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UC Merced Electronic Theses and Dissertations

Cover page of Catalysis at aqueous interfaces

Catalysis at aqueous interfaces


Many chemical reactions occurring at aqueous interfaces show different kinetics and thermodynamics than the same reactions occurring in the bulk. The nature of these chemical reactions is central in understanding environmental, industrial, and biological processes; but remains incompletely understood due to its complexity and experimental difficulties in tuning and characterizing reactions at aqueous interfaces. In this dissertation, different experimental approaches are utilized to generate large, well-characterized aqueous interfaces for kinetic studies of chemical reactions. Chapter 1 introduces deviations of chemistry at aqueous interfaces that can alter physiochemical properties of chemical processes. In chapter 2, I study mechanistic rate accelerations of organic reactions at the organic-water interface and find that free OH groups of interfacial water molecules play an essential role in catalysis. In chapter 3, I revisit the effects of electric fields at the air-water interface of water microdroplets on directly converting water into hydrogen peroxide which is thermodynamically unfavorable in solution. Contrast to previous reports, no hydrogen peroxide production is observed in water microdroplets when tuning the electric fields at droplet surfaces. In chapter 4, I discuss claims of spontaneous hydrogen peroxide formation at the air-water interface and pinpoint potential experiments that can help to clarify them. Chapter 5 is the conclusion of the work presented in this dissertation.

Racial and Ethnic Socialization in Latinx Families


This dissertation examines how Latinx families talk about racial and cultural identity and racism and how these conversations about and experiences with racial and ethnic inclusion and exclusion vary by family and community resources. I conducted 65 in-depth, semi-structured interviews with U.S.-born Latinx children of immigrants from Florida and California, young adult siblings, and parents. My dissertation contributes to research on race and ethnicity by developing the processes of how structural racism unfolds in Latinx families through racial and ethnic socialization and how individual, family, and community resources can help resist racial oppression.

Giant vesicles as cell-mimetic vessels: Induced cellular variation and confinement on a cyanobacterial circadian clock


Giant unilamellar vesicles (GUVs) are spherical structures composed of an aqueous compartment enclosed by a bilayer membrane. They are often seen as a simplified analog of a cell membrane and can be utilized as minimal cell models for studying cellular systems due to their cell-like sizes and capacity to mediate membrane interactions. A paper-based diffusive loading technique termed, OSM-PAPYRUS, is shown to assemble GUVs in physiologically relevant salt solutions with gentle loading of proteins. Characterization of this loading process reveals cell-like variation of encapsulated protein concentrations and a gamma distribution often cited for protein distributions in the cell. This ability to mimic cellular variability in vitro reveals potential in bridging the gap between in vitro and in vivo experimentation. The highly controlled environment of in vitro experiments can be combined with cell-like volumes, phospholipid bilayer, and cellular variation in GUVs. A practical application is explored, encapsulating the post-translation oscillator (PTO) of the cyanobacteria circadian clock system which shows membrane interactions in vivo. The results showed that cellular variation and membrane binding significantly hampers the fidelity of the clock, in contrast to bulk experiments where concentration did not matter once a critical concentration is met. An increase in concentration to cellular levels helps counteract the effect of variation. Modeling the clock reaction using expected distributions and variation of encapsulated proteins, corroborated with the hypothesis that intercellular variation and membrane binding were responsible for trends in the experimental data. The experimental data and model showed that the PTO by itself was not capable of achieving the near 100% fidelity observed in the native cyanobacteria, instead, other cellular components, like SasA and CikA or transcriptional-translational feedback loop (TTFL) would be necessary to achieve in vivo clock fidelity. The GUV model demonstrated advantages over in vivo studies, particularly in the isolation of the PTO, which allowed for the determination that large period variations seen in vivo cannot be produced by the PTO even under cell-like variability and volumes. This demonstrates the ability of GUV in vitro models to obtain context on behaviors not appreciated by either previous bulk in vitro or in vivo studies.

Cover page of Sustainability Knowledge and Governance in Environmental Problems. The cases of Illegal Wildlife Trade and National Park Systems Management

Sustainability Knowledge and Governance in Environmental Problems. The cases of Illegal Wildlife Trade and National Park Systems Management


The effective management of complex systems necessitates a fundamental understanding of the role of knowledge, especially in environmental systems comprising ecological and social complex dynamics and inherent uncertainties. The social dimension of environmental problems involves not only practices, symbolisms, and forms of organization around a particular resource, but also the dynamics of knowledge and its connection to collective action and decision-making. The interests, perceptions, and power dynamics inherent to the social construction of the problem are revealed by the knowledge produced for a given problem and who produces and uses such knowledge. However, the ways in which knowledge is purposely produced have been poorly explored in the analysis of environmental systems. This dissertation contributes conceptually and empirically to the understanding of knowledge in environmental systems – referred here as sustainability knowledge- by assessing the nature of such knowledge and its interconnection with management and policy of two environmental problems, namely, “illegal trade of wildlife” and “National parks system management”. To be specific, this dissertation addresses i) How knowledge enables illegal operation and its implications to tackle the activity. ii) How a problem’s ill-definition and solution uncertainty affect the scholarly production of knowledge about environmental problems, emphasizing on illegal trade of wildlife. iii) In which ways the perceptions regarding illegal trade of wildlife differ between multiple stakeholders and how this affects possible strategies to manage the problem. iv) How the U.S. national parks are understood and represented by multiple communication channels. v) To what extent the production of knowledge about national parks in multiple countries involves transdisciplinary teams (i.e. teams comprising of individuals from distinct sectors). vi) What is the potential of research about national parks to meet the managerial needs for knowledge-based governance. Altogether, these analyses show how knowledge is used in different management regimes. To be specific, the data-driven approach used here enables the characterization of distinctive features of sustainability knowledge. Overall, this dissertation indicates that sustainability knowledge about the two problems studied lacks proper conceptual and social consolidation at several scales, largely owing to the disparity in stakeholders’ perceptions, preferences, and interests. These findings imply the existence of diverse knowledges that might result in difficulties to make them actionable. Furthermore, such difficulties can affect the capability of managers to deal with multiple, and sometimes conflicting, worldviews, priorities, and interests. As such, the findings suggest that achieving inclusive governance regimes might be hindered by the ability of managers to mobilize diverse actors towards common goals.

Improving Theoretical Modeling of Water in Condensed Phases


Liquid water, a ubiquitous and vital substance, exhibits a range of fascinating and anomalous properties. Understanding its molecular behaviors is crucial for elucidating its unique roles in chemistry, biology, and materials science. This dissertation presents a series of theoretical investigations aimed at improving the theoretical modeling of liquid water and advancing our knowledge of its structure, dynamics, and thermodynamic properties.

First, we systematically scrutinize various electronic structure methods and charge models, evaluating their performance in predicting dipole moments of isolated water, water clusters, and liquid water, as well as charge transfer in water dimer and liquid water. We identify the Iterative Hirshfeld method as the best performing charge model to assign partial atomic charges for liquid water. Our final pragmatic quantum-chemical charge assigning protocol for liquid water is the Iterative Hirshfeld method and a quantum region with a cutoff radius of 5.5 Å.

With the training data from our quantum-chemical charge-assigning protocol, we develop a machine-learning (ML) model trained to assign point charges for water in a post-MD manner. The resulting model improves the predictions of the dielectric constant and low-frequency IR spectrum of liquid water. Our analysis reveals that polarization dominates the enhancement of the dielectric constant of liquid water, and charge transfer is primarily responsible for the hydrogen-bond stretch peak at 200 cm$^{-1}$ in the IR spectrum.

Furthermore, we develop an ML potential for liquid water, including long-range interactions. This model reproduces the potential energy of a water dimer as a function of the separation between the water molecules, and reliably predicts the dipole moment of the dimer simultaneously, allowing a consistent treatment of potential energy and dipole moment surfaces.

Finally, we examine the behaviors of an antifreeze protein (AFP), DAFP-1, in two rigid non-polarizable water models. DAFP-1, an amphiphilic protein, remains at the water/air interface in the experiment. However, it sinks in the simulation with TIP3P water model, whereas staying at the interface with TIP4P/2005. Using umbrella sampling, we calculate the free energy profiles for the sinking process for each water model and investigate how different water models influence the behaviors of AFPs.

Exploring the Dynamics of Confined Microtubule-Based Active Matter


Active matter is ubiquitous in nature, from the flocking of birds to the swarming of bacteria. Both living and non-living systems are out of equilibrium and exhibit rich dynamics that are of fundamental interest. This thesis focuses on the microtubule based active matter, a fascinating material class that has attracted significant attention in recent years. The first project, investigates the dynamics of microtubules propelled by diffusive motor proteins on lipid bilayers, providing insights into bidirectional lanes and nematic phase behavior. The second, third, and fourth projects explore dense microtubule-kinesin systems bundled together with motor protein clusters. In 2D, these systems exhibit self-organizing patterns and nematic-like behavior. In chapter four, I discussed different parameters can change the morphology of active nematics, such as oil viscosity and the fuel source or ATP. In chapter five, I discussed a robust experimental method to confine the active nematic laterally in different geometries. In chapter six, the material is confined in cardioid-shape geometry, and an efficient mixing pattern or golden braid pattern and more controlled dynamics have been shown. Overall, this dissertation advances our understanding of microtubule-based active matter systems, providing a framework for controlling and manipulating these materials for practical applications in the future for drug delivery systems and soft robotics.

Digital Twin Enabled Collective Sensing and Steering for Source Determination Problems


Motivated by climate change and the global warming potential of methane (86 times more potent than CO_2), this dissertation focuses on the source determination problem using collective sensing and Digital Twins. Recently, Digital Twins have been developed to provide better performance assessment, fault prognosis and predict future behavior of complex systems. The term `collective', refers to the group of mobile sensors that, as a whole, provide more information than a single mobile sensor can. The mitigation of methane emissions into the atmosphere is important to focus on in reducing the effects of global warming in the near term. In order to mitigate emissions, the leaks have to first be detected and assessed before they can be repaired. Many of these emissions can be modeled as a point source governed by partial differential equations (PDE), which, solutions are typically time-stepped into the future (i.e. the forward problem). In many cases, the emission plume is subject to turbulence which requires the use of turbulence models, such as large eddy simulations (LES), to compute. In both cases, the computational requirements and run-time can prevent real-time or near real-time analysis. Considering hybrid modeling approaches (e.g. deterministic and stochastic), the forward behavior matched Digital Twin model can be computed in near-real time and used for improving emission quantification methodologies as well as perform optimization (e.g. sensor placement and mobile or fixed-location sensing / actuation policy). The dissertation is broken into four main parts: the first part is on source seeking based optimization using random search, collective foraging, Fluxotaxis, and Extremum Seeking Control; the second part is on the application of leak detection and quantification with sUAS (including: sensors, platforms, and methods) as well as controlled release and real world field campaigns; the third part is on Digital Twins (POSIM and MOABS/DT) and how to use them for environmental sensing, method development, and performance evaluation case studies; the last part is on the sensor placement problem and how the observability Gramian combined with Digital Twins, can be used for smarter collective sensing and steering.

Engineering Multifunctional Nanoparticle Assemblies through DNA Guided Self-Assembly


DNA nanotechnology is a rapidly evolving field that exploits the remarkable properties of DNA molecules to create complex and functional nanostructures. One of the key techniques in DNA nanotechnology is self-assembly, wherein DNA molecules are designed to interact and assemble into specific structures with precise control over their size, shape, and composition. This dissertation focuses on the self-assembly of plasmonic, fluorescent, and magnetic nanoparticles in both 2D and 3D using DNA as a programmable scaffold, and explores their applications in various areas, including biosensing and magnetic metamaterials.Chapter 1 provides a comprehensive overview of DNA nanotechnology, self-assembly techniques, and DNA origami. The principles of DNA self-assembly are discussed, including the design rules for creating DNA nanostructures with precise control over their shape and size. The versatility of DNA as a programmable scaffold is highlighted, allowing for the assembly of diverse nanoparticles with unique functionalities. The chapter also discusses the fundamentals of DNA origami, a powerful technique that utilizes the folding of a long single-stranded DNA template to create complex nanostructures with high precision. In Chapter 2, a novel ligand exchange method is presented, which allows for the functionalization of quantum dots (QDs) with DNA to form self-assembled heterodimers. The heterodimers serve as probes for detection, with one QD acting as a reporter and the other AuNP (gold nanoparticle) as a quencher. The chapter elaborates on the design and fabrication of the QD-AuNP heterodimer. The changes in photoluminescence (PL) signals upon binding of the heterodimers to target DNA molecules are investigated. Chapter 3 focuses on the application of the heterodimer probes in the development of a biosensor for nucleic acid detection. The biosensor is designed based on the change in PL signal upon target DNA binding, allowing for sensitive and selective detection. The chapter provides details on the fabrication and characterization of the biosensor, including the optimization of xvi experimental parameters such as probe design and concentration, and target DNA concentration. The performance of the biosensor is also evaluated using different target DNA concentrations. The kinetics of the DNA displacement process in the biosensor are also investigated, shedding light on the dynamics of target DNA binding and release from the heterodimers. In Chapter 4, a novel method for the self-assembly of gold-coated magnetic nanoparticles in 3D using DNA as a scaffold is presented. The chapter discusses the fabrication of DNA-modified magnetic nanoparticles and their subsequent self-assembly into 3D structures by exploiting the programmable base-pairing interactions of DNA molecules. The chapter highlights the unique capabilities of this 3D self-assembly approach and discusses the future prospects and potential directions for further research in this area. In conclusion, this dissertation presents a comprehensive investigation into the use of DNA nanotechnology for the self-assembly of plasmonic, fluorescent, and magnetic nanoparticles in 2D and 3D. The methods and findings presented in this dissertation contribute to the advancement of DNA nanotechnology and demonstrate the potential of self-assembled nanostructures for various applications, including biosensing, nucleic acid detection, DNA data storage and magneto-plasmonic measurements.

Invoking Halogen Bonding: An Investigation of Halogenation Reactions Promoted By the Halogen Bonding Phenomenon Between Commercially Available Halogenating Reagents And Lewis Basic Additives


Halogenation has become of great interest. Recent studies show the biological advantages when pharmaceuticals contain various halogen-carbon bonds. Halogenated compounds are used in metal-catalyzed cross-coupling reactions, pesticides, and natural products. Previously, our lab has reported radical benzylic C-H fluorination using Selectfluor, as a mild oxidant and fluorine source, in the presence of unprotected amino acids and Ag(I). In doing control reactions, we found that N-protected amino acids did not yield any product, suggesting free nitrogen was required. In contrast to the amino acid/Ag(I) method, we found that a carboxylate group was not required as long as a free nitrogen additive, such as pyridine. Herein, we report an effective method for C-H fluorination via halogen-bonding between Selectfluor and monosubstituted pyridine additives. Computational and NMR studies showed that the Lewis basicity of the pyridine additive must be optimum for halogen-bonding but not strong enough to promote unwanted side reactions.

Using this knowledge, we expanded this strategy to include C-H bromination using N-bromosuccinimide. During the initial investigation, we discovered aromatic bromination was favored over benzylic. While optimizing the reaction conditions, we determined lactic acid was an efficient catalyst for aromatic bromination. Several lactic acid derivatives were investigated and found to affect the efficiency of aromatic bromination via halogen-bonding interactions. This method gives access to a relatively less toxic procedure using catalytic mandelic acid under aqueous conditions at room temperature.

The last chapter of this dissertation depicts the development of an alternative method for halohydrin formation of alkenes. Several brominating reagents were assessed, and the best for this reaction was N-bromosaccharin. Through experimental investigations, it was determined that the carbonyl in the substrate was essential for the halohydrin formation to occur. Future work will further explore the reactivity of the carbonyl oxygen.

Chapter 2 is work adapted from J. Org. Chem. 2022, 87, 8492-8502.

Investigating the Appropriate Unit of Analysis for Creative Cognition


Understanding creativity begins with investigating its appropriate unit of analysis: what elements constitute and can sufficiently explain creative cognition, without leaving out any essential aspect? Is creativity sufficiently explained by the study of the human brain alone? Or does it go beyond the boundaries of the skull, such that the investigation of the external representations—movement, body, environment, etc.—can also provide essential insights into the nature of creative cognition?This dissertation investigates the appropriate units of analysis of expert-level mathematical creativity as a canonical example of highly abstract creative cognition. To do so, it draws on mathematicians’ self-report accounts, empirical studies, and formal modeling. It provides both causal and correlational evidence of multiple mechanisms through which external representations contribute to creative cognition. It thus argues that even highly abstract creative breakthroughs benefit—and in some cases arise—from interactions across distributed components including the brain, the body, the environment, and their interactions. Thus, a comprehensive account of mathematical creativity, and creative cognition in general, must go beyond the boundaries of the human skull and embrace external representations as well.