Fuel cells are the tools which convert chemical energy into electricity directly by the effective utilization of hydrogen and oxygen (or air). One of the most important barriers for the fuel cell commercialization is the durability of the fuel cell components in the long term operations. In this study, the durability of the PEM fuel cell electrocatalysts were investigated via cyclic voltammetry (CV) and rotating disk electrode (RDE) experiments in order to determine the hydrogen oxidation reaction (HOR) and oxygen reduction reaction (ORR) which corresponds to the half cell reactions in the fuel cell. PEM fuel cell electrodes mainly composed of carbon supported Pt catalysts. In long term operations due to Pt dissolution and carbon corrosion some properties of the electrocatalysts can be changed. Performance losses in catalysts mainly depend on / i) decrease in the total metal surface area (SA) and the electrochemically active surface area (ESA) due to the increase in the particle size ii) decrease in the tafel slope potential in ORR and iii) increase in carbon corrosion. In this study, these properties were examined via accelerated degradation tests performed in CV and RDE. The catalysts having different Pt loadings, synthesized with different ink compositions, pH values and microwave durations were investigated. The commercial catalysts having Pt loadings of 20, 50 and 70 (wt %) were tried and best results were obtained for Pt/V (50 wt %) catalyst. Different carbon to Nafion&reg / ratios of 4, 8, 12 in the ink composition were tried. C/N ratio of 8 gave the best result in Pt dissolution and carbon corrosion degradation tests. The catalysts prepared at different pH values of 1.4, 6.25 and 10 were tried and the catalyst prepared at pH of 10 was less degraded in Pt dissolution test and the catalyst prepared at pH of 6.25 showed better resistance to carbon corrosion. Catalysts prepared under different microwave durations of 50, 60 and 120 s were tried and the catalyst prepared at 60 s gave the best performances.

AN ABSTRACT OF THE DISSERTATION OFElizabeth W. Salako, for the Doctor of Philosophy degree in Chemistry, presented on March 27, 2024, at Southern Illinois University Carbondale. TITLE: SYNTHESIS OF NOVEL PERFLUORINATED ION EXCHANGE MEMBRANES AGAINST HYDROGEN PEROXIDE DEGRADATION IN ELECTROCHEMICAL ENERGY STORAGE DEVICES MAJOR PROFESSOR: Dr. Yong GaoThe continuous burning of fossil fuels to meet the energy needs of the ever-growing population has extensive and enduring effects on the environment, human health, and the economy. Adopting cleaner and more sustainable energy sources is crucial to reducing the impact and tackling the difficulties posed by climate change. Renewable energy, which is derived from sources that are naturally replenished, presents a compelling solution to address these pressing challenges. Due to the inherent intermittency of renewable energy available, which relies on weather conditions and daylight hours, incorporating energy storage technology into the power grid can effectively handle unforeseeable power demands.An ion exchange membrane (IEM) is an important part of electrochemical energy storage and conversion devices like fuel cells, flow batteries, and electrolyzers. Without it, these devices would not work properly. The IEM has significantly enhanced these devices by enabling higher operating temperatures and improving their durability and efficiency. The proton exchange membrane (PEM) has been greatly studied, with Nafion® (a product of DuPont) as the state-of-the-art membrane. Even though Nafion®, which belongs to the perfluorosulfonic acid (PFSA) group, has been commercialized, it suffers from low working temperatures, high cost, low tolerance to fuel impurities, and most importantly, degradation of the membrane over a short period of time. The membrane undergoes three main types of degradation: mechanical, thermal, and chemical degradation. Although the mechanical and thermal degradation of the membranes can be managed, the chemical degradation is a more intricate and challenging issue to address. The degradation of Nafion® occurs through the process of radical-induced disintegration of the polymer structure. This selectively targets the weakest points in the polymer structure, thereby fragmenting the polymer and leading to a loss of ionic conductivity. These vulnerable sites include carboxylic acid groups, C-S linkages, tertiary carbons, and fluoro-ether groups. Studies have shown the fluoro-ether groups to be more susceptible to hydroxyl radical attacks. In our aim to reduce membrane degradation, we designed and synthesized novel fluoro-monomers void of the fluoro-ether groups. We used the emulsion polymerization process in a high-pressure reactor to polymerize our synthesized monomers with a commercially available monomer to make different ionomers with -SO3H and -PO3H2 ion exchange groups. We measured the molecular weight of the polymers through the viscometry method. The mechanical properties of the polymers were not as great, and it became difficult to cast them into a thin film. Polytetrafluoroethylene (PTFE) films were used as a support for the polymers to make them stronger and to also measure their ion conductivities in comparison with NafionTM 115. Fenton’s test was employed to measure the susceptibility of the polymers to hydroxyl radical attack. Our polymers were not as good at conducting ions as NafionTM 115, but they were better at protecting against hydroxyl radical attacks, both at room temperature and higher temperatures. The results showed an inverse relationship between the number of fluoroalkyl ether groups present in the polymers and their resistance to hydroxyl radical attacks.

This thesis deals with alternative thermal degradation of electrical insulating liquids. The theoretical part describes the current state of the problem, electrical insulating liquids, their classification and types. There are described properties of electrical insulating liquids, their models of aging and diagnostic methods in accordance with the appropriate standards. In the practical part are chosen by representatives of alternative electrical insulating liquids and there are determined their dielectric characteristics in the initial state. According to the measured values is chosen five candidates for which it is designed and implemented an accelerated degradation test. During the test are measured dielectric parameters of the selected oils. Measured dependencies are mathematically approximated. In conclusion, the results are compared and physically interpreted in view of oxidation stability.

In this dissertation, we introduce three projects in machine learning and reliability applications after the general introductions in Chapter 1. The first project concentrates on the multivariate sensory data, the second project is related to the bivariate recurrent process, and the third project introduces thermal index (TI) estimation in accelerated destructive degradation test (ADDT) data, in which an R package is developed. All three projects are related to and can be used to solve certain reliability problems. Specifically, in Chapter 2, we introduce a clustering method for multivariate functional data. In order to cluster the customized events extracted from multivariate functional data, we apply the functional principal component analysis (FPCA), and use a model based clustering method on a transformed matrix. A penalty term is imposed on the likelihood so that variable selection is performed automatically. In Chapter 3, we propose a covariate-adjusted model to predict next event in a bivariate recurrent event system. Inspired by geyser eruptions in Yellowstone National Park, we consider two event types and model their event gap time relationship. External systematic conditions are taken account into the model with covariates. The proposed covariate adjusted recurrent process (CARP) model is applied to the Yellowstone National Park geyser data. In Chapter 4, we compare estimation methods for TI. In ADDT, TI is an important index indicating the reliability of materials, when the accelerating variable is temperature. Three methods are introduced in TI estimations, which are least-squares method, parametric model and semi-parametric model. An R package is implemented for all three methods. Applications of R functions are introduced in Chapter 5 with publicly available ADDT datasets. Chapter 6 includes conclusions and areas for future works. / Doctor of Philosophy / This dissertation focuses on three projects that are all related to machine learning and reliability. Specifically, in the first project, we propose a clustering method designated for events extracted from multivariate sensory data. When the customized event is corresponding to reliability issues, such as aging procedures, clustering results can help us learn different event characteristics by examining events belonging to the same group. Applications include diving behavior segmentation based on vehicle sensory data, where multiple sensors are measuring vehicle conditions simultaneously and events are defined as vehicle stoppages. In our project, we also proposed to conduct sensor selection by three different penalizations including individual, variable and group. Our method can be applied for multi-dimensional sensory data clustering, when optimal sensor design is also an objective. The second project introduces a covariate-adjusted model accommodated to a bivariate recurrent event process system. In such systems, events can occur repeatedly and event occurrences for each type can affect each other with certain dependence. Events in the system can be mechanical failures which is related to reliability, while next event time and type predictions are usually of interest. Precise predictions on the next event time and type can essentially prevent serious safety and economy consequences following the upcoming event. We propose two CARP models with marginal behaviors as well as the dependence structure characterized in the bivariate system. We innovate to incorporate external information to the model so that model results are enhanced. The proposed model is evaluated in simulation studies, while geyser data from Yellowstone National Park is applied. In the third project, we comprehensively discuss three estimation methods for thermal index. They are the least-square method, parametric model and semi-parametric model. When temperature is the accelerating variable, thermal index indicates the temperature at which our materials can hold up to a certain time. In reality, estimating the thermal index precisely can prolong lifetime of certain product by choosing the right usage temperature. Methods evaluations are conducted by simulation study, while applications are applied to public available datasets.

Ces dernières années ont vu apparaître sur le marché les premiers transistors de puissance de type MOSFET en carbure de silicium. Ce type de composant est particulièrement adapté à la réalisation d’équipement électrique à haut rendement et capable de fonctionner à haute température. Néanmoins, la question de la fiabilité doit être posée avant de pouvoir envisager la mise en œuvre de ces composants dans des applications aéronautiques ou spatiales. Les mécanismes de défaillance liés à l’oxyde de grille ont pendant longtemps retardé la mise sur le marché des transistors à grille isolée en carbure de silicium. Cette étude s’attache donc à estimer la durée de vie des MOSFET SiC de 1ére génération. Dans un premier temps, le mécanisme connu sous le nom de Time Dependent Dielectric Breakdown(TDDB) a été étudié au travers de résultats expérimentaux issus de la bibliographie. Notre analyse nous a permis de justifier de l’emploi d’une loi de Weibull pour modéliser la distribution des temps à défaillance issue de ces tests. Les résultats nous ont également permis de confirmer l’amélioration significative de la fiabilité de ces structures vis-à-vis de ce mécanisme. Dans un second temps, l’impact du mécanisme d’instabilité de la tension de seuil sur la fiabilité a été quantifié au travers de tests de vieillissement de type HTGB. Les données de dégradation ainsi collectées ont été modélisées à l’aide d’un processus gamma non-homogène, qui nous a permis de prendre en compte la variabilité entre les composants testés dans des conditions identiques et de proposer des facteurs d’accélération en tension et en température pour ce mécanisme. Enfin, ces travaux ont permis d’ouvrir la voie à la mise en œuvre d’outils de pronostic de la durée de vie résiduelle pour les équipements électriques. / Recent years have seen SiC MOSFET reach the industrial market. This type of device is particularly adapted to the design of power electronics equipment with high efficiency and high reliability capable to operate in high ambient temperature. Nevertheless the question of the SiC MOSFET reliability has to be addressed prior to considering the implementation of such devices in an aeronautic application. The failure mechanisms linked to the gate oxide of the SiC MOSFET have for a long time prevented the introduction of the device. In this manuscript we propose to study the reliability of the first generation of SiC MOSFET. First, the mechanism known as the Time–Dependent Dielectric Breakdown is studied through experimental results extracted from literature. Our study shows the successful application of a Weibull law to model the time-to-failure distribution extracted from the accelerated tests. The results show also a significant improvement of the SiC MOSFET structure with respect to this phenomenon. In a second step, the impact of the threshold voltage instability is quantified through accelerated tests known as High Temperature Gate Bias. The collected degradation data are modeled using a non-homogeneous Gamma process. This approach allows taking into account the variability between devices tested under the same conditions. Acceleration factors have been proposed with respect to temperature and gate voltage. Eventually the study delivers a primary estimation of the remaining useful lifetime of the SiC MOSFET in a typical aeronautic application.

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