Charge to Breakdown Testing of MOSFETs for Reliability Analysis in Electric Vehicles

With a ban on fossil-fuel vehicles coming into place in 2030, the window for developing electric vehicles (EVs) with reliable performance is rapidly closing. Investigating the reliability of emerging materials, specifically silicon carbide (SiC) metal-oxide-semiconductor field-effect-transistors (MOSFETs) and gallium nitride (GaN) cascode silicon (Si) MOSFETs, this thesis uses a charge to breakdown (QBD) technique to test the reliability in electric vehicle (EV) power electronics. While traditional Si insulated gate bipolar transistors (IGBTs) have been widely studied, SiC devices offer enhanced thermal and electrical properties, making them a more efficient alternative for EV applications. However, their long-term reliability under high-voltage and high-temperature conditions remains a critical area of study.During this research, an accelerated age testing setup was designed to assess the breakdown behavior of SiC and Si MOSFETs through QBD testing, in which the results revealed distinct differences in oxide reliability between the two materials. The SiC MOSFETs exhibited QBD values of 0.134C and 0.174C, however, the Si MOSFETs did not exhibit breakdown, highlighting the need for further investigation into their failure mechanisms and performance limitations in EV applications. By focusing on SiC MOSFETs and Si MOSFETs, this study offers insights into the importance of gate oxide reliability in maintaining MOSFET performance and therefore the reliability of EV power electronics, as well as the potential of QBD testing for future innovations of EV technology

Deriving safe limits for N-nitroso-bisoprolol by error-corrected next-generation sequencing (ecNGS) and benchmark dose (BMD) analysis, integrated with QM modeling and CYP-docking analysis

N-nitroso-bisoprolol (NBP) is a nitrosamine drug substance-related impurity (NDSRI) of bisoprolol, which is used to treat cardiac diseases since decades. To investigate the mutagenic potential of NBP, in vitro methods such as Enhanced Ames Test (EAT) and a mammalian cell gene mutation (HPRT) assay were used. To assess the in vivo mutagenicity, a 28-day repeat-dose study was conducted in wild-type NMRI mice, and liver and bone marrow samples were subjected to error-corrected next-generation sequencing (i.e., duplex sequencing) followed by benchmark dose analysis (BMD). NBP did not show mutagenic effects in Ames tests using 10 % and 30 % induced rat or 30 % uninduced hamster S9. However, relevant increases in mutation frequencies were observed in an EAT in the presence of 30 % induced hamster S9 in strains TA100 and TA1535, confirming that the most stringent conditions of the EAT are appropriate to detect the mutagenic activity of weak mutagens, such as NBP. In the HPRT assay conducted in V79 cells, nitroso-diethylamine (NDEA) relevantly induced the mutation frequency, but not NBP. The highly sensitive error-corrected Next-Generation Sequencing (ecNGS) method to detect mutations across the genome represents an appropriate in vivo mutagenicity investigation equally suitable as a TGR assay to assess the mutagenic potential of nitrosamines. A weak induction of mutation frequencies was detected by ecNGS in the liver and the bone marrow of mice. Using BMD analysis, new safe limits were calculated for NBP, which are higher than the published AI of 1.5 µg/person/day. Using the approach to calculate Permissible Daily Exposure (PDE) limits according to ICH Q3C, a lifetime PDE of 400 µg/person/day was derived. Based on the ICH M7 framework for derivation of Acceptable Intake (AI) limits, an AI of 64 µg/person/day was established. Consistent with regulatory emphasis on mechanistic interpretation, in vivo modeling was further supported by in silico calculations. Specifically, the validated Computer-Aided Discovery and RE-design (CADRE) tool was used to predict the potency of NBP and further differentiate its metabolic activity from the anchor nitrosamine NDEA via quantum mechanics (QM) calculations and CYP-binding predictions. Outcomes of this analysis were consistent with in vivo studies, while offering a deeper understanding of the fundamental biochemistry using a physics-led method. The integrated in vivo–in silico investigation provides a data-based determination of safe limits, suggesting that the AI based on structural considerations solely might be over-conservative and should not be capped at the TTC

Experimental and Monte Carlo simulation study on a core–shell NiFe2O4@HKUST-1/graphene oxide nanocomposite for Congo Red adsorption

A copper-based metal–organic framework, nickel ferrite and graphene oxide were prepared as constituents of a new core–shell nanocomposite formed by a layer-by-layer method, then it was applied to absorb Congo Red dye as an organic contaminant. The nanocomposite was studied by XRD, FTIR, EDS, FESEM and VSM methods. Investigating the main factors affecting the adsorption shows that the optimum pH of the dye solution is 7, the best contact time is 60 min with an initial solution concentration of 5 ppm and 0.05 g of adsorbent is the optimum amount. Adaptation of Langmuir, Freundlich, Temkin and Dubinin–Radushkevich adsorption isotherms showed that the dye adsorption process is consistent with two first isotherm models. Regarding the adsorption kinetics and according to the calculations, it was found that the adsorption process follows second-order kinetics. The composite NiFe2O4@HKUST-1/GO demonstrated a maximum adsorption capacity of 25.64 mg g−1 for Congo Red dye removal from aqueous solutions. Monte Carlo simulations were used to simulate the adsorption nature between NiFe2O4 (311) molecules and the HKUST-1 surface, GO molecules and NiFe2O4@HKUST-1, and CR and NiFe2O4@HKUST-1/GO

Thermodynamic principles for optimizing multi-junction photovoltaics—Exemplified for perovskite-based indoor photovoltaics

Multi-junction architectures are utilized in photovoltaic (PV) technology to widen spectral range, increase voltage and/or current, and hence deliver higher overall power conversion efficiencies (PCEs). However, accurate approaches for simulating multi-junction PVs using the electro-optical properties of real materials are somewhat scarce—particularly in the context of novel applications such as indoor PVs, where the illumination spectrum differs from natural sunlight. Herein, we present a robust methodology—alongside an open-source simulation tool—for modeling multi-junction PVs while accounting for intrinsic PV features, including sub-gap absorption, band-filling effects, and radiative couplings between junctions. Although we primarily focus our investigation on perovskite-based multi-junction devices, our approach is extendable to any class of PV material. We apply it in the context of indoor PVs by assuming the LED-B4 spectrum as a representative light source. At a typical illuminance of 1000 lux, we find that PCEs above 60% are possible by combining a 2.1 eV wide-gap top cell with a 1.0–2.0 eV narrow-gap bottom cell, meaning that a suitable wide-gap semiconductor could be coupled with almost any conventional solar cell to achieve high performance. Using the spectral responses of real PV devices, we then predict optimal material configurations under LED-B4 illumination, before probing the spectral versatility of these devices under a variety of indoor light sources and intensities. We find that the maximum power point voltage is mostly independent of light source, while PCE is more sensitive due to changes in current density, which provides insight into how laboratory-optimized devices may perform in realistic scenarios

Diversity and Plasticity in Mosquito Feeding Patterns: A Meta‐Analysis of ‘Universal’ DNA Diet Studies

Although mosquitoes can have innate preferences for particular blood-meal hosts, their realised feeding patterns on different host species can be modified under climate and land use change with implications for disease spread. Therefore, it is important to understand the niche breadth of vectors and the extent to which shifts in feeding patterns can be predicted. Six prominent disease-vectoring mosquitoes: Aedes aegypti, Ae. albopictus, Anopheles funestus, An. gambiae, Culex pipiens, Cx. quinquefasciatus. Focusing on blood-meal studies that used ‘universal’ molecular methods with broad taxonomic coverage, we compiled evidence from > 15,600 blood-meals. We estimated mosquito's host niche breadth and we used hierarchical Dirichlet regression models to investigate shifts in feeding patterns among different functional and taxonomic groups of host species in relation to host and environmental factors. We estimated host ranges of 179–321 species for each of the two Culex mosquitoes and 26–65 species for Aedes mosquitoes, comprising considerably broader host niche breadths than previously anticipated. For the two Anopheles species, we estimated host ranges of 7–29 species. We found some evidence that shifts in feeding patterns among different host functional and taxonomic groups were associated with environmental conditions such as temperature and livestock density, while our results also demonstrate that with the currently available evidence, global predictions of shifts in mosquito feeding patterns are impeded by significant uncertainty. Our global meta-analysis afforded first insights into the shifts of feeding patterns in variable environments, suggesting that host choice is not a simple function of host availability, but contingent on other environmental drivers. Improving resolution and consistency of data gathering and reporting will improve the precision of how blood-meal studies can inform us of present and potential risks of pathogen transmission events

Synthesis of Solid-state NASICON Electrolytes for Sodium-ion Batteries

This thesis aims to synthesise the sodium super ionic conductor (NASICON) solid-state electrolyte for sodium-ion battery applications using two methodologies: solid-state and sol synthesis. The objective is to develop NASICON with enhanced chemical properties and minimised secondary phases by employing less energy-intensive techniques. This involves transitioning from conventional oven heating to near-infrared (NIR) radiation sintering, achieved through the fabrication of NASICON as a thin film. All samples were characterised by x-ray diffraction (XRD) and scanning electron microscopy (SEM).NASICON was successfully synthesised using the solid-state technique, forming dense pellets (density: 2.80 g/cm3) that are 15 mm in diameter and 2 mm thick (±0.05 mm). The final sintering step was conducted in a tube furnace in an inert environment (argon) at 1180 ºC for 16 hours, with the total oven time for the process being 70 hours and only 1.5% weight fraction secondary phase of ZrO2. Alumina crucibles and a powder bed were used to prevent the pellet fusing to the crucible (Al3+ diffusion) and reduce sodium volatilisation.The sol method also successfully formed a NASICON powder with a low secondary phase content of <2%, aligned with literature values. The powder was prepared in a conventional oven (in air) with reduced sintering times of 3 hours and temperatures of 1000 ºC. Additionally, NASICON was synthesised as a dense 10 µm thin film on a quartz substrate, utilising a sol spray coating technique. The same sintering times were employed as the powder (3 hours) but at lower a lower temperature of 950 ºC. The spray coating technique allowed the film to dry on a hot plate reducing the overall oven heating time from 16 hours (powder) to 3 hours (thin film).NIR radiation was successfully employed to synthesise NASICON as a thin film, a novel technique that has not previously been used in this field. This significantly reduced sintering times to 60 seconds, and overall oven/NIR heating times to 2 hours 2 minutes

A guide to frames, 2π’s, scales and corrections in string compactifications

This note is intended to serve as a reference for conventions used in the literature on string compactifications, and how to move between them, collected in a single and easy-to-find place, using type IIB as an illustrative example. It will be useful to beginners in the field and busy experts. E.g. string constructions proposed to address the moduli stabilisation problem are generically in regions of parameter space at the boundaries of control, so that consistent use of 2π’s and frame conventions can be pivotal when computing their potentially dangerous correction

Functional connectivity and GABAergic signaling modulate the enhancement effect of neurostimulation on mathematical learning

Effortful learning and practice are integral to academic attainment in areas like reading, language, and mathematics, shaping future career prospects, socioeconomic status, and health outcomes. However, academic learning outcomes often exhibit disparities, with initial cognitive advantages leading to further advantages (the Matthew effect). One of the areas in which learners frequently exhibit difficulties is mathematical learning. Neurobiological research has underscored the involvement of the dorsolateral prefrontal cortex (dlPFC), the posterior parietal cortex (PPC), and the hippocampus in mathematical learning. However, their causal contributions remain unclear. Moreover, recent findings have highlighted the potential role of excitation/inhibition (E/I) balance in neuroplasticity and learning. To deepen our understanding of the mechanisms driving mathematical learning, we employed a novel approach integrating double-blind excitatory neurostimulation—high-frequency transcranial random noise stimulation (tRNS)—and examined its effect at the behavioral, functional, and neurochemical levels. During a 5-day mathematical learning paradigm (n = 72) active tRNS was applied over the dlPFC or the PPC, and we compared the effects versus sham tRNS. Individuals exhibiting stronger positive baseline frontoparietal connectivity demonstrated greater improvement in calculation learning. Subsequently, utilizing tRNS to modulate frontoparietal connectivity, we found that participants with weaker positive baseline frontoparietal connectivity, typically associated with poorer learning performance, experienced enhanced learning outcomes following dlPFC-tRNS only. Further analyses revealed that dlPFC-tRNS improved learning outcomes for participants who showed reductions in dlPFC GABA when it was accompanied by a reduced positive frontoparietal connectivity, but this effect was reversed for participants who showed increased positive frontoparietal connectivity. Our multimodal approach elucidates the causal role of the dlPFC and frontoparietal network in a critical academic learning skill, shedding light on the interplay between functional connectivity and GABAergic modulation in the efficacy of brain-based interventions to augment learning outcomes, particularly benefiting individuals who would learn less optimally based on their neurobiological profile

Stochastic dispersion behavior and optimal design of locally resonant metamaterial nanobeams using nonlocal strain gradient theory

This study examines the stochastic response of a metamaterial (MM) nanobeam, focusing on bandgap formation and analyzed using machine learning. The nanobeam is modeled as an infinitely long Euler Bernoulli beam with two length scale parameters: the nonlocal and strain gradient parameter. Periodically distributed linear resonators along its length introduce periodicity. The deterministic analysis is conducted by estimating bandgap edge frequencies using the dispersion of elastic waves in a representative unit cell. The impact of uncertainties on wave propagation behavior indicate that geometric properties predominantly influence variability in frequency response, followed by material properties, affecting the location and width of the bandgap. Scale dependent parameters, however, have a negligible effect. A Gaussian process (GP) surrogate model is employed to efficiently capture the stochastic behavior of the nanobeam. To highlight the utility of machine learning in computationally intensive tasks, a multi-objective optimization problem is formulated to tailor the bandgap features of the nanobeam. The offline-trained GP model yields a Pareto front of design configurations closely aligned with actual simulations, eliminating the need for repeated analyses during optimization. This surrogate based optimizer efficiently facilitates reverse engineering of MM designs for user defined wave dispersion characteristics, showcasing its potential for large scale optimization. Importantly, the stochastic dispersion framework grounded in nonlocal strain gradient theory can be directly applied to other periodic MM nanostructures. By varying unit cell configurations and materials within the same computational pipeline, new insights into bandgap emergence across applications ranging from phononic waveguides, nanoscale acoustic devices to structure–property relationships in next-generation MMs can be rapidly obtained

Falling and Landing Framework (FLF): A Consensus on a Novel Falling and Landing Video Analysis Framework for Use Across Rugby Codes

Understanding how players experience head-acceleration events (HAE) whilst playing rugby is a priority area of research. In both rugby union and league, video analysis frameworks have been developed to comprehensively define key features of contact events. However, these frameworks were developed prior to recent advances in our understanding regarding the proportion of HAEs that occur due to head-to-ground mechanisms and do not consider important post-contact variables. Therefore, there is a need to supplement the existing frameworks in order to capture how players fall and land post-tackle. This study used the Delphi method with an interdisciplinary, international team of researchers, coaches, and video analysts (working with a variety of playing levels in rugby union and league) to establish a consensus for defining falling and landing events. Subsequently, a draft framework was developed on which the research team provided feedback via online meetings, culminating in the Falling/Landing Framework that each member of the research team rated agreement on, via a 9-point Likert-type scale, with consensus deemed to be reached when the median score was ≥ 7. The median scores were 8.0 (7.8 - 8.0), 8.0 (7.0 - 9.0), and 8.0 (8.0 - 9.0) for ‘Additional Contextual Characteristics for Carry and Tackle Events,’ ‘Falling Characteristics of Tackle and Carry Events,’ and ‘Landing Characteristics of Tackle and Carry Events,’ respectively. This novel framework defines more comprehensive falling and landing variables to capture post-contact injury and performance markers in both rugby union and league, through a standardised approach