500 Naturwissenschaften und Mathematik
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Encapsulant-free N.I.C.E. modules have strong ecological advantages compared to conventional laminated modules but suffer generally from lower electrical performance. Via long-term outdoor monitoring of fullsize industrial modules of both types with identical solar cells, we investigated if the performance difference remains constant over time and which parameters influence its value. After assessing about a full year’s data, two obvious levers for N.I.C.E. optimization are identified: The usage of textured glass and transparent adhesives on the module rear side. Also, the performance loss could be alleviated using tracking systems due to lower AOI values. Our measurements show additionally that N.I.C.E. module surfaces are in average about 2.5°C cooler compared to laminated modules. With these findings, we lay out a roadmap to reduce today’s LIV gap of about 5%rel by different optimizations.
Im Jahre 2010 bot die Hochschule Offenburg ein Medizintechnikstudium mit dem Schwerpunkt ’Kardiologie, Elektrophysiologie und elektronische kardiologische Implantate’ als Bachelor- und später auch Masterstudiengang an. Ziel des auf diesen Schwerpunkt ausgelegten didaktischen Lehrkonzeptes ist die Vermittlung sofort anwendungsbereiten theoretischen Wissens und praktischen Könnens, welches die Absolventinnen und Absolventen in ihrer künftigen Berufsausübung in der Industrie oder als technische Partner der behandelnden Ärztinnen und Ärzte in hochspezialisierten klinischen Einrichtungen benötigen.
Aufgrund fehlender kommerzieller Angebote ist zur Umsetzung dieses Lehrkonzeptes die ingenieurtechnische Realisierung geeigneter Lehrmittel zwingend erforderlich. Dies betrifft die hard- und softwareseitige Erstellung visueller Demonstrationsmöglichkeiten für pathologische und implantatinduzierte Herzrhythmen, sowie die synthetische Bereitstellung originalgetreuer elektrokardiographischer Ableitsignale aus der klinischen Routine. Des Weiteren den Aufbau von in-vitro Trainingssystemen zu Therapien mit elektronischen kardiologischen Implantaten sowie zur Hochfrequenz-Katheterablation.
Insbesondere die Wahlfächer ’Programmierung von Herzschrittmachern’ und ‚Programmierung von Defibrillatoren’, deren Besuch den Teilnehmenden einen besonders raschen Berufseinstieg ermöglichen sollte, wurden in didaktischer Hinsicht in engem Bezug zum 4-Komponenten-Instruktionsdesign-Modell der Lehre gestaltet.
Durch den kontinuierlichen Einsatz der Instrumente der formativen Evaluation gelangen sowohl deutliche Verbesserungen am Gesamtkonzept der Lehrveranstaltungen als auch an den dort eingesetzten, selbst realisierten Lösungen des benannten speziellen Lehr- und Trainingsequipments.
Eine summative Evaluation des Lehrkonzeptes ist aufgrund seines Alleinstellungsmerkmals schwierig. Aus diesem Grund erschien die quantitative Prüfung des Einflusses eines Besuchs des praktisch orientierten Wahlfachs ’Programmierung von Herzschrittmachern’ auf die Note der kombinierten Abschlussklausur in den Fächern ’Elektrokardiographie’ und ’Elektrostimulation’ sinnvoll. In diese Evaluation eingeschlossen wurde eine Kohorte von 221 Studierenden, 76 Frauen und 145 Männer, von denen 93 am Wahlfach nicht teilnahmen und 128 die es besucht hatten.
Über 7 zusammengefasste Studienjahre zeigte sich, dass die praktische Ausbildung im Wahlfach ’Programmierung von Herzschrittmachern’ das Leistungsniveau der Studierenden der Medizintechnik in der kombinierten Abschlussprüfung ’Elektrokardiographie und Elektrostimulation’ deutlich beeinflusste.
Das im Rahmen dieser Arbeit mitgestaltete Lehrkonzept, die realisierten Lehrmaterialien und Lehrumgebungen wurden im Bachelor- und Masterstudiengang der Medizintechnik an der Hochschule Offenburg in den Praktika, Seminaren und Vorlesungen des Schwerpunktes ’Kardiologie, Elektrophysiologie und elektronische kardiologische Implantate’ vielfältig genutzt. Sie ermöglichten die Gestaltung interaktiver praktischer Weiterbildungsveranstaltungen für ärztliches und mittleres medizinisches Personal und für auf diesen Gebieten tätige medizintechnische Firmen.
Sustainable chemical processes should be designed to combine the technological advantages and progress with lower safety risks and minimization of environmental impact such as, for example, reduction of raw materials, energy and water consumption, and avoidance of hazardous waste and pollution with toxic chemical agents. A number of novel eco-friendly chemical technologies have been developed in the recent decades with the help of the eco-innovations approaches and methods such as Life Cycle Analysis, Green Process Engineering, Process Intensification, Process Design for Sustainability, and others. An emerging approach to the sustainable process design in process engineering builds on the innovative solutions inspired from nature. However, the implementation of the eco-friendly technologies often faces secondary ecological problems. The study postulates that the eco-inventive principles identified in natural systems allow to avoid secondary eco-problems and proposes to apply these principles for sustainable design in chemical process engineering. The research work critically examines how this approach differs from the biomimetics, as it is commonly used for copying natural systems. The application of nature-inspired eco-design principles is illustrated with an example of a sustainable technology for extraction of nickel from pyrophyllite.
The manufacturing of conventional electronics has become a highly complicated process, which requires intensive investment. In this context, printed electronics keeps attracting attention from both academia and industry. The primary reason is the simplification of the manufacturing process via additive printing technology such as ink-jet printing. Consequently, advantages are realized such as on-demand fabrication, minimal material waste and versatile choice of substrate materials. Central to the development of printed electronic circuits are printed transistors. Recently, metal oxide semiconductors such as indium oxide have become promising materials for the fabrication of printed transistors due to their high charge mobility. Furthermore, electrolyte-gating also provides benefits such as the low-voltage operation in sub-1 V regime due to the large gate capacitance provided by electrical double layers. This opens new possibilities to fabricate printed devices and circuits for niche applications.
To facilitate the design and fabrication of printed circuits, the development of compact models is necessary. However, most of the current works have focused on the study of the static behavior of transistors, while the in-depth understanding of other characteristics such as the dynamic or noise behavior is missing. To this end, the purpose of this work is the comprehensive study on capacitance and noise properties of inkjet-printed electrolyte-gated thin-film transistors (EGT) based on indium oxide semiconductors. Proper modeling approaches are also proposed to capture accurately the electrical behaviour, which can be further utilized to enable advanced analysis of digital, analog and mixed-signal circuits.
In this work, the capacitance of EGTs is characterized using voltage-dependent impedance spectroscopy. Intrinsic and extrinsic effects are carefully separated by using de-embedding test structures. Also, a dedicated equivalent circuit model is established to offer accurate simulations of the measured frequency response of the gate impedance. Based on that, it is revealed that top-gated EGTs have the potential to reach operation frequency in the kHz regime with proper optimizations of materials and printing process. Furthermore, a Meyer-like model is proposed to accurately capture the capacitance-voltage characteristics of the lumped terminal capacitance. Both parasitic and nonquasi-static effects are considered. This further enables the AC and transient analysis of complex circuits in circuit simulators.
Following, the study of noise properties in the field of printed electronics is conducted. Low-frequency noise of EGTs is characterized using a reliable experimental setup. By examining measured noise spectra of the drain current at various gate voltages, the number fluctuation with correlated mobility fluctuation has been determined as the primary noise mechanism. Based on that, normalized flat-band voltage noise can be determined as the key performance metrics, which is only 1.08 × 10−7 V^2 µm^2, significantly lower in comparison with other thin-film technologies, which are based on dielectric gating and semiconductors such as IZO and IGZO. A plausible reason could be the large gate capacitance offered by the electrical double layers. This renders EGT technology useful for low-noise and sensitive applications such as sensor periphery circuits.
Last but not least, various circuit designs based on EGT technology are proposed, including basic digital circuits such as inverters and ring oscillators. Their performance metrics such as the propagation delay and power consumption are extensively characterized. Also, the first design of a printed full-wave rectifier is presented by using diode-connected EGTs, which features near-zero threshold voltage. As a consequence, the presented rectifier can effectively process input voltage with a small amplitude of 100 mV and a cut-off frequency of 300 Hz, which is particularly attractive for the application domain of energy harvesting. Additionally, the previously established capacitance models are verified on those circuits, which provide a satisfactory agreement between the simulation and measurement data.
Diffracted waves carry high‐resolution information that can help interpreting fine structural details at a scale smaller than the seismic wavelength. However, the diffraction energy tends to be weak compared to the reflected energy and is also sensitive to inaccuracies in the migration velocity, making the identification of its signal challenging. In this work, we present an innovative workflow to automatically detect scattering points in the migration dip angle domain using deep learning. By taking advantage of the different kinematic properties of reflected and diffracted waves, we separate the two types of signals by migrating the seismic amplitudes to dip angle gathers using prestack depth imaging in the local angle domain. Convolutional neural networks are a class of deep learning algorithms able to learn to extract spatial information about the data in order to identify its characteristics. They have now become the method of choice to solve supervised pattern recognition problems. In this work, we use wave equation modelling to create a large and diversified dataset of synthetic examples to train a network into identifying the probable position of scattering objects in the subsurface. After giving an intuitive introduction to diffraction imaging and deep learning and discussing some of the pitfalls of the methods, we evaluate the trained network on field data and demonstrate the validity and good generalization performance of our algorithm. We successfully identify with a high‐accuracy and high‐resolution diffraction points, including those which have a low signal to noise and reflection ratio. We also show how our method allows us to quickly scan through high dimensional data consisting of several versions of a dataset migrated with a range of velocities to overcome the strong effect of incorrect migration velocity on the diffraction signal.
Extracting horizon surfaces from key reflections in a seismic image is an important step of the interpretation process. Interpreting a reflection surface in a geologically complex area is a difficult and time-consuming task, and it requires an understanding of the 3D subsurface geometry. Common methods to help automate the process are based on tracking waveforms in a local window around manual picks. Those approaches often fail when the wavelet character lacks lateral continuity or when reflections are truncated by faults. We have formulated horizon picking as a multiclass segmentation problem and solved it by supervised training of a 3D convolutional neural network. We design an efficient architecture to analyze the data over multiple scales while keeping memory and computational needs to a practical level. To allow for uncertainties in the exact location of the reflections, we use a probabilistic formulation to express the horizons position. By using a masked loss function, we give interpreters flexibility when picking the training data. Our method allows experts to interactively improve the results of the picking by fine training the network in the more complex areas. We also determine how our algorithm can be used to extend horizons to the prestack domain by following reflections across offsets planes, even in the presence of residual moveout. We validate our approach on two field data sets and show that it yields accurate results on nontrivial reflectivity while being trained from a workable amount of manually picked data. Initial training of the network takes approximately 1 h, and the fine training and prediction on a large seismic volume take a minute at most.