazine redox-active molecule called toxoflavin can all modulate antibiotic susceptibility in Bcc species. Depending on the antibiotic's class, the metabolites' presence can either antagonize or potentiate the drug's efficacy. All the studied metabolites are produced by clinical isolates that infect cystic fibrosis and other immunocompromised patients. I demonstrate that the modulator effect of redox-active molecules in the pathogens is dependent on the transcription factor SoxR, which senses the presence of the metabolites and induces specific redox-regulated efflux systems that are effective in transporting both the metabolites and the structurally related drugs. To end, I provide a proof-of-principle that including such metabolites during clinical drug susceptibility tests may lead to a more accurate assessment of pathogens' resistance profile.
Taken together, the findings presented in this thesis demonstrate that redox-active secondary metabolites have profound effects on the physiology and antibiotic sensitivity levels of opportunistic pathogens. Their modulator effect on antibiotic susceptibility is likely a widespread phenomenon in polymicrobial communities that has been overlooked and may have direct consequences for the evolution of antibiotic resistance. Understanding the physiological roles of these metabolites at the molecular level is essential for accurate predictions of the drugs and pathogens affected, which may lead to more effective treatment strategies.</p