@phdthesis{Canil2021,
  author    = {Canil, Laura},
  title     = {Tuning Interfacial Properties in Perovskite Solar Cells through Defined Molecular Assemblies},
  doi       = {10.25932/publishup-54633},
  url       = {http://nbn-resolving.de/urn:nbn:de:kobv:517-opus4-546333},
  school      = {Universit{\"a}t Potsdam},
  pages     = {vii, 157},
  year      = {2021},
  abstract  = {In the frame of a world fighting a dramatic global warming caused by human-related activities, research towards the development of renewable energies plays a crucial role. Solar energy is one of the most important clean energy sources and its role in the satisfaction of the global energy demand is set to increase. In this context, a particular class of materials captured the attention of the scientific community for its attractive properties: halide perovskites. Devices with perovskite as light-absorber saw an impressive development within the last decade, reaching nowadays efficiencies comparable to mature photovoltaic technologies like silicon solar cells. Yet, there are still several roadblocks to overcome before a wide-spread commercialization of this kind of devices is enabled. One of the critical points lies at the interfaces: perovskite solar cells (PSCs) are made of several layers with different chemical and physical features. In order for the device to function properly, these properties have to be well-matched. This dissertation deals with some of the challenges related to interfaces in PSCs, with a focus on the interface between the perovskite material itself and the subsequent charge transport layer. In particular, molecular assemblies with specific properties are deposited on the perovskite surface to functionalize it. The functionalization results in energy level alignment adjustment, interfacial losses reduction, and stability improvement. First, a strategy to tune the perovskite's energy levels is introduced: self-assembled monolayers of dipolar molecules are used to functionalize the surface, obtaining simultaneously a shift in the vacuum level position and a saturation of the dangling bonds at the surface. A shift in the vacuum level corresponds to an equal change in work function, ionization energy, and electron affinity. The direction of the shift depends on the direction of the collective interfacial dipole. The magnitude of the shift can be tailored by controlling the deposition parameters, such as the concentration of the solution used for the deposition. The shift for different molecules is characterized by several non-invasive techniques, including in particular Kelvin probe. Overall, it is shown that it is possible to shift the perovskite energy levels in both directions by several hundreds of meV. Moreover, interesting insights on the molecules deposition dynamics are revealed. Secondly, the application of this strategy in perovskite solar cells is explored. Devices with different perovskite compositions ("triple cation perovskite" and MAPbBr3) are prepared. The two resulting model systems present different energetic offsets at the perovskite/hole-transport layer interface. Upon tailored perovskite surface functionalization, the devices show a stabilized open circuit voltage (Voc) enhancement of approximately 60 meV on average for devices with MAPbBr3, while the impact is limited on triple-cation solar cells. This suggests that the proposed energy level tuning method is valid, but its effectiveness depends on factors such as the significance of the energetic offset compared to the other losses in the devices. Finally, the above presented method is further developed by incorporating the ability to interact with the perovskite surface directly into a novel hole-transport material (HTM), named PFI. The HTM can anchor to the perovskite halide ions via halogen bonding (XB). Its behaviour is compared to that of another HTM (PF) with same chemical structure and properties, except for the ability of forming XB. The interaction of perovskite with PFI and PF is characterized through UV-Vis, atomic force microscopy and Kelvin probe measurements combined with simulations. Compared to PF, PFI exhibits enhanced resilience against solvent exposure and improved energy level alignment with the perovskite layer. As a consequence, devices comprising PFI show enhanced Voc and operational stability during maximum-power-point tracking, in addition to hysteresis reduction. XB promotes the formation of a high-quality interface by anchoring to the halide ions and forming a stable and ordered interfacial layer, showing to be a particularly interesting candidate for the development of tailored charge transport materials in PSCs. Overall, the results exposed in this dissertation introduce and discuss a versatile tool to functionalize the perovskite surface and tune its energy levels. The application of this method in devices is explored and insights on its challenges and advantages are given. Within this frame, the results shed light on XB as ideal interaction for enhancing stability and efficiency in perovskite-based devices.},
  language  = {en}
}
@phdthesis{Kegelmann2019,
  author    = {Kegelmann, Lukas},
  title     = {Advancing charge selective contacts for efficient monolithic perovskite-silicon tandem solar cells},
  doi       = {10.25932/publishup-42642},
  url       = {http://nbn-resolving.de/urn:nbn:de:kobv:517-opus4-426428},
  school      = {Universit{\"a}t Potsdam},
  pages     = {v, 155},
  year      = {2019},
  abstract  = {Hybrid organic-inorganic perovskites are one of the most promising material classes for photovoltaic energy conversion. In solar cells, the perovskite absorber is sandwiched between n- and p-type contact layers which selectively transport electrons and holes to the cell's cathode and anode, respectively. This thesis aims to advance contact layers in perovskite solar cells and unravel the impact of interface and contact properties on the device performance. Further, the contact materials are applied in monolithic perovskite-silicon heterojunction (SHJ) tandem solar cells, which can overcome the single junction efficiency limits and attract increasing attention. Therefore, all contact layers must be highly transparent to foster light harvesting in the tandem solar cell design. Besides, the SHJ device restricts processing temperatures for the selective contacts to below 200°C. A comparative study of various electron selective contact materials, all processed below 180°C, in n-i-p type perovskite solar cells highlights that selective contacts and their interfaces to the absorber govern the overall device performance. Combining fullerenes and metal-oxides in a TiO2/PC60BM (phenyl-C60-butyric acid methyl ester) double-layer contact allows to merge good charge extraction with minimized interface recombination. The layer sequence thereby achieved high stabilized solar cell performances up to 18.0\% and negligible current-voltage hysteresis, an otherwise pronounced phenomenon in this device design. Double-layer structures are therefore emphasized as a general concept to establish efficient and highly selective contacts. Based on this success, the concept to combine desired properties of different materials is transferred to the p-type contact. Here, a mixture of the small molecule Spiro-OMeTAD [2,2',7,7'-tetrakis(N,N-di-p-methoxyphenylamine)-9,9'-spirobifluoren] and the doped polymer PEDOT [poly(3,4-ethylenedioxythiophene)] is presented as a novel hole selective contact. PEDOT thereby remarkably suppresses charge recombination at the perovskite surface, allowing an increase of quasi-Fermi level splitting in the absorber. Further, the addition of Spiro-OMeTAD into the PEDOT layer is shown to enhance charge extraction at the interface and allow high efficiencies up to 16.8\%. Finally, the knowledge on contact properties is applied to monolithic perovskite-SHJ tandem solar cells. The main goal is to optimize the top contact stack of doped Spiro-OMeTAD/molybdenum oxide(MoOx)/ITO towards higher transparency by two different routes. First, fine-tuning of the ITO deposition to mitigate chemical reduction of MoOx and increase the transmittance of MoOx/ITO stacks by 25\%. Second, replacing Spiro-OMeTAD with the alternative hole transport materials PEDOT/Spiro-OMeTAD mixtures, CuSCN or PTAA [poly(triaryl amine)]. Experimental results determine layer thickness constrains and validate optical simulations, which subsequently allow to realistically estimate the respective tandem device performances. As a result, PTAA represents the most promising replacement for Spiro-OMeTAD, with a projected increase of the optimum tandem device efficiency for the herein used architecture by 2.9\% relative to 26.5\% absolute. The results also reveal general guidelines for further performance gains of the technology.},
  language  = {en}
}
@phdthesis{Doerries2024,
  author    = {D{\"o}rries, Timo Julian},
  title     = {Anomalous transport and non-Gaussian dynamics in mobile-immobile models},
  doi       = {10.25932/publishup-63495},
  url       = {http://nbn-resolving.de/urn:nbn:de:kobv:517-opus4-634959},
  school      = {Universit{\"a}t Potsdam},
  pages     = {ii, 177},
  year      = {2024},
  abstract  = {The mobile-immobile model (MIM) has been established in geoscience in the context of contaminant transport in groundwater. Here the tracer particles effectively immobilise, e.g., due to diffusion into dead-end pores or sorption. The main idea of the MIM is to split the total particle density into a mobile and an immobile density. Individual tracers switch between the mobile and immobile state following a two-state telegraph process, i.e., the residence times in each state are distributed exponentially. In geoscience the focus lies on the breakthrough curve (BTC), which is the concentration at a fixed location over time. We apply the MIM to biological experiments with a special focus on anomalous scaling regimes of the mean squared displacement (MSD) and non-Gaussian displacement distributions. As an exemplary system, we have analysed the motion of tau proteins, that diffuse freely inside axons of neurons. Their free diffusion thereby corresponds to the mobile state of the MIM. Tau proteins stochastically bind to microtubules, which effectively immobilises the tau proteins until they unbind and continue diffusing. Long immobilisation durations compared to the mobile durations give rise to distinct non-Gaussian Laplace shaped distributions. It is accompanied by a plateau in the MSD for initially mobile tracer particles at relevant intermediate timescales. An equilibrium fraction of initially mobile tracers gives rise to non-Gaussian displacements at intermediate timescales, while the MSD remains linear at all times. In another setting bio molecules diffuse in a biosensor and transiently bind to specific receptors, where advection becomes relevant in the mobile state. The plateau in the MSD observed for the advection-free setting and long immobilisation durations persists also for the case with advection. We find a new clear regime of anomalous diffusion with non-Gaussian distributions and a cubic scaling of the MSD. This regime emerges for initially mobile and for initially immobile tracers. For an equilibrium fraction of initially mobile tracers we observe an intermittent ballistic scaling of the MSD. The long-time effective diffusion coefficient is enhanced by advection, which we physically explain with the variance of mobile durations. Finally, we generalize the MIM to incorporate arbitrary immobilisation time distributions and focus on a Mittag-Leffler immobilisation time distribution with power-law tail ~ t^(-1-mu) with 0<mu<1 and diverging mean immobilisation durations. A fit of our model to the BTC of experimental data from tracer particles in aquifers matches the BTC including the power-law tail. We use the fit parameters for plotting the displacement distributions and the MSD. We find Gaussian normal diffusion at short times and long-time power-law decay of mobile mass accompanied by anomalous diffusion at long times. The long-time diffusion is subdiffusive in the advection-free setting, while it is either subdiffusive for 0<mu<1/2 or superdiffusive for 1/2<mu<1 when advection is present. In the long-time limit we show equivalence of our model to a bi-fractional diffusion equation.},
  language  = {en}
}
@phdthesis{Banerjee2022,
  author    = {Banerjee, Abhirup},
  title     = {Characterizing the spatio-temporal patterns of extreme events},
  doi       = {10.25932/publishup-55983},
  url       = {http://nbn-resolving.de/urn:nbn:de:kobv:517-opus4-559839},
  school      = {Universit{\"a}t Potsdam},
  pages     = {xiv, 91},
  year      = {2022},
  abstract  = {Over the past decades, there has been a growing interest in 'extreme events' owing to the increasing threats that climate-related extremes such as floods, heatwaves, droughts, etc., pose to society. While extreme events have diverse definitions across various disciplines, ranging from earth science to neuroscience, they are characterized mainly as dynamic occurrences within a limited time frame that impedes the normal functioning of a system. Although extreme events are rare in occurrence, it has been found in various hydro-meteorological and physiological time series (e.g., river flows, temperatures, heartbeat intervals) that they may exhibit recurrent behavior, i.e., do not end the lifetime of the system. The aim of this thesis to develop some sophisticated methods to study various properties of extreme events. One of the main challenges in analyzing such extreme event-like time series is that they have large temporal gaps due to the paucity of the number of observations of extreme events. As a result, existing time series analysis tools are usually not helpful to decode the underlying information. I use the edit distance (ED) method to analyze extreme event-like time series in their unaltered form. ED is a specific distance metric, mainly designed to measure the similarity/dissimilarity between point process-like data. I combine ED with recurrence plot techniques to identify the recurrence property of flood events in the Mississippi River in the United States. I also use recurrence quantification analysis to show the deterministic properties and serial dependency in flood events. After that, I use this non-linear similarity measure (ED) to compute the pairwise dependency in extreme precipitation event series. I incorporate the similarity measure within the framework of complex network theory to study the collective behavior of climate extremes. Under this architecture, the nodes are defined by the spatial grid points of the given spatio-temporal climate dataset. Each node is associated with a time series corresponding to the temporal evolution of the climate observation at that grid point. Finally, the network links are functions of the pairwise statistical interdependence between the nodes. Various network measures, such as degree, betweenness centrality, clustering coefficient, etc., can be used to quantify the network's topology. We apply the methodology mentioned above to study the spatio-temporal coherence pattern of extreme rainfall events in the United States and the Ganga River basin, which reveals its relation to various climate processes and the orography of the region. The identification of precursors associated with the occurrence of extreme events in the near future is extremely important to prepare the masses for an upcoming disaster and mitigate the potential risks associated with such events. Under this motivation, I propose an in-data prediction recipe for predicting the data structures that typically occur prior to extreme events using the Echo state network, a type of Recurrent Neural Network which is a part of the reservoir computing framework. However, unlike previous works that identify precursory structures in the same variable in which extreme events are manifested (active variable), I try to predict these structures by using data from another dynamic variable (passive variable) which does not show large excursions from the nominal condition but carries imprints of these extreme events. Furthermore, my results demonstrate that the quality of prediction depends on the magnitude of events, i.e., the higher the magnitude of the extreme, the better is its predictability skill. I show quantitatively that this is because the input signals collectively form a more coherent pattern for an extreme event of higher magnitude, which enhances the efficiency of the machine to predict the forthcoming extreme events.},
  language  = {en}
}
@phdthesis{Aue2024,
  author    = {Aue, Lars},
  title     = {Cyclone impacts on sea ice in the Atlantic Arctic Ocean},
  doi       = {10.25932/publishup-63445},
  url       = {http://nbn-resolving.de/urn:nbn:de:kobv:517-opus4-634458},
  school      = {Universit{\"a}t Potsdam},
  pages     = {VIII, 131},
  year      = {2024},
  abstract  = {The Arctic is the hot spot of the ongoing, global climate change. Over the last decades, near-surface temperatures in the Arctic have been rising almost four times faster than on global average. This amplified warming of the Arctic and the associated rapid changes of its environment are largely influenced by interactions between individual components of the Arctic climate system. On daily to weekly time scales, storms can have major impacts on the Arctic sea-ice cover and are thus an important part of these interactions within the Arctic climate. The sea-ice impacts of storms are related to high wind speeds, which enhance the drift and deformation of sea ice, as well as to changes in the surface energy budget in association with air mass advection, which impact the seasonal sea-ice growth and melt. The occurrence of storms in the Arctic is typically associated with the passage of transient cyclones. Even though the above described mechanisms how storms/cyclones impact the Arctic sea ice are in principal known, there is a lack of statistical quantification of these effects. In accordance with that, the overarching objective of this thesis is to statistically quantify cyclone impacts on sea-ice concentration (SIC) in the Atlantic Arctic Ocean over the last four decades. In order to further advance the understanding of the related mechanisms, an additional objective is to separate dynamic and thermodynamic cyclone impacts on sea ice and assess their relative importance. Finally, this thesis aims to quantify recent changes in cyclone impacts on SIC. These research objectives are tackled utilizing various data sets, including atmospheric and oceanic reanalysis data as well as a coupled model simulation and a cyclone tracking algorithm. Results from this thesis demonstrate that cyclones are significantly impacting SIC in the Atlantic Arctic Ocean from autumn to spring, while there are mostly no significant impacts in summer. The strength and the sign (SIC decreasing or SIC increasing) of the cyclone impacts strongly depends on the considered daily time scale and the region of the Atlantic Arctic Ocean. Specifically, an initial decrease in SIC (day -3 to day 0 relative to the cyclone) is found in the Greenland, Barents and Kara Seas, while SIC increases following cyclones (day 0 to day 5 relative to the cyclone) are mostly limited to the Barents and Kara Seas. For the cold season, this results in a pronounced regional difference between overall (day -3 to day 5 relative to the cyclone) SIC-decreasing cyclone impacts in the Greenland Sea and overall SIC-increasing cyclone impacts in the Barents and Kara Seas. A cyclone case study based on a coupled model simulation indicates that both dynamic and thermodynamic mechanisms contribute to cyclone impacts on sea ice in winter. A typical pattern consisting of an initial dominance of dynamic sea-ice changes followed by enhanced thermodynamic ice growth after the cyclone passage was found. This enhanced ice growth after the cyclone passage most likely also explains the (statistical) overall SIC-increasing effects of cyclones in the Barents and Kara Seas in the cold season. Significant changes in cyclone impacts on SIC over the last four decades have emerged throughout the year. These recent changes are strongly varying from region to region and month to month. The strongest trends in cyclone impacts on SIC are found in autumn in the Barents and Kara Seas. Here, the magnitude of destructive cyclone impacts on SIC has approximately doubled over the last four decades. The SIC-increasing effects following the cyclone passage have particularly weakened in the Barents Sea in autumn. As a consequence, previously existing overall SIC-increasing cyclone impacts in this region in autumn have recently disappeared. Generally, results from this thesis show that changes in the state of the sea-ice cover (decrease in mean sea-ice concentration and thickness) and near-surface air temperature are most important for changed cyclone impacts on SIC, while changes in cyclone properties (i.e. intensity) do not play a significant role.},
  language  = {en}
}
@phdthesis{Kraemer2021,
  author    = {Kr{\"a}mer, Kai Hauke},
  title     = {Towards a robust framework for recurrence analysis},
  doi       = {10.25932/publishup-53874},
  url       = {http://nbn-resolving.de/urn:nbn:de:kobv:517-opus4-538743},
  school      = {Universit{\"a}t Potsdam},
  pages     = {xlii, 217},
  year      = {2021},
  abstract  = {In our daily life, recurrence plays an important role on many spatial and temporal scales and in different contexts. It is the foundation of learning, be it in an evolutionary or in a neural context. It therefore seems natural that recurrence is also a fundamental concept in theoretical dynamical systems science. The way in which states of a system recur or develop in a similar way from similar initial states makes it possible to infer information about the underlying dynamics of the system. The mathematical space in which we define the state of a system (state space) is often high dimensional, especially in complex systems that can also exhibit chaotic dynamics. The recurrence plot (RP) enables us to visualize the recurrences of any high-dimensional systems in a two-dimensional, binary representation. Certain patterns in RPs can be related to physical properties of the underlying system, making the qualitative and quantitative analysis of RPs an integral part of nonlinear systems science. The presented work has a methodological focus and further develops recurrence analysis (RA) by addressing current research questions related to an increasing amount of available data and advances in machine learning techniques. By automatizing a central step in RA, namely the reconstruction of the state space from measured experimental time series, and by investigating the impact of important free parameters this thesis aims to make RA more accessible to researchers outside of physics. The first part of this dissertation is concerned with the reconstruction of the state space from time series. To this end, a novel idea is proposed which automates the reconstruction problem in the sense that there is no need to preprocesse the data or estimate parameters a priori. The key idea is that the goodness of a reconstruction can be evaluated by a suitable objective function and that this function is minimized in the embedding process. In addition, the new method can process multivariate time series input data. This is particularly important because multi-channel sensor-based observations are ubiquitous in many research areas and continue to increase. Building on this, the described minimization problem of the objective function is then processed using a machine learning approach. In the second part technical and methodological aspects of RA are discussed. First, we mathematically justify the idea of setting the most influential free parameter in RA, the recurrence threshold ε, in relation to the distribution of all pairwise distances in the data. This is especially important when comparing different RPs and their quantification statistics and is fundamental to any comparative study. Second, some aspects of recurrence quantification analysis (RQA) are examined. As correction schemes for biased RQA statistics, which are based on diagonal lines, we propose a simple method for dealing with border effects of an RP in RQA and a skeletonization algorithm for RPs. This results in less biased (diagonal line based) RQA statistics for flow-like data. Third, a novel type of RQA characteristic is developed, which can be viewed as a generalized non-linear powerspectrum of high dimensional systems. The spike powerspectrum transforms a spike-train like signal into its frequency domain. When transforming the diagonal line-dependent recurrence rate (τ-RR) of a RP in this way, characteristic periods, which can be seen in the state space representation of the system can be unraveled. This is not the case, when Fourier transforming τ-RR. Finally, RA and RQA are applied to climate science in the third part and neuroscience in the fourth part. To the best of our knowledge, this is the first time RPs and RQA have been used to analyze lake sediment data in a paleoclimate context. Therefore, we first elaborate on the basic formalism and the interpretation of visually visible patterns in RPs in relation to the underlying proxy data. We show that these patterns can be used to classify certain types of variability and transitions in the Potassium record from six short (< 17m) sediment cores collected during the Chew Bahir Drilling Project. Building on this, the long core (∼ m composite) from the same site is analyzed and two types of variability and transitions are identified and compared with ODP Site  wetness index from the eastern Mediterranean. Type  variability likely reflects the influence of precessional forcing in the lower latitudes at times of maximum values of the long eccentricity cycle ( kyr) of the earth's orbit around the sun, with a tendency towards extreme events. Type  variability appears to be related to the minimum values of this cycle and corresponds to fairly rapid transitions between relatively dry and relatively wet conditions. In contrast, RQA has been applied in the neuroscientific context for almost two decades. In the final part, RQA statistics are used to quantify the complexity in a specific frequency band of multivariate EEG (electroencephalography) data. By analyzing experimental data, it can be shown that the complexity of the signal measured in this way across the sensorimotor cortex decreases as motor tasks are performed. The results are consistent with and comple- ment the well known concepts of motor-related brain processes. We assume that the thus discovered features of neuronal dynamics in the sensorimotor cortex together with the robust RQA methods for identifying and classifying these contribute to the non-invasive EEG-based development of brain-computer interfaces (BCI) for motor control and rehabilitation. The present work is an important step towards a robust analysis of complex systems based on recurrence.},
  language  = {en}
}
@phdthesis{Braun2023,
  author    = {Braun, Tobias},
  title     = {Recurrences in past climates},
  doi       = {10.25932/publishup-58690},
  url       = {http://nbn-resolving.de/urn:nbn:de:kobv:517-opus4-586900},
  school      = {Universit{\"a}t Potsdam},
  pages     = {xxviii, 251},
  year      = {2023},
  abstract  = {Our ability to predict the state of a system relies on its tendency to recur to states it has visited before. Recurrence also pervades common intuitions about the systems we are most familiar with: daily routines, social rituals and the return of the seasons are just a few relatable examples. To this end, recurrence plots (RP) provide a systematic framework to quantify the recurrence of states. Despite their conceptual simplicity, they are a versatile tool in the study of observational data. The global climate is a complex system for which an understanding based on observational data is not only of academical relevance, but vital for the predurance of human societies within the planetary boundaries. Contextualizing current global climate change, however, requires observational data far beyond the instrumental period. The palaeoclimate record offers a valuable archive of proxy data but demands methodological approaches that adequately address its complexities. In this regard, the following dissertation aims at devising novel and further developing existing methods in the framework of recurrence analysis (RA). The proposed research questions focus on using RA to capture scale-dependent properties in nonlinear time series and tailoring recurrence quantification analysis (RQA) to characterize seasonal variability in palaeoclimate records ('Palaeoseasonality'). In the first part of this thesis, we focus on the methodological development of novel approaches in RA. The predictability of nonlinear (palaeo)climate time series is limited by abrupt transitions between regimes that exhibit entirely different dynamical complexity (e.g. crossing of 'tipping points'). These possibly depend on characteristic time scales. RPs are well-established for detecting transitions and capture scale-dependencies, yet few approaches have combined both aspects. We apply existing concepts from the study of self-similar textures to RPs to detect abrupt transitions, considering the most relevant time scales. This combination of methods further results in the definition of a novel recurrence based nonlinear dependence measure. Quantifying lagged interactions between multiple variables is a common problem, especially in the characterization of high-dimensional complex systems. The proposed 'recurrence flow' measure of nonlinear dependence offers an elegant way to characterize such couplings. For spatially extended complex systems, the coupled dynamics of local variables result in the emergence of spatial patterns. These patterns tend to recur in time. Based on this observation, we propose a novel method that entails dynamically distinct regimes of atmospheric circulation based on their recurrent spatial patterns. Bridging the two parts of this dissertation, we next turn to methodological advances of RA for the study of Palaeoseasonality. Observational series of palaeoclimate 'proxy' records involve inherent limitations, such as irregular temporal sampling. We reveal biases in the RQA of time series with a non-stationary sampling rate and propose a correction scheme. In the second part of this thesis, we proceed with applications in Palaeoseasonality. A review of common and promising time series analysis methods shows that numerous valuable tools exist, but their sound application requires adaptions to archive-specific limitations and consolidating transdisciplinary knowledge. Next, we study stalagmite proxy records from the Central Pacific as sensitive recorders of mid-Holocene El Ni{\~n}o-Southern Oscillation (ENSO) dynamics. The records' remarkably high temporal resolution allows to draw links between ENSO and seasonal dynamics, quantified by RA. The final study presented here examines how seasonal predictability could play a role for the stability of agricultural societies. The Classic Maya underwent a period of sociopolitical disintegration that has been linked to drought events. Based on seasonally resolved stable isotope records from Yok Balum cave in Belize, we propose a measure of seasonal predictability. It unveils the potential role declining seasonal predictability could have played in destabilizing agricultural and sociopolitical systems of Classic Maya populations. The methodological approaches and applications presented in this work reveal multiple exciting future research avenues, both for RA and the study of Palaeoseasonality.},
  language  = {en}
}
@phdthesis{Roland2017,
  author    = {Roland, Steffen},
  title     = {Charge carrier recombination and open circuit voltage in organic solar cells},
  url       = {http://nbn-resolving.de/urn:nbn:de:kobv:517-opus4-397721},
  school      = {Universit{\"a}t Potsdam},
  pages     = {VI, 145},
  year      = {2017},
  abstract  = {Tremendous progress in the development of thin film solar cell techniques has been made over the last decade. The field of organic solar cells is constantly developing, new material classes like Perowskite solar cells are emerging and different types of hybrid organic/inorganic material combinations are being investigated for their physical properties and their applicability in thin film electronics. Besides typical single-junction architectures for solar cells, multi-junction concepts are also being investigated as they enable the overcoming of theoretical limitations of a single-junction. In multi-junction devices each sub-cell operates in different wavelength regimes and should exhibit optimized band-gap energies. It is exactly this tunability of the band-gap energy that renders organic solar cell materials interesting candidates for multi-junction applications. Nevertheless, only few attempts have been made to combine inorganic and organic solar cells in series connected multi-junction architectures. Even though a great diversity of organic solar cells exists nowadays, their open circuit voltage is usually low compared to the band-gap of the active layer. Hence, organic low band-gap solar cells in particular show low open circuit voltages and the key factors that determine the voltage losses are not yet fully understood. Besides open circuit voltage losses the recombination of charges in organic solar cells is also a prevailing research topic, especially with respect to the influence of trap states. The exploratory focus of this work is therefore set, on the one hand, on the development of hybrid organic/inorganic multi-junctions and, on the other hand, on gaining a deeper understanding of the open circuit voltage and the recombination processes of organic solar cells. In the first part of this thesis, the development of a hybrid organic/inorganic triple-junction will be discussed which showed at that time (Jan. 2015) a record power conversion efficiency of 11.7\%. The inorganic sub-cells of these devices consist of hydrogenated amorphous silicon and were delivered by the Competence Center Thin-Film and Nanotechnology for Photovoltaics in Berlin. Different recombination contacts and organic sub-cells were tested in conjunction with these inorganic sub-cells on the basis of optical modeling predictions for the optimal layer thicknesses to finally reach record efficiencies for this type of solar cells. In the second part, organic model systems will be investigated to gain a better understanding of the fundamental loss mechanisms that limit the open circuit voltage of organic solar cells. First, bilayer systems with different orientation of the donor and acceptor molecules were investigated to study the influence of the donor/acceptor orientation on non-radiative voltage loss. Secondly, three different bulk heterojunction solar cells all comprising the same amount of fluorination and the same polymer backbone in the donor component were examined to study the influence of long range electrostatics on the open circuit voltage. Thirdly, the device performance of two bulk heterojunction solar cells was compared which consisted of the same donor polymer but used different fullerene acceptor molecules. By this means, the influence of changing the energetics of the acceptor component on the open circuit voltage was investigated and a full analysis of the charge carrier dynamics was presented to unravel the reasons for the worse performance of the solar cell with the higher open circuit voltage. In the third part, a new recombination model for organic solar cells will be introduced and its applicability shown for a typical low band-gap cell. This model sheds new light on the recombination process in organic solar cells in a broader context as it re-evaluates the recombination pathway of charge carriers in devices which show the presence of trap states. Thereby it addresses a current research topic and helps to resolve alleged discrepancies which can arise from the interpretation of data derived by different measurement techniques.},
  language  = {en}
}
@phdthesis{Kotha2018,
  author    = {Kotha, Sreeram Reddy},
  title     = {Quantification of uncertainties in seismic ground-motion prediction},
  url       = {http://nbn-resolving.de/urn:nbn:de:kobv:517-opus4-415743},
  school      = {Universit{\"a}t Potsdam},
  pages     = {xii, 101},
  year      = {2018},
  abstract  = {The purpose of Probabilistic Seismic Hazard Assessment (PSHA) at a construction site is to provide the engineers with a probabilistic estimate of ground-motion level that could be equaled or exceeded at least once in the structure's design lifetime. A certainty on the predicted ground-motion allows the engineers to confidently optimize structural design and mitigate the risk of extensive damage, or in worst case, a collapse. It is therefore in interest of engineering, insurance, disaster mitigation, and security of society at large, to reduce uncertainties in prediction of design ground-motion levels. In this study, I am concerned with quantifying and reducing the prediction uncertainty of regression-based Ground-Motion Prediction Equations (GMPEs). Essentially, GMPEs are regressed best-fit formulae relating event, path, and site parameters (predictor variables) to observed ground-motion values at the site (prediction variable). GMPEs are characterized by a parametric median (μ) and a non-parametric variance (σ) of prediction. μ captures the known ground-motion physics i.e., scaling with earthquake rupture properties (event), attenuation with distance from source (region/path), and amplification due to local soil conditions (site); while σ quantifies the natural variability of data that eludes μ. In a broad sense, the GMPE prediction uncertainty is cumulative of 1) uncertainty on estimated regression coefficients (uncertainty on μ,σ_μ), and 2) the inherent natural randomness of data (σ). The extent of μ parametrization, the quantity, and quality of ground-motion data used in a regression, govern the size of its prediction uncertainty: σ_μ and σ. In the first step, I present the impact of μ parametrization on the size of σ_μ and σ. Over-parametrization appears to increase the σ_μ, because of the large number of regression coefficients (in μ) to be estimated with insufficient data. Under-parametrization mitigates σ_μ, but the reduced explanatory strength of μ is reflected in inflated σ. For an optimally parametrized GMPE, a ~10\% reduction in σ is attained by discarding the low-quality data from pan-European events with incorrect parametric values (of predictor variables). In case of regions with scarce ground-motion recordings, without under-parametrization, the only way to mitigate σ_μ is to substitute long-term earthquake data at a location with short-term samples of data across several locations - the Ergodic Assumption. However, the price of ergodic assumption is an increased σ, due to the region-to-region and site-to-site differences in ground-motion physics. σ of an ergodic GMPE developed from generic ergodic dataset is much larger than that of non-ergodic GMPEs developed from region- and site-specific non-ergodic subsets - which were too sparse to produce their specific GMPEs. Fortunately, with the dramatic increase in recorded ground-motion data at several sites across Europe and Middle-East, I could quantify the region- and site-specific differences in ground-motion scaling and upgrade the GMPEs with 1) substantially more accurate region- and site-specific μ for sites in Italy and Turkey, and 2) significantly smaller prediction variance σ. The benefit of such enhancements to GMPEs is quite evident in my comparison of PSHA estimates from ergodic versus region- and site-specific GMPEs; where the differences in predicted design ground-motion levels, at several sites in Europe and Middle-Eastern regions, are as large as ~50\%. Resolving the ergodic assumption with mixed-effects regressions is feasible when the quantified region- and site-specific effects are physically meaningful, and the non-ergodic subsets (regions and sites) are defined a priori through expert knowledge. In absence of expert definitions, I demonstrate the potential of machine learning techniques in identifying efficient clusters of site-specific non-ergodic subsets, based on latent similarities in their ground-motion data. Clustered site-specific GMPEs bridge the gap between site-specific and fully ergodic GMPEs, with their partially non-ergodic μ and, σ ~15\% smaller than the ergodic variance. The methodological refinements to GMPE development produced in this study are applicable to new ground-motion datasets, to further enhance certainty of ground-motion prediction and thereby, seismic hazard assessment. Advanced statistical tools show great potential in improving the predictive capabilities of GMPEs, but the fundamental requirement remains: large quantity of high-quality ground-motion data from several sites for an extended time-period.},
  language  = {en}
}
@phdthesis{Eickelmann2018,
  author    = {Eickelmann, Stephan Felix},
  title     = {Experimental Study of Liquid Interfaces with Compositional Gradients},
  doi       = {10.17617/2.3010222},
  school      = {Universit{\"a}t Potsdam},
  pages     = {153},
  year      = {2018},
  abstract  = {Der Inhalt dieser Arbeit ist die experimentelle Untersuchung von verdunstenen d{\"u}nnen Filmen auf glatten Oberfl{\"a}chen, und die Anreicherung, das Kristallwachstum so wie Marangoni-Fluss in der N{\"a}he der Dreiphasenlinie bei partiell benetzenden Mischungen aus fl{\"u}chtigen und nichtfl{\"u}chtigen Fl{\"u}ssigkeiten. Im Detail werden die Eigenschaften von planaren Fl{\"u}ssigkeitsfilmen und d{\"u}nnen Fl{\"u}ssigkeitsabschnitten in der N{\"a}he der dreiphasigen Kontaktlinie behandelt. In beiden F{\"a}llen verliert die Fl{\"u}ssigkeit kontinuierlich eine Komponente durch Verdampfung. Ein Thema ist das ntnetzungsveralten ultrad{\"u}nner Filme aus bin{\"a}ren Mischungen eines fl{\"u}chtigen L{\"o}sungsmittels und eines nichtfl{\"u}chtigen Stoffes. Dabei wird analysiert wie die Dicke, bei der der Film reißt, mit der Kristallisation des gel{\"o}sten Stoffes an der Grenzfl{\"a}che zwischen Fl{\"u}ssigkeit und Substrat in Verbindung steht, sobald der gel{\"o}ste Stoff seine {\"U}bers{\"a}ttigung erreicht. Die Resultate dieses Projektes zeigen eine universelle Beziehung zwischen der Entnetzungdicke und dem S{\"a}ttigungsverhalten. Das zweite Forschungsgebiet sind einzelne Nanopartikel, die in molekular d{\"u}nne Filme auf planaren Substraten eingebettet sind. Es zeigt sich, dass die Nanopartikel eine unerwartet große Filmoberfl{\"a}chenverzerrung (Meniskus) verursachen. Diese Verzerrung kann durch herk{\"o}mmliche Reflexionsmikroskopie quantitativ gemessen werden, obwohl die Nanopartikel viel kleiner als die Rayleigh-Beugungsgrenze sind. Untersuchungen mit bin{\"a}ren Mischungen fl{\"u}chtiger L{\"o}sungsmittel und nichtfl{\"u}chtiger Stoffe (Polymere) zielen auf ein besseres Verst{\"a}ndnis/Vorhersage der finalen Schichtdicke, zeitaufgel{\"o}sten Verd{\"u}nnung, zeitaufgel{\"o}sten Verdunstung und der Entwicklung der Konzentration des gel{\"o}sten Stoffes innerhalb des verd{\"u}nnenden Filmes. Eine quantitative theoretische Beschreibung der experimentellen Ergebnisse wird hergeleitet. Unerwarteterweise zeigen die Experimente, mit vollst{\"a}ndig mischbarer bin{\"a}rer Mischungen fl{\"u}chtiger Fl{\"u}ssigkeiten, die einzeln glatte Filme bilden, dass Filme dieser Mischungen nicht notwendigerweise kontinuierlich und glatt sind. Vielmehr k{\"o}nnen sie Oberfl{\"a}chenwellen bilden oder sogar aufreißen. Dies wird mit Oberfl{\"a}chen-Marangoni-Str{\"o}mungen erkl{\"a}rt. Es wird eine neue Methode f{\"u}r die schnelle Herstellung von ultralang gerichtetn Diphenylalanin-Einkristallen (Dip- Casting) (mm/min) vorgestellt. Dabei viii wird gezeigt, wie die spezifischen Verdunstungsbedingen an der Dreiphasenlinie f{\"u}r einen kontrollierten Peptidkristallwachstumsprozess verwendet werden k{\"o}nnen. Abschließend wird gezeigt, wie die Beschr{\"a}nkung innerhalb einer kleinen Kapillare die Peptidkristallisation beeinflusst, diese verstanden und verwendet werden kann.},
  language  = {en}
}