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Numerical Analysis of Folding and Deployment Dynamics of Thin Shell Structures with Localized Folds
Get PDFThis thesis focuses on the analysis of tape springs folded in the opposite sense and their dynamic deployment, and aims to use methods to reduce the computational cost of the analysis. The tape spring is a thin shell deployable structure that has features in common with other deployable structures. The deployment process of such structures can be difficult to predict, and the use of numerical models can be a more cost-effective alternative to experimental testing. Approaches to reduce the computational cost of the analysis of tape springs are investigated such as adaptive meshing and reduced order models. The thesis also presents an accurate analysis of tape spring deployment and a detailed study of the energies and the physics of the deployment. This is used to investigate the energy leak observed in previous tape spring deployment work. Overall, this thesis contributes to improving the efficiency and accuracy of the analysis of deployable structures, particularly tape springs, which can have significant applications in spacecraft technology
Optimization of Photovoltaic Performance for Luminescent Solar Concentrator Systems
Get PDFThe luminescent solar concentrator (LSC), an emerging photovoltaic (PV) technology with myriad potential application areas, could help further spur global solar adoption. At its core, an LSC absorbs and down-shifts incident irradiation via luminophores, and then redirects the emitted photoluminescence through a dielectric waveguide towards small-area PV cells. Given this design, an LSC describes a planar concentrating technology that i) maintains low system costs by using small amounts of high-efficiency PV material and ii) enables concentration of both direct and diffuse irradiation. The underlying structure of an LSC—including flexibility, material versatility, and variable transparency—facilitates application areas that span utility, building integrated, and space-based solar power.
This thesis explores the optimization of LSC systems, measured based on photovoltaic performance, across each application area. We begin by examining photovoltaic device considerations for LSC integration, including device form factor, luminophore pairing, and microcell fabrication. We outline ideal component parameters for optimal LSC performance and fabricate a silicon heterojunction microcell with a record VOC of 588mV. Next, we design and fabricate single-junction LSCs for two application areas: building-integrated PV and space-based solar power. Through simulations and technoeconomic analyses, we find that such designs are able to achieve 7% efficiency with a forecasted cost as low as 2.22 /W for the space-based application. Finally, we investigate the potential to combine luminescent concentration with conventional solar technologies, including each a silicon subcell and geometric concentrators. We demonstrate that hybridization of luminescent concentrators with certain conventional designs has the potential to boost PV performance in both direct and diffuse lighting.
We conclude by investigating future directions for LSCs, including improved overall system performance, as well as next-generation designs for each building-integrated and space-based applications.</p
Optimization of Electrodes Towards More Practical Electrochemical Water Treatment
Get PDFDue to water scarcity and water pollution, the world suffers from continuing water sanitation issues, which lead to billions of water-borne disease cases every year. Decentralized water treatment is regarded as an important supplement to the conventional wastewater treatment system to address the water sanitation and water pollution issues in rural, remote, and undeveloped regions. Electrochemical water treatment technology has been demonstrated to be feasible for decentralized water treatment systems because of the ambient operation conditions, robust performance, modular design, small footprint, and environmental compatibility. The performance of electrochemical water treatment systems relies heavily on the choice of electrodes. This thesis presents a comprehensive study towards understanding and optimizing the electrodes to enhance the performance and lower the cost of electrochemical water treatment systems. The research work on anodes followed an “understanding – development” approach and spanned both the scientific and engineering sides of the spectrum. Specifically, a comprehensive review was assembled through the analysis of existing literature on mixed metal oxide anodes. This review pointed towards potential future research directions. With the advancement of material sciences, it is important to focus not only on single catalytic metal elements, but also on the intermetallic electronic interaction to gain a deeper understanding of the catalytic activity of mixed metal oxides. The microscopic steric effects imposed by crystalline structures may also be a nonnegligible contributor to the catalytic properties.
Following the review, this thesis scrutinized the catalytic sites of crystalline CoSb₂O₆, an emerging anode for chlorine evolution reaction (CER) catalysis. It has been demonstrated to be a promising alternative for the conventional Ru- and Ir-based anodes based on its high activity and excellent stability, but its catalytic sites and mechanism are still unknown. By fabricating and testing a series of anodes with different Sb/Co ratios, it was discovered that the surface Sb/Co ratios in CoSb₂O₆ were ~50% higher than in the bulk. At the same time, it was surprising to find through scanning electrochemical microscopy (SECM) that Sb-rich samples showed higher catalytic activities, indicating that Sb sites may be even more active catalytic sites than the Co-sites. This was attributed to the electronic interaction between Co and Sb, as revealed by X-ray photoelectron spectroscopy (XPS).
On the engineering side, a Ni–Sb–SnO₂ reactive electrochemical membrane (REM) was developed to treat primary effluent and greywater. In 30 min, the REM removed up to 78 ± 2% COD and 94 ± 0.6% turbidity from the primary effluent. The REM had ~100% COD removal and 89 ± 4% turbidity removal from greywater, with the effluent meeting the NSF/ANSI 350 standard. Compared to the conventional plate-type electrodes under the same conditions, the REM had 36% lower energy consumption for primary effluent treatment and 22% lower energy consumption for greywater treatment while yielding better treatment results. The REM-based electrochemical system was demonstrated to be a promising solution for decentralized wastewater treatment and recycling for single households and for vehicles.
Last but not the least, this thesis presents the 3D-printing-derived carbon lattice as a monolithic electro-Fenton cathode. The Fenton reaction is one of the most important advanced oxidation processes (AOPs) that is widely used in water treatment to remove non-biodegradable pollutants, and heterogeneous electro-Fenton (HEF) process catalyzed by carbon-based cathodes has received considerable attention as an evolving branch due to its wide working pH range and independence from chemical dosing. However, the conventional carbon cathodes suffered from poorly controlled porosities, which hampered the mass transport and limited the overall catalytic performance. Three rationally-designed carbon lattice cathodes with different macroscopic porosities were fabricated and tested, showing that it was feasible to facilitate the mass transport by tuning the macroscopic electrode structure. Specifically, Grid-2% cathode, which had the largest macroscopic porosity, showed 157% higher specific activity for electrochemical H₂O₂ production and 256% higher specific activity for trimethoprim degradation than the Star-2%, the one with the smallest macroscopic porosity. Grid-2% achieved 97% aqueous trimethoprim removal in 60 min, demonstrating the potential of the carbon lattice cathode to be used for water treatment and remediation.</p
Chirped Pulse Rotational Spectroscopy of Small Molecule Clusters
Get PDFDespite the ubiquity of water and alcohol mixtures in every realm of science, the hydrogen bond network governing the unique properties of these mixtures is still under investigation. To aid in the determination of hydrogen bond energetics and dynamics in alcohol and water mixtures, herein a ground up approach studying small alcohol:water clusters is presented. Novel instrumentation for chirped pulse Fourier-transform microwave spectroscopy was developed, and subsequently benchmarked against the detection and characterization of ethanol and water trimers. From there, cluster size was gradually increased, first studying ethanol and water tetramers, then switching to methanol for larger cluster studies of pentamers and hexamers. Throughout this thesis, the over-arching questions as to microaggregation in clusters and trends in geometry and relative energy ordering were investigated, and evidence supporting the facile mixing of small alcohols and water is presented at the few-molecule cluster scale. In the final studies of methanol and water hexamers, the first `3-dimensional' bonding motifs of methanol and water clusters are observed, marking the transition from the planar conformers of small clusters to the complex and higher cooperativity bonding patterns in larger clusters and in bulk mixtures
DNA-Guided Genome Manipulation in Escherichia coli
Get PDFArgonaute proteins (Agos) were initially discovered in eukaryotes as key players in RNA interference (RNAi) pathways and later found in prokaryotes. Some prokaryotic argonautes (pAgos) have been shown to mediate nucleic acid-guided cleavage of DNA targets, reminiscent of the nucleic acid-guided DNase activity of the CRISPR/Cas9 system. It has been postulated that pAgo variants might be used as a novel genome-editing tool. However, genome manipulation induced by pAgo-mediated DNA cleavage has never been established. To demonstrate that pAgo-mediated DNA cleavage can introduce genomic mutations in Escherichia coli, we first created a recombination system and showed that CbAgo, a pAgo from Clostridium butyricum, can be directed by plasmid-encoded guide sequences to cleave the genome target site and induce chromosome recombination between downstream direct repeat sequences. Results from testing different pAgo variants suggest that the recombination rate correlates well with pAgo DNA cleavage activity, and the mechanistic study suggests the recombination involves DSB generation and RecBCD processing. In RecA-deficient E. coli strain, guide-directed CbAgo cleavage on chromosomes severely impairs cell growth, which can be utilized as counter-selection to assist Lambda-Red recombineering. These findings demonstrate the guide-directed cleavage of pAgo on the host genome is mutagenic and can lead to different outcomes according to the function of the host DNA repair machinery. Furthermore, we created a dCbAgo-based deaminase and showed that it can not only act as a random mutagen in vivo but also has the potential to be directed by plasmid-encoded guide sequences. We anticipate the novel DNA-guided interference by pAgo only or by its fusion protein to be useful in broader genetic manipulation. My work of engineering fluorescent protein-based nicotine biosensors via computational design and experimental evolution is also described in the thesis.</p
The Development of Ni-Catalyzed Methods for Application in Total Synthesis
Get PDFThe total synthesis of complex natural products often requires the development of mild, selective transformations. Once developed, these methods can serve as starting points for related methodologies, fundamental mechanistic studies, or applied in other total syntheses. Herein, a series of projects that embody this relationship are described. Inspired by unexpected challenges in the synthesis of complex diterpenoid alkaloid talatisamine, a Ni-catalyzed enol-triflate-halogen exchange reaction was developed. In addition to finding application toward the synthesis of talatisamine, this reaction found further use in an attempted route toward enmein-type ent-kauranoid natural products. En route to the synthesis of these natural products, a need for meso-anhydride functionalization was identified which inspired a research program dedicated to Ni-catalyzed reductive functionalization of anhydrides.</p
A Unified Data-Informed Model of Turbulence and Convection for Climate Prediction
Get PDFResolving atmospheric turbulent and convective processes in global climate simulations is, and will remain for decades, an intractable computational problem. The strong influence of these processes on cloud formation and maintenance makes the task of modeling turbulence and convection one of the grand challenges in climate modeling, due to the outsized effect of clouds on climate. Current operational climate models fail to represent atmospheric turbulence and convection accurately and consistently across dynamical regimes and vertical levels; errors in the representation of these processes explain about half of the spread in climate projections. This dissertation seeks to reduce such representation errors by improving a recently proposed unified framework for modeling turbulence and convection, known as the extended eddy-diffusivity mass-flux scheme, in several ways. First, the framework is rederived by systematically coarse-graining the governing fluid equations, highlighting the assumptions about atmospheric motion that are necessary to yield the scheme. New terms related to turbulent entrainment processes are shown to arise from the derivation. Second, a generalized formulation of turbulent diffusion consistent with the framework is presented. This novel formulation is shown to accurately represent turbulent processes under statically stable and unstable conditions, including regimes with sharp lapse rate inversions such as the stratocumulus-topped boundary layer. Finally, a methodology to calibrate free parameters within the model from indirect data is proposed. The methodology, based on Kalman filtering, is shown to be efficient at calibrating imperfect black-box models from noisy data, and in its regularized unscented version approximately quantifies parametric uncertainty. The resulting unified data-informed model of turbulence and convection is shown to accurately represent a range of low-cloud regimes that are associated with the largest biases in current operational climate models. The response of the model to realistic climate perturbations is also shown to be consistent with the resolved climate response, although structural errors in the amount of condensate are still important at realistic vertical resolutions
Laser-Engraved Wearable Sweat Sensor for Metabolic Monitoring
Get PDFWearable sensors have shown great potential in health diagnostics and monitoring. Continuous monitoring of metabolites in sweat could potentially offer great insight into a person’s health, but current sweat sensing technology faces challenges in different realms: The sensing strategies are limited and there is a need to achieve high sensitivity for low-concentration targets and widen the detection spectrum of chemical targets. The lack of efficient sweat sampling creates inaccurate sensing results from sweat mixing with skin contaminants or sensing byproducts. Moreover, the lack of evaluation of sweat metabolites with respect to relevant clinical conditions and the lack of scalable fabrication technique pose hurdles in the eventual applications of non-invasive sweat monitoring. In this thesis, efforts advancing progress in these fronts are presented. Chapter 1 establishes a brief topical overview of the sweat-sensing background. In Chapter 2, we demonstrate how to utilize laser-engraving technique to achieve high-performance graphene sensors for electroactive metabolite sensing and vital signs detection. Chapter 3 describes subsequent efforts built on laser-engraved graphene sensors to improve sensing selectivity and widen the detection spectrum to detect non-electroactive targets in sweat. In Chapter 4, design and performance of our laser-engraved microfluidics are described and shown to improve sweat sampling in both exercise-induced and iontophoresis-induced sweating individuals. Chapter 5 presents our endeavors in evaluating sweat biomarkers with clinical conditions in pilot studies involving individuals with gout and metabolic syndrome. In total, the works summarized here expand biology, chemistry, material science, and mechanical engineering, and could potentially facilitate future applications in precision nutrition
Non-Invasive Functional Gene Delivery to the Central and Peripheral Nervous System Across Species
Get PDFThe normal function of the central nervous system (CNS) and peripheral nervous system (PNS) relies on precise regulation. When this regulation breaks down in diseases, genetic access to the nervous system is critical for therapeutic intervention. However, access to the nervous system remains difficult, reflecting the critical need for development of effective and non-invasive gene delivery vectors across species. By applying directed evolution approach, we identified 2 capsids, AAV-MaCPNS1 and AAV-MaCPNS2, which efficiently transduced the PNS in rodents following intravenous administration. Combining with rational optimization, we also identified AAV-X1 capsid family, which transduce brain endothelial cells specifically and efficiently following systemic administration in wild-type mice with diverse genetic backgrounds and rats. Some previously-engineered AAVs that target the nervous system fail to translate across non-human primate (NHP). We thus also further tested our novel vectors across species and showed that AAV-MaCPNS1/2 efficiently transduced both the PNS and CNS in NHPs. AAV-X1.1 also exhibit superior transduction of the CNS in rhesus macaques and ex vivo human brain slices although the endothelial tropism is not conserved across species.
With these enhanced systemic AAVs, we wanted to explore whether they could enable neuronal recording and modulation which has been challenging with the nature AAV serotypes. We used AAV-MaCPNS1 to systemically deliver the neuronal sensor jGCaMP8s to record calcium signal dynamics in nodose ganglia. We observed specific nodose neuronal response to physiological modulation in the gut. Furthermore, we showed that the MaCPNS1-delivered neuronal actuator DREADD to dorsal root ganglia could enable non-invasive neuronal modulation and create a model of pain. The functional utility of the novel systemic vectors demonstrated here provide a non-invasive approach to better explore the nervous system, which would lead to better therapeutic intervention. To this end, we also demonstrated that the X1 capsids can be used to genetically engineer the blood-brain barrier by transforming the mouse brain vasculature into a functional biofactory for production of therapeutic agents for CNS. We showed that vasculature-secreted Hevin (a synaptogenic protein), whose coding sequence is delivered by X1 vectors, rescued synaptic deficits in a mouse model.
AAV repeated dosing could be favorable for certain therapeutic applications, however, neutralizing antibody generated following the first injection creates major obstacle for second injection. We explored whether serotype switching with 2 AAV capsids that have a distinguished neutralizing antibody profile could be a potential solution. To this end, we firstly showed that the X1 capsid modifications translate from AAV9 to other serotypes such as AAV1 and AAV-DJ. We then combined the different engineered serotype to enable serotype switching for sequential AAV administration in mice, showing the first AAV-delivered receptor for the second AAV could boost its CNS targeting.
In general, we developed strategies to enable non-invasive functional gene delivery to the central and peripheral nervous system across species, which would be incremental for both basic neuroscience research and gene therapies for neurological disorders.</p
Computational Methods in the Study of Political Behavior
Get PDFIn this thesis, I explore how individual-level actions contribute to aggregate political outcomes. In each chapter, I aim to understand an observed political behavior using data or methodologies previously unused in their contexts. The subject matter ranges from protest activity and vote choice to theoretical opinion models and re-examining how socioeconomic class is understood in quantitative work.
In the first two chapters I employ novel datasets to understand phenomena where popular theories differ from empirical observations. In Chapter 1 I examine protest behavior, which is not the equilibrium prediction of models of collective action. I investigate what aspects of published language can predict protest participation and how these change leading up to and following protests. Specifically, I collect and, using natural language processing methods, analyze 4 million tweets of individuals who participated in the Black Lives Matter protests during the summer of 2020. Using geographical and temporal variation to isolate results, I find evidence that interest in the subject, measured as percentage of online time discussing the matter, is correlated with protest behavior. However, I also find that collective identity, measured through pronoun use, does not have a strong relationship with protest behavior.
Next, in Chapter 2, I use a survey---which I helped to develop and field---to understand the 2020 midterm elections' surprising results. While most accepted models of midterm elections predicted massive Democratic losses (averaging around 40 seats in the House), these predictions were not met. In fact, the Democratic party did well---they did not lose a single state legislature, expanded some majorities, and lost only 9 seats in the House of Representatives. Testing various models of midterm elections, I show that the 2020 midterms were issue-based elections, where views on abortion had a large impact on vote choice.
In the second half of the thesis I focus on methodologies. Specifically, in Chapter 3, I expanded on mathematical models of consensus building to better mimic reality. Bounded confidence models have historically been used to explain convergence of opinions. In this chapter I add a repulsive element, modeling the inclination to differentiate oneself from someone who otherwise has similar beliefs. With this added component, convergence is no longer assumed. I explore both analytical and simulated numerical results to understand the dynamics of opinions in this new context.
Finally, in Chapter 4, I introduce a method for operationalizing socioeconomic class as a latent variable in regression models. While there has been a plethora of research which shows that class affects opinions, views, and actions, the definition of class is nebulous. I argue that this is a result of the nature of class, which is context dependent. Therefore, rather than explicitly determining class, I present using class within a mixture model framework. This allows for the exact definition of class to change within the context being analyzed and enables researchers to use class within their work. Following the theoretical arguments, I present the efficacy of the approach using the American National Election Studies survey from 2020 to show how class differs when related to views of the U.S. Immigration and Customs Enforcement agency and the Black Lives Matter movement.</p