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Institute
- Hasso-Plattner-Institut für Digital Engineering GmbH (50)
- Extern (47)
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- Department Psychologie (19)
Gravitational-wave (GW) astrophysics is a field in full blossom. Since the landmark detection of GWs from a binary black hole on September 14th 2015, fifty-two compact-object binaries have been reported by the LIGO-Virgo collaboration. Such events carry astrophysical and cosmological information ranging from an understanding of how black holes and neutron stars are formed, what neutron stars are composed of, how the Universe expands, and allow testing general relativity in the highly-dynamical strong-field regime. It is the goal of GW astrophysics to extract such information as accurately as possible. Yet, this is only possible if the tools and technology used to detect and analyze GWs are advanced enough. A key aspect of GW searches are waveform models, which encapsulate our best predictions for the gravitational radiation under a certain set of parameters, and that need to be cross-correlated with data to extract GW signals. Waveforms must be very accurate to avoid missing important physics in the data, which might be the key to answer the fundamental questions of GW astrophysics. The continuous improvements of the current LIGO-Virgo detectors, the development of next-generation ground-based detectors such as the Einstein Telescope or the Cosmic Explorer, as well as the development of the Laser Interferometer Space Antenna (LISA), demand accurate waveform models. While available models are enough to capture the low spins, comparable-mass binaries routinely detected in LIGO-Virgo searches, those for sources from both current and next-generation ground-based and spaceborne detectors must be accurate enough to detect binaries with large spins and asymmetry in the masses. Moreover, the thousands of sources that we expect to detect with future detectors demand accurate waveforms to mitigate biases in the estimation of signals’ parameters due to the presence of a foreground of many sources that overlap in the frequency band. This is recognized as one of the biggest challenges for the analysis of future-detectors’ data, since biases might hinder the extraction of important astrophysical and cosmological information from future detectors’ data. In the first part of this thesis, we discuss how to improve waveform models for binaries with high spins and asymmetry in the masses. In the second, we present the first generic metrics that have been proposed to predict biases in the presence of a foreground of many overlapping signals in GW data.
For the first task, we will focus on several classes of analytical techniques. Current models for LIGO and Virgo studies are based on the post-Newtonian (PN, weak-field, small velocities) approximation that is most natural for the bound orbits that are routinely detected in GW searches. However, two other approximations have risen in prominence, the post-Minkowskian (PM, weak- field only) approximation natural for unbound (scattering) orbits and the small-mass-ratio (SMR) approximation typical of binaries in which the mass of one body is much bigger than the other. These are most appropriate to binaries with high asymmetry in the masses that challenge current waveform models. Moreover, they allow one to “cover” regions of the parameter space of coalescing binaries, thereby improving the interpolation (and faithfulness) of waveform models. The analytical approximations to the relativistic two-body problem can synergically be included within the effective-one-body (EOB) formalism, in which the two-body information from each approximation can be recast into an effective problem of a mass orbiting a deformed Schwarzschild (or Kerr) black hole. The hope is that the resultant models can cover both the low-spin comparable-mass binaries that are routinely detected, and the ones that challenge current models. The first part of this thesis is dedicated to a study about how to best incorporate information from the PN, PM, SMR and EOB approaches in a synergistic way. We also discuss how accurate the resulting waveforms are, as compared against numerical-relativity (NR) simulations. We begin by comparing PM models, whether alone or recast in the EOB framework, against PN models and NR simulations. We will show that PM information has the potential to improve currently-employed models for LIGO and Virgo, especially if recast within the EOB formalism. This is very important, as the PM approximation comes with a host of new computational techniques from particle physics to exploit. Then, we show how a combination of PM and SMR approximations can be employed to access previously-unknown PN orders, deriving the third subleading PN dynamics for spin-orbit and (aligned) spin1-spin2 couplings. Such new results can then be included in the EOB models currently used in GW searches and parameter estimation studies, thereby improving them when the binaries have high spins. Finally, we build an EOB model for quasi-circular nonspinning binaries based on the SMR approximation (rather than the PN one as usually done). We show how this is done in detail without incurring in the divergences that had affected previous attempts, and compare the resultant model against NR simulations. We find that the SMR approximation is an excellent approximation for all (quasi-circular nonspinning) binaries, including both the equal-mass binaries that are routinely detected in GW searches and the ones with highly asymmetric masses. In particular, the SMR-based models compare much better than the PN models, suggesting that SMR-informed EOB models might be the key to model binaries in the future. In the second task of this thesis, we work within the linear-signal ap- proximation and describe generic metrics to predict inference biases on the parameters of a GW source of interest in the presence of confusion noise from unfitted foregrounds and from residuals of other signals that have been incorrectly fitted out. We illustrate the formalism with simple (yet realistic) LISA sources, and demonstrate its validity against Monte-Carlo simulations. The metrics we describe pave the way for more realistic studies to quantify the biases with future ground-based and spaceborne detectors.
Das Rahmenkonzept der Universitätsschule Potsdam beschreibt die Wertegrundlage und das pädagogisch-didaktische sowie das wissenschaftliche Fundament einer zu gründenden Universitätsschule Potsdam. Wie andere Universitätsschulen soll sich auch diese Schule durch eine enge und institutionalisierte Beziehung zwischen Schule und Universität auszeichnen, die den ständigen Wissenstransfer zwischen Schulpraxis, Wissenschaft, Lehrkräftebildung und Schulverwaltung unterstützt. Das Rahmenkonzept legt die Grundlagen für eine inklusive Schule, deren Schüler:innen einen Querschnitt der Gesellschaft abbilden, und die in ungleichheitssensiblen Bildungsangeboten alle Bildungsabschlüsse des Landes Brandenburg anbietet. Die Universitätsschule soll den starken Segregationsprozessen in Potsdam entgegenwirken.
Im Leitbild werden die Grundwerte (Nachhaltigkeit, Inklusion und Bildungsgerechtigkeit, Menschenrechte und Demokratie, Gemeinschaft, Ganzheitlichkeit) und die Bildungsziele (Transferfähigkeit, kritisch-reflexives Denken und lebensbegleitendes Lernen, Diversitätsbewusstsein und Transkulturalität, Selbstkompetenz und Beziehungskompetenz, Kulturtechniken und digitale Kompetenz) der Universitätsschule dargestellt. Das Pädagogische Konzept veranschaulicht, wie Werte und Bildungsziele in den Bereichen Schulform, Schulkultur, Lernkultur sowie Lernorte und Lernumgebung ausgestaltet werden können. Schließlich wird die Universitätsschule als lernende und lehrende Institution beschrieben, die ein Ort des Transfers von Bildungsinnovationen ist. Dafür soll eine Transferwerkstatt in der Schule verankert werden, die den Wissensaustausch der schulrelevanten Akteur:innen unterstützt und gestaltet.
In den vergangenen Jahren hat sich die Politikdidaktik zunehmend mit dem Einsatz von Narrationen im Politikunterricht beschäftigt, denn neben Sachtexten bietet auch die Belletristik die Möglichkeit, sich mit politischen Themen auseinanderzusetzen. Insbesondere die Literatur von Ferdinand von Schirach hat in den letzten Jahren zunehmend Anklang in der Gesellschaft gefunden. Von Schirachs Texte greifen gesellschaftskritische Themen auf, beleuchten diese aus verschiedenen Perspektiven und fordern zur Meinungsbildung heraus. Aus diesem Grund weisen von Schirachs Narrationen ein hohes Potential für die Politische Bildung auf. Politische Bildung schließt auch die Rechterziehung ein. Der Fall Collini von Ferdinand von Schirach setzt sich sowohl mit rechtlichen, als auch mit politischen Themen im Sinne der Rechtserziehung auseinander. In der vorliegenden Masterarbeit wird der Frage nachgegangen, inwieweit der Roman Der Fall Collini von Ferdinand von Schirach als Narration eine Chance für politisch-rechtliches Lernen im Politikunterricht darstellt. Um die Forschungsfrage zu beantworten, werden die Lernchancen und -grenzen des Romans hinsichtlich seiner Thematik und seines Genres, sowie durch den Roman geförderten Kompetenzen herausgearbeitet und die durch ihn möglichen fächerübergreifenden Bezüge verdeutlicht. Durch die Auseinandersetzung mit von Schirachs Werk beschäftigen sich die Schülerinnen und Schüler mit politisch-rechtlichen Themen, wie dem Spannungsverhältnis von Recht und Gerechtigkeit, dem Ablauf von Strafgerichtsverfahren, dem theoretischen Anspruch des Rechtsstaates und dessen realen Schwächen. Zudem fördert die Auseinandersetzung mit dem Roman Der Fall Collini die vier fachbezogenen Kompetenzen der Politischen Bildung, sowie Multiperspektivität und exemplarisches Lernen. Des Weiteren verknüpft der Roman historische, politisch-rechtliche und moralisch-ethische Aspekte miteinander, wodurch fächerübergreifende Bezüge mit den Fächern Geschichte, Deutsch und L-E-R hergestellt werden können. Darüber hinaus spricht der Justizroman als Narration seine Leserinnen und Leser auch emotional an und fördert somit eine ganzheitliche und nachhaltige Wissensvermittlung im Sinne der Rechtserziehung. Es hat sich gezeigt, dass Der Fall Collini von Ferdinand von Schirach sich für die unterrichtliche Beschäftigung innerhalb der Politischen Bildung besonders eignet.
En el presente trabajo se realizó una investigación multidisciplinaria combinando métodos de geomorfología tectónica con estudios geofisicos y estructurales, focalizados principalmente en la caracterización neotectónica de ambos faldeos de la sierra de La Candelaria y del extremo sur de la cuenca de Metán. La zona de estudio se encuentra ubicada en la región limítrofe entre las provincias de Salta y Tucumán y pertenece a la provincia geológica del Sistema Santa Bárbara.
El principal objetivo consistió en contextualizar las evidencias de actividad tectónica cuaternaria de la región mediante la propuesta de un modelo estructural novedoso, con el propósito de incrementar la información disponible sobre estructuras neotectónicas y su potencial sismogénico. Con este fin, se aplicaron e integraron diversas técnicas tales como la interpretación de líneas sísmicas de reflexión, construcción de secciones estructurales balanceadas, y métodos geofísicos someros, para constatar el comportamiento en profundidad tanto de las estructuras geológicas identificadas en superficie como de las posibles fallas ciegas corticales involucradas.
En primer lugar, se realizó un relevamiento regional del área de estudio empleando imágenes satelitales multiespectrales LANDSAT y SENTINEL 2, que permitieron reconocer diferentes niveles de abanicos aluviales y terrazas fluviales cuaternarios. Mediante la determinación de diferentes indicadores morfométricos en modelos de elevación digital (MED), junto con observaciones de campo, fue posible identificar evidencias de deformación sobre dichos niveles cuaternarios que han sido relacionadas genéticamente con cuatro fallas neotectónicas. Tres de ellas (fallas Arias, El Quemado y Copo Quile) fueron seleccionadas para efectuar estudios de mayor detalle por medio de la aplicación de métodos de geofísica somera (tomografía eléctrica resistiva (ERT) y tomografía sísmica de refracción Sísmica (SRT)), que permitieron corroborar su existencia en profundidad, realizar inferencias geométricas y cinemáticas, y estimar la magnitud de la deformación reciente. Las fallas Arias y El Quemado fueron interpretadas como fallas inversas relacionadas con deslizamiento flexural interstratal, mientras que la falla Copo Quile se interpretó como una falla inversa ciega de bajo ángulo.También se realizó una interpretación conjunta de líneas sísmicas de reflexión y pozos exploratorios pertenecientes a áreas hidrocarburíferas de las cuencas de Choromoro y Metán con el fin de contextualizar las principales estructuras reconocidas en el marco estratigráfico y tectónico regional. Toda la información fue integrada en una sección estructural balanceada mediante técnicas de modelado cinemático. Dicho modelo permite inferir que la deformación cuaternaria reconocida está relacionada al desplazamiento del basamento a lo largo de un corrimiento ciego, responsable del levantamiento de la sierra de La Candelaria y el cerr Cantero. Asimismo, el modelo cinemático permite interpretar la ubicación aproximada de los principales niveles de despegue que controlan el estilo de deformación. El nivel de despegue más somero, que controla la deformación de la cobertura sedimentaria se encuentra a 4 km de profundidad, a 21 km se estima la presencia de otra zona de cizalla subhorizontal dentro del basamento.
Finalmente, a partir de la integración de todos los resultados obtenidos, se evaluó el potencial sismogénico de las fallas en la zona de estudio. Las fallas de primer orden que controlan la deformación en la zona son las responsables de los grandes terremotos. Mientras, las fallas Cuaternarias flexodeslizantes e inversas afectan solamente a la cobertura sedimentaria y serían estructuras de segundo orden que acomodan la deformación y fueron activadas durante el cuaternario con movimientos asísmicos y/o sísmicos de muy baja magnitud.
Estos resultados permiten inferir que el corrimiento La Candelaria constituye una fuente sismogénica potencial de importancia para la región, donde se ubican numerosas poblaciones y obras civiles de envergadura. Por otra parte, la sección estructural balanceada implica la presencia de otras fallas ciegas de distinto orden de magnitud que podrían ser posibles fuentes sismogénicas profundas adicionales, marcando la necesidad de continuar con el desarrollo de este tipo de estudios en esta región tectónicamente activa.
Polymeric semiconductors are strong contenders for replacing traditional inorganic semiconductors in electronic applications requiring low power, low cost and flexibility, such as biosensors, flexible solar cells and electronic displays. Molecular doping has the potential to enable this revolution by improving the conductivity and charge transport properties of this class of materials. Despite decades of research in this field, gaps in our understanding of the nature of dopant–polymer interactions has resulted in limited commercialization of this technology. This work aims at providing a deeper insight into the underlying mechanisms of molecular p-doping of semiconducting polymers in the solution and solid-state, and thereby bring the scientific community closer to realizing the dream of making organic semiconductors commonplace in the electronics industry. The role of 1) dopant size/shape, 2) polymer chain aggregation and 3) charge delocalization on the doping mechanism and efficiency is addressed using optical (UV-Vis-NIR) and electron paramagnetic resonance (EPR) spectroscopies. By conducting a comprehensive study of the nature and concentration of the doping-induced species in solutions of the polymer poly(3-hexylthiophene) (P3HT) with 3 different dopants, we identify the unique optical signatures of the delocalized polaron, localized polaron and charge-transfer complex, and report their extinction coefficient values. Furthermore, with X-ray diffraction, atomic force microscopy and electrical conductivity measurements, we study the impact of processing technique and doping mechanism on the morphology and thereby, charge transport through the doped films.
This work demonstrates that the doping mechanism and type of doping-induced species formed are strongly influenced by the polymer backbone arrangement rather than dopant shape/size. The ability of the polymer chain to aggregate is found to be crucial for efficient charge transfer (ionization) and polaron delocalization. At the same time, our results suggest that the high ionization efficiency of a dopant–polymer system in solution may subsequently hinder efficient charge transport in the solid-state due to the reduction in the fraction of tie chains, which enable charges to move efficiently between aggregated domains in the films. This study demonstrates the complex multifaceted nature of polymer doping while providing important hints for the future design of dopant-host systems and film fabrication techniques.
Mental health problems are highly prevalent worldwide. Fortunately, psychotherapy has proven highly effective in the treatment of a number of mental health issues, such as depression and anxiety disorders. In contrast, psychotherapy training as is practised currently cannot be considered evidence-based. Thus, there is much room for improvement. The integration of simulated patients (SPs) into psychotherapy training and research is on the rise. SPs originate from the medical education and have, in a number of studies, been demonstrated to contribute to effective learning environments. Nevertheless, there has been voiced criticism regarding the authenticity of SP portrayals, but few studies have examined this to date.
Based on these considerations, this dissertation explores SPs’ authenticity while portraying a mental disorder, depression. Altogether, the present cumulative dissertation consists of three empirical papers. At the time of printing, Paper I and Paper III have been accepted for publication, and Paper II is under review after a minor revision.
First, Paper I develops and validates an observer-based rating-scale to assess SP authenticity in psychotherapeutic contexts. Based on the preliminary findings, it can be concluded that the Authenticity of Patient Demonstrations scale is a reliable and valid tool that can be used for recruiting, training, and evaluating the authenticity of SPs.
Second, Paper II tests whether student SPs are perceived as more authentic after they receive an in-depth role-script compared to those SPs who only receive basic information on the patient case. To test this assumption, a randomised controlled study design was implemented and the hypothesis could be confirmed. As a consequence, when engaging SPs, an in-depth role-script with details, e.g. on nonverbal behaviour and feelings of the patient, should be provided.
Third, Paper III demonstrates that psychotherapy trainees cannot distinguish between trained SPs and real patients and therefore suggests that, with proper training, SPs are a promising training method for psychotherapy.
Altogether, the dissertation shows that SPs can be trained to portray a depressive patient authentically and thus delivers promising evidence for the further dissemination of SPs.
Precipitation forecasting has an important place in everyday life – during the day we may have tens of small talks discussing the likelihood that it will rain this evening or weekend. Should you take an umbrella for a walk? Or should you invite your friends for a barbecue? It will certainly depend on what your weather application shows.
While for years people were guided by the precipitation forecasts issued for a particular region or city several times a day, the widespread availability of weather radars allowed us to obtain forecasts at much higher spatiotemporal resolution of minutes in time and hundreds of meters in space. Hence, radar-based precipitation nowcasting, that is, very-short-range forecasting (typically up to 1–3 h), has become an essential technique, also in various professional application contexts, e.g., early warning, sewage control, or agriculture.
There are two major components comprising a system for precipitation nowcasting: radar-based precipitation estimates, and models to extrapolate that precipitation to the imminent future. While acknowledging the fundamental importance of radar-based precipitation retrieval for precipitation nowcasts, this thesis focuses only on the model development: the establishment of open and competitive benchmark models, the investigation of the potential of deep learning, and the development of procedures for nowcast errors diagnosis and isolation that can guide model development.
The present landscape of computational models for precipitation nowcasting still struggles with the availability of open software implementations that could serve as benchmarks for measuring progress. Focusing on this gap, we have developed and extensively benchmarked a stack of models based on different optical flow algorithms for the tracking step and a set of parsimonious extrapolation procedures based on image warping and advection. We demonstrate that these models provide skillful predictions comparable with or even superior to state-of-the-art operational software. We distribute the corresponding set of models as a software library, rainymotion, which is written in the Python programming language and openly available at GitHub (https://github.com/hydrogo/rainymotion). That way, the library acts as a tool for providing fast, open, and transparent solutions that could serve as a benchmark for further model development and hypothesis testing.
One of the promising directions for model development is to challenge the potential of deep learning – a subfield of machine learning that refers to artificial neural networks with deep architectures, which may consist of many computational layers. Deep learning showed promising results in many fields of computer science, such as image and speech recognition, or natural language processing, where it started to dramatically outperform reference methods.
The high benefit of using "big data" for training is among the main reasons for that. Hence, the emerging interest in deep learning in atmospheric sciences is also caused and concerted with the increasing availability of data – both observational and model-based. The large archives of weather radar data provide a solid basis for investigation of deep learning potential in precipitation nowcasting: one year of national 5-min composites for Germany comprises around 85 billion data points.
To this aim, we present RainNet, a deep convolutional neural network for radar-based precipitation nowcasting. RainNet was trained to predict continuous precipitation intensities at a lead time of 5 min, using several years of quality-controlled weather radar composites provided by the German Weather Service (DWD). That data set covers Germany with a spatial domain of 900 km x 900 km and has a resolution of 1 km in space and 5 min in time. Independent verification experiments were carried out on 11 summer precipitation events from 2016 to 2017. In these experiments, RainNet was applied recursively in order to achieve lead times of up to 1 h. In the verification experiments, trivial Eulerian persistence and a conventional model based on optical flow served as benchmarks. The latter is available in the previously developed rainymotion library.
RainNet significantly outperformed the benchmark models at all lead times up to 60 min for the routine verification metrics mean absolute error (MAE) and critical success index (CSI) at intensity thresholds of 0.125, 1, and 5 mm/h. However, rainymotion turned out to be superior in predicting the exceedance of higher intensity thresholds (here 10 and 15 mm/h). The limited ability of RainNet to predict high rainfall intensities is an undesirable property which we attribute to a high level of spatial smoothing introduced by the model. At a lead time of 5 min, an analysis of power spectral density confirmed a significant loss of spectral power at length scales of 16 km and below.
Obviously, RainNet had learned an optimal level of smoothing to produce a nowcast at 5 min lead time. In that sense, the loss of spectral power at small scales is informative, too, as it reflects the limits of predictability as a function of spatial scale. Beyond the lead time of 5 min, however, the increasing level of smoothing is a mere artifact – an analogue to numerical diffusion – that is not a property of RainNet itself but of its recursive application. In the context of early warning, the smoothing is particularly unfavorable since pronounced features of intense precipitation tend to get lost over longer lead times. Hence, we propose several options to address this issue in prospective research on model development for precipitation nowcasting, including an adjustment of the loss function for model training, model training for longer lead times, and the prediction of threshold exceedance.
The model development together with the verification experiments for both conventional and deep learning model predictions also revealed the need to better understand the source of forecast errors. Understanding the dominant sources of error in specific situations should help in guiding further model improvement. The total error of a precipitation nowcast consists of an error in the predicted location of a precipitation feature and an error in the change of precipitation intensity over lead time. So far, verification measures did not allow to isolate the location error, making it difficult to specifically improve nowcast models with regard to location prediction.
To fill this gap, we introduced a framework to directly quantify the location error. To that end, we detect and track scale-invariant precipitation features (corners) in radar images. We then consider these observed tracks as the true reference in order to evaluate the performance (or, inversely, the error) of any model that aims to predict the future location of a precipitation feature. Hence, the location error of a forecast at any lead time ahead of the forecast time corresponds to the Euclidean distance between the observed and the predicted feature location at the corresponding lead time.
Based on this framework, we carried out a benchmarking case study using one year worth of weather radar composites of the DWD. We evaluated the performance of four extrapolation models, two of which are based on the linear extrapolation of corner motion; and the remaining two are based on the Dense Inverse Search (DIS) method: motion vectors obtained from DIS are used to predict feature locations by linear and Semi-Lagrangian extrapolation.
For all competing models, the mean location error exceeds a distance of 5 km after 60 min, and 10 km after 110 min. At least 25% of all forecasts exceed an error of 5 km after 50 min, and of 10 km after 90 min. Even for the best models in our experiment, at least 5 percent of the forecasts will have a location error of more than 10 km after 45 min. When we relate such errors to application scenarios that are typically suggested for precipitation nowcasting, e.g., early warning, it becomes obvious that location errors matter: the order of magnitude of these errors is about the same as the typical extent of a convective cell. Hence, the uncertainty of precipitation nowcasts at such length scales – just as a result of locational errors – can be substantial already at lead times of less than 1 h. Being able to quantify the location error should hence guide any model development that is targeted towards its minimization. To that aim, we also consider the high potential of using deep learning architectures specific to the assimilation of sequential (track) data.
Last but not least, the thesis demonstrates the benefits of a general movement towards open science for model development in the field of precipitation nowcasting. All the presented models and frameworks are distributed as open repositories, thus enhancing transparency and reproducibility of the methodological approach. Furthermore, they are readily available to be used for further research studies, as well as for practical applications.
Manganese (Mn) and zinc (Zn) are not only essential trace elements, but also potential exogenous risk factors for various diseases. Since the disturbed homeostasis of single metals can result in detrimental health effects, concerns have emerged regarding the consequences of excessive exposures to multiple metals, either via nutritional supplementation or parenteral nutrition. This study focuses on Mn-Zn-interactions in the nematode Caenorhabditis elegans (C. elegans) model, taking into account aspects related to aging and age-dependent neurodegeneration.
Zentrales Element dieser Arbeit ist die Synthese und Charakterisierung praktisch nutzbarer Ionogele. Die Basis der Polymerionogele bildet das Modellpolymer Polymethylmethacrylat. Als Additive kommen ionische Flüssigkeiten zum Einsatz, deren Grundlage Derivate des vielfach verwendeten Imidazoliumkations sind. Die Eigenschaften der eingebetteten ionischen Flüssigkeiten sind für die Ionogele funktionsgebend. Die Funktionalität der jeweiligen Gele und damit der Transfer der Eigenschaften von ionischen Flüssigkeiten auf die Ionogele wurde in der vorliegenden Arbeit mittels zahlreicher Charakterisierungstechniken überprüft und bestätigt. In dieser Arbeit wurden durch Ionogelbildung makroskopische Ionogelobjekte in Form von Folien und Vliesen erzeugt. Dabei kamen das Filmgießen und das Elektrospinnen als Methoden zur Erzeugung dieser Folien und Vliese zum Einsatz, woraus jeweils ein Modellsystem resultiert. Dadurch wird die vorliegende Arbeit in die Themenkomplexe „elektrisch halbleitende Ionogelfolien“ und „antimikrobiell aktive Ionogelvliese“ gegliedert. Der Einsatz von triiodidhaltigen ionischen Flüssigkeiten und einer Polymermatrix in einem diskontinuierlichen Gießprozess resultiert in elektrisch halbleitenden Ionogelfolien. Die flexiblen und transparenten Folien können Mittelpunkt zahlreicher neuer Anwendungsfelder im Bereich flexibler Elektronik sein. Das Elektrospinnen von Polymethylmethacrylat mit einer ionischen Flüssigkeit führte zu einem homogen Ionogelvlies, welches ein Modell für die Übertragung antimikrobiell aktiver Eigenschaften ionischer Flüssigkeiten auf poröse Strukturen zur Filtration darstellt. Gleichzeitig ist es das erste Beispiel für ein kupferchloridhaltiges Ionogel. Ionogele sind attraktive Materialien mit zahlreichen Anwendungsmöglichkeiten. Mit der vorliegenden Arbeit wird das Spektrum der Ionogele um ein elektrisch halbleitendes und ein antimikrobiell aktives Ionogel erweitert. Gleichzeitig wurden durch diese Arbeit der Gruppe der ionischen Flüssigkeiten drei Beispiele für elektrisch halbleitende ionische Flüssigkeiten sowie zahlreiche kupfer(II)chloridbasierte ionische Flüssigkeiten hinzugefügt.