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Solar photocatalysis is the one of leading concepts of research in the current paradigm of sustainable chemical industry. For actual practical implementation of sunlight-driven catalytic processes in organic synthesis, a cheap, efficient, versatile and robust heterogeneous catalyst is necessary. Carbon nitrides are a class of organic semiconductors who are known to fulfill these requirements.
First, current state of solar photocatalysis in economy, industry and lab research is overviewed, outlining EU project funding, prospective synthetic and reforming bulk processes, small scale solar organic chemistry, and existing reactor designs and prototypes, concluding feasibility of the approach.
Then, the photocatalytic aerobic cleavage of oximes to corresponding aldehydes and ketones by anionic poly(heptazine imide) carbon nitride is discussed. The reaction provides a feasible method of deprotection and formation of carbonyl compounds from nitrosation products and serves as a convenient model to study chromoselectivity and photophysics of energy transfer in heterogeneous photocatalysis.
Afterwards, the ability of mesoporous graphitic carbon nitride to conduct proton-coupled electron transfer was utilized for the direct oxygenation of 1,3-oxazolidin-2-ones to corresponding 1,3-oxazlidine-2,4-diones. This reaction provides an easier access to a key scaffold of diverse types of drugs and agrochemicals.
Finally, a series of novel carbon nitrides based on poly(triazine imide) and poly(heptazine imide) structure was synthesized from cyanamide and potassium rhodizonate. These catalysts demonstrated a good performance in a set of photocatalytic benchmark reactions, including aerobic oxidation, dual nickel photoredox catalysis, hydrogen peroxide evolution and chromoselective transformation of organosulfur precursors.
Concluding, the scope of carbon nitride utilization for net-oxidative and net-neutral photocatalytic processes was expanded, and a new tunable platform for catalyst synthesis was discovered.
Es ist bekannt, dass Änderungen im Kohlenstoff- bzw. Stickstoffstaus der Pflanzen zu einer parallelen statt reziproken Änderung der kohlenstoff- und stickstoffhaltigen Primärmetabolite führen. Unter diesem Gesichtspunkt wurden in der vorliegenden Arbeit der Aminosäurestoffwechsel und der Sekundärstoffwechsel unter reduzierten Stickstoffbedingungen untersucht. Zur Beeinflussung des Stickstoffstoffwechsels wurden nitratmangelernährte Tabakwildtyppflanzen und Genotypen mit unterschiedlich stark reduzierter Nitratreduktase-Aktivität verwendet. Dieses experimentelle System erlaubt zusätzlich durch den Vergleich Nitrat defizienter Wildtyppflanzen mit Nitrat akkumulierenden NIA-Transformanten Prozesse zu identifizieren, die durch Nitrat gesteuert werden. Die Analysen der Primär- und Sekundärmetabolite wurde in allen Genotypen diurnal durchgeführt, um auch tageszeitlich abhängige Prozesse zu identifizieren. Die Analyse der absoluten Gehalte aller individuellen Aminosäuren enthüllte bei den meisten erstaunlich stabile diurnale Muster mit einem Anstieg während des Tages und einem Abfall in der Nacht in Wildtyppflanzen gewachsen mit ausreichend Nitrat. Dieses Ergebnis legt die Schlussfolgerung nahe, dass die Biosynthese der Aminosäuren koordiniert abläuft. In Pflanzen mit reduziertem Stickstoffstatus haben diese diurnalen Muster jedoch keinen Bestand. Die Kombination des erzeugten stickstoffbasierten Aminosäuredatensatz in Kombination mit einem bereits erzeugten Aminosäuredatensatz unter kohlenstofflimitierten Bedingungen von Matt et al. (2002) führte durch Hauptkomponentenanalyse (PCA) und Korrelationsanalyse zu dem Ergebnis, dass die Hypothese nach einer koordinierten Aminosäurebiosynthese nicht allgemeine Gültigkeit hat. Die PCA identifizierte Glutamin, Glutamat, Aspartat, Glycin, Pheny-lalanin und Threonin als Faktoren, die den Datensätzen ihre charakteristische Eigenschaft und deren Varianz verleihen. Die Korrelationsanalyse zeigte, dass die sehr guten Korrelationen der individuellen Aminosäuren untereinander in reduzierten Stickstoff- und Kohlenstoffbedingungen sich verschlechtern. Das Verhältnis einer einzelnen Aminosäure relativ zu den anderen führte zur Identifizierung einiger Aminosäuren, die individuelle Antworten auf Stickstoff- und/oder Kohlenstoffstatus zeigen, und/oder speziell auf Nitrat, Licht und/oder den E-nergiestatus der Thylakoidmembran. Glutamat beispielsweise verhält sich in den meisten Situationen stabil, Phenylalanin dagegen zeigt in jeder physiologischen Situation eine individuelle Antwort. Die Ergebnisse dieser Arbeit führen zu einer Erweiterung der Hypothese einer koordinierten Synthese der Aminosäuren dahingehend, dass diese nicht generell für alle Aminosäuren angenommen werden kann. Es gibt einige Aminosäuren deren, Anteile sich situationsbedingt anpassen. Die Reduktion des Stickstoffstatus in nitratmangelernährten Tabakwildtyppflanzen führte zu der, nach der „Carbon-Nutrient-Balance“ Hypothese erwarteten Verlagerung der kohlenstoffreichen Phenylpropanoide und des stickstoffreichen Nikotins. Die Erhöhung der Phenylpropanoidgehalte war nicht in der Nitrat akkumulierenden NIA-Transformante zu beobachten und somit konnte Nitrat als regulatorisches Element identifiziert werden. Ein Einfluss der Vorläufermetabolite konnte ausgeschlossen werden, da sowohl nitratmangelernährter Wildtyp als auch die Nitrat akkumulierende NIA-Transformante ähnliche Gehalte dieser aufwiesen. Genexpressionsanalysen über Mikroarray-Hybridisierung und quantitative RT-PCR zeigten, dass Nitrat durch noch nicht geklärte Mechanismen Einfluss auf die Expression einiger Gene nimmt, die dem Phenylpropanoidstoffwechsels zugeordnet sind. Aus der Arbeit hervorgegangene Veröffentlichungen: Christina Fritz, Natalia Palacios-Rojas, Regina Feil und Mark Stitt (2006) Regulation of Secondary Metabolism by the Carbon-Nitrogen Status in Tobacco: Nitrate Inhibits Large Sectors of Phenylpropanoid Metabolism. Plant Journal 46, 533 - 548 Christina Fritz, Petra Matt, Cathrin Müller, Regina Feil und Mark Stitt (2006) Impact of the Carbon-Nitrogen Status on the Amino Acid Profile in Tobacco Source Leaves. Plant, Cell and Environment 29 (11), 2009 - 2111
Aminosäuren sind lebensnotwendige Moleküle für alle Organismen. Ihre Erkennung im Körper ermöglicht eine bedarfsgerechte Regulation ihrer Aufnahme und ihrer Verwertung. Welcher Chemosensor für diese Erkennung jedoch hauptverantwortlich ist, ist bisher unklar. In der vorliegenden Arbeit wurde die Rolle der Umamigeschmacksrezeptoruntereinheit Tas1r1 jenseits ihrer gustatorischen Bedeutung für die Aminosäuredetektion in der Mundhöhle untersucht.
In der histologischen Tas1r1-Expressionsanalyse nichtgustatorischer Gewebe der Mauslinie Tas1r1-Cre/ROSA26-tdRFP wurde über die Detektion des Reporterproteins tdRFP die Expression des Tas1r1 in allen untersuchten Geweben (Speiseröhre, Magen, Darm, Bauchspeicheldrüse, Leber, Niere, Muskel- und Fettgewebe, Milz, Thymus, Lymphknoten, Lunge sowie Hoden) nachgewiesen. Mit Ausnahme von Dünndarm und Hoden gelang hierbei der Nachweis erstmals spezifisch auf zellulärer Ebene. Caecum und Lymphknoten wurden zudem neu als Expressionsorte des Tas1r1 identifiziert.
Trotz der beobachteten weiten Verbreitung des Tas1r1 im Organismus – unter anderem auch in Geweben, die für den Proteinstoffwechsel besonders relevant sind – waren im Zuge der durchgeführten Untersuchung potentieller extraoraler Funktionen des Rezeptors durch phänotypische Charakterisierung der Mauslinie Tas1r1-BLiR nur schwache Auswirkungen auf Aminosäurestoffwechsel bzw. Stickstoffhaushalt im Falle eines Tas1r1-Knockouts detektierbar. Während sich Ernährungsverhalten, Gesamtphysiologie, Gewebemorphologie sowie Futterverdaulichkeit unverändert zeigten, war die renale Stickstoffausscheidung bei Tas1r1-Knockout-Mäusen auf eiweißarmer sowie auf eiweißreicher Diät signifikant verringert. Eine Überdeckung der Auswirkungen des Tas1r1-Knockouts aufgrund kompensatorischer Effekte durch den Aminosäuresensor CaSR oder den Peptidsensor Gpr93 war nicht nachweisbar. Es bleibt offen, ob andere Mechanismen oder andere Chemosensoren an einer Kompensation beteiligt sind oder aber Tas1r1 in extraoralem Gewebe andere Funktionen als die der Aminosäuredetektion übernimmt. Unterschiede im extraoralen Expressionsmuster der beiden Umamirezeptor-untereinheiten Tas1r1 und Tasr3 lassen Spekulationen über andere Partner, Liganden und Funktionen zu.
The echo chamber model describes the development of groups in heterogeneous social networks. By heterogeneous social network we mean a set of individuals, each of whom represents exactly one opinion. The existing relationships between individuals can then be represented by a graph. The echo chamber model is a time-discrete model which, like a board game, is played in rounds. In each round, an existing relationship is randomly and uniformly selected from the network and the two connected individuals interact. If the opinions of the individuals involved are sufficiently similar, they continue to move closer together in their opinions, whereas in the case of opinions that are too far apart, they break off their relationship and one of the individuals seeks a new relationship. In this paper we examine the building blocks of this model. We start from the observation that changes in the structure of relationships in the network can be described by a system of interacting particles in a more abstract space.
These reflections lead to the definition of a new abstract graph that encompasses all possible relational configurations of the social network. This provides us with the geometric understanding necessary to analyse the dynamic components of the echo chamber model in Part III. As a first step, in Part 7, we leave aside the opinions of the inidividuals and assume that the position of the edges changes with each move as described above, in order to obtain a basic understanding of the underlying dynamics. Using Markov chain theory, we find upper bounds on the speed of convergence of an associated Markov chain to its unique stationary distribution and show that there are mutually identifiable networks that are not apparent in the dynamics under analysis, in the sense that the stationary distribution of the associated Markov chain gives equal weight to these networks.
In the reversible cases, we focus in particular on the explicit form of the stationary distribution as well as on the lower bounds of the Cheeger constant to describe the convergence speed.
The final result of Section 8, based on absorbing Markov chains, shows that in a reduced version of the echo chamber model, a hierarchical structure of the number of conflicting relations can be identified.
We can use this structure to determine an upper bound on the expected absorption time, using a quasi-stationary distribution. This hierarchy of structure also provides a bridge to classical theories of pure death processes. We conclude by showing how future research can exploit this link and by discussing the importance of the results as building blocks for a full theoretical understanding of the echo chamber model. Finally, Part IV presents a published paper on the birth-death process with partial catastrophe. The paper is based on the explicit calculation of the first moment of a catastrophe. This first part is entirely based on an analytical approach to second degree recurrences with linear coefficients. The convergence to 0 of the resulting sequence as well as the speed of convergence are proved. On the other hand, the determination of the upper bounds of the expected value of the population size as well as its variance and the difference between the determined upper bound and the actual value of the expected value. For these results we use almost exclusively the theory of ordinary nonlinear differential equations.
The central gas in half of all galaxy clusters shows short cooling times. Assuming unimpeded cooling, this should lead to high star formation and mass cooling rates, which are not observed. Instead, it is believed that condensing gas is accreted by the central black hole that powers an active galactic nuclei jet, which heats the cluster. The detailed heating mechanism remains uncertain. A promising mechanism invokes cosmic ray protons that scatter on self-generated magnetic fluctuations, i.e. Alfvén waves. Continuous damping of Alfvén waves provides heat to the intracluster medium. Previous work has found steady state solutions for a large sample of clusters where cooling is balanced by Alfvénic wave heating. To verify modeling assumptions, we set out to study cosmic ray injection in three-dimensional magnetohydrodynamical simulations of jet feedback in an idealized cluster with the moving-mesh code arepo. We analyze the interaction of jet-inflated bubbles with the turbulent magnetized intracluster medium.
Furthermore, jet dynamics and heating are closely linked to the largely unconstrained jet composition. Interactions of electrons with photons of the cosmic microwave background result in observational signatures that depend on the bubble content. Those recent observations provided evidence for underdense bubbles with a relativistic filling while adopting simplifying modeling assumptions for the bubbles. By reproducing the observations with our simulations, we confirm the validity of their modeling assumptions and as such, confirm the important finding of low-(momentum) density jets.
In addition, the velocity and magnetic field structure of the intracluster medium have profound consequences for bubble evolution and heating processes. As velocity and magnetic fields are physically coupled, we demonstrate that numerical simulations can help link and thereby constrain their respective observables. Finally, we implement the currently preferred accretion model, cold accretion, into the moving-mesh code arepo and study feedback by light jets in a radiatively cooling magnetized cluster. While self-regulation is attained independently of accretion model, jet density and feedback efficiencies, we find that in order to reproduce observed cold gas morphology light jets are preferred.
Seit den 60er Jahren gibt es im deutschsprachigen Raum Diskussionen um die Begriffe Schlüsselqualifikation und (Schlüssel-)Kompetenz, welche seit ca. 2000 auch in der Informatikdidaktik angekommen sind. Die Diskussionen der Fachdisziplinen und ihre Bedeutung für die Informatikdidaktik sind Gegenstand des ersten Teils dieser Dissertation. Es werden Rahmenmodelle zur Strukturierung und Einordnung von Kompetenzen entworfen, die für alle Fachdisziplinen nutzbar sind. Im zweiten Teil wird ein methodologischer Weg gezeigt, Schlüsselkompetenzen herzuleiten, ohne normativ vorgehen zu müssen. Hierzu wird das Verfahren der Qualitativen Inhaltsanalyse (QI) auf informatikdidaktische Ansätze angewendet. Die resultierenden Kompetenzen werden in weiteren Schritten verfeinert und in die zuvor entworfenen Rahmenmodelle eingeordnet. Das Ergebnis sind informatische Schlüsselkompetenzen, welche ein spezifisches Bild der Informatik zeichnen und zur Analyse bereits bestehender Curricula genutzt werden können. Zusätzlich zeigt das Verfahren einen Weg auf, wie Schlüsselkompetenzen auf nicht-normativem Wege generell hergeleitet werden können.
This thesis rests on two large Active Galactic Nuclei (AGNs) surveys. The first survey deals with galaxies that host low-level AGNs (LLAGN) and aims at identifying such galaxies by quantifying their variability. While numerous studies have shown that AGNs can be variable at all wavelengths, the nature of the variability is still not well understood. Studying the properties of LLAGNs may help to understand better galaxy evolution, and how AGNs transit between active and inactive states. In this thesis, we develop a method to extract variability properties of AGNs. Using multi-epoch deep photometric observations, we subtract the contribution of the host galaxy at each epoch to extract variability and estimate AGN accretion rates. This pipeline will be a powerful tool in connection with future deep surveys such as PANSTARS. The second study in this thesis describes a survey of X-ray selected AGN hosts at redshifts z>1.5 and compares them to quiescent galaxies. This survey aims at studying environments, sizes and morphologies of star-forming high-redshift AGN hosts in the COSMOS Survey at the epoch of peak AGN activity. Between redshifts 1.5<z<3.8, the COSMOS HST/ACS imaging probes the UV regime, where separating the AGN flux from its host galaxy is very challenging. Nevertheless, we successfully derived the structural properties of 249 AGN hosts using two-dimensional surface-brightness profile fitting with the GALFIT package. This is the largest sample of AGN hosts at redshift z>1.5 to date. We analyzed the evolution of structural parameters of AGN and non-AGN host galaxies with redshift, and compared their disturbance rates to identify the more probable AGN triggering mechanism in the 43.5<log_10 L_X<45 luminosity range. We also conducted mock AGN and quiescent galaxies observations to determine errors and corrections for the derived parameters. We find that the size-absolute magnitude relations of AGN hosts and non-AGN galaxies are very similar, with estimated mean sizes in both samples decreasing by ~50% between redshifts z=1.5 and z=3.5. Morphological classification of both active and quiescent galaxies shows that the majority of the AGN host galaxies are disc-dominated, with disturbance rates that are significantly lower than among the non-AGN galaxies. Such a finding suggests that Major Mergers are probably not responsible for triggering AGN accretion in most of these galaxies. Other secular mechanisms should therefore be responsible.
Selenium (Se) is an essential trace element that is ubiquitously present in the environment in small concentrations. Essential functions of Se in the human body are manifested through the wide range of proteins, containing selenocysteine as their active center. Such proteins are called selenoproteins which are found in multiple physiological processes like antioxidative defense and the regulation of thyroid hormone functions. Therefore, Se deficiency is known to cause a broad spectrum of physiological impairments, especially in endemic regions with low Se content. Nevertheless, being an essential trace element, Se could exhibit toxic effects, if its intake exceeds tolerable levels. Accordingly, this range between deficiency and overexposure represents optimal Se supply. However, this range was found to be narrower than for any other essential trace element. Together with significantly varying Se concentrations in soil and the presence of specific bioaccumulation factors, this represents a noticeable difficulty in the assessment of Se
epidemiological status. While Se is acting in the body through multiple selenoproteins, its intake occurs mainly in form of small organic or inorganic molecular mass species. Thus, Se exposure not only depends on daily intake but also on the respective chemical form, in which it is present.
The essential functions of selenium have been known for a long time and its primary forms in different food sources have been described. Nevertheless, analytical capabilities for a comprehensive investigation of Se species and their derivatives have been introduced only in the last decades. A new Se compound was identified in 2010 in the blood and tissues of bluefin tuna. It was called selenoneine (SeN) since it is an isologue of naturally occurring antioxidant ergothioneine (ET), where Se replaces sulfur. In the following years, SeN was identified in a number of edible fish species and attracted attention as a new dietary Se source and potentially strong antioxidant. Studies in populations whose diet largely relies on fish revealed that SeN
represents the main non-protein bound Se pool in their blood. First studies, conducted with enriched fish extracts, already demonstrated the high antioxidative potential of SeN and its possible function in the detoxification of methylmercury in fish. Cell culture studies demonstrated, that SeN can utilize the same transporter as ergothioneine, and SeN metabolite was found in human urine.
Until recently, studies on SeN properties were severely limited due to the lack of ways to obtain the pure compound. As a predisposition to this work was firstly a successful approach to SeN synthesis in the University of Graz, utilizing genetically modified yeasts. In the current study, by use of HepG2 liver carcinoma cells, it was demonstrated, that SeN does not cause toxic effectsup to 100 μM concentration in hepatocytes. Uptake experiments showed that SeN is not bioavailable to the used liver cells.
In the next part a blood-brain barrier (BBB) model, based on capillary endothelial cells from the porcine brain, was used to describe the possible transfer of SeN into the central nervous system (CNS). The assessment of toxicity markers in these endothelial cells and monitoring of barrier conditions during transfer experiments demonstrated the absence of toxic effects from SeN on the BBB endothelium up to 100 μM concentration. Transfer data for SeN showed slow but substantial transfer. A statistically significant increase was observed after 48 hours following SeN incubation from the blood-facing side of the barrier. However, an increase in Se content was clearly visible already after 6 hours of incubation with 1 μM of SeN. While the transfer rate of SeN after application of 0.1 μM dose was very close to that for 1 μM, incubation with 10 μM of SeN resulted in a significantly decreased transfer rate. Double-sided application of SeN caused no side-specific transfer of SeN, thus suggesting a passive diffusion mechanism of SeN across the BBB. This data is in accordance with animal studies, where ET accumulation was observed in the rat brain, even though rat BBB does not have the primary ET transporter – OCTN1. Investigation of capillary endothelial cell monolayers after incubation with SeN and reference selenium compounds showed no significant increase of intracellular selenium concentration. Speciesspecific Se measurements in medium samples from apical and basolateral compartments, as good as in cell lysates, showed no SeN metabolization. Therefore, it can be concluded that SeN may reach the brain without significant transformation.
As the third part of this work, the assessment of SeN antioxidant properties was performed in Caco-2 human colorectal adenocarcinoma cells. Previous studies demonstrated that the intestinal epithelium is able to actively transport SeN from the intestinal lumen to the blood side and accumulate SeN. Further investigation within current work showed a much higher antioxidant potential of SeN compared to ET. The radical scavenging activity after incubation with SeN was close to the one observed for selenite and selenomethionine. However, the SeN effect on the viability of intestinal cells under oxidative conditions was close to the one caused by ET. To answer the question if SeN is able to be used as a dietary Se source and induce the activity of selenoproteins, the activity of glutathione peroxidase (GPx) and the secretion of selenoprotein P (SelenoP) were measured in Caco-2 cells, additionally. As expected, reference selenium compounds selenite and selenomethionine caused efficient induction of GPx activity. In contrast to those SeN had no effect on GPx activity. To examine the possibility of SeN being embedded into the selenoproteome, SelenoP was measured in a culture medium. Even though Caco-2 cells effectively take up SeN in quantities much higher than selenite or selenomethionine, no secretion of SelenoP was observed after SeN incubation.
Summarizing, we can conclude that SeN can hardly serve as a Se source for selenoprotein synthesis. However, SeN exhibit strong antioxidative properties, which appear when sulfur in ET is exchanged by Se. Therefore, SeN is of particular interest for research not as part of Se metabolism, but important endemic dietary antioxidant.
Complex emulsions are dispersions of kinetically stabilized multiphasic emulsion droplets comprised of two or more immiscible liquids that provide a novel material platform for the generation of active and dynamic soft materials. In recent years, the intrinsic reconfigurable morphological behavior of complex emulsions, which can be attributed to the unique force equilibrium between the interfacial tensions acting at the various interfaces, has become of fundamental and applied interest. As such, particularly biphasic Janus droplets have been investigated as structural templates for the generation of anisotropic precision objects, dynamic optical elements or as transducers and signal amplifiers in chemo- and bio-sensing applications. In the present thesis, switchable internal morphological responses of complex droplets triggered by stimuli-induced alterations of the balance of interfacial tensions have been explored as a universal building block for the design of multiresponsive, active, and adaptive liquid colloidal systems. A series of underlying principles and mechanisms that influence the equilibrium of interfacial tensions have been uncovered, which allowed the targeted design of emulsion bodies that can alter their shape, bind and roll on surfaces, or change their geometrical shape in response to chemical stimuli. Consequently, combinations of the unique triggerable behavior of Janus droplets with designer surfactants, such as a stimuli-responsive photosurfactant (AzoTAB) resulted for instance in shape-changing soft colloids that exhibited a jellyfish inspired buoyant motion behavior, holding great promise for the design of biological inspired active material architectures and transformable soft robotics.
In situ observations of spherical Janus emulsion droplets using a customized side-view microscopic imaging setup with accompanying pendant dropt measurements disclosed the sensitivity regime of the unique chemical-morphological coupling inside complex emulsions and enabled the recording of calibration curves for the extraction of critical parameters of surfactant effectiveness. The deduced new "responsive drop" method permitted a convenient and cost-efficient quantification and comparison of the critical micelle concentrations (CMCs) and effectiveness of various cationic, anionic, and nonionic surfactants. Moreover, the method allowed insightful characterization of stimuli-responsive surfactants and monitoring of the impact of inorganic salts on the CMC and surfactant effectiveness of ionic and nonionic surfactants. Droplet functionalization with synthetic crown ether surfactants yielded a synthetically minimal material platform capable of autonomous and reversible adaptation to its chemical environment through different supramolecular host-guest recognition events. Addition of metal or ammonium salts resulted in the uptake of the resulting hydrophobic complexes to the hydrocarbon hemisphere, whereas addition of hydrophilic ammonium compounds such as amino acids or polypeptides resulted in supramolecular assemblies at the hydrocarbon-water interface of the droplets. The multiresponsive material platform enabled interfacial complexation and
thus triggered responses of the droplets to a variety of chemical triggers including metal ions, ammonium compounds, amino acids, antibodies, carbohydrates as well as amino-functionalized solid surfaces.
In the final chapter, the first documented optical logic gates and combinatorial logic circuits based on complex emulsions are presented. More specifically, the unique reconfigurable and multiresponsive properties of complex emulsions were exploited to realize droplet-based logic gates of varying complexity using different stimuli-responsive surfactants in combination with diverse readout methods. In summary, different designs for multiresponsive, active, and adaptive liquid colloidal systems were presented and investigated, enabling the design of novel transformative chemo-intelligent soft material platforms.
In this thesis we mainly generalize two theorems from Mackaay-Picken and Picken (2002, 2004). In the first paper, Mackaay and Picken show that there is a bijective correspondence between Deligne 2-classes $\xi \in \check{H}^2(M,\mathcal{D}^2)$ and holonomy maps from the second thin-homotopy group $\pi_2^2(M)$ to $U(1)$. In the second one, a generalization of this theorem to manifolds with boundaries is given: Picken shows that there is a bijection between Deligne 2-cocycles and a certain variant of 2-dimensional topological quantum field theories. In this thesis we show that these two theorems hold in every dimension. We consider first the holonomy case, and by using simplicial methods we can prove that the group of smooth Deligne $d$-classes is isomorphic to the group of smooth holonomy maps from the $d^{th}$ thin-homotopy group $\pi_d^d(M)$ to $U(1)$, if $M$ is $(d-1)$-connected. We contrast this with a result of Gajer (1999). Gajer showed that Deligne $d$-classes can be reconstructed by a different class of holonomy maps, which not only include holonomies along spheres, but also along general $d$-manifolds in $M$. This approach does not require the manifold $M$ to be $(d-1)$-connected. We show that in the case of flat Deligne $d$-classes, our result differs from Gajers, if $M$ is not $(d-1)$-connected, but only $(d-2)$-connected. Stiefel manifolds do have this property, and if one applies our theorem to these and compare the result with that of Gajers theorem, it is revealed that our theorem reconstructs too many Deligne classes. This means, that our reconstruction theorem cannot live without the extra assumption on the manifold $M$, that is our reconstruction needs less informations about the holonomy of $d$-manifolds in $M$ at the price of assuming $M$ to be $(d-1)$-connected. We continue to show, that also the second theorem can be generalized: By introducing the concept of Picken-type topological quantum field theory in arbitrary dimensions, we can show that every Deligne $d$-cocycle induces such a $d$-dimensional field theory with two special properties, namely thin-invariance and smoothness. We show that any $d$-dimensional topological quantum field theory with these two properties gives rise to a Deligne $d$-cocycle and verify that this construction is surjective and injective, that is both groups are isomorphic.
In recent decades, astronomy has seen a boom in large-scale stellar surveys of the Galaxy. The detailed information obtained about millions of individual stars in the Milky Way is bringing us a step closer to answering one of the most outstanding questions in astrophysics: how do galaxies form and evolve? The Milky Way is the only galaxy where we can dissect many stars into their high-dimensional chemical composition and complete phase space, which analogously as fossil records can unveil the past history of the genesis of the Galaxy. The processes that lead to large structure formation, such as the Milky Way, are critical for constraining cosmological models; we call this line of study Galactic archaeology or near-field cosmology.
At the core of this work, we present a collection of efforts to chemically and dynamically characterise the disks and bulge of our Galaxy. The results we present in this thesis have only been possible thanks to the advent of the Gaia astrometric satellite, which has revolutionised the field of Galactic archaeology by precisely measuring the positions, parallax distances and motions of more than a billion stars. Another, though not less important, breakthrough is the APOGEE survey, which has observed spectra in the near-infrared peering into the dusty regions of the Galaxy, allowing us to determine detailed chemical abundance patterns in hundreds of thousands of stars. To accurately depict the Milky Way structure, we use and develop the Bayesian isochrone fitting tool/code called StarHorse; this software can predict stellar distances, extinctions and ages by combining astrometry, photometry and spectroscopy based on stellar evolutionary models. The StarHorse code is pivotal to calculating distances where Gaia parallaxes alone cannot allow accurate estimates.
We show that by combining Gaia, APOGEE, photometric surveys and using StarHorse, we can produce a chemical cartography of the Milky way disks from their outermost to innermost parts. Such a map is unprecedented in the inner Galaxy. It reveals a continuity of the bimodal chemical pattern previously detected in the solar neighbourhood, indicating two populations with distinct formation histories. Furthermore, the data reveals a chemical gradient within the thin disk where the content of 𝛼-process elements and metals is higher towards the centre. Focusing on a sample in the inner MW we confirm the extension of the chemical duality to the innermost regions of the Galaxy. We find stars with bar shape orbits to show both high- and low-𝛼 abundances, suggesting the bar formed by secular evolution trapping stars that already existed. By analysing the chemical orbital space of the inner Galactic regions, we disentangle the multiple populations that inhabit this complex region. We reveal the presence of the thin disk, thick disk, bar, and a counter-rotating population, which resembles the outcome of a perturbed proto-Galactic disk. Our study also finds that the inner Galaxy holds a high quantity of super metal-rich stars up to three times solar suggesting it is a possible repository of old super-metal-rich stars found in the solar neighbourhood.
We also enter into the complicated task of deriving individual stellar ages. With StarHorse, we calculate the ages of main-sequence turn-off and sub-giant stars for several public spectroscopic surveys. We validate our results by investigating linear relations between chemical abundances and time since the 𝛼 and neutron capture elements are sensitive to age as a reflection of the different enrichment timescales of these elements. For further study of the disks in the solar neighbourhood, we use an unsupervised machine learning algorithm to delineate a multidimensional separation of chrono-chemical stellar groups revealing the chemical thick disk, the thin disk, and young 𝛼-rich stars. The thick disk is shown to have a small age dispersion indicating its fast formation contrary to the thin disk that spans a wide range of ages.
With groundbreaking data, this thesis encloses a detailed chemo-dynamical view of the disk and bulge of our Galaxy. Our findings on the Milky Way can be linked to the evolution of high redshift disk galaxies, helping to solve the conundrum of galaxy formation.
Carbon nitride and poly(ionic liquid)s (PILs) have been successfully applied in various fields of materials science owing to their outstanding properties. This thesis aims at the successful application of these polymers as innovative materials in the interfaces of hybrid organic–inorganic perovskite solar cells. A critical problem in harnessing the full thermodynamic potential of halide perovskites in solar cells is the design and modification of interfaces to reduce carrier recombination. Therefore, the interface must be properly studied and improved. This work investigated the effect of applying carbon nitride and PILs on a perovskite surface on the device performance. The facile synthetic method for modifying carbon nitride with vinyl thiazole and barbituric acid (CMB-vTA) yields 2.3 nm layers when solution processing is performed using isopropanol. The nanosheets were applied as a metal-free electron transport layer in inverted perovskite solar cells. The application of carbon nitride layers (CMB-vTA) resulted in negligible current-voltage hysteresis with a high open circuit voltage (Voc) of 1.1 V and a short-circuit current (Jsc) of 20.28 mA cm-2, which afforded efficiencies of up to 17%. Thus, the successful implementation of a carbon nitride-based structure enabled good charge extraction with minimized interface recombination between the perovskite and PCBM. Similarly, PILs represent a new strategy of interfacial modification using an ionic polymer in an n-i-p perovskite architecture.. The application of PILs as an interfacial modifier resulted in solar cell devices with an extraordinarily high efficiency of 21.8% and a Voc of 1.17 V. The implementation reduced non-radiative recombination at the perovskite surface through defect passivation. Finally, our work proposes a novel method to efficiently suppress non-radiative charge recombination using the unexplored properties of carbon nitride and PILs in the solar cell field. Additionally, the method for interfacial modification has general applicability because of the simplicity of the post-treatment approach, and therefore has potential applicability in other solar cells. Thus, this work opens the door to a new class of materials to be implemented.
Microswimmers, i.e. swimmers of micron size experiencing low Reynolds numbers, have received a great deal of attention in the last years, since many applications are envisioned in medicine and bioremediation. A promising field is the one of magnetic swimmers, since magnetism is biocom-patible and could be used to direct or actuate the swimmers. This thesis studies two examples of magnetic microswimmers from a physics point of view.
The first system to be studied are magnetic cells, which can be magnetic biohybrids (a swimming cell coupled with a magnetic synthetic component) or magnetotactic bacteria (naturally occurring bacteria that produce an intracellular chain of magnetic crystals). A magnetic cell can passively interact with external magnetic fields, which can be used for direction. The aim of the thesis is to understand how magnetic cells couple this magnetic interaction to their swimming strategies, mainly how they combine it with chemotaxis (the ability to sense external gradient of chemical species and to bias their walk on these gradients). In particular, one open question addresses the advantage given by these magnetic interactions for the magnetotactic bacteria in a natural environment, such as porous sediments. In the thesis, a modified Active Brownian Particle model is used to perform simulations and to reproduce experimental data for different systems such as bacteria swimming in the bulk, in a capillary or in confined geometries. I will show that magnetic fields speed up chemotaxis under special conditions, depending on parameters such as their swimming strategy (run-and-tumble or run-and-reverse), aerotactic strategy (axial or polar), and magnetic fields (intensities and orientations), but it can also hinder bacterial chemotaxis depending on the system.
The second example of magnetic microswimmer are rigid magnetic propellers such as helices or random-shaped propellers. These propellers are actuated and directed by an external rotating magnetic field. One open question is how shape and magnetic properties influence the propeller behavior; the goal of this research field is to design the best propeller for a given situation. The aim of the thesis is to propose a simulation method to reproduce the behavior of experimentally-realized propellers and to determine their magnetic properties. The hydrodynamic simulations are based on the use of the mobility matrix. As main result, I propose a method to match the experimental data, while showing that not only shape but also the magnetic properties influence the propellers swimming characteristics.
The utilization of lignin as renewable electrode material for electrochemical energy storage is a sustainable approach for future batteries and supercapacitors. The composite electrode was fabricated from Kraft lignin and conductive carbon and the charge storage contribution was determined in terms of electrical double layer (EDL) and redox reactions. The important factors at play for achieving high faradaic charge storage capacity contribute to high surface area, accessibility of redox sites in lignin and their interaction with conductive additives. A thinner layer of lignin covering the high surface area of carbon facilitates the electron transfer process with a shorter pathway from the active sites of nonconductive lignin to the current collector leading to the improvement of faradaic charge storage capacity.
Composite electrodes from lignin and carbon would be even more sustainable if the fluorinated binder can be omitted. A new route to fabricate a binder-free composite electrode from Kraft lignin and high surface area carbon has been proposed by crosslinking lignin with glyoxal. A high molecular weight of lignin is obtained to enhance both electroactivity and binder capability in composite electrodes. The order of the processing step of crosslinking lignin on the composite electrode plays a crucial role in achieving a stable electrode and high charge storage capacity. The crosslinked lignin based electrodes are promising since they allow for more stable, sustainable, halogen-free and environmentally benign devices for energy storage applications. Furthermore, improvement of the amount of redox active groups (quinone groups) in lignin is useful to enhance the capacity in lithium battery applications. Direct oxidative demethylation by cerium ammonium nitrate has been carried out under mild conditions. This proves that an increase of quinone groups is able to enhance the performance of lithium battery. Thus, lignin is a promising material and could be a good candidate for application in sustainable energy storage devices.
Technological progress allows for producing ever more complex predictive models on the basis of increasingly big datasets. For risk management of natural hazards, a multitude of models is needed as basis for decision-making, e.g. in the evaluation of observational data, for the prediction of hazard scenarios, or for statistical estimates of expected damage. The question arises, how modern modelling approaches like machine learning or data-mining can be meaningfully deployed in this thematic field. In addition, with respect to data availability and accessibility, the trend is towards open data. Topic of this thesis is therefore to investigate the possibilities and limitations of machine learning and open geospatial data in the field of flood risk modelling in the broad sense. As this overarching topic is broad in scope, individual relevant aspects are identified and inspected in detail.
A prominent data source in the flood context is satellite-based mapping of inundated areas, for example made openly available by the Copernicus service of the European Union. Great expectations are directed towards these products in scientific literature, both for acute support of relief forces during emergency response action, and for modelling via hydrodynamic models or for damage estimation. Therefore, a focus of this work was set on evaluating these flood masks. From the observation that the quality of these products is insufficient in forested and built-up areas, a procedure for subsequent improvement via machine learning was developed. This procedure is based on a classification algorithm that only requires training data from a particular class to be predicted, in this specific case data of flooded areas, but not of the negative class (dry areas). The application for hurricane Harvey in Houston shows the high potential of this method, which depends on the quality of the initial flood mask.
Next, it is investigated how much the predicted statistical risk from a process-based model chain is dependent on implemented physical process details. Thereby it is demonstrated what a risk study based on established models can deliver. Even for fluvial flooding, such model chains are already quite complex, though, and are hardly available for compound or cascading events comprising torrential rainfall, flash floods, and other processes. In the fourth chapter of this thesis it is therefore tested whether machine learning based on comprehensive damage data can offer a more direct path towards damage modelling, that avoids explicit conception of such a model chain. For that purpose, a state-collected dataset of damaged buildings from the severe El Niño event 2017 in Peru is used. In this context, the possibilities of data-mining for extracting process knowledge are explored as well. It can be shown that various openly available geodata sources contain useful information for flood hazard and damage modelling for complex events, e.g. satellite-based rainfall measurements, topographic and hydrographic information, mapped settlement areas, as well as indicators from spectral data. Further, insights on damaging processes are discovered, which mainly are in line with prior expectations. The maximum intensity of rainfall, for example, acts stronger in cities and steep canyons, while the sum of rain was found more informative in low-lying river catchments and forested areas. Rural areas of Peru exhibited higher vulnerability in the presented study compared to urban areas. However, the general limitations of the methods and the dependence on specific datasets and algorithms also become obvious.
In the overarching discussion, the different methods – process-based modelling, predictive machine learning, and data-mining – are evaluated with respect to the overall research questions. In the case of hazard observation it seems that a focus on novel algorithms makes sense for future research. In the subtopic of hazard modelling, especially for river floods, the improvement of physical models and the integration of process-based and statistical procedures is suggested. For damage modelling the large and representative datasets necessary for the broad application of machine learning are still lacking. Therefore, the improvement of the data basis in the field of damage is currently regarded as more important than the selection of algorithms.
Recent years witnessed a vast advent of stalagmites as palaeoclimate archives. The multitude of geochemical and physical proxies and a promise of a precise and accurate age model greatly appeal to palaeoclimatologists. Although substantial progress was made in speleothem-based palaeoclimate research and despite high-resolution records from low-latitudinal regions, proving that palaeo-environmental changes can be archived on sub-annual to millennial time scales our comprehension of climate dynamics is still fragmentary. This is in particular true for the summer monsoon system on the Indian subcontinent. The Indian summer monsoon (ISM) is an integral part of the intertropical convergence zone (ITCZ). As this rainfall belt migrates northward during boreal summer, it brings monsoonal rainfall. ISM strength depends however on a variety of factors, including snow cover in Central Asia and oceanic conditions in the Indic and Pacific. Presently, many of the factors influencing the ISM are known, though their exact forcing mechanism and mutual relations remain ambiguous. Attempts to make an accurate prediction of rainfall intensity and frequency and drought recurrence, which is extremely important for South Asian countries, resemble a puzzle game; all interaction need to fall into the right place to obtain a complete picture. My thesis aims to create a faithful picture of climate change in India, covering the last 11,000 ka. NE India represents a key region for the Bay of Bengal (BoB) branch of the ISM, as it is here where the monsoon splits into a northwestward and a northeastward directed arm. The Meghalaya Plateau is the first barrier for northward moving air masses and receives excessive summer rainfall, while the winter season is very dry. The proximity of Meghalaya to the Tibetan Plateau on the one hand and the BoB on the other hand make the study area a key location for investigating the interaction between different forcings that governs the ISM. A basis for the interpretation of palaeoclimate records, and a first important outcome of my thesis is a conceptual model which explains the observed pattern of seasonal changes in stable isotopes (d18O and d2H) in rainfall. I show that although in tropical and subtropical regions the amount effect is commonly called to explain strongly depleted isotope values during enhanced rainfall, alone it cannot account for observed rainwater isotope variability in Meghalaya. Monitoring of rainwater isotopes shows no expected negative correlation between precipitation amount and d18O of rainfall. In turn I find evidence that the runoff from high elevations carries an inherited isotopic signature into the BoB, where during the ISM season the freshwater builds a strongly depleted plume on top of the marine water. The vapor originating from this plume is likely to memorize' and transmit further very negative d18O values. The lack of data does not allow for quantication of this plume effect' on isotopes in rainfall over Meghalaya but I suggest that it varies on seasonal to millennial timescales, depending on the runoff amount and source characteristics. The focal point of my thesis is the extraction of climatic signals archived in stalagmites from NE India. High uranium concentration in the stalagmites ensured excellent age control required for successful high-resolution climate reconstructions. Stable isotope (d18O and d13C) and grey-scale data allow unprecedented insights into millennial to seasonal dynamics of the summer and winter monsoon in NE India. ISM strength (i. e. rainfall amount) is recorded in changes in d18Ostalagmites. The d13C signal, reflecting drip rate changes, renders a powerful proxy for dry season conditions, and shows similarities to temperature-related changes on the Tibetan Plateau. A sub-annual grey-scale profile supports a concept of lower drip rate and slower stalagmite growth during dry conditions. During the Holocene, ISM followed a millennial-scale decrease of insolation, with decadal to centennial failures resulting from atmospheric changes. The period of maximum rainfall and enhanced seasonality corresponds to the Holocene Thermal Optimum observed in Europe. After a phase of rather stable conditions, 4.5 kyr ago, the strengthening ENSO system dominated the ISM. Strong El Nino events weakened the ISM, especially when in concert with positive Indian Ocean dipole events. The strongest droughts of the last 11 kyr are recorded during the past 2 kyr. Using the advantage of a well-dated stalagmite record at hand I tested the application of laser ablation-inductively coupled plasma-mass spectrometry (LA-ICP-MS) to detect sub-annual to sub-decadal changes in element concentrations in stalagmites. The development of a large ablation cell allows for ablating sample slabs of up to 22 cm total length. Each analyzed element is a potential proxy for different climatic parameters. Combining my previous results with the LAICP- MS-generated data shows that element concentration depends not only on rainfall amount and associated leaching from the soil. Additional factors, like biological activity and hydrogeochemical conditions in the soil and vadose zone can eventually affect the element content in drip water and in stalagmites. I present a theoretical conceptual model for my study site to explain how climatic signals can be transmitted and archived in stalagmite carbonate. Further, I establish a first 1500 year long element record, reconstructing rainfall variability. Additionally, I hypothesize that volcanic eruptions, producing large amounts of sulfuric acid, can influence soil acidity and hence element mobilization.
The Milky Way is only one out of billions of galaxies in the universe. However, it is a special galaxy because it allows to explore the main mechanisms involved in its evolution and formation history by unpicking the system star-by-star. Especially, the chemical fingerprints of its stars provide clues and evidence of past events in the Galaxy’s lifetime. These information help not only to decipher the current structure and building blocks of the Milky Way, but to learn more about the general formation process of galaxies.
In the past decade a multitude of stellar spectroscopic Galactic surveys have scanned millions of stars far beyond the rim of the solar neighbourhood. The obtained spectroscopic information provide unprecedented insights to the chemo-dynamics of the Milky Way. In addition analytic models and numerical simulations of the Milky Way provide necessary descriptions and predictions suited for comparison with observations in order to decode the physical properties that underlie the complex system of the Galaxy.
In the thesis various approaches are taken to connect modern theoretical modelling of galaxy formation and evolution with observations from Galactic stellar surveys. With its focus on the chemo-kinematics of the Galactic disk this work aims to determine new observational constraints on the formation of the Milky Way providing also proper comparisons with two different models. These are the population synthesis model TRILEGAL based on analytical distribution functions, which aims to simulate the number and distribution of stars in the Milky Way and its different components, and a hybrid model (MCM) that combines an N-body simulation of a Milky Way like galaxy in the cosmological framework with a semi-analytic chemical evolution model for the Milky Way. The major observational data sets in use come from two surveys, namely the “Radial Velocity Experiment” (RAVE) and the “Sloan Extension for Galactic Understanding and Exploration” (SEGUE).
In the first approach the chemo-kinematic properties of the thin and thick disk of the Galaxy as traced by a selection of about 20000 SEGUE G-dwarf stars are directly compared to the predictions by the MCM model. As a necessary condition for this, SEGUE's selection function and its survey volume are evaluated in detail to correct the spectroscopic observations for their survey specific selection biases. Also, based on a Bayesian method spectro-photometric distances with uncertainties below 15% are computed for the selection of SEGUE G-dwarfs that are studied up to a distance of 3 kpc from the Sun.
For the second approach two synthetic versions of the SEGUE survey are generated based on the above models. The obtained synthetic stellar catalogues are then used to create mock samples best resembling the compiled sample of observed SEGUE G-dwarfs. Generally, mock samples are not only ideal to compare predictions from various models. They also allow validation of the models' quality and improvement as with this work could be especially achieved for TRILEGAL. While TRILEGAL reproduces the statistical properties of the thin and thick disk as seen in the observations, the MCM model has shown to be more suitable in reproducing many chemo-kinematic correlations as revealed by the SEGUE stars. However, evidence has been found that the MCM model may be missing a stellar component with the properties of the thick disk that the observations clearly show. While the SEGUE stars do indicate a thin-thick dichotomy of the stellar Galactic disk in agreement with other spectroscopic stellar studies, no sign for a distinct metal-poor disk is seen in the MCM model.
Usually stellar spectroscopic surveys are limited to a certain volume around the Sun covering different regions of the Galaxy’s disk. This often prevents to obtain a global view on the chemo-dynamics of the Galactic disk. Hence, a suitable combination of stellar samples from independent surveys is not only useful for the verification of results but it also helps to complete the picture of the Milky Way. Therefore, the thesis closes with a comparison of the SEGUE G-dwarfs and a sample of RAVE giants. The comparison reveals that the chemo-kinematic relations agree in disk regions where the samples of both surveys show a similar number of stars. For those parts of the survey volumes where one of the surveys lacks statistics they beautifully complement each other. This demonstrates that the comparison of theoretical models on the one side, and the combined observational data gathered by multiple surveys on the other side, are key ingredients to understand and disentangle the structure and formation history of the Milky Way.
Metals are often used in environments that are conducive to corrosion, which leads to a reduction in their mechanical properties and durability. Coatings are applied to corrosion-prone metals such as aluminum alloys to inhibit the destructive surface process of corrosion in a passive or active way. Standard anticorrosive coatings function as a physical barrier between the material and the corrosive environment and provide passive protection only when intact. In contrast, active protection prevents or slows down corrosion even when the main barrier is damaged. The most effective industrially used active corrosion inhibition for aluminum alloys is provided by chromate conversion coatings. However, their toxicity and worldwide restriction provoke an urgent need for finding environmentally friendly corrosion preventing systems. A promising approach to replace the toxic chromate coatings is to embed particles containing nontoxic inhibitor in a passive coating matrix. This work presents the development and optimization of effective anticorrosive coatings for the industrially important aluminum alloy, AA2024-T3 using this approach. The protective coatings were prepared by dispersing mesoporous silica containers, loaded with the nontoxic corrosion inhibitor 2-mercaptobenzothiazole, in a passive sol-gel (SiOx/ZrOx) or organic water-based layer. Two types of porous silica containers with different sizes (d ≈ 80 and 700 nm, respectively) were investigated. The studied robust containers exhibit high surface area (≈ 1000 m² g-1), narrow pore size distribution (dpore ≈ 3 nm) and large pore volume (≈ 1 mL g-1) as determined by N2 sorption measurements. These properties favored the subsequent adsorption and storage of a relatively large amount of inhibitor as well as its release in response to pH changes induced by the corrosion process. The concentration, position and size of the embedded containers were varied to ascertain the optimum conditions for overall anticorrosion performance. Attaining high anticorrosion efficiency was found to require a compromise between delivering an optimal amount of corrosion inhibitor and preserving the coating barrier properties. This study broadens the knowledge about the main factors influencing the coating anticorrosion efficiency and assists the development of optimum active anticorrosive coatings doped with inhibitor loaded containers.
In the first section of the thesis graphitic carbon nitride was for the first time synthesised using the high-temperature condensation of dicyandiamide (DCDA) – a simple molecular precursor – in a eutectic salt melt of lithium chloride and potassium chloride. The extent of condensation, namely next to complete conversion of all reactive end groups, was verified by elemental microanalysis and vibrational spectroscopy. TEM- and SEM-measurements gave detailed insight into the well-defined morphology of these organic crystals, which are not based on 0D or 1D constituents like known molecular or short-chain polymeric crystals but on the packing motif of extended 2D frameworks. The proposed crystal structure of this g-C3N4 species was derived in analogy to graphite by means of extensive powder XRD studies, indexing and refinement. It is based on sheets of hexagonally arranged s-heptazine (C6N7) units that are held together by covalent bonds between C and N atoms. These sheets stack in a graphitic, staggered fashion adopting an AB-motif, as corroborated by powder X-ray diffractometry and high-resolution transmission electron microscopy. This study was contrasted with one of many popular – yet unsuccessful – approaches in the last 30 years of scientific literature to perform the condensation of an extended carbon nitride species through synthesis in the bulk. The second section expands the repertoire of available salt melts introducing the lithium bromide and potassium bromide eutectic as an excellent medium to obtain a new phase of graphitic carbon nitride. The combination of SEM, TEM, PXRD and electron diffraction reveals that the new graphitic carbon nitride phase stacks in an ABA’ motif forming unprecedentedly large crystals. This section seizes the notion of the preceding chapter, that condensation in a eutectic salt melt is the key to obtain a high degree of conversion mainly through a solvatory effect. At the close of this chapter ionothermal synthesis is seen established as a powerful tool to overcome the inherent kinetic problems of solid state reactions such as incomplete polymerisation and condensation in the bulk especially when the temperature requirement of the reaction in question falls into the proverbial “no man’s land” of classical solvents, i.e. above 250 to 300 °C. The following section puts the claim to the test, that the crystalline carbon nitrides obtained from a salt melt are indeed graphitic. A typical property of graphite – namely the accessibility of its interplanar space for guest molecules – is transferred to the graphitic carbon nitride system. Metallic potassium and graphitic carbon nitride are converted to give the potassium intercalation compound, K(C6N8)3 designated according to its stoichiometry and proposed crystal structure. Reaction of the intercalate with aqueous solvents triggers the exfoliation of the graphitic carbon nitride material and – for the first time – enables the access of singular (or multiple) carbon nitride sheets analogous to graphene as seen in the formation of sheets, bundles and scrolls of carbon nitride in TEM imaging. The thus exfoliated sheets form a stable, strongly fluorescent solution in aqueous media, which shows no sign in UV/Vis spectroscopy that the aromaticity of individual sheets was subject to degradation. The final section expands on the mechanism underlying the formation of graphitic carbon nitride by literally expanding the distance between the covalently linked heptazine units which constitute these materials. A close examination of all proposed reaction mechanisms to-date in the light of exhaustive DSC/MS experiments highlights the possibility that the heptazine unit can be formed from smaller molecules, even if some of the designated leaving groups (such as ammonia) are substituted by an element, R, which later on remains linked to the nascent heptazine. Furthermore, it is suggested that the key functional groups in the process are the triazine- (Tz) and the carbonitrile- (CN) group. On the basis of these assumptions, molecular precursors are tailored which encompass all necessary functional groups to form a central heptazine unit of threefold, planar symmetry and then still retain outward functionalities for self-propagated condensation in all three directions. Two model systems based on a para-aryl (ArCNTz) and para-biphenyl (BiPhCNTz) precursors are devised via a facile synthetic procedure and then condensed in an ionothermal process to yield the heptazine based frameworks, HBF-1 and HBF-2. Due to the structural motifs of their molecular precursors, individual sheets of HBF-1 and HBF-2 span cavities of 14.2 Å and 23.0 Å respectively which makes both materials attractive as potential organic zeolites. Crystallographic analysis confirms the formation of ABA’ layered, graphitic systems, and the extent of condensation is confirmed as next-to-perfect by elemental analysis and vibrational spectroscopy.
In the living cell, the organization of the complex internal structure relies to a large extent on molecular motors. Molecular motors are proteins that are able to convert chemical energy from the hydrolysis of adenosine triphosphate (ATP) into mechanical work. Being about 10 to 100 nanometers in size, the molecules act on a length scale, for which thermal collisions have a considerable impact onto their motion. In this way, they constitute paradigmatic examples of thermodynamic machines out of equilibrium. This study develops a theoretical description for the energy conversion by the molecular motor myosin V, using many different aspects of theoretical physics. Myosin V has been studied extensively in both bulk and single molecule experiments. Its stepping velocity has been characterized as a function of external control parameters such as nucleotide concentration and applied forces. In addition, numerous kinetic rates involved in the enzymatic reaction of the molecule have been determined. For forces that exceed the stall force of the motor, myosin V exhibits a 'ratcheting' behaviour: For loads in the direction of forward stepping, the velocity depends on the concentration of ATP, while for backward loads there is no such influence. Based on the chemical states of the motor, we construct a general network theory that incorporates experimental observations about the stepping behaviour of myosin V. The motor's motion is captured through the network description supplemented by a Markov process to describe the motor dynamics. This approach has the advantage of directly addressing the chemical kinetics of the molecule, and treating the mechanical and chemical processes on equal grounds. We utilize constraints arising from nonequilibrium thermodynamics to determine motor parameters and demonstrate that the motor behaviour is governed by several chemomechanical motor cycles. In addition, we investigate the functional dependence of stepping rates on force by deducing the motor's response to external loads via an appropriate Fokker-Planck equation. For substall forces, the dominant pathway of the motor network is profoundly different from the one for superstall forces, which leads to a stepping behaviour that is in agreement with the experimental observations. The extension of our analysis to Markov processes with absorbing boundaries allows for the calculation of the motor's dwell time distributions. These reveal aspects of the coordination of the motor's heads and contain direct information about the backsteps of the motor. Our theory provides a unified description for the myosin V motor as studied in single motor experiments.
Cargo transport by molecular motors is ubiquitous in all eukaryotic cells and is typically driven cooperatively by several molecular motors, which may belong to one or several motor species like kinesin, dynein or myosin. These motor proteins transport cargos such as RNAs, protein complexes or organelles along filaments, from which they unbind after a finite run length. Understanding how these motors interact and how their movements are coordinated and regulated is a central and challenging problem in studies of intracellular transport. In this thesis, we describe a general theoretical framework for the analysis of such transport processes, which enables us to explain the behavior of intracellular cargos based on the transport properties of individual motors and their interactions. Motivated by recent in vitro experiments, we address two different modes of transport: unidirectional transport by two identical motors and cooperative transport by actively walking and passively diffusing motors. The case of cargo transport by two identical motors involves an elastic coupling between the motors that can reduce the motors’ velocity and/or the binding time to the filament. We show that this elastic coupling leads, in general, to four distinct transport regimes. In addition to a weak coupling regime, kinesin and dynein motors are found to exhibit a strong coupling and an enhanced unbinding regime, whereas myosin motors are predicted to attain a reduced velocity regime. All of these regimes, which we derive both by analytical calculations and by general time scale arguments, can be explored experimentally by varying the elastic coupling strength. In addition, using the time scale arguments, we explain why previous studies came to different conclusions about the effect and relevance of motor-motor interference. In this way, our theory provides a general and unifying framework for understanding the dynamical behavior of two elastically coupled molecular motors. The second mode of transport studied in this thesis is cargo transport by actively pulling and passively diffusing motors. Although these passive motors do not participate in active transport, they strongly enhance the overall cargo run length. When an active motor unbinds, the cargo is still tethered to the filament by the passive motors, giving the unbound motor the chance to rebind and continue its active walk. We develop a stochastic description for such cooperative behavior and explicitly derive the enhanced run length for a cargo transported by one actively pulling and one passively diffusing motor. We generalize our description to the case of several pulling and diffusing motors and find an exponential increase of the run length with the number of involved motors.
Hyperspectral remote sensing of the spatial and temporal heterogeneity of low Arctic vegetation
(2019)
Arctic tundra ecosystems are experiencing warming twice the global average and Arctic vegetation is responding in complex and heterogeneous ways. Shifting productivity, growth, species composition, and phenology at local and regional scales have implications for ecosystem functioning as well as the global carbon and energy balance. Optical remote sensing is an effective tool for monitoring ecosystem functioning in this remote biome. However, limited field-based spectral characterization of the spatial and temporal heterogeneity limits the accuracy of quantitative optical remote sensing at landscape scales. To address this research gap and support current and future satellite missions, three central research questions were posed:
• Does canopy-level spectral variability differ between dominant low Arctic vegetation communities and does this variability change between major phenological phases?
• How does canopy-level vegetation colour images recorded with high and low spectral resolution devices relate to phenological changes in leaf-level photosynthetic pigment concentrations?
• How does spatial aggregation of high spectral resolution data from the ground to satellite scale influence low Arctic tundra vegetation signatures and thereby what is the potential of upcoming hyperspectral spaceborne systems for low Arctic vegetation characterization?
To answer these questions a unique and detailed database was assembled. Field-based canopy-level spectral reflectance measurements, nadir digital photographs, and photosynthetic pigment concentrations of dominant low Arctic vegetation communities were acquired at three major phenological phases representing early, peak and late season. Data were collected in 2015 and 2016 in the Toolik Lake Research Natural Area located in north central Alaska on the North Slope of the Brooks Range. In addition to field data an aerial AISA hyperspectral image was acquired in the late season of 2016. Simulations of broadband Sentinel-2 and hyperspectral Environmental and Mapping Analysis Program (EnMAP) satellite reflectance spectra from ground-based reflectance spectra as well as simulations of EnMAP imagery from aerial hyperspectral imagery were also obtained.
Results showed that canopy-level spectral variability within and between vegetation communities differed by phenological phase. The late season was identified as the most discriminative for identifying many dominant vegetation communities using both ground-based and simulated hyperspectral reflectance spectra. This was due to an overall reduction in spectral variability and comparable or greater differences in spectral reflectance between vegetation communities in the visible near infrared spectrum.
Red, green, and blue (RGB) indices extracted from nadir digital photographs and pigment-driven vegetation indices extracted from ground-based spectral measurements showed strong significant relationships. RGB indices also showed moderate relationships with chlorophyll and carotenoid pigment concentrations. The observed relationships with the broadband RGB channels of the digital camera indicate that vegetation colour strongly influences the response of pigment-driven spectral indices and digital cameras can track the seasonal development and degradation of photosynthetic pigments.
Spatial aggregation of hyperspectral data from the ground to airborne, to simulated satel-lite scale was influenced by non-photosynthetic components as demonstrated by the distinct shift of the red edge to shorter wavelengths. Correspondence between spectral reflectance at the three scales was highest in the red spectrum and lowest in the near infra-red. By artificially mixing litter spectra at different proportions to ground-based spectra, correspondence with aerial and satellite spectra increased. Greater proportions of litter were required to achieve correspondence at the satellite scale.
Overall this thesis found that integrating multiple temporal, spectral, and spatial data is necessary to monitor the complexity and heterogeneity of Arctic tundra ecosystems. The identification of spectrally similar vegetation communities can be optimized using non-peak season hyperspectral data leading to more detailed identification of vegetation communities. The results also highlight the power of vegetation colour to link ground-based and satellite data. Finally, a detailed characterization non-photosynthetic ecosystem components is crucial for accurate interpretation of vegetation signals at landscape scales.
Antarctic glacier forfields are extreme environments and pioneer sites for ecological succession. The Antarctic continent shows microbial community development as a natural laboratory because of its special environment, geographic isolation and little anthropogenic influence. Increasing temperatures due to global warming lead to enhanced deglaciation processes in cold-affected habitats and new terrain is becoming exposed to soil formation and accessible for microbial colonisation. This study aims to understand the structure and development of glacier forefield bacterial communities, especially how soil parameters impact the microorganisms and how those are adapted to the extreme conditions of the habitat. To this effect, a combination of cultivation experiments, molecular, geophysical and geochemical analysis was applied to examine two glacier forfields of the Larsemann Hills, East Antarctica. Culture-independent molecular tools such as terminal restriction length polymorphism (T-RFLP), clone libraries and quantitative real-time PCR (qPCR) were used to determine bacterial diversity and distribution. Cultivation of yet unknown species was carried out to get insights in the physiology and adaptation of the microorganisms. Adaptation strategies of the microorganisms were studied by determining changes of the cell membrane phospholipid fatty acid (PLFA) inventory of an isolated bacterium in response to temperature and pH fluctuations and by measuring enzyme activity at low temperature in environmental soil samples. The two studied glacier forefields are extreme habitats characterised by low temperatures, low water availability and small oligotrophic nutrient pools and represent sites of different bacterial succession in relation to soil parameters. The investigated sites showed microbial succession at an early step of soil formation near the ice tongue in comparison to closely located but rather older and more developed soil from the forefield. At the early step the succession is influenced by a deglaciation-dependent areal shift of soil parameters followed by a variable and prevalently depth-related distribution of the soil parameters that is driven by the extreme Antarctic conditions. The dominant taxa in the glacier forefields are Actinobacteria, Acidobacteria, Proteobacteria, Bacteroidetes, Cyanobacteria and Chloroflexi. The connection of soil characteristics with bacterial community structure showed that soil parameter and soil formation along the glacier forefield influence the distribution of certain phyla. In the early step of succession the relative undifferentiated bacterial diversity reflects the undifferentiated soil development and has a high potential to shift according to past and present environmental conditions. With progressing development environmental constraints such as water or carbon limitation have a greater influence. Adapting the culturing conditions to the cold and oligotrophic environment, the number of culturable heterotrophic bacteria reached up to 108 colony forming units per gram soil and 148 isolates were obtained. Two new psychrotolerant bacteria, Herbaspirillum psychrotolerans PB1T and Chryseobacterium frigidisoli PB4T, were characterised in detail and described as novel species in the family of Oxalobacteraceae and Flavobacteriaceae, respectively. The isolates are able to grow at low temperatures tolerating temperature fluctuations and they are not specialised to a certain substrate, therefore they are well-adapted to the cold and oligotrophic environment. The adaptation strategies of the microorganisms were analysed in environmental samples and cultures focussing on extracellular enzyme activity at low temperature and PLFA analyses. Extracellular phosphatases (pH 11 and pH 6.5), β-glucosidase, invertase and urease activity were detected in the glacier forefield soils at low temperature (14°C) catalysing the conversion of various compounds providing necessary substrates and may further play a role in the soil formation and total carbon turnover of the habitat. The PLFA analysis of the newly isolated species C. frigidisoli showed that the cold-adapted strain develops different strategies to maintain the cell membrane function under changing environmental conditions by altering the PLFA inventory at different temperatures and pH values. A newly discovered fatty acid, which was not found in any other microorganism so far, significantly increased at decreasing temperature and low pH and thus plays an important role in the adaption of C. frigidisoli. This work gives insights into the diversity, distribution and adaptation mechanisms of microbial communities in oligotrophic cold-affected soils and shows that Antarctic glacier forefields are suitable model systems to study bacterial colonisation in connection to soil formation.
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.
Genome-scale metabolic models are mathematical representations of all known reactions occurring in a cell. Combined with constraints based on physiological measurements, these models have been used to accurately predict metabolic fluxes and effects of perturbations (e.g. knock-outs) and to inform metabolic engineering strategies. Recently, protein-constrained models have been shown to increase predictive potential (especially in overflow metabolism), while alleviating the need for measurement of nutrient uptake rates. The resulting modelling frameworks quantify the upkeep cost of a certain metabolic flux as the minimum amount of enzyme required for catalysis. These improvements are based on the use of in vitro turnover numbers or in vivo apparent catalytic rates of enzymes for model parameterization. In this thesis several tools for the estimation and refinement of these parameters based on in vivo proteomics data of Escherichia coli, Saccharomyces cerevisiae, and Chlamydomonas reinhardtii have been developed and applied. The difference between in vitro and in vivo catalytic rate measures for the three microorganisms was systematically analyzed. The results for the facultatively heterotrophic microalga C. reinhardtii considerably expanded the apparent catalytic rate estimates for photosynthetic organisms. Our general finding pointed at a global reduction of enzyme efficiency in heterotrophy compared to other growth scenarios. Independent of the modelled organism, in vivo estimates were shown to improve accuracy of predictions of protein abundances compared to in vitro values for turnover numbers. To further improve the protein abundance predictions, machine learning models were trained that integrate features derived from protein-constrained modelling and codon usage. Combining the two types of features outperformed single feature models and yielded good prediction results without relying on experimental transcriptomic data. The presented work reports valuable advances in the prediction of enzyme allocation in unseen scenarios using protein constrained metabolic models. It marks the first successful application of this modelling framework in the biotechnological important taxon of green microalgae, substantially increasing our knowledge of the enzyme catalytic landscape of phototrophic microorganisms.
Adsorption layers of soluble surfactants enable and govern a variety of phenomena in surface and colloidal sciences, such as foams. The ability of a surfactant solution to form wet foam lamellae is governed by the surface dilatational rheology. Only systems having a non-vanishing imaginary part in their surface dilatational modulus, E, are able to form wet foams. The aim of this thesis is to illuminate the dissipative processes that give rise to the imaginary part of the modulus. There are two controversial models discussed in the literature. The reorientation model assumes that the surfactants adsorb in two distinct states, differing in their orientation. This model is able to describe the frequency dependence of the modulus E. However, it assumes reorientation dynamics in the millisecond time regime. In order to assess this model, we designed a SHG pump-probe experiment that addresses the orientation dynamics. Results obtained reveal that the orientation dynamics occur in the picosecond time regime, being in strong contradiction with the two states model. The second model regards the interface as an interphase. The adsorption layer consists of a topmost monolayer and an adjacent sublayer. The dissipative process is due to the molecular exchange between both layers. The assessment of this model required the design of an experiment that discriminates between the surface compositional term and the sublayer contribution. Such an experiment has been successfully designed and results on elastic and viscoelastic surfactant provided evidence for the correctness of the model. Because of its inherent surface specificity, surface SHG is a powerful analytical tool that can be used to gain information on molecular dynamics and reorganization of soluble surfactants. They are central elements of both experiments. However, they impose several structural elements of the model system. During the course of this thesis, a proper model system has been identified and characterized. The combination of several linear and nonlinear optical techniques, allowed for a detailed picture of the interfacial architecture of these surfactants.
The aim of this work was the generation of carbon materials with high surface area, exhibiting a hierarchical pore system in the macro- and mesorange. Such a pore system facilitates the transport through the material and enhances the interaction with the carbon matrix (macropores are pores with diameters > 50 nm, mesopores between 2 – 50 nm). Thereto, new strategies for the synthesis of novel carbon materials with designed porosity were developed that are in particular useful for the storage of energy. Besides the porosity, it is the graphene structure itself that determines the properties of a carbon material. Non-graphitic carbon materials usually exhibit a quite large degree of disorder with many defects in the graphene structure, and thus exhibit inherent microporosity (d < 2nm). These pores are traps and oppose reversible interaction with the carbon matrix. Furthermore they reduce the stability and conductivity of the carbon material, which was undesired for the proposed applications. As one part of this work, the graphene structures of different non-graphitic carbon materials were studied in detail using a novel wide-angle x-ray scattering model that allowed precise information about the nature of the carbon building units (graphene stacks). Different carbon precursors were evaluated regarding their potential use for the synthesis shown in this work, whereas mesophase pitch proved to be advantageous when a less disordered carbon microstructure is desired. By using mesophase pitch as carbon precursor, two templating strategies were developed using the nanocasting approach. The synthesized (monolithic) materials combined for the first time the advantages of a hierarchical interconnected pore system in the macro- and mesorange with the advantages of mesophase pitch as carbon precursor. In the first case, hierarchical macro- / mesoporous carbon monoliths were synthesized by replication of hard (silica) templates. Thus, a suitable synthesis procedure was developed that allowed the infiltration of the template with the hardly soluble carbon precursor. In the second case, hierarchical macro- / mesoporous carbon materials were synthesized by a novel soft-templating technique, taking advantage of the phase separation (spinodal decomposition) between mesophase pitch and polystyrene. The synthesis also allowed the generation of monolithic samples and incorporation of functional nanoparticles into the material. The synthesized materials showed excellent properties as an anode material in lithium batteries and support material for supercapacitors.