@phdthesis{Mazzanti2022,
  author    = {Mazzanti, Stefano},
  title     = {Novel photocatalytic processes mediated by carbon nitride photocatalysis},
  doi       = {10.25932/publishup-54209},
  url       = {http://nbn-resolving.de/urn:nbn:de:kobv:517-opus4-542099},
  school      = {Universit{\"a}t Potsdam},
  pages     = {418},
  year      = {2022},
  abstract  = {The key to reduce the energy required for specific transformations in a selective manner is the employment of a catalyst, a very small molecular platform that decides which type of energy to use. The field of photocatalysis exploits light energy to shape one type of molecules into others, more valuable and useful. However, many challenges arise in this field, for example, catalysts employed usually are based on metal derivatives, which abundance is limited, they cannot be recycled and are expensive. Therefore, carbon nitrides materials are used in this work to expand horizons in the field of photocatalysis. Carbon nitrides are organic materials, which can act as recyclable, cheap, non-toxic, heterogeneous photocatalysts. In this thesis, they have been exploited for the development of new catalytic methods, and shaped to develop new types of processes. Indeed, they enabled the creation of a new photocatalytic synthetic strategy, the dichloromethylation of enones by dichloromethyl radical generated in situ from chloroform, a novel route for the making of building blocks to be used for the productions of active pharmaceutical compounds. Then, the ductility of these materials allowed to shape carbon nitride into coating for lab vials, EPR capillaries, and a cell of a flow reactor showing the great potential of such flexible technology in photocatalysis. Afterwards, their ability to store charges has been exploited in the reduction of organic substrates under dark conditions, gaining new insights regarding multisite proton coupled electron transfer processes. Furthermore, the combination of carbon nitrides with flavins allowed the development of composite materials with improved photocatalytic activity in the CO2 photoreduction. Concluding, carbon nitrides are a versatile class of photoactive materials, which may help to unveil further scientific discoveries and to develop a more sustainable future.},
  language  = {en}
}
@phdthesis{Simsek2022,
  author    = {Simsek, Ibrahim},
  title     = {Ink-based preparation of chalcogenide perovskites as thin films for PV applications},
  doi       = {10.25932/publishup-57271},
  url       = {http://nbn-resolving.de/urn:nbn:de:kobv:517-opus4-572711},
  school      = {Universit{\"a}t Potsdam},
  pages     = {iv, 113},
  year      = {2022},
  abstract  = {The increasing demand for energy in the current technological era and the recent political decisions about giving up on nuclear energy diverted humanity to focus on alternative environmentally friendly energy sources like solar energy. Although silicon solar cells are the product of a matured technology, the search for highly efficient and easily applicable materials is still ongoing. These properties made the efficiency of halide perovskites comparable with silicon solar cells for single junctions within a decade of research. However, the downside of halide perovskites are poor stability and lead toxicity for the most stable ones. On the other hand, chalcogenide perovskites are one of the most promising absorber materials for the photovoltaic market, due to their elemental abundance and chemical stability against moisture and oxygen. In the search of the ultimate solar absorber material, combining the good optoelectronic properties of halide perovskites with the stability of chalcogenides could be the promising candidate. Thus, this work investigates new techniques for the synthesis and design of these novel chalcogenide perovskites, that contain transition metals as cations, e.g., BaZrS3, BaHfS3, EuZrS3, EuHfS3 and SrHfS3. There are two stages in the deposition techniques of this study: In the first stage, the binary compounds are deposited via a solution processing method. In the second stage, the deposited materials are annealed in a chalcogenide atmosphere to form the perovskite structure by using solid-state reactions. The research also focuses on the optimization of a generalized recipe for a molecular ink to deposit precursors of chalcogenide perovskites with different binaries. The implementation of the precursor sulfurization resulted in either binaries without perovskite formation or distorted perovskite structures, whereas some of these materials are reported in the literature as they are more favorable in the needle-like non-perovskite configuration. Lastly, there are two categories for the evaluation of the produced materials: The first category is about the determination of the physical properties of the deposited layer, e.g., crystal structure, secondary phase formation, impurities, etc. For the second category, optoelectronic properties are measured and compared to an ideal absorber layer, e.g., band gap, conductivity, surface photovoltage, etc.},
  language  = {en}
}
@phdthesis{Brill2022,
  author    = {Brill, Fabio Alexander},
  title     = {Applications of machine learning and open geospatial data in flood risk modelling},
  doi       = {10.25932/publishup-55594},
  url       = {http://nbn-resolving.de/urn:nbn:de:kobv:517-opus4-555943},
  school      = {Universit{\"a}t Potsdam},
  pages     = {xix, 124},
  year      = {2022},
  abstract  = {Der technologische Fortschritt erlaubt es, zunehmend komplexe Vorhersagemodelle auf Basis immer gr{\"o}ßerer Datens{\"a}tze zu produzieren. F{\"u}r das Risikomanagement von Naturgefahren sind eine Vielzahl von Modellen als Entscheidungsgrundlage notwendig, z.B. in der Auswertung von Beobachtungsdaten, f{\"u}r die Vorhersage von Gefahrenszenarien, oder zur statistischen Absch{\"a}tzung der zu erwartenden Sch{\"a}den. Es stellt sich also die Frage, inwiefern moderne Modellierungsans{\"a}tze wie das maschinelle Lernen oder Data-Mining in diesem Themenbereich sinnvoll eingesetzt werden k{\"o}nnen. Zus{\"a}tzlich ist im Hinblick auf die Datenverf{\"u}gbarkeit und -zug{\"a}nglichkeit ein Trend zur {\"O}ffnung (open data) zu beobachten. Thema dieser Arbeit ist daher, die M{\"o}glichkeiten und Grenzen des maschinellen Lernens und frei verf{\"u}gbarer Geodaten auf dem Gebiet der Hochwasserrisikomodellierung im weiteren Sinne zu untersuchen. Da dieses {\"u}bergeordnete Thema sehr breit ist, werden einzelne relevante Aspekte herausgearbeitet und detailliert betrachtet. Eine prominente Datenquelle im Bereich Hochwasser ist die satellitenbasierte Kartierung von {\"U}berflutungsfl{\"a}chen, die z.B. {\"u}ber den Copernicus Service der Europ{\"a}ischen Union frei zur Verf{\"u}gung gestellt werden. Große Hoffnungen werden in der wissenschaftlichen Literatur in diese Produkte gesetzt, sowohl f{\"u}r die akute Unterst{\"u}tzung der Einsatzkr{\"a}fte im Katastrophenfall, als auch in der Modellierung mittels hydrodynamischer Modelle oder zur Schadensabsch{\"a}tzung. Daher wurde ein Fokus in dieser Arbeit auf die Untersuchung dieser Flutmasken gelegt. Aus der Beobachtung, dass die Qualit{\"a}t dieser Produkte in bewaldeten und urbanen Gebieten unzureichend ist, wurde ein Verfahren zur nachtr{\"a}glichenVerbesserung mittels maschinellem Lernen entwickelt. Das Verfahren basiert auf einem Klassifikationsalgorithmus der nur Trainingsdaten von einer vorherzusagenden Klasse ben{\"o}tigt, im konkreten Fall also Daten von {\"U}berflutungsfl{\"a}chen, nicht jedoch von der negativen Klasse (trockene Gebiete). Die Anwendung f{\"u}r Hurricane Harvey in Houston zeigt großes Potenzial der Methode, abh{\"a}ngig von der Qualit{\"a}t der urspr{\"u}nglichen Flutmaske. Anschließend wird anhand einer prozessbasierten Modellkette untersucht, welchen Einfluss implementierte physikalische Prozessdetails auf das vorhergesagte statistische Risiko haben. Es wird anschaulich gezeigt, was eine Risikostudie basierend auf etablierten Modellen leisten kann. Solche Modellketten sind allerdings bereits f{\"u}r Flusshochwasser sehr komplex, und f{\"u}r zusammengesetzte oder kaskadierende Ereignisse mit Starkregen, Sturzfluten, und weiteren Prozessen, kaum vorhanden. Im vierten Kapitel dieser Arbeit wird daher getestet, ob maschinelles Lernen auf Basis von vollst{\"a}ndigen Schadensdaten einen direkteren Weg zur Schadensmodellierung erm{\"o}glicht, der die explizite Konzeption einer solchen Modellkette umgeht. Dazu wird ein staatlich erhobener Datensatz der gesch{\"a}digten Geb{\"a}ude w{\"a}hrend des schweren El Ni{\~n}o Ereignisses 2017 in Peru verwendet. In diesem Kontext werden auch die M{\"o}glichkeiten des Data-Mining zur Extraktion von Prozessverst{\"a}ndnis ausgelotet. Es kann gezeigt werden, dass diverse frei verf{\"u}gbare Geodaten n{\"u}tzliche Informationen f{\"u}r die Gefahren- und Schadensmodellierung von komplexen Flutereignissen liefern, z.B. satellitenbasierte Regenmessungen, topographische und hydrographische Information, kartierte Siedlungsfl{\"a}chen, sowie Indikatoren aus Spektraldaten. Zudem zeigen sich Erkenntnisse zu den Sch{\"a}digungsprozessen, die im Wesentlichen mit den vorherigen Erwartungen in Einklang stehen. Die maximale Regenintensit{\"a}t wirkt beispielsweise in St{\"a}dten und steilen Schluchten st{\"a}rker sch{\"a}digend, w{\"a}hrend die Niederschlagssumme in tiefliegenden Flussgebieten und bewaldeten Regionen als aussagekr{\"a}ftiger befunden wurde. L{\"a}ndliche Gebiete in Peru weisen in der pr{\"a}sentierten Studie eine h{\"o}here Vulnerabilit{\"a}t als die Stadtgebiete auf. Jedoch werden auch die grunds{\"a}tzlichen Grenzen der Methodik und die Abh{\"a}ngigkeit von spezifischen Datens{\"a}tzen and Algorithmen offenkundig. In der {\"u}bergreifenden Diskussion werden schließlich die verschiedenen Methoden - prozessbasierte Modellierung, pr{\"a}diktives maschinelles Lernen, und Data-Mining - mit Blick auf die Gesamtfragestellungen evaluiert. Im Bereich der Gefahrenbeobachtung scheint eine Fokussierung auf neue Algorithmen sinnvoll. Im Bereich der Gefahrenmodellierung, insbesondere f{\"u}r Flusshochwasser, wird eher die Verbesserung von physikalischen Modellen, oder die Integration von prozessbasierten und statistischen Verfahren angeraten. In der Schadensmodellierung fehlen nach wie vor die großen repr{\"a}sentativen Datens{\"a}tze, die f{\"u}r eine breite Anwendung von maschinellem Lernen Voraussetzung ist. Daher ist die Verbesserung der Datengrundlage im Bereich der Sch{\"a}den derzeit als wichtiger einzustufen als die Auswahl der Algorithmen.},
  language  = {en}
}
@phdthesis{Jongejans2022,
  author    = {Jongejans, Loeka Laura},
  title     = {Organic matter stored in ice-rich permafrost},
  doi       = {10.25932/publishup-56491},
  url       = {http://nbn-resolving.de/urn:nbn:de:kobv:517-opus4-564911},
  school      = {Universit{\"a}t Potsdam},
  pages     = {xxiii, 178},
  year      = {2022},
  abstract  = {The Arctic is changing rapidly and permafrost is thawing. Especially ice-rich permafrost, such as the late Pleistocene Yedoma, is vulnerable to rapid and deep thaw processes such as surface subsidence after the melting of ground ice. Due to permafrost thaw, the permafrost carbon pool is becoming increasingly accessible to microbes, leading to increased greenhouse gas emissions, which enhances the climate warming. The assessment of the molecular structure and biodegradability of permafrost organic matter (OM) is highly needed. My research revolves around the question "how does permafrost thaw affect its OM storage?" More specifically, I assessed (1) how molecular biomarkers can be applied to characterize permafrost OM, (2) greenhouse gas production rates from thawing permafrost, and (3) the quality of OM of frozen and (previously) thawed sediments. I studied deep (max. 55 m) Yedoma and thawed Yedoma permafrost sediments from Yakutia (Sakha Republic). I analyzed sediment cores taken below thermokarst lakes on the Bykovsky Peninsula (southeast of the Lena Delta) and in the Yukechi Alas (Central Yakutia), and headwall samples from the permafrost cliff Sobo-Sise (Lena Delta) and the retrogressive thaw slump Batagay (Yana Uplands). I measured biomarker concentrations of all sediment samples. Furthermore, I carried out incubation experiments to quantify greenhouse gas production in thawing permafrost. I showed that the biomarker proxies are useful to assess the source of the OM and to distinguish between OM derived from terrestrial higher plants, aquatic plants and microbial activity. In addition, I showed that some proxies help to assess the degree of degradation of permafrost OM, especially when combined with sedimentological data in a multi-proxy approach. The OM of Yedoma is generally better preserved than that of thawed Yedoma sediments. The greenhouse gas production was highest in the permafrost sediments that thawed for the first time, meaning that the frozen Yedoma sediments contained most labile OM. Furthermore, I showed that the methanogenic communities had established in the recently thawed sediments, but not yet in the still-frozen sediments. My research provided the first molecular biomarker distributions and organic carbon turnover data as well as insights in the state and processes in deep frozen and thawed Yedoma sediments. These findings show the relevance of studying OM in deep permafrost sediments.},
  language  = {en}
}
@phdthesis{Siegmund2022,
  author    = {Siegmund, Nicole},
  title     = {Wind driven soil particle uptake Quantifying drivers of wind erosion across the particle size spectrum},
  doi       = {10.25932/publishup-57489},
  url       = {http://nbn-resolving.de/urn:nbn:de:kobv:517-opus4-574897},
  school      = {Universit{\"a}t Potsdam},
  pages     = {ix, 56},
  year      = {2022},
  abstract  = {Among the multitude of geomorphological processes, aeolian shaping processes are of special character, Pedogenic dust is one of the most important sources of atmospheric aerosols and therefore regarded as a key player for atmospheric processes. Soil dust emissions, being complex in composition and properties, influence atmospheric processes and air quality and has impacts on other ecosystems. In this because even though their immediate impact can be considered low (exceptions exist), their constant and large-scale force makes them a powerful player in the earth system. dissertation, we unravel a novel scientific understanding of this complex system based on a holistic dataset acquired during a series of field experiments on arable land in La Pampa, Argentina. The field experiments as well as the generated data provide information about topography, various soil parameters, the atmospheric dynamics in the very lower atmosphere (4m height) as well as measurements regarding aeolian particle movement across a wide range of particle size classes between 0.2μm up to the coarse sand. The investigations focus on three topics: (a) the effects of low-scale landscape structures on aeolian transport processes of the coarse particle fraction, (b) the horizontal and vertical fluxes of the very fine particles and (c) the impact of wind gusts on particle emissions. Among other considerations presented in this thesis, it could in particular be shown, that even though the small-scale topology does have a clear impact on erosion and deposition patterns, also physical soil parameters need to be taken into account for a robust statistical modelling of the latter. Furthermore, specifically the vertical fluxes of particulate matter have different characteristics for the particle size classes. Finally, a novel statistical measure was introduced to quantify the impact of wind gusts on the particle uptake and its application on the provided data set. The aforementioned measure shows significantly increased particle concentrations during points in time defined as gust event. With its holistic approach, this thesis further contributes to the fundamental understanding of how atmosphere and pedosphere are intertwined and affect each other.},
  language  = {en}
}
@phdthesis{Pellegrino2022,
  author    = {Pellegrino, Antonio},
  title     = {miRNA profiling for diagnosis of chronic pain in polyneuropathy},
  doi       = {10.25932/publishup-58385},
  url       = {http://nbn-resolving.de/urn:nbn:de:kobv:517-opus4-583858},
  school      = {Universit{\"a}t Potsdam},
  pages     = {viii, 97, xi},
  year      = {2022},
  abstract  = {This dissertation aimed to determine differential expressed miRNAs in the context of chronic pain in polyneuropathy. For this purpose, patients with chronic painful polyneuropathy were compared with age matched healthy patients. Taken together, all miRNA pre library preparation quality controls were successful and none of the samples was identified as an outlier or excluded for library preparation. Pre sequencing quality control showed that library preparation worked for all samples as well as that all samples were free of adapter dimers after BluePippin size selection and reached the minimum molarity for further processing. Thus, all samples were subjected to sequencing. The sequencing control parameters were in their optimal range and resulted in valid sequencing results with strong sample to sample correlation for all samples. The resulting FASTQ file of each miRNA library was analyzed and used to perform a differential expression analysis. The differentially expressed and filtered miRNAs were subjected to miRDB to perform a target prediction. Three of those four miRNAs were downregulated: hsa-miR-3135b, hsa-miR-584-5p and hsa-miR-12136, while one was upregulated: hsa-miR-550a-3p. miRNA target prediction showed that chronic pain in polyneuropathy might be the result of a combination of miRNA mediated high blood flow/pressure and neural activity dysregulations/disbalances. Thus, leading to the promising conclusion that these four miRNAs could serve as potential biomarkers for the diagnosis of chronic pain in polyneuropathy. Since TRPV1 seems to be one of the major contributors of nociception and is associated with neuropathic pain, the influence of PKA phosphorylated ARMS on the sensitivity of TRPV1 as well as the part of AKAP79 during PKA phosphorylation of ARMS was characterized. Therefore, possible PKA-sites in the sequence of ARMS were identified. This revealed five canonical PKA-sites: S882, T903, S1251/52, S1439/40 and S1526/27. The single PKA-site mutants of ARMS revealed that PKA-mediated ARMS phosphorylation seems not to influence the interaction rate of TRPV1/ARMS. While phosphorylation of ARMST903 does not increase the interaction rate with TRPV1, ARMSS1526/27 is probably not phosphorylated and leads to an increased interaction rate. The calcium flux measurements indicated that the higher the interaction rate of TRPV1/ARMS, the lower the EC50 for capsaicin of TRPV1, independent of the PKA phosphorylation status of ARMS. In addition, the western blot analysis confirmed the previously observed TRPV1/ARMS interaction. More importantly, AKAP79 seems to be involved in the TRPV1/ARMS/PKA signaling complex. To overcome the problem of ARMS-mediated TRPV1 sensitization by interaction, ARMS was silenced by shRNA. ARMS silencing resulted in a restored TRPV1 desensitization without affecting the TRPV1 expression and therefore could be used as new topical therapeutic analgesic alternative to stop ARMS mediated TRPV1 sensitization.},
  language  = {en}
}
@phdthesis{Zeuschner2022,
  author    = {Zeuschner, Steffen Peer},
  title     = {Magnetoacoustics observed with ultrafast x-ray diffraction},
  doi       = {10.25932/publishup-56109},
  url       = {http://nbn-resolving.de/urn:nbn:de:kobv:517-opus4-561098},
  school      = {Universit{\"a}t Potsdam},
  pages     = {V, 128, IX},
  year      = {2022},
  abstract  = {In the present thesis I investigate the lattice dynamics of thin film hetero structures of magnetically ordered materials upon femtosecond laser excitation as a probing and manipulation scheme for the spin system. The quantitative assessment of laser induced thermal dynamics as well as generated picosecond acoustic pulses and their respective impact on the magnetization dynamics of thin films is a challenging endeavor. All the more, the development and implementation of effective experimental tools and comprehensive models are paramount to propel future academic and technological progress. In all experiments in the scope of this cumulative dissertation, I examine the crystal lattice of nanoscale thin films upon the excitation with femtosecond laser pulses. The relative change of the lattice constant due to thermal expansion or picosecond strain pulses is directly monitored by an ultrafast X-ray diffraction (UXRD) setup with a femtosecond laser-driven plasma X-ray source (PXS). Phonons and spins alike exert stress on the lattice, which responds according to the elastic properties of the material, rendering the lattice a versatile sensor for all sorts of ultrafast interactions. On the one hand, I investigate materials with strong magneto-elastic properties; The highly magnetostrictive rare-earth compound TbFe2, elemental Dysprosium or the technological relevant Invar material FePt. On the other hand I conduct a comprehensive study on the lattice dynamics of Bi1Y2Fe5O12 (Bi:YIG), which exhibits high-frequency coherent spin dynamics upon femtosecond laser excitation according to the literature. Higher order standing spinwaves (SSWs) are triggered by coherent and incoherent motion of atoms, in other words phonons, which I quantified with UXRD. We are able to unite the experimental observations of the lattice and magnetization dynamics qualitatively and quantitatively. This is done with a combination of multi-temperature, elastic, magneto-elastic, anisotropy and micro-magnetic modeling. The collective data from UXRD, to probe the lattice, and time-resolved magneto-optical Kerr effect (tr-MOKE) measurements, to monitor the magnetization, were previously collected at different experimental setups. To improve the precision of the quantitative assessment of lattice and magnetization dynamics alike, our group implemented a combination of UXRD and tr-MOKE in a singular experimental setup, which is to my knowledge, the first of its kind. I helped with the conception and commissioning of this novel experimental station, which allows the simultaneous observation of lattice and magnetization dynamics on an ultrafast timescale under identical excitation conditions. Furthermore, I developed a new X-ray diffraction measurement routine which significantly reduces the measurement time of UXRD experiments by up to an order of magnitude. It is called reciprocal space slicing (RSS) and utilizes an area detector to monitor the angular motion of X-ray diffraction peaks, which is associated with lattice constant changes, without a time-consuming scan of the diffraction angles with the goniometer. RSS is particularly useful for ultrafast diffraction experiments, since measurement time at large scale facilities like synchrotrons and free electron lasers is a scarce and expensive resource. However, RSS is not limited to ultrafast experiments and can even be extended to other diffraction techniques with neutrons or electrons.},
  language  = {en}
}