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In the present thesis, self-assembly of hydrophilic polymers, reinforced hydrogels and inorganic/polymer hybrids were examined. The thesis describes an avenue from polymer synthesis via various methods over polymer self-assembly to the formation of polymer materials that have promising properties for future applications.
Hydrophilic polymers were utilized to form multi-phase systems, water-in-water emulsions and self-assembled structures, e.g. particles/aggregates or hollow structures from completely water-soluble building blocks. The structuring of aqueous environments by hydrophilic homo and block copolymers was further utilized in the formation of supramolecular hydrogels with compartments or specific thermal behavior. Furthermore, inorganic graphitic carbon nitride (g-CN) was utilized as photoinitiator for hydrogel formation and as reinforcer for hydrogels. As such, hydrogels with remarkable mechanical properties were synthesized, e.g. high compressibility, high storage modulus or lubricity. In addition, g-CN was combined with polymers for a broad range of materials, e.g. coatings, films or latex, that could be utilized in photocatalytic applications. Another inorganic material class was combined with polymers in the present thesis as well, namely metal-organic frameworks (MOFs). It was shown that the pore structure of MOFs enables improved control over tacticity and achievement of high molar masses. Furthermore, MOF-based polymerization catalysis was introduced with improved control for coordinating monomers, catalyst recyclability and decreased metal contamination in the product. Finally, the effect of external influence on MOF morphology was studied, e.g. via solvent or polymer additives, which allowed the formation of various MOF structures.
Overall, advances in several areas of polymer science are presented in here. A major topic of the thesis was hydrophilic polymers and hydrogels that currently constitute significant materials in the polymer field due to promising future applications in biomedicine. Moreover, the combination of polymers with materials from other areas of research, i.e. g-CN and MOFs, provided various new materials with remarkable properties also of interest for applications in the future, e.g. coatings, particle structures and catalysis.
Rivers have always flooded their floodplains. Over 2.5 billion people worldwide have been affected by flooding in recent decades. The economic damage is also considerable, averaging 100 billion US dollars per year. There is no doubt that damage and other negative effects of floods can be avoided. However, this has a price: financially and politically. Costs and benefits can be estimated through risk assessments. Questions about the location and frequency of floods, about the objects that could be affected and their vulnerability are of importance for flood risk managers, insurance companies and politicians. Thus, both variables and factors from the fields of hydrology and sociol-economics play a role with multi-layered connections. One example are dikes along a river, which on the one hand contain floods, but on the other hand, by narrowing the natural floodplains, accelerate the flood discharge and increase the danger of flooding for the residents downstream. Such larger connections must be included in the assessment of flood risk. However, in current procedures this is accompanied by simplifying assumptions. Risk assessments are therefore fuzzy and associated with uncertainties.
This thesis investigates the benefits and possibilities of new data sources for improving flood risk assessment. New methods and models are developed, which take the mentioned interrelations better into account and also quantify the existing uncertainties of the model results, and thus enable statements about the reliability of risk estimates. For this purpose, data on flood events from various sources are collected and evaluated. This includes precipitation and flow records at measuring stations as well as for instance images from social media, which can help to delineate the flooded areas and estimate flood damage with location information. Machine learning methods have been successfully used to recognize and understand correlations between floods and impacts from a wide range of data and to develop improved models.
Risk models help to develop and evaluate strategies to reduce flood risk. These tools also provide advanced insights into the interplay of various factors and on the expected consequences of flooding. This work shows progress in terms of an improved assessment of flood risks by using diverse data from different sources with innovative methods as well as by the further development of models. Flood risk is variable due to economic and climatic changes, and other drivers of risk. In order to keep the knowledge about flood risks up-to-date, robust, efficient and adaptable methods as proposed in this thesis are of increasing importance.