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Ion Management And Mass Transport For (photo-) Electrochemical Conversions
Get PDF(Photo-) electrochemistry hold great potential for storing the surplus energy of renewably generated electrons in the form of energy-dense chemicals that can be stored for long periods of time at low cost. The efficiency of converting between electrical- and chemical- energy depends on the charge transfer kinetics at the anode and cathode, as well as ion and mass transport; the latter two have been shown to cause energy losses comparable to those of the electrode charge transfer processes. The first three chapters of the thesis explore the use of bipolar membranes (BPMs) for managing ion transport in CO2 electrolysis, fuel cells, and redox flow batteries, while focusing on understanding the fundamental aspects of BPMs. Chapter 1 introduces the potential of using electrolysis for renewable energy storage and summarizes recent progress in BPM research. In Chapter 2, we combine electrochemical impedance spectroscopy and finite element method based numerical modeling to elucidate the relation between the electric field and interfacial catalysis in enhancing water dissociation reaction in BPMs. Chapter 3 presents the results of managing interfacial protons in a BPM-based CO2 electrolyzer. The acidic local environment at the membrane/catalyst interface facilitates the competing hydrogen evolution reaction and leads to a low CO2 reduction efficiency. This problem was mitigated by coating the membrane with a weak acid polyelectrolyte film of ~ 50 nm, the local pH within which was monitored using ratiometric pH indicators covalently attached to the polyelectrolyte. Chapter 4 explores the forward-biased BPM in a redox flow battery that operates the positive/negative electrode in an alkaline/acidic environment. This unique configuration enables a battery potential that is ~ 0.7 V higher than the conventional ones using a single pH condition. The acid-base recombination reaction was found to be inefficient in forward-biased BPMs, being rate-limited by the narrow reaction zone in the junction region. In chapter 5, we designed a novel architecture for the catalyst layer in alkaline fuel cells, which allows for a better control of the microstructure and thus the study of mass transport in a membrane electrode assembly (MEA) configuration. The last chapter summaries the thesis and proposes future directions
Development Of Transition Metal Cluster Complexes With Macrocyclic Redox-Active Ligands Of Increasing Pocket Size
No full textDinuclear molecular complexes are increasingly sought in order to exploit metal-metal cooperativity to obtain new catalytic activity in a myriad of applications, including small molecule activation, organic transformations and polymerization methods. These complexes are also desirable for their potential utility in elucidating mechanistic understanding of biological systems and heterogeneous catalysis. Achieving new reactivity necessitates the synthesis of new ligands and new dinuclear molecular complexes. A series of 2,6-diiminopyridine-derived macrocyclic ligands with ring sizes of 18, 20 and 22 members have been synthesized along with the corresponding homobimetallic 3d metal complexes of Mn, Fe, Co, Ni and Cu. The solubility of these ligands and metallic complexes in aprotic organic solvents not only enables systematic characterization of the structural and physical properties with changes to both metals and macrocyclic ring sizes using techniques such as NMR spectroscopy, mass spectrometry, solution-phase UV-Vis spectroscopy, cyclic voltammetry and single-crystal X-ray crystallography in addition to solid state measurements using IR spectroscopy and SQUID magnetometry. The electronic understanding of both the ligand and metal complexes was further developed through computational studies
On The Interaction And Motion Of Inclusions In Lipid Membranes
Get PDFIn this thesis, we analytically investigate three problems in the mechanics of lipid membranes and inclusions embedded in them. We study (a) irradiation-induced oxidation of membranes, (b) elastic and entropic interactions of inclusions on membranes due to bending and kinetics of their self-assembly, and (c) membrane thickness mediated interactions of inclusions and kinetics of self-assembly.Oxidative damage to cell lipid membranes is a ubiquitous phenomenon in biology that often leads to cell death. Oxidative damage occurs in two steps: per molecule area increase, followed by vesicle shrinkage due to formation of pores. We employ a model to study the first step which focuses on thermal fluctuations, tension-area relation and the change in per molecule area caused by irradiation. Our model makes predictions of vesicle shapes, adhesion kinetics and forces under various conditions of irradiation. These results may potentially be applied in photodynamic therapy where controlled oxidative damage is harnessed for killing diseased cells. Interactions between inclusions in lipid membranes is an important topic in biophysics as it can lead to self-assembly (of proteins) that influences a host of biological process, including exo- and endo-cytosis. The rate of self-assembly of inclusions is of interest since interventions made at the right time could help to block the assembly of viruses. We develop a model based on theory of stochastic processes that casts self-assembly of two inclusions as a first passage time problem. A partial differential equation (PDE) is derived to compute the mean first passage time of self-assembly. The validity of the PDE is verified by running Langevin dynamic simulations to estimate the mean first passage time. Our methods provide new ways to study self-assembly which could complement existing methods based on molecular dynamics simulations. We separately study the interactions between inclusions caused by bending deformations and thickness deformations and use both the PDE and Langevin equation to compute the mean first passage time for self-assembly of variously shaped inclusions. Hydrodynamic interactions and rotational diffusion are also considered in our models. Finally, we apply our stochastic model mentioned above to compute the mean and second moment of the first passage time of MutSα protein searching for lesions through one-dimensional diffusion on DNA. The results could enhance the understanding of post-replicative mismatch repair (MMR) for fixing errors in DNA
Increasing Employee Engagement and Satisfaction Through Positive Interventions for Human Flourishing
Get PDFApproximately four out of five employees globally are either disengaged or actively disengaged at work. Recent data shows a rapid drop in engagement in the leadership and management sector of organizations. This literature review explores the efficacy of Positive Psychology Interventions (PPIs) designed to increase the state-like constructs of Positive Emotions and Psychological Capital (PsyCap), a construct combining Hope, Efficacy, Resilience, and Optimism). Results from multiple studies show small to medium effect sizes for Positive Emotion and PsyCap Interventions for increasing employee engagement, productivity, job satisfaction, commitment to the organization, organizational citizenship behavior and reductions in stress, absenteeism, and intention to leave the organization. Individuals low in targeted state-like traits pre-intervention experience greater growth than individuals high in the state-like traits. Brief, online, self-directed interventions were effective and longer, in-person, group interventions showed greater benefit. No negative side effects were discovered. Limitations are discussed and an appendix of evidence-based interventions is provided. PPIs targeting Positive Emotions and PsyCap are a scalable, cost-effective strategy to increase employee engagement and satisfaction at the workplace
Urban Transportation in the United States During the COVID-19 Pandemic
Get PDFThis paper aims to perform initial research into transportation patterns in the continental U.S. during the COVID-19 pandemic. In order to do this, I will apply methods from previous research on data collected during the pandemic era, and then compare this to pre-COVID data to see if there are any relevant conclusions. This research would allow us to see how cities in the U.S. have responded to the COVID crisis in terms of urban mobility, and if there are lessons to be learned for future pandemics. With initial observations, the most reasonable conclusion to draw is that, although COVID-19 has impacted transportation, along with most other areas of life in the U.S., that these changes in transportation patterns do not change the fundamental predictors of urban mobility that we have seen in previous research. Although specific data points and overall measures of travel characteristics may change, many of the underlying patterns remain roughly the same
How Lagging Financial Metrics Affect Next Year Hospital Patient Metrics
Get PDFThis paper analyzes a dataset of 231 California general acute hospitals from 2013-2018 to determine whether there are differences between how financial metrics affect following year patient metrics in non-profit versus investor-owned hospitals. The primary patient metric used in this paper is the average length of stay, excluding long-term care. The primary financial metrics used in this paper are lagged gross patient revenue. Secondary outcomes measured include how c-section births, inpatient operating room minutes, and total discharges are affected by financial metrics from non-profit and investor-owned hospitals. The main finding of this paper is that investor-owned hospitals decrease the average length of stay while non-profit hospitals increase the average length of stay as the previous year\u27s net income increases
Pensions and ESG: An Institutional and Historical Perspective
Get PDFSustainable investing is growing into its moment. Funded pensions, which were among the first institutions to respond to sustainability concerns, are showing renewed interest in better ways to reflect responsible investing objectives, along with regulators, asset managers and shareholder groups. Looking back, the principal elements of sustainability—environmental, social and governance (ESG)—all have different origins and took different pathways. Looking across, sustainable investing developed differently depending on region and country. Viewing it today, we see the trend toward E, S, & G convergence—of definition, process and organization—toward a more integrated investment perspective and process. With growing asset size, funded pensions, sovereign wealth funds and other large institutional investors became ‘universal owners’ and, along with thought leaders and regulators, drove the evolution of sustainable investing toward the more systematic set of tools and policies we see today. Looking forward, questions remain, such as who will be most influential in determining the future of sustainable investing—pensions and other institutional investors, governments, shareholders and companies-- as well as what it will look like. As such, sustainability remains a work in progress and pensions are in a strong position to shape its evolution
Detecting OODs as datapoints with High Uncertainty
Get PDFDeep neural networks (DNNs) are known to produce incorrect predictions with very high confidence on out-of-distribution inputs (OODs). This limitation is one of the key challenges in the adoption of DNNs in high-assurance systems such as autonomous driving, air traffic management, and medical diagnosis. This challenge has received significant attention recently, and several techniques have been developed to detect inputs where the model’s prediction cannot be trusted. These techniques detect OODs as datapoints with either high epistemic uncertainty or high aleatoric uncertainty. We demonstrate the difference in the detection ability of these techniques and propose an ensemble approach for detection of OODs as datapoints with high uncertainty (epistemic or aleatoric). We perform experiments on vision datasets with multiple DNN architectures, achieving state-of-the-art results in most cases
Mechanistic Insight into the Partial Oxidation of Methane to Methanol Using Platinum and Palladium Complexes
No full textThe efficient conversion of methane into methanol is a priority in energy research as it converts methane, a greenhouse gas, to methanol, a transportable liquid that is ubiquitous in chemical industry. Detailed within are efforts towards the development of palladium and platinum homogeneous catalysts that will selectively oxidize methane to methanol using dioxygen as the oxidant. There are three proposed steps for this challenging transformation: 1) selective C–H activation 2) oxidation and 3) release of product. A potential mechanism for C–H activation is metal-ligand cooperation (MLC) in which the metal and ligand are involved in cleaving C–H bonds. As the C–H activation step can be thermodynamically unfavorable, Chapter 2 focuses on mechanistic studies of the microscopic reverse of C–H activation - the formation of methane from metal-methyl complexes with a protonated ligand backbone. For these studies, [H(BPI)M(CH3)][X] (BPI = 1,3-bis(2-pyridylimino)isoindole , M = Pd, Pt, X = OTf-, NTf2-, BF4-, BArF20-, IMP-CF-, Cl-), the proposed product of MLC C–H activation, were used. Thermolysis of these compounds resulted in the formation of methane. Kinetic and computational studies were carried out to investigate the mechanism of C–H bond formation. Chapter 3 evaluates different MLC mechanisms for the activation of C–H and H-X bonds. Three types of MLC were investigated: C–H bond activation, in which the BPI ligand is protonated, Concerted Metalation Deprotonation (CMD) and Internal Electrophilic Substitution (IES). C–H activation of benzene was observed to form a Pt-phenyl complex bearing a protonated imine BPI ligand when a non-coordinating anion was used. Attempts to promote activation of benzene by CMD or IES with (BPI)M complexes were unproductive. However, (BPI)Pt(OH) did activate H2 to afford (BPI)PtH. Chapter 4 investigates the reaction of oxygen with (BPI)M(CH3) (M = Pd, Pt). Oxygen was found to insert into the metal-methyl bond to form (BPI)M(OOCH3). (BPI)Pt(OOCH3), was the third reported crystallographically characterized Pt(OOCH3) complex. The results of kinetic and mechanistic studies are consistent with a radical chain pathway for the oxygen insertion reaction. Further reaction of (BPI)Pt(OOCH3) leads to the release of methanol in up to 55% yield
Stochastic OLG Models and Transition Path, with Numerical Computation Using GPU Computing and Machine Learning
No full textThis paper provides with a general framework for solving stochastic overlapping-generations model with heterogeneous finitely-lived households with elastic labor supply, exposed to both idiosyncratic income risk and aggregate production risk, using GPU computing in parallel. Markets are incomplete both within and between generations, and the fiscal policy space includes debt, progressive taxes, and a lifetime-based redistribution program (social security). Machine learning methods including neural networks and kernel regression are applied in order to improve the accuracy of the perceived law of motions. The presence of idiosyncratic shocks enables the Krusell-Smith algorithm to perform well in stochastic steady-state by smoothing out zero-wealth corner constraints across the measure of households. The model produces a wide range of realistic pricing moments (equity premium, risk-free rate, and key covariances) with standard CRRA preferences set at a modest level of risk aversion (γ=3). Auto-correlation in productivity shocks produces realistic business cycles that typically cause the demand for safe assets to increase after a negative shock by more than the supply of new debt consistent with realistic counter-cyclical government spending. As a result, the risk-free rate falls despite an increase in the debt-output ratio. However, a systemic increase in debt produced by a change in the fiscal policy itself produces sharp increases in the risk-free rate and marginal product of capital, while reducing the equity premium, wages, and GDP. Transition paths are calculated by including a large matrix of Krusell-Smith coefficients indexed by time into the fixed-point algorithm, which also allows for the construction of confidence intervals across time. Changes in welfare, calculated as equivalent variations, can be reported across the measure of households and across generations, including households of different ages at the time of reform as well as future generations (the unborn). Additional model enhancements, including a distinct unemployment risk, are considered to investigate the role of risk vulnerability\u27\u27 (Gollier and Pratt (1996)) caused by the interaction of idiosyncratic and aggregate risk