89 research outputs found

    Artificial Light at Night Disrupts Pain Behavior and Cerebrovascular Structure in Mice

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    Artificial Light at Night Disrupts Pain Behavior and Cerebrovascular Structure in Mice Jacob R. Bumgarner Circadian rhythms are intrinsic biological processes that fluctuate in function with a period of approximately 24 hours. These rhythms are precisely synchronized to the 24- hour day of the Earth by external rhythmic signaling cues. Solar light-dark cycles are the most potent environmental signaling cue for terrestrial organisms to align internal rhythms with the external day. Proper alignment and synchrony of internal circadian rhythms with external environmental rhythms are essential for health and optimal biological function. The modern human environment on Earth is no longer conducive to properly aligned circadian rhythms. Following the industrial revolution, artificial lighting and an ever-growing 24-hour global economy have shifted humans away from natural environments suited for rhythmic behavior and physiology. Humans, and much of the natural environment, are routinely exposed to circadian rhythm disruptors. The most pervasive disruptor of circadian rhythms is artificial light at night (ALAN). A growing 80% of humans on Earth are exposed to ALAN beyond natural nighttime environmental lighting levels. ALAN exposure is associated with numerous negative consequences on behavior and physiology, including neuroinflammation, cardiovascular disease, and altered immune function. This dissertation examines two previously uninvestigated effects of ALAN exposure on physiology and behavior in mice. In Part 1, I investigated the effects of ALAN exposure on pain behavior in mice. I observed that ALAN exposure had detrimental effects on rodent pain behavior in contexts of both health and models of human disease. ALAN exposure heightened responsiveness to noxious cold stimuli and innocuous mechanical touch. Differences in these effects were noted based on sex and disease state. I conclude this section with a report on the mechanisms by which ALAN exposure altered pain behavior. In Part 2, I investigated the effects of ALAN exposure on cerebrovascular structure in mice. To conduct these investigations, I first developed VesselVio, an open-source application for the analysis and visualization of vasculature datasets. Using this application and additional analytical frameworks, I examined the effects of short-term ALAN exposure on hippocampal vasculature in mice. ALAN exposure reduced hippocampal vascular density in mice, with notable regional sex differences. I also observed that ALAN exposure altered hippocampal vascular network connectivity and structure, with persistent regional sex differences. The data in this dissertation contribute to the ever-growing field of circadian rhythm biology focused on studying circadian rhythm disruption. These data highlight the continuing need to mitigate the pervasiveness of ALAN in human and natural environments. Most importantly, the results presented in this dissertation emphasize the need to consider ALAN as a mitigating factor for the treatment of both cardiovascular disease and pain

    Using super resolution microscopy to investigate the role of actin in adenosine receptor organisation

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    Membrane receptors are key to how cells interact with other cells and their environment. G Protein-Coupled Receptors (GPCRs) are a major drug target, with approximately a third of all FDA approved drugs acting on a GPCR [1]. The organisation of GPCRs in the cell membrane can play a key role in determining signalling responses and associated pharmacological parameters. There is significant evidence that the cortical actin skeleton can contribute to this organisation via the picket fence model. The direct contribution of actin architecture and dynamics to organisation of specific receptors requires further study. Therefore, this thesis applies a range of super-resolution microscopy techniques to investigate the role of cortical actin in the organisation of the human adenosine-A2A (A2AR) and -A2B receptors (A2BR). Using A549 cells transiently transfected with N-terminally SNAP-tagged receptor constructs, clustering analysis using dSTORM (direct stochastic optical reconstruction microscopy) indicates effects of actin disruption on A2AR clustering but not A2BR, while assessment of dynamic behaviour via single particle tracking (SPT) indicates differential effects on the motion patterns of each receptor. This was further supported by 3D-SIM (structured illumination microscopy) imaging of actin and receptors together. Assessment of actin using SRRF (super resolved radial fluctuations) processing showed a change in actin architecture after receptor stimulation. Workflows for imaging and analysing finer actin filaments using 3D-SIM expansion microscopy (ExM) were also developed, with incorporation of the A2R interacting protein α-actinin-1 serving both as investigation of a potential actin link and as a demonstration of two colour ExM. Initial experiments using SRRF processing indicated super-resolution imaging of actin was possible on a timescale which allowed concurrent single particle tracking of receptors, opening potential for correlated analysis. These findings indicate a role for actin in mediating A2AR and A2BR membrane organisation, with potential for different regulatory contributions between receptors and across organisational scales. [A thesis submitted to the University of Birmingham and the University of Nottingham for the dual degree of Doctor of Philosophy, August 2022.

    Computational Geometry Contributions Applied to Additive Manufacturing

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    This Doctoral Thesis develops novel articulations of Computation Geometry for applications on Additive Manufacturing, as follows: (1) Shape Optimization in Lattice Structures. Implementation and sensitivity analysis of the SIMP (Solid Isotropic Material with Penalization) topology optimization strategy. Implementation of a method to transform density maps, resulting from topology optimization, into surface lattice structures. Procedure to integrate material homogenization and Design of Experiments (DOE) to estimate the stress/strain response of large surface lattice domains. (2) Simulation of Laser Metal Deposition. Finite Element Method implementation of a 2D nonlinear thermal model of the Laser Metal Deposition (LMD) process considering temperaturedependent material properties, phase change and radiation. Finite Element Method implementation of a 2D linear transient thermal model for a metal substrate that is heated by the action of a laser. (3) Process Planning for Laser Metal Deposition. Implementation of a 2.5D path planning method for Laser Metal Deposition. Conceptualization of a workflow for the synthesis of the Reeb Graph for a solid region in ℝ" denoted by its Boundary Representation (B-Rep). Implementation of a voxel-based geometric simulator for LMD process. Conceptualization, implementation, and validation of a tool for the minimization of the material over-deposition at corners in LMD. Implementation of a 3D (non-planar) slicing and path planning method for the LMD-manufacturing of overhanging features in revolute workpieces. The aforementioned contributions have been screened by the international scientific community via Journal and Conference submissions and publications

    Experimental and Data-driven Workflows for Microstructure-based Damage Prediction

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    Materialermüdung ist die häufigste Ursache für mechanisches Versagen. Die Degradationsmechanismen, welche die Lebensdauer von Bauteilen bei vergleichsweise ausgeprägten zyklischen Belastungen bestimmen, sind gut bekannt. Bei Belastungen im makroskopisch elastischen Bereich hingegen, der (sehr) hochzyklischen Ermüdung, bestimmen die innere Struktur eines Werkstoffs und die Wechselwirkung kristallografischer Defekte die Lebensdauer. Unter diesen Umständen sind die inneren Degradationsphänomene auf der mikroskopischen Skala weitgehend reversibel und führen nicht zur Bildung kritischer Schädigungen, die kontinuierlich wachsen können. Allerdings sind einige Kornensembles in polykristallinen Metallen, je nach den lokalen mikrostrukturellen Gegebenheiten, anfällig für Schädigungsinitiierung, Rissbildung und -wachstum und wirken daher als Schwachstellen. Daher weisen Bauteile, die solchen Belastungen ausgesetzt sind, oft eine ausgeprägte Lebensdauerstreuung auf. Die Tatsache, dass ein umfassendes mechanistisches Verständnis für diese Degradationsprozesse in verschiedenen Werkstoffen nicht vorliegt, hat zur Folge, dass die derzeitigen Modellierungsbemühungen die mittlere Lebensdauer und ihre Varianz in der Regel nur mit unbefriedigender Genauigkeit vorhersagen. Dies wiederum erschwert die Bauteilauslegung und macht die Nutzung von Sicherheitsfaktoren während des Dimensionierungsprozesses erforderlich. Abhilfe kann geschaffen werden, indem umfangreiche Daten zu Einflussfaktoren und deren Wirkung auf die Bildung initialer Ermüdungsschädigungen erhoben werden. Die Datenknappheit wirkt sich nach wie vor negativ auf Datenwissenschaftler und Modellierungsexperten aus, die versuchen, trotz geringer Stichprobengröße und unvollständigen Merkmalsräumen, mikrostrukturelle Abhängigkeiten abzuleiten, datengetriebene Vorhersagemodelle zu trainieren oder physikalische, regelbasierte Modelle zu parametrisieren. Die Tatsache, dass nur wenige kritische Schädigungen bezogen auf das gesamte Probenvolumen auftreten und die hochzyklische Ermüdung eine Vielzahl unterschiedlicher Abhängigkeiten aufweist, impliziert einige Anforderungen an die Datenerfassung und -verarbeitung. Am wichtigsten ist, dass die Messtechniken so empfindlich sind, dass nuancierte Schwankungen im Probenzustand erfasst werden können, dass die gesamte Routine effizient ist und dass die korrelative Mikroskopie räumliche Informationen aus verschiedenen Messungen miteinander verbindet. Das Hauptziel dieser Arbeit besteht darin, einen Workflow zu etablieren, der den Datenmangel behebt, so dass die zukünftige virtuelle Auslegung von Komponenten effizienter, zuverlässiger und nachhaltiger gestaltet werden kann. Zu diesem Zweck wird in dieser Arbeit ein kombinierter experimenteller und datenverarbeitender Workflow vorgeschlagen, um multimodale Datensätze zu Ermüdungsschädigungen zu erzeugen. Der Schwerpunkt liegt dabei auf dem Auftreten von lokalen Gleitbändern, der Rissinitiierung und dem Wachstum mikrostrukturell kurzer Risse. Der Workflow vereint die Ermüdungsprüfung von mesoskaligen Proben, um die Empfindlichkeit der Schädigungsdetektion zu erhöhen, die ergänzende Charakterisierung, die multimodale Registrierung und Datenfusion der heterogenen Daten, sowie die bildverarbeitungsbasierte Schädigungslokalisierung und -bewertung. Mesoskalige Biegeresonanzprüfung ermöglicht das Erreichen des hochzyklischen Ermüdungszustands in vergleichsweise kurzen Zeitspannen bei gleichzeitig verbessertem Auflösungsvermögen der Schädigungsentwicklung. Je nach Komplexität der einzelnen Bildverarbeitungsaufgaben und Datenverfügbarkeit werden entweder regelbasierte Bildverarbeitungsverfahren oder Repräsentationslernen gezielt eingesetzt. So sorgt beispielsweise die semantische Segmentierung von Schädigungsstellen dafür, dass wichtige Ermüdungsmerkmale aus mikroskopischen Abbildungen extrahiert werden können. Entlang des Workflows wird auf einen hohen Automatisierungsgrad Wert gelegt. Wann immer möglich, wurde die Generalisierbarkeit einzelner Workflow-Elemente untersucht. Dieser Workflow wird auf einen ferritischen Stahl (EN 1.4003) angewendet. Der resultierende Datensatz verknüpft unter anderem große verzerrungskorrigierte Mikrostrukturdaten mit der Schädigungslokalisierung und deren zyklischer Entwicklung. Im Zuge der Arbeit wird der Datensatz wird im Hinblick auf seinen Informationsgehalt untersucht, indem detaillierte, analytische Studien zur einzelnen Schädigungsbildung durchgeführt werden. Auf diese Weise konnten unter anderem neuartige, quantitative Erkenntnisse über mikrostrukturinduzierte plastische Verformungs- und Rissstopmechanismen gewonnen werden. Darüber hinaus werden aus dem Datensatz abgeleitete kornweise Merkmalsvektoren und binäre Schädigungskategorien verwendet, um einen Random-Forest-Klassifikator zu trainieren und dessen Vorhersagegüte zu bewerten. Der vorgeschlagene Workflow hat das Potenzial, die Grundlage für künftiges Data Mining und datengetriebene Modellierung mikrostrukturempfindlicher Ermüdung zu legen. Er erlaubt die effiziente Erhebung statistisch repräsentativer Datensätze mit gleichzeitig hohem Informationsgehalt und kann auf eine Vielzahl von Werkstoffen ausgeweitet werden

    Accessible software frameworks for reproducible image analysis of host-pathogen interactions

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    Um die Mechanismen hinter lebensgefährlichen Krankheiten zu verstehen, müssen die zugrundeliegenden Interaktionen zwischen den Wirtszellen und krankheitserregenden Mikroorganismen bekannt sein. Die kontinuierlichen Verbesserungen in bildgebenden Verfahren und Computertechnologien ermöglichen die Anwendung von Methoden aus der bildbasierten Systembiologie, welche moderne Computeralgorithmen benutzt um das Verhalten von Zellen, Geweben oder ganzen Organen präzise zu messen. Um den Standards des digitalen Managements von Forschungsdaten zu genügen, müssen Algorithmen den FAIR-Prinzipien (Findability, Accessibility, Interoperability, and Reusability) entsprechen und zur Verbreitung ebenjener in der wissenschaftlichen Gemeinschaft beitragen. Dies ist insbesondere wichtig für interdisziplinäre Teams bestehend aus Experimentatoren und Informatikern, in denen Computerprogramme zur Verbesserung der Kommunikation und schnellerer Adaption von neuen Technologien beitragen können. In dieser Arbeit wurden daher Software-Frameworks entwickelt, welche dazu beitragen die FAIR-Prinzipien durch die Entwicklung von standardisierten, reproduzierbaren, hochperformanten, und leicht zugänglichen Softwarepaketen zur Quantifizierung von Interaktionen in biologischen System zu verbreiten. Zusammenfassend zeigt diese Arbeit wie Software-Frameworks zu der Charakterisierung von Interaktionen zwischen Wirtszellen und Pathogenen beitragen können, indem der Entwurf und die Anwendung von quantitativen und FAIR-kompatiblen Bildanalyseprogrammen vereinfacht werden. Diese Verbesserungen erleichtern zukünftige Kollaborationen mit Lebenswissenschaftlern und Medizinern, was nach dem Prinzip der bildbasierten Systembiologie zur Entwicklung von neuen Experimenten, Bildgebungsverfahren, Algorithmen, und Computermodellen führen wird

    Cochlear Compartments Segmentation and Pharmacokinetics using Micro Computed Tomography Images

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    Local drug delivery to the inner ear via micropump implants has the potential to be much more effective than oral drug delivery for treating patients with sensorineural hearing loss and to protect hearing from ototoxic insult due to noise exposure. Delivering appropriate concentrations of drugs to the necessary cochlear compartments is of paramount importance; however, directly measuring local drug concentrations over time throughout the cochlea is not possible. Indirect measurement using otoacoustic emissions and auditory brainstem response are ineffective as they only provide an estimate of concentration and are susceptible to non-linear sensitivity effects. Imaging modalities such as MRI with infused gadolinium contrast agent are limited due to the high spatial resolution requirement for pharmacokinetic analysis, especially in mice with cochlear length in the micron scale. We develop an intracochlear pharmacokinetic model using micro-computed tomography imaging of the cochlea during in vivo infusion of a contrast agent at the basal end of scala tympani through a cochleostomy. This approach requires accurately segmenting the main cochlear compartments: scala tympani (ST), scala media (SM) and scala vestibuli (SV). Each scan was segmented using 1) atlas-based deformable registration, and 2) V-Net, a encoder-decoder style convolutional neural network. The segmentation of these cochlear regions enable concentrations to be extracted along the length of each scala. These spatio-temporal concentration profiles are used to learn a concentration dependent diffusion coefficient, and transport parameters between the major scalae and to clearance. The pharmacokinetic model results are comparable to the current state of the art model, and can simulate concentrations for cases involving different infusion molecules and drug delivery protocols. While our model shows promising results, to extend the approach to larger animals and to generate accurate further experimental data, computational constraints, and time requirements of previous segmentation methods need to be mitigated. To this end, we extended the V-Net architecture with inclusion of spatial attention. Moreover, to enable segmentation in hardware restricted environments, we designed a 3D segmentation network using Capsule Networks that can provide improved segmentation performance along with 90% reduction in trainable parameters. Finally, to demonstrate the effectiveness of these networks, we test them on multiple public datasets. They are also tested on the cochlea dataset and pharmacokinetic model simulations will be validated against existing results

    DeepACSON automated segmentation of white matter in 3D electron microscopy

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    Tracing the entirety of ultrastructures in large three-dimensional electron microscopy (3D-EM) images of the brain tissue requires automated segmentation techniques. Current segmentation techniques use deep convolutional neural networks (DCNNs) and rely on high-contrast cellular membranes and high-resolution EM volumes. On the other hand, segmenting low-resolution, large EM volumes requires methods to account for severe membrane discontinuities inescapable. Therefore, we developed DeepACSON, which performs DCNN-based semantic segmentation and shape-decomposition-based instance segmentation. DeepACSON instance segmentation uses the tubularity of myelinated axons and decomposes under-segmented myelinated axons into their constituent axons. We applied DeepACSON to ten EM volumes of rats after sham-operation or traumatic brain injury, segmenting hundreds of thousands of long-span myelinated axons, thousands of cell nuclei, and millions of mitochondria with excellent evaluation scores. DeepACSON quantified the morphology and spatial aspects of white matter ultrastructures, capturing nanoscopic morphological alterations five months after the injury. With DeepACSON, Abdollahzadeh et al. combines existing deep learning-based methods for semantic segmentation and a novel shape decomposition technique for the instance segmentation. The pipeline is used to segment low-resolution 3D-EM datasets allowing quantification of white matter morphology in large fields-of-view.Peer reviewe

    Characterization and Morphological Analysis of Porous Electrodes for Lithium-Ion Batteries

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    Climate change is one of the greatest challenges of the century. Compared to the pre-industrial era, the average global temperature has already risen by 1 °C (1.5 °C in Germany). The temperature increase is caused by the emission of greenhouse gases, which convert sunlight reflected into the atmosphere into heat. Around 20% of the CO2 emissions in Germany are attributed to the transport sector. Electro mobility represents a possible solution to this problem. Powerful lithium-ion batteries (LIBs) are needed for mobile storage of electricity from renewable energy. Three main approaches are being pursued to improve the performance of LIBs: the search for new materials, the development of new battery concepts, and the optimization of existing systems. This work takes the latter approach by imaging and studying the electrode morphology using tomography. Detailed morphological analysis and simulations are used to identify microstructural kinetic limitations. The results are compared with electrochemical characterization methods. In the following, the results of the five chapters of this cumulative dissertation are summarized. Chapters 1–3 are related to the study of transport limitations in batteries using a liquid electrolyte, Chapter 4 deals with all-solid-state batteries, and Chapter 5 applies the reconstruction approach to a hierarchical porous material. In Chapter 1, transport limitations of an electrode are detected by both reconstruction-simulation (RS) and electrochemical measurements, and the results of the two approaches are compared to each other. The aim of the study is to determine the ionic tortuosity in both ways to quantify transport limitations in the pore space, filled by a liquid electrolyte. Graphite, which is a common anode material, is chosen as the active material. First, graphite electrodes with different thicknesses are investigated by electrochemical impedance spectroscopy (EIS) in a symmetrical cell setup. The resulting spectra are fitted using the transmission line model (TLM), which describes the impedance of porous electrodes. The analysis reveals an ionic tortuosity of τ_EIS=7.3. Second, one graphite electrode is physically reconstructed over the entire cross-section using FIB-SEM tomography. For this purpose, the pore space of the electrode is infiltrated by an osmium-based contrast agent. The space which is filled by the liquid electrolyte in normal battery operation is thus directly imaged and the contrast of the resulting image stack is enhanced, facilitating an accurate reconstruction. A comprehensive morphological analysis is conducted featuring porosity profiles, the geometric tortuosity, and a chord length distribution (CLD) of the solid phase and void space. All analyses are performed with regards to the spatial direction, showing that the flaky graphite particles form a distinct anisotropic microstructure. This leads to strong transport hindrances in the direction perpendicular to the current collector. The reconstruction volume is verified to be representative by a finite-size analysis, which is indispensable in order to obtain reliable results. Diffusion simulations based on a random-walk approach yield a similar tortuosity value of τ_RS=6.55, which is within the experimental error of τ_EIS. Consequently, this study shows that long-range transport simulations (without considering double-layer formation) and EIS combined with TLM (ion transport in the pores and double-layer formation) give comparable results even for a highly anisotropic microstructure. Compared to FIB-SEM tomography along with numerical simulations, EIS is significantly faster, cheaper, and easier to apply, and it is available in almost every electrochemical laboratory. However, the underlying microstructural features causing steric transport hindrances can only be analyzed by appropriate tomography methods. EIS screenings can be used to detect transport limitations of newly designed electrodes. Thus, the results of this study may contribute to the future development of more powerful electrodes. In Chapter 2, the impedance of electrodes with variable thickness is examined for different liquid electrolyte systems. For this purpose, batteries are first cycled using a tetraglyme-based solvate ionic liquid (IL), a conventional carbonate-based electrolyte, and a LiFSI in IL electrolyte system. The area-specific resistances are estimated based on the overvoltages at 50% state of charge, which increase in the order; carbonate-based electrolyte < IL < solvate IL. The different electrolyte systems are characterized based on the electrode thickness by means of EIS. Special attention is paid to the impedance at 10 4 Hz, since this frequency corresponds approximately to the time scale of typical cyclization rates of 1–2 C. The impedances of the electrolyte systems increase in the same order as it was observed in the cycling experiments. Next, the analytical model of Huang and Zhang is used to shed light on the individual contributions to the overall electrode impedance for the carbonate-based electrolyte and the solvate IL. This model calculates the electrode impedance, taking into account salt concentration polarization in the electrolyte-filled pore space. It is applicable to electrolyte systems consisting of one type each of cation and anion in a solvent. Therefore, the LiFSI in IL electrolyte cannot be analyzed by this model. At 10-4 Hz, only a weak dependence on the electrode thickness is observed for the real part and the modulus of the complex impedance in the range of 50–100 µm. Using a generalized TLM, the impedance contributions of ion transport and thickness-dependent charge transfer as well as solid phase diffusion are analyzed separately. The impedance of ion transport for both the solvate IL and the carbonate-based electrolyte is higher than the contribution from charge transfer and solid phase diffusion at 10 4 Hz and for thicknesses between 50–150 µm. This explains the low dependence on thickness of the impedance spectra and leads to the conclusion that greater electrode thicknesses than the conventional 80 µm would be possible, given that the morphological properties can be kept constant over the entire electrode. Chapter 3 examines the influence of the carbon-binder domain (CBD) on Li+ charge transport in the electrolyte phase by using a RS approach and compares the results with EIS experiments. The morphology of the electrolyte-filled pore space in LIBs is influenced by the microstructure of the solid components: active material (AM) particles, binder, and conductive carbon. The binder and conductive carbon form an interpenetrating nanoporous phase, the CBD. While the µm-scaled AM particles can be easily reconstructed by 3D tomography, the CBD is often not taken into account due to its small feature size. In this chapter, a LiCoO2 (LCO) composite cathode is physically reconstructed by means of FIB-SEM tomography to determine the Li+ transport tortuosity and to morphologically characterize the CBD. EIS experiments in the framework of the TLM are conducted to determine the ionic tortuosity experimentally and are compared with the RS approach. The three-phase reconstruction provides both the hitherto highest reported resolution down to a voxel size of (13.9 × 13.9 × 20.0) nm3, and an unprecedented large volume with a minimum edge length of 20 µm. This enables a representative description of the interstitial pore space. A detailed morphological analysis is presented to characterize the morphology of the void space featuring CLD, specific surface area determination, connectivity analysis, and calculation of the geometric tortuosity. The results show that the microstructural properties of the cathode are affected by the presence of the CBD spanning the void space as a convoluted network and leading to more tortuous and constricted Li+ transport pathways. Pore-scale numerical diffusion simulations reveal a significantly higher ionic tortuosity of 1.9 when the CBD is taken into account compared to 1.5 without CBD, which cannot be solely attributed to the lower porosity. The RS analysis underscores that only pore-scale simulations in physical reconstructions including the CBD can reproduce experimental tortuosity values derived from EIS. In Chapter 4, the morphology of two sheet-type all-solid-state battery (ST-ASSB) cathodes with different solid electrolytes (SEs) is investigated to identify kinetic limiting features. The slurry-based manufacturing process of ST-ASSBs is comparable to that of conventional lithium-ion batteries and is thus relevant for eventual mass production. The sulfur-based SEs are β-LPS (β-Li3PS4) and LPSI (2 Li3PS4∙LiI) with conductivities of 0.2 mS cm 1 and 0.8 mS cm 1, respectively. While β-LPS is composed of mesoporous nanoparticles, the LPSI particles exhibit sizes up to the µm range and no intrinsic porosity. Small state-of-the-art NMC 85|05|10 particles coated with LiNbO3 are used as cathode active material (CAM). Three-phase FIB-SEM based reconstructions of large cathode volumes in high resolution reveal structurally representative and realistic models of the SE, CAM particles, and void space. The binder, which is distributed as a thin layer over all surfaces, cannot be resolved due to its small feature size and poor contrast. The volume fractions found in the reconstructions suggest that, for β-LPS, the binder accumulates predominantly within the nanoparticulate SE phase due to the high intrinsic surface area. For LPSI, it is distributed over all interfaces. Void space is dead space in ASSBs as it makes transport paths in SE more tortuous, prevents charge transfer at the SE–CAM interface, and reduces the volumetric energy density of the battery. For the β-LPS-based cathode, a small void fraction of 1 vol% can be found, while LPSI exhibits a much higher fraction of 11 vol%. The voids in the LPSI-based cathode are larger compared to β-LPS and mainly found at the SE or SE–CAM interface. For β-LPS, the voids are predominantly surrounded by CAM. This explains the larger active surface area of 87% for β-LPS, while 62% of the CAM surface is in direct contact with the SE for LPSI. An analysis of CAM connectivity shows that >99% of the CAM volume is directly connected in each case, making electron transport within the cathode uncritical. Numerical transport simulations show that the ionic tortuosity of the electrolyte phase of the LPSI sample is twice that of the β-LPS sample. In contrast, cycling experiments reveal that the LPSI sample has a higher discharge capacity (178 mAh/g vs. 150 mAh/g) and lower overvoltage. Using a general TLM, the individual contributions to the battery impedance were estimated to draw conclusions about kinetic limitations in the two samples. The charge transfer at the SE–CAM interface accounts for by far the largest impedance, while Li chemical diffusion in the CAM and ionic transport in the SE phase account for only a comparably small fraction. Due to their similar chemical composition, both electrolytes exhibit a similar charge transfer resistance. However, due to the higher effective SE–CAM interface of the LPSI sample, a lower effective charge transfer resistance is obtained, which explains the lower overvoltage. Consequently, especially in lowering the interfacial impedance, there is still great potential to further improve the performance of ST-ASSB. In the fifth Chapter, the physical reconstruction technique is applied to hierarchical porous materials (HPMs), which have a high potential for use in the field of energy storage and conversion. HPMs are a class of functional materials characterized by a large specific surface area and an interconnected pore space with high accessibility. The study presents a universal laser-based procedure for generating metal oxide HPMs with cauliflower-like morphology. Based on a facile nanosecond pulsed laser-treatment, the manufacturing process is easy to implement, solvent-free, and scalable. The resulting hybrid micro-/nanostructures can be generated on a variety of metallic substrates over a wide range of melting points. The morphology of the superstructures can be directly controlled by varying the laser parameters. The formation process is investigated in detail by means of FIB-SEM tomography. For this purpose, the cauliflower-like structures are generated on copper metal, embedded in epoxy resin, and physically reconstructed. Cross-sections of the superstructures show a ring-like pattern similar to tree rings. These rings can be fitted by ellipses with a constant center point and linearly rising elliptical axes, which makes the structures resemble an ideal ellipsoid. The distance between the single rings is constant and depends on the laser scan line distance. The porosity increases towards the outer surface, resulting in a large external surface area. A hierarchical network of pores with diameters from nanometer to micrometer is created. During generation, the laser scans the metal surface in a linear pattern, causing material to melt and partially evaporate whereby the metal partially oxidizes. In a self-organization process, microstructures are created which grow layer by layer through stepwise recondensation due to the meandering laser path. A complex and over several orders of magnitude self-similar morphology is formed. Interestingly, the determined fractal dimension corresponds to that of natural cauliflower. The concept can be applied to a variety of materials, especially transition metals, such as those used as cathode material in LIBs. In conclusion, this work provides new insights into the microstructure of battery electrodes. For this purpose, a protocol for two- and three-phase reconstructions is developed and applied to a variety of different samples. It is shown that only direct imaging of the morphology provides reliable conclusions about the reason for transport limitations and morphological heterogeneity. FIB-SEM tomography is the method of choice for physical reconstructions of electrodes as it achieves a sufficiently high resolution and provides a high sensitivity towards light elements, such as those found in the CBD. Optimization of the electrode morphology to reduce transport limitations will help to make LIBs even more efficient in the future

    Measuring blood flow and pro-inflammatory changes in the rabbit aorta

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    Atherosclerosis is a chronic inflammatory disease that develops as a consequence of progressive entrapment of low density lipoprotein, fibrous proteins and inflammatory cells in the arterial intima. Once triggered, a myriad of inflammatory and atherogenic factors mediate disease progression. However, the role of pro-inflammatory activity in the initiation of atherogenesis and its relation to altered mechanical stresses acting on the arterial wall is unclear. Estimation of wall shear stress (WSS) and the inflammatory mediator NF-κB is consequently useful. In this thesis novel ultrasound tools for accurate measurement of spatiotemporally varying 2D and 3D blood flow, with and without the use of contrast agents, have been developed. This allowed for the first time accurate, broad-view quantification of WSS around branches of the rabbit abdominal aorta. A thorough review of the evidence for a relationship between flow, NF-κB and disease was performed which highlighted discrepancies in the current literature and was used to guide the study design. Subsequently, methods for the measurement and colocalization of the spatial distribution of NF-κB, arterial permeability and nuclear morphology in the aorta of New Zealand White rabbits were developed. It was demonstrated that endothelial pro-inflammatory changes are spatially correlated with patterns of WSS, nuclear morphology and arterial permeability in vivo in the rabbit descending and abdominal aorta. The data are consistent with a causal chain between WSS, macromolecule uptake, inflammation and disease, and with the hypothesis that lipids are deposited first, through flow-mediated naturally occurring transmigration that, in excessive amounts, leads to subsequent inflammation and disease.Open Acces
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