In the field of DNA Nanotechnology, a field that utilizes synthetically designed DNA strands and their inherent complimentary nature to create two and three dimensional structures, there are many approaches to creating structures to act as vehicles for drug delivery and platforms for diagnostics. However, this field holds an expense due to the number of oligonucleotide strands that are required in order to form the structures. These structures are also limited by the amount of therapeutic load they can carry, the density of targeting modalities that can be attached to the surface, and the variability of other proteins or small molecules that we may wish to include. Our approach to this problem was to utilize a natural, bacterial DNA amplification process known as Rolling Circle Amplification, or RCA. With RCA, we can amplify a sequence into a long stranded DNA sequence carrying the complementary sequence repeating multiple times sequentially on one strand. Using these repetitive sequences, we can create structures by binding them to themselves in certain manners. Any unbound sequences can be used to bind to, and capture, many different modalities and carry a large variety of materials or therapeutics to a designated target. They can also be of great use in capturing large quantities of an analyte in detection motifs and modalities. My RCA structures are capable of binding large quantities of materials, thusly delivering them to targeted cell populations through the use of binding many ligands on the surface. These structures are also capable of undergoing morphological changes in order to facilitate delivery or act as a diagnostic modality in the presence of the target analyte or environment. Thusly, these structures, created by amplifying a single DNA strand, as opposed to utilizing many, have a great advantage over current DNA Nanotechnology techniques for creating delivery and diagnostic vehicles due to their great flexibility in binding capabilities and the decreased cost for synthesis.

This dissertation is devoted to understanding household saving rates of the two largest countries in the world - China and the United States. The first two chapters explain why the Chinese elderly save at extraordinarily high rates and the third chapter explains why the U.S. personal saving rate has been falling since 1980's.

Chapter 1 explores the potential explanation of the high saving rates of the Chinese elderly. The high saving rate of China has attracted global attention. Furthermore, the saving rates of the Chinese elderly are especially high. Understanding why the elderly in China save at high rates is important for two reasons: (1) it partially explains the high aggregate saving rate in China, and (2) the fact that the elderly save more than the middle-aged contradicts the predictions of the life-cycle model. In this chapter, I present evidence that pension income is the primary explanation for the high saving rates of elderly Chinese households. I provide this evidence in two steps. First, I document three stylized facts that are consistent with this hypothesis: (1) saving rates are higher in years with higher pensions, (2) saving rates are higher for those with more generous pension plans, and (3) policy reforms that exogenously increase pensions also increase saving rates. However, a higher pension income on its own cannot explain the entire pattern because a household can simultaneously adjust its consumption. Therefore, in the second step, I demonstrate that concerns regarding future medical expenditures and bequest motives can explain why households do not increase their consumption commensurate with increases in pension income.

In Chapter 2, I build and estimate a dynamic life cycle model for two purposes. The first is to quantify the effect of pension income. The second purpose is to carry out counterfactual policy simulations. The model is a standard life-cycle model with three main components. First, pension income is properly modeled to capture the increase observed in the data. The second part of the model is about uncertainty. In the model, I cover income uncertainty, health status and medical expenditures as the main source of uncertainty for the elderly. Finally, individuals have bequest motives. I estimate the model using the method of simulated moments. The estimation results show that it is possible to match the data with reasonable parameters. It is noteworthy that the estimated degree of relative risk aversion for the Chinese elderly is similar to that of U.S. population in other studies. This implies when factors including pension income, medical expenditures and bequest motives are properly taken into account, it is not necessary to assume Chinese elderly to be highly risk averse to explain their high saving rates. With the model, I am able to carry out various policy simulations. The most interesting simulation is if the Chinese pension and economy growth rate becomes similar to those in the United States, the saving rates of the Chinese elderly will fall to the level of the U.S.

Chapter 3 is a joint work with Maurizio Mazzocco and Bela Szemely. In this chapter we provide evidence that most of the decline in the U.S. personal saving rate from 9 percent in the early eighties to 2 percent in 2007 can be explained by the steep increase in health expenditure experienced by the U.S. economy during the same period. The most convincing evidence is provided using the FDA approval of new drugs as a source of exogenous variation in medical expenses. Employing this source of variation, we find that a $1$ percentage point increase in health expenditure generates a decline in the U.S. saving rate that is between $0.58$ and $0.67$ percentage points. Using this result, we calculate that the rise in health expenditure explains about 83 percent of the drop in the U.S. saving rate. To evaluate whether households changed their consumption decisions to mitigate the effect of higher medical expenses, we develop a stylized model of household's and government's decisions. Using the model jointly with our empirical results, we find that the households' response to the rise in health expenses was negligible. This is why the saving rate dropped by a significant amount. Finally, with the objective of better understanding why households did not respond, we provide evidence on how the increase in medical expenditure was funded. We find that it was paid almost exclusively by an increase in government debt, a reduction in other government expenses, and an increase in employer contributions to health funds. The main implication of these findings is that the households were barely affected by the rise in health expenditure. The households' negligible response was, therefore, rational.

Bacteria live in environments featuring various complex chemical concentration gradients. The formation of these gradients is a combined result of mass transfer and bacterial activities, creating a challenge in isolating the effects of specific gradients on bacterial activities. Since the size of a single microbe is 1 to 10 μm, the spatial profiles of the surrounding chemical concentration gradients are at a resolution similar to the size of a single microbe. Moreover, time-dependent bacterial activities could lead to temporal variation in these chemical concentration gradients. Both spatial chemical heterogeneity and temporal chemical variation are important in shaping bacterial activities. The critical roles of gradients on bacterial behavior poses a significant challenge: the coexistence of numerous gradients simultaneously complicates discerning the effects of a specific gradient on a particular bacterial activity. To address this challenge, I established a workflow using electrochemistry to create electrochemical gradients to simulate specific chemical gradients in bacterial environments. Additionally, I leveraged electrode design to control the generated gradients and minimize side effects where electrochemistry could introduce to the bacterial environment. In my first research project (Chapter 2), I fabricated a flow device equipped with a microwire electrode to generate and control oxygen (O2) and hydrogen peroxide (H2O2) gradient to mimic bacterial gradients. The first highlight of this research is that I generated electrochemical H2O2 gradients through an electrocatalyst design to simulate spatial distributions of reactive oxygen species in bacterial environments. By coating the electrodes with Au catalysts to facilitate the two-electron oxygen reduction reaction (2e-ORR), I achieved a spatial profile of H2O2 perpendicular to the electrode surface. The second highlight of this project is that I enabled spatial and temporal control of both electrochemical gradients via electrode morphology design and applying time-dependent external voltages, respectively. This spatiotemporal control strategy achieved spatial control at a precision within 10 μm and temporal control on a second scale, comparable to the spatiotemporal variation of bacterial environments. Moreover, the concentration ranges of O2 (0 to 250 μM) and H2O2 (0 to 40 μM) in the electrochemical gradient, aligned with the conditions of microbiological research. To advance this research, I, together with co-authors Jingyu Wang and Ben Hoar, developed a machine-learning-assisted design strategy for desired O2 and H2O2 gradients, offering a rapid and efficient solution for electrode optimization. I created another novel electrochemical flow device for my second research project (Chapter 3). I introduced two main electrode design strategies to enhance compatibility and convenience in gradient-bacteria research. Firstly, I deposited a layer of porous copolymer on the electrode surface to increase the oxygen reduction reaction (ORR) selectivity and create a “clean” O2 gradient for bacterial research. This innovation was particularly critical in mitigating the interference of external voltages with bacterial redox systems, thus increasing the integrity of experimental results. Based on size exclusion, the coated polymer maintained the ORR current while rejecting the access of larger bacterial redox molecules to the electrode surface. Using a redox molecule produced by Pseudomonas aeruginosa, pyocyanin, as an example, the coated polymer enabled the ORR current to remain around 70% compared to that on the current on a bare electrode while suppressing almost 100% of the redox current of pyocyanin. Secondly, I adopted planar electrode arrays instead of the vertical electrode arrays used in Chapter 2. The planar geometry enabled spatial O2 concentration profile gradient generation both perpendicular and parallel to the electrode surface. This innovation proved instrumental as it facilitated microscopic microbial research while also allowing the replication of gradients within three-dimensional bacterial structures. In summary, this porous polymer coating enabled the creation of a “clean” O2 gradient, allowing investigation of the effect of O2 on microbial behavior. In Chapter 4, I proposed a potential application using electrochemical gradients in microbiological research. I describe utilizing electrochemical O2 gradients to investigate the effect of O2 variation on bacterial adenosine triphosphate concentration ([ATP]) at the single-cell level. I reviewed the published literature on the relationship between O2 concentration and microbial ATP activity. Most previous research is focused on the average effect of O2 on adenosine energy charge (AEC) and [ATP] in a bulk bacteria culture. As such, the effect of O2 on single cells is missing. I proposed an experiment plan to utilize the electrochemical O2 gradient platform to study the effects of O2 on single microbes and explored the feasibility of this plan. I raised several potential concerns or questions that could arise from an electrochemistry-microbiology hybrid system. In summary, my thesis comprehensively explores electrochemical concentration gradients and their implications for microbial behavior. By employing innovative electrode design strategies, I explored the feasibility of using electrochemical concentration gradients for nuanced investigations into the intricate dynamics of bacterial ecosystems. Looking ahead, I believe the fusion of electrochemistry and microbiology holds promise for advancing our understanding of bacterial behavior, especially at the single-cell level and beyond.

A platform is established to embed functional proteins onto a DNA nanogel. These proteins are embedded through self-assembly mechanisms between the DNA and the proteins. The DNA nanogel is composed of single stranded DNAs, which forms a 3-dimensional nanogel structure by using a G-Quadruplex secondary structure as the crosslinker. The DNA nanogel has multiple overhanging 20 adenosine sequences, which can be used to modify the nanogels by simple Watson-Crick base pairing. In this thesis, we have demonstrated that the DNA nanogel has embedded the unmodified proteins while still preserving the structure and function of the proteins. As a proof of concept, we have confirmed the embedding with mobility shift assay of native agarose gel electrophoresis, imaged embedded proteins onto DNA nanogel with Atomic Force Microscopy(AFM), characterized the size and zeta potential with Dynamic Light Scattering(DLS). In addition, we have proved that embedded proteins preserved their function by showing the nanogel-IgG system can be conjugated to the surface proteins of E.Coli. To develop our system as a Nanomedicine platform, we have modified our nanogel to be used as targeted drug delivery vehicle for KB cells. Doxorubicin was loaded onto the DNA nanogel with embedded Immunoglobulin G(IgG)s. The nanogel-IgG system showed higher cytotoxicity to KB cells than controls due to the ability of the IgG to function as a targeting ligand. This result demonstrates the self-assembled system can function as a possible next generation drug delivery vehicle.

DNA nanotechnology as a versatile tool has been growing rapidly in the past three decades, with applications across different disciplines such as drug delivery, imaging, and even computing. Its versatility stems from the programmable nature of DNA and the ability to self-assemble through the A-T and C-G base pairing by hydrogen bonds. These features make DNA nanotechnology the ideal tool for mimicking and studying biological processes that are hard to otherwise model. In this work, the process we focus on is Clathrin-Mediated Endocytosis (CME). CME is one of the major mechanisms for cell entry. This pathway internalizes Influenza A, vesicular stomatitis virus, and many others. Mimicking this process will help us further understand it, and also find ways to utilize it in the field of drug delivery.The process of CME can be described in 3 major steps. The first step is array formation. Clathrin can cluster on cell surface in the form of triskelion, to form small patches of an array. The second step involves the recruitment of adaptor proteins, that transforms the array into three-dimensional lattices to encapsulate the foreign agent. The third step is the transportation of the vehicle inside the cell, along with the disassembly of the vehicle to release the agent. Specifically for each stage, we designed mimicking mechanisms using DNA nano-structures. For the array stage, we designed a three-point-star motif to mimic the triskelion structure, and functionalized it with cholesterol to integrate the array to the cell membrane. Characterization through liquid atomic force microscopy (AFM) showed clear hexagonal pattern, and in vitro cell experiment also showed the integration of the arrays to cell membranes. For the transition stage, we designed a reversible 2D-3D transition mechanism that allowed the 2D arrays to transform into 3D particles with the addition of a particular stimuli, that is a DNA single strand. The transformability and reversibility were confirmed through polyacrylamide gel electrophoresis (PAGE), dynamic light scattering (DLS) and AFM. For the delivery stage, we designed several 3D DNA structures for better drug delivery efficiency, and the designs were also characterized by PAGE and AFM. The successful designs for all three stages led us closer to understanding the structural transformation of clathrin triskelion during CME. The 2D-3D transition mechanism also has the potential to be used in other systems, such as stimuli-controlled drug release and DNA computing using single strand DNA.

As electric grids transition toward higher penetrations of renewable energy, battery energy storage systems (BESS) and aggregated electric vehicle (EV) charging stations are becoming critical assets for maintaining grid reliability and flexibility. This dissertation develops advanced control and optimization algorithms for BESS operators and EV aggregators to enhance grid support capabilities while maximizing economic returns through participation in demand response markets. The proposed methods address key operational challenges, including forecast uncertainty, demand charge management, and real-time market constraints.

First, an economic control model is presented for a BESS deployed at the UC San Diego campus, incorporating real-world complexities such as demand charge management and participation in the Day-ahead (DA) demand response market. The model yields \$96,025 in net revenue over two summer months, validating its practicality in operational settings.

Second, a practical Economic Model Predictive Control (EMPC) framework is developed for BESS to manage monthly demand charges. Acknowledging the limitations of full-month forecasts, a trajectory-based EMPC using 24–48 hour prediction horizons is introduced. The proposed 48-hour rolling-horizon EMPC reduces annual costs by 2\% compared to traditional methods, demonstrating improved cost-efficiency under forecast uncertainty.

Third, a two-layer control strategy is proposed for EV aggregators participating in DA demand response markets. The framework combines offline optimization for DA scheduling with online model predictive control (MPC) to adapt to real-time (RT) deviations in EV charging behavior. Tested on one year of workplace charging data, the method demonstrates the ability to leverage EV flexibility to offset forecast errors and achieve economic gains.

Finally, the two-layer architecture is extended with a RT optimizer that uses a two-stage MPC structure to manage the non-convexity of the problem. The first stage schedules cost-optimal charging at full service levels, while the second stage adjusts service levels to mitigate forecast uncertainty and maximize market revenue, accounting for controlled baselines. Simulations show a fivefold increase in net revenue compared to conventional strategies, achieving \$0.21 per kWh of demand reduction under forecast uncertainty.

Collectively, these contributions offer scalable, economically viable solutions for integrating BESS and EVs into grid operations, enabling more reliable and profitable participation in demand response markets.

Transition metal compounds have long been known to host intriguing many-body ground states owing to a fine interplay between electron-electron interactions, electron-phonon coupling, and low dimensionality. Recent advances in van der Waals synthesis have enabled the experimental studies of transition metal dichalcogenides in the extreme two-dimensional limit. In this dissertation I will discuss experiments that probe novel many-body ground states in single-layer transition metal dichalcogenides via the use of scanning tunneling microscopy and spectroscopy (STM/STS). The first part of the dissertation focuses on charge density wave states in single-layer 1H-NbSe2, single-layer 1H-TaSe2, and single-layer 1T-TaSe2 and the mechanisms by which they arise. The second part of the dissertation discusses an exotic Mott insulator electronic ground state in single-layer 1T-TaSe2 and its collapse with increased temperature and/or the introduction of interlayer coupling. In the third part of the dissertation I present evidence supporting a novel many-body magnetic ground state called a quantum spin liquid in the spin channel of single-layer 1T-TaSe2. This gapless spin liquid state was probed by spectroscopic STM imaging as well as by its response to single magnetic impurities. The results discussed here establish new platforms for investigating correlation physics in two dimensions and provide insights into the intriguing many-body phases therein.