Refine
Has Fulltext
- yes (135) (remove)
Year of publication
- 2017 (135) (remove)
Document Type
- Doctoral Thesis (135) (remove)
Keywords
- FRET (3)
- Klimawandel (3)
- Nanopartikel (3)
- climate change (3)
- Adipositas (2)
- Arbeitsmarktpolitik (2)
- Bioraffinerie (2)
- Calciumphosphat (2)
- DNA origami (2)
- Depression (2)
Institute
- Institut für Chemie (25)
- Institut für Geowissenschaften (21)
- Institut für Physik und Astronomie (14)
- Institut für Biochemie und Biologie (13)
- Department Psychologie (8)
- Wirtschaftswissenschaften (7)
- Department Linguistik (6)
- Institut für Mathematik (6)
- Department Sport- und Gesundheitswissenschaften (5)
- Institut für Ernährungswissenschaft (5)
Functional nanoporous carbon-based materials derived from oxocarbon-metal coordination complexes
(2017)
Nanoporous carbon based materials are of particular interest for both science and industry due to their exceptional properties such as a large surface area, high pore volume, high electroconductivity as well as high chemical and thermal stability. Benefiting from these advantageous properties, nanoporous carbons proved to be useful in various energy and environment related applications including energy storage and conversion, catalysis, gas sorption and separation technologies. The synthesis of nanoporous carbons classically involves thermal carbonization of the carbon precursors (e.g. phenolic resins, polyacrylonitrile, poly(vinyl alcohol) etc.) followed by an activation step and/or it makes use of classical hard or soft templates to obtain well-defined porous structures. However, these synthesis strategies are complicated and costly; and make use of hazardous chemicals, hindering their application for large-scale production. Furthermore, control over the carbon materials properties is challenging owing to the relatively unpredictable processes at the high carbonization temperatures.
In the present thesis, nanoporous carbon based materials are prepared by the direct heat treatment of crystalline precursor materials with pre-defined properties. This synthesis strategy does not require any additional carbon sources or classical hard- or soft templates. The highly stable and porous crystalline precursors are based on coordination compounds of the squarate and croconate ions with various divalent metal ions including Zn2+, Cu2+, Ni2+, and Co2+, respectively. Here, the structural properties of the crystals can be controlled by the choice of appropriate synthesis conditions such as the crystal aging temperature, the ligand/metal molar ratio, the metal ion, and the organic ligand system. In this context, the coordination of the squarate ions to Zn2+ yields porous 3D cube crystalline particles. The morphology of the cubes can be tuned from densely packed cubes with a smooth surface to cubes with intriguing micrometer-sized openings and voids which evolve on the centers of the low index faces as the crystal aging temperature is raised. By varying the molar ratio, the particle shape can be changed from truncated cubes to perfect cubes with right-angled edges.
These crystalline precursors can be easily transformed into the respective carbon based materials by heat treatment at elevated temperatures in a nitrogen atmosphere followed by a facile washing step. The resulting carbons are obtained in good yields and possess a hierarchical pore structure with well-organized and interconnected micro-, meso- and macropores. Moreover, high surface areas and large pore volumes of up to 1957 m2 g-1 and 2.31 cm3 g-1 are achieved, respectively, whereby the macroscopic structure of the precursors is preserved throughout the whole synthesis procedure.
Owing to these advantageous properties, the resulting carbon based materials represent promising supercapacitor electrode materials for energy storage applications. This is exemplarily demonstrated by employing the 3D hierarchical porous carbon cubes derived from squarate-zinc coordination compounds as electrode material showing a specific capacitance of 133 F g-1 in H2SO4 at a scan rate of 5 mV s-1 and retaining 67% of this specific capacitance when the scan rate is increased to 200 mV s-1.
In a further application, the porous carbon cubes derived from squarate-zinc coordination compounds are used as high surface area support material and decorated with nickel nanoparticles via an incipient wetness impregnation. The resulting composite material combines a high surface area, a hierarchical pore structure with high functionality and well-accessible pores. Moreover, owing to their regular micro-cube shape, they allow for a good packing of a fixed-bed flow reactor along with high column efficiency and a minimized pressure drop throughout the packed reactor. Therefore, the composite is employed as heterogeneous catalyst in the selective hydrogenation of 5-hydroxymethylfurfural to 2,5-dimethylfuran showing good catalytic performance and overcoming the conventional problem of column blocking.
Thinking about the rational design of 3D carbon geometries, the functions and properties of the resulting carbon-based materials can be further expanded by the rational introduction of heteroatoms (e.g. N, B, S, P, etc.) into the carbon structures in order to alter properties such as wettability, surface polarity as well as the electrochemical landscape. In this context, the use of crystalline materials based on oxocarbon-metal ion complexes can open a platform of highly functional materials for all processes that involve surface processes.
In littoral zones of lakes, multiple processes determine lake ecology and water quality. Lacustrine groundwater discharge (LGD), most frequently taking place in littoral zones, can transport or mobilize nutrients from the sediments and thus contribute significantly to lake eutrophication. Furthermore, lake littoral zones are the habitat of benthic primary producers, namely submerged macrophytes and periphyton, which play a key role in lake food webs and influence lake water quality. Groundwater-mediated nutrient-influx can potentially affect the asymmetric competition between submerged macrophytes and periphyton for light and nutrients. While rooted macrophytes have superior access to sediment nutrients, periphyton can negatively affect macrophytes by shading. LGD may thus facilitate periphyton production at the expense of macrophyte production, although studies on this hypothesized effect are missing.
The research presented in this thesis is aimed at determining how LGD influences periphyton, macrophytes, and the interactions between these benthic producers. Laboratory experiments were combined with field experiments and measurements in an oligo-mesotrophic hard water lake.
In the first study, a general concept was developed based on a literature review of the existing knowledge regarding the potential effects of LGD on nutrients and inorganic and organic carbon loads to lakes, and the effect of these loads on periphyton and macrophytes. The second study includes a field survey and experiment examining the effects of LGD on periphyton in an oligotrophic, stratified hard water lake (Lake Stechlin). This study shows that LGD, by mobilizing phosphorus from the sediments, significantly promotes epiphyton growth, especially at the end of the summer season when epilimnetic phosphorus concentrations are low. The third study focuses on the potential effects of LGD on submerged macrophytes in Lake Stechlin. This study revealed that LGD may have contributed to an observed change in macrophyte community composition and abundance in the shallow littoral areas of the lake. Finally, a laboratory experiment was conducted which mimicked the conditions of a seepage lake. Groundwater circulation was shown to mobilize nutrients from the sediments, which significantly promoted periphyton growth. Macrophyte growth was negatively affected at high periphyton biomasses, confirming the initial hypothesis.
More generally, this thesis shows that groundwater flowing into nutrient-limited lakes may import or mobilize nutrients. These nutrients first promote periphyton, and subsequently provoke radical changes in macrophyte populations before finally having a possible influence on the lake’s trophic state. Hence, the eutrophying effect of groundwater is delayed and, at moderate nutrient loading rates, partly dampened by benthic primary producers. The present research emphasizes the importance and complexity of littoral processes, and the need to further investigate and monitor the benthic environment. As present and future global changes can significantly affect LGD, the understanding of these complex interactions is required for the sustainable management of lake water quality.
In the wake of 21st century, humanity witnessed a phenomenal raise of urban agglomerations as powerhouses for innovation and socioeconomic growth. Driving much of national (and in few instances even global) economy, such a gargantuan raise of cities is also accompanied by subsequent increase in energy, resource consumption and waste generation. Much of anthropogenic transformation of Earth's environment in terms of environmental pollution at local level to planetary scale in the form of climate change is currently taking place in cities. Projected to be crucibles for entire humanity by the end of this century, the ultimate fate of humanity predominantly lies in the hands of technological innovation, urbanites' attitudes towards energy/resource consumption and development pathways undertaken by current and future cities. Considering the unparalleled energy, resource consumption and emissions currently attributed to global cities, this thesis addresses these issues from an efficiency point of view. More specifically, this thesis addresses the influence of population size, density, economic geography and technology in improving urban greenhouse gas (GHG) emission efficiency and identifies the factors leading to improved eco-efficiency in cities. In order to investigate the in uence of these factors in improving emission and resource efficiency in cities, a multitude of freely available datasets were coupled with some novel methodologies and analytical approaches in this thesis.
Merging the well-established Kaya Identity to the recently developed urban scaling laws, an Urban Kaya Relation is developed to identify whether large cities are more emission efficient and the intrinsic factors leading to such (in)efficiency. Applying Urban Kaya Relation to a global dataset of 61 cities in 12 countries, this thesis identifed that large cities in developed regions of the world will bring emission efficiency gains because of the better technologies implemented in these cities to produce and utilize energy consumption while the opposite is the case for cities in developing regions. Large cities in developing countries are less efficient mainly because of their affluence and lack of efficient technologies. Apart from the in uence of population size on emission efficiency, this thesis identified the crucial role played by population density in improving building and on-road transport sector related emission efficiency in cities. This is achieved by applying the City Clustering Algorithm (CCA) on two different gridded land use datasets and a standard emission inventory to attribute these sectoral emissions to all inhabited settlements in the USA. Results show that doubling the population density would entail a reduction in the total CO2 emissions in buildings and on-road sectors typically by at least 42 %. Irrespective of their population size and density, cities are often blamed for their intensive resource consumption that threatens not only local but also global sustainability. This thesis merged the concept of urban metabolism with benchmarking and identified cities which are eco-efficient. These cities enable better socioeconomic conditions while being less burden to the environment. Three environmental burden indicators (annual average NO2 concentration, per capita waste generation and water consumption) and two socioeconomic indicators (GDP per capita and employment ratio) for 88 most populous European cities are considered in this study. Using two different non-parametric ranking methods namely regression residual ranking and Data Envelopment Analysis (DEA), eco-efficient cities and their determining factors are identified. This in-depth analysis revealed that mature cities with well-established economic structures such as Munich, Stockholm and Oslo are eco-efficient. Further, correlations between objective eco-efficiency ranking with each of the indicator rankings and the ranking of urbanites' subjective perception about quality of life are analyzed. This analysis revealed that urbanites' perception about quality of life is not merely confined to the socioeconomic well-being but rather to their combination with lower environmental burden.
In summary, the findings of this dissertation has three general conclusions for improving emission and ecological efficiency in cities. Firstly, large cities in emerging nations face a huge challenge with respect to improving their emission efficiency. The task in front of these cities is threefold: (1) deploying efficient technologies for the generation of electricity and improvement of public transportation to unlock their leap frogging potential, (2) addressing the issue of energy poverty and (3) ensuring that these cities do not develop similar energy consumption patterns with infrastructure lock-in behavior similar to those of cities in developed regions. Secondly, the on-going urban sprawl as a global phenomenon will decrease the emission efficiency within the building and transportation sector. Therefore, local policy makers should identify adequate fiscal and land use policies to curb urban sprawl. Lastly, since mature cities with well-established economic structures are more eco-efficient and urbanites' perception re ects its combination with decreasing environmental burden; there is a need to adopt and implement strategies which enable socioeconomic growth in cities whilst decreasing their environment burden.
Lithospheric plates move over the low viscosity asthenosphere balancing several forces. The driving forces include basal shear stress exerted by mantle convection and plate boundary forces such as slab pull and ridge push, whereas the resisting forces include inter-plate friction, trench resistance, and cratonic root resistance. These generate plate motions, the lithospheric stress field and dynamic topography which are observed with different geophysical methods. The orientation and tectonic regime of the observed crustal/lithospheric stress field further contribute to our knowledge of different deformation processes occurring within the Earth's crust and lithosphere. Using numerical models previous studies were able to identify major forces generating stresses in the crust and lithosphere which also contribute to the formation of topography as well as driving lithospheric plates. They showed that the first-order stress pattern explaining about 80\,\% of the stress field originates from a balance of forces acting at the base of the moving lithospheric plates due to convective flow in the underlying mantle. The remaining second-order stress pattern is due to lateral density variations in the crust and lithosphere in regions of pronounced topography and high gravitational potential, such as the Himalayas and mid-ocean ridges. By linking global lithosphere dynamics to deep mantle flow this study seeks to evaluate the influence of shallow and deep density heterogenities on plate motions, lithospheric stress field and dynamic topography using the geoid as a major constraint for mantle rheology. We use the global 3D lithosphere-asthenosphere model SLIM3D with visco-elasto-plastic rheology coupled at 300 km depth to a spectral model of mantle flow. The complexity of the lithosphere-asthenosphere component allows for the simulation of power-law rheology with creep parameters accounting for both diffusion and dislocation creep within the uppermost 300 km.
First we investigate the influence of intra-plate friction and asthenospheric viscosity on present-day plate motions. Previous modelling studies have suggested that small friction coefficients (µ < 0.1, yield stress ~ 100 MPa) can lead to plate tectonics in models of mantle convection. Here we show that, in order to match present-day plate motions and net rotation, the frictional parameter must be less than 0.05. We are able to obtain a good fit with the magnitude and orientation of observed plate velocities (NUVEL-1A) in a no-net-rotation (NNR) reference frame with µ < 0.04 and minimum asthenosphere viscosity ~ 5*10e19 Pas to 10e20 Pas. Our estimates of net rotation (NR) of the lithosphere suggest that amplitudes ~ 0.1-0.2 °/Ma, similar to most observation-based estimates, can be obtained with asthenosphere viscosity cutoff values of ~ 10e19 Pas to 5*10e19 Pas and friction coefficient µ < 0.05.
The second part of the study investigates further constraints on shallow and deep mantle heterogeneities causing plate motion by predicting lithosphere stress field and topography and validating with observations. Lithosphere stresses and dynamic topography are computed using the modelling setup and rheological parameters for prescribed plate motions. We validate our results with the World Stress Map 2016 (WSM2016) and the observed residual topography. Here we tested a number of upper mantle thermal-density structures. The one used to calculate plate motions is considered the reference thermal-density structure. This model is derived from a heat flow model combined with a sea floor age model. In addition we used three different thermal-density structures derived from global S-wave velocity models to show the influence of lateral density heterogeneities in the upper 300 km on model predictions. A large portion of the total dynamic force generating stresses in the crust/lithosphere has its origin in the deep mantle, while topography is largely influenced by shallow heterogeneities. For example, there is hardly any difference between the stress orientation patterns predicted with and without consideration of the heterogeneities in the upper mantle density structure across North America, Australia, and North Africa. However, the crust is dominant in areas of high altitude for the stress orientation compared to the all deep mantle contribution.
This study explores the sensitivity of all the considered surface observables with regards to model parameters providing insights into the influence of the asthenosphere and plate boundary rheology on plate motion as we test various thermal-density structures to predict stresses and topography.
This cumulative dissertation consists of five chapters. In terms of research content, my thesis can be divided into two parts. Part one examines local interactions and spillover effects between small regional governments using spatial econometric methods. The second part focuses on patterns within municipalities and inspects which institutions of citizen participation, elections and local petitions, influence local housing policies.
The central aim of this thesis is to demonstrate the benefits of innovative frequency-based methods to better explain the variability observed in lake ecosystems. Freshwater ecosystems may be the most threatened part of the hydrosphere. Lake ecosystems are particularly sensitive to changes in climate and land use because they integrate disturbances across their entire catchment. This makes understanding the dynamics of lake ecosystems an intriguing and important research priority. This thesis adds new findings to the baseline knowledge regarding variability in lake ecosystems. It provides a literature-based, data-driven and methodological framework for the investigation of variability and patterns in environmental parameters in the time frequency domain.
Observational data often show considerable variability in the environmental parameters of lake ecosystems. This variability is mostly driven by a plethora of periodic and stochastic processes inside and outside the ecosystems. These run in parallel and may operate at vastly different time scales, ranging from seconds to decades. In measured data, all of these signals are superimposed, and dominant processes may obscure the signals of other processes, particularly when analyzing mean values over long time scales. Dominant signals are often caused by phenomena at long time scales like seasonal cycles, and most of these are well understood in the limnological literature. The variability injected by biological, chemical and physical processes operating at smaller time scales is less well understood. However, variability affects the state and health of lake ecosystems at all time scales. Besides measuring time series at sufficiently high temporal resolution, the investigation of the full spectrum of variability requires innovative methods of analysis.
Analyzing observational data in the time frequency domain allows to identify variability at different time scales and facilitates their attribution to specific processes. The merit of this approach is subsequently demonstrated in three case studies. The first study uses a conceptual analysis to demonstrate the importance of time scales for the detection of ecosystem responses to climate change. These responses often occur during critical time windows in the year, may exhibit a time lag and can be driven by the exceedance of thresholds in their drivers. This can only be detected if the temporal resolution of the data is high enough. The second study applies Fast Fourier Transform spectral analysis to two decades of daily water temperature measurements to show how temporal and spatial scales of water temperature variability can serve as an indicator for mixing in a shallow, polymictic lake. The final study uses wavelet coherence as a diagnostic tool for limnology on a multivariate high-frequency data set recorded between the onset of ice cover and a cyanobacteria summer bloom in the year 2009 in a polymictic lake. Synchronicities among limnological and meteorological time series in narrow frequency bands were used to identify and disentangle prevailing limnological processes.
Beyond the novel empirical findings reported in the three case studies, this thesis aims to more generally be of interest to researchers dealing with now increasingly available time series data at high temporal resolution. A set of innovative methods to attribute patterns to processes, their drivers and constraints is provided to help make more efficient use of this kind of data.
Natural products and their derivatives have always been a source of drug leads. In particular, bacterial compounds have played an important role in drug development, for example in the field of antibiotics. A decrease in the discovery of novel leads from natural sources and the hope of finding new leads through the generation of large libraries of drug-like compounds by combinatorial chemistry aimed at specific molecular targets drove the pharmaceutical companies away from research on natural products. However, recent technological advances in genetics, bioinformatics and analytical chemistry have revived the interest in natural products. The ribosomally synthesized and post-translationally modified peptides (RiPPs) are a group of natural products generated by the action of post-translationally modifying enzymes on precursor peptides translated from mRNA by ribosomes. The great substrate promiscuity exhibited by many of the enzymes from RiPP biosynthetic pathways have led to the generation of hundreds of novel synthetic and semisynthetic variants, including variants carrying non-canonical amino acids (ncAAs). The microviridins are a family of RiPPs characterized by their atypical tricyclic structure composed of lactone and lactam rings, and their activity as serine protease inhibitors. The generalities of their biosynthetic pathway have already been described, however, the lack of information on details such as the protease responsible for cleaving off the leader peptide from the cyclic core peptide has impeded the fast and cheap production of novel microviridin variants. In the present work, knowledge on leader peptide activation of enzymes from other RiPP families has been extrapolated to the microviridin family, making it possible to bypass the need of a leader peptide. This feature allowed for the exploitation of the microviridin biosynthetic machinery for the production of novel variants through the establishment of an efficient one-pot in vitro platform. The relevance of this chemoenzymatic approach has been exemplified by the synthesis of novel potent serine protease inhibitors from both rationally-designed peptide libraries and bioinformatically predicted microviridins. Additionally, new structure-activity relationships (SARs) could be inferred by screening microviridin intermediates. The significance of this technique was further demonstrated by the simple incorporation of ncAAs into the microviridin scaffold.
Start-up incentives targeted at unemployed individuals have become an important tool of the Active Labor Market Policy (ALMP) to fight unemployment in many countries in recent years. In contrast to traditional ALMP instruments like training measures, wage subsidies, or job creation schemes, which are aimed at reintegrating unemployed individuals into dependent employment, start-up incentives are a fundamentally different approach to ALMP, in that they intend to encourage and help unemployed individuals to exit unemployment by entering self-employment and, thus, by creating their own jobs. In this sense, start-up incentives for unemployed individuals serve not only as employment and social policy to activate job seekers and combat unemployment but also as business policy to promote entrepreneurship. The corresponding empirical literature on this topic so far has been mainly focused on the individual labor market perspective, however. The main part of the thesis at hand examines the new start-up subsidy (“Gründungszuschuss”) in Germany and consists of four empirical analyses that extend the existing evidence on start-up incentives for unemployed individuals from multiple perspectives and in the following directions:
First, it provides the first impact evaluation of the new start-up subsidy in Germany. The results indicate that participation in the new start-up subsidy has significant positive and persistent effects on both reintegration into the labor market as well as the income profiles of participants, in line with previous evidence on comparable German and international programs, which emphasizes the general potential of start-up incentives as part of the broader ALMP toolset. Furthermore, a new innovative sensitivity analysis of the applied propensity score matching approach integrates findings from entrepreneurship and labor market research about the key role of an individual’s personality on start-up decision, business performance, as well as general labor market outcomes, into the impact evaluation of start-up incentives. The sensitivity analysis with regard to the inclusion and exclusion of usually unobserved personality variables reveals that differences in the estimated treatment effects are small in magnitude and mostly insignificant. Consequently, concerns about potential overestimation of treatment effects in previous evaluation studies of similar start-up incentives due to usually unobservable personality variables are less justified, as long as the set of observed control variables is sufficiently informative (Chapter 2).
Second, the thesis expands our knowledge about the longer-term business performance and potential of subsidized businesses arising from the start-up subsidy program. In absolute terms, the analysis shows that a relatively high share of subsidized founders successfully survives in the market with their original businesses in the medium to long run. The subsidy also yields a “double dividend” to a certain extent in terms of additional job creation. Compared to “regular”, i.e., non-subsidized new businesses founded by non-unemployed individuals in the same quarter, however, the economic and growth-related impulses set by participants of the subsidy program are only limited with regard to employment growth, innovation activity, or investment. Further investigations of possible reasons for these differences show that differential business growth paths of subsidized founders in the longer run seem to be mainly limited by higher restrictions to access capital and by unobserved factors, such as less growth-oriented business strategies and intentions, as well as lower (subjective) entrepreneurial persistence. Taken together, the program has only limited potential as a business and entrepreneurship policy intended to induce innovation and economic growth (Chapters 3 and 4).
And third, an empirical analysis on the level of German regional labor markets yields that there is a high regional variation in subsidized start-up activity relative to overall new business formation. The positive correlation between regular start-up intensity and the share among all unemployed individuals who participate in the start-up subsidy program suggests that (nascent) unemployed founders also profit from the beneficial effects of regional entrepreneurship capital. Moreover, the analysis of potential deadweight and displacement effects from an aggregated regional perspective emphasizes that the start-up subsidy for unemployed individuals represents a market intervention into existing markets, which affects incumbents and potentially produces inefficiencies and market distortions. This macro perspective deserves more attention and research in the future (Chapter 5).
In Germany more than 200.000 persons die of cancer every year, which makes it the second most common cause of death. Chemotherapy and radiation therapy are often combined to exploit a supra-additive effect, as some chemotherapeutic agents like halogenated nucleobases sensitize the cancerous tissue to radiation. The radiosensitizing action of certain therapeutic agents can be at least partly assigned to their interaction with secondary low energy electrons (LEEs) that are generated along the track of the ionizing radiation. In the therapy of cancer DNA is an important target, as severe DNA damage like double strand breaks induce the cell death. As there is only a limited number of radiosensitizing agents in clinical practice, which are often strongly cytotoxic, it would be beneficial to get a deeper understanding of the interaction of less toxic potential radiosensitizers with secondary reactive species like LEEs. Beyond that LEEs can be generated by laser illuminated nanoparticles that are applied in photothermal therapy (PTT) of cancer, which is an attempt to treat cancer by an increase of temperature in the cells. However, the application of halogenated nucleobases in PTT has not been taken into account so far. In this thesis the interaction of the potential radiosensitizer 8-bromoadenine (8BrA) with LEEs was studied. In a first step the dissociative electron attachment (DEA) in the gas phase was studied in a crossed electron-molecular beam setup. The main fragmentation pathway was revealed as the cleavage of the C-Br bond. The formation of a stable parent anion was observed for electron energies around 0 eV. Furthermore, DNA origami nanostructures were used as platformed to determine electron induced strand break cross sections of 8BrA sensitized oligonucleotides and the corresponding nonsensitized sequence as a function of the electron energy. In this way the influence of the DEA resonances observed for the free molecules on the DNA strand breaks was examined. As the surrounding medium influences the DEA, pulsed laser illuminated gold nanoparticles (AuNPs) were used as a nanoscale electron source in an aqueous environment. The dissociation of brominated and native nucleobases was tracked with UV-Vis absorption spectroscopy and the generated fragments were identified with surface enhanced Raman scattering (SERS). Beside the electron induced damage, nucleobase analogues are decomposed in the vicinity of the laser illuminatednanoparticles due to the high temperatures. In order to get a deeper understanding of the different dissociation mechanisms, the thermal decomposition of the nucleobases in these systems was studied and the influence of the adsorption kinetics of the molecules was elucidated. In addition to the pulsed laser experiments, a dissociative electron transfer from plasmonically generated ”hot electrons” to 8BrA was observed under low energy continuous wave laser illumination and tracked with SERS. The reaction was studied on AgNPs and AuNPs as a function of the laser intensity and wavelength. On dried samples the dissociation of the molecule was described by fractal like kinetics. In solution, the dissociative electron transfer was observed as well. It turned out that the timescale of the reaction rates were slightly below typical integration times of Raman spectra. In consequence such reactions need to be taken into account in the interpretation of SERS spectra of electrophilic molecules. The findings in this thesis help to understand the interaction of brominated nucleobases with plasmonically generated electrons and free electrons. This might help to evaluate the potential radiosensitizing action of such molecules in cancer radiation therapy and PTT.
Data profiling is the computer science discipline of analyzing a given dataset for its metadata. The types of metadata range from basic statistics, such as tuple counts, column aggregations, and value distributions, to much more complex structures, in particular inclusion dependencies (INDs), unique column combinations (UCCs), and functional dependencies (FDs). If present, these statistics and structures serve to efficiently store, query, change, and understand the data. Most datasets, however, do not provide their metadata explicitly so that data scientists need to profile them.
While basic statistics are relatively easy to calculate, more complex structures present difficult, mostly NP-complete discovery tasks; even with good domain knowledge, it is hardly possible to detect them manually. Therefore, various profiling algorithms have been developed to automate the discovery. None of them, however, can process datasets of typical real-world size, because their resource consumptions and/or execution times exceed effective limits.
In this thesis, we propose novel profiling algorithms that automatically discover the three most popular types of complex metadata, namely INDs, UCCs, and FDs, which all describe different kinds of key dependencies. The task is to extract all valid occurrences from a given relational instance. The three algorithms build upon known techniques from related work and complement them with algorithmic paradigms, such as divide & conquer, hybrid search, progressivity, memory sensitivity, parallelization, and additional pruning to greatly improve upon current limitations. Our experiments show that the proposed algorithms are orders of magnitude faster than related work. They are, in particular, now able to process datasets of real-world, i.e., multiple gigabytes size with reasonable memory and time consumption.
Due to the importance of data profiling in practice, industry has built various profiling tools to support data scientists in their quest for metadata. These tools provide good support for basic statistics and they are also able to validate individual dependencies, but they lack real discovery features even though some fundamental discovery techniques are known for more than 15 years. To close this gap, we developed Metanome, an extensible profiling platform that incorporates not only our own algorithms but also many further algorithms from other researchers. With Metanome, we make our research accessible to all data scientists and IT-professionals that are tasked with data profiling. Besides the actual metadata discovery, the platform also offers support for the ranking and visualization of metadata result sets.
Being able to discover the entire set of syntactically valid metadata naturally introduces the subsequent task of extracting only the semantically meaningful parts. This is challenge, because the complete metadata results are surprisingly large (sometimes larger than the datasets itself) and judging their use case dependent semantic relevance is difficult. To show that the completeness of these metadata sets is extremely valuable for their usage, we finally exemplify the efficient processing and effective assessment of functional dependencies for the use case of schema normalization.