This research investigates TiO₂-based catalysts for the CO₂ methanation in the context of Power-to-Gas applications, contributing to long-term energy storage and decarbonization in line with the Paris Agreement. Open-cell ceramic foam carriers coated with nanostructured nickel layers are utilized to enable stable and efficient operation under dynamic conditions. The developed systems exhibit high methane yields close to thermodynamic equilibrium, demonstrate long-term stability and are suitable for economically viable load-change operations.

Mo Si based alloys with high Ti concentrations represent a new class of high temperature materials offering promising properties for structural applications such as gas turbines, exceeding the thermal capabilities of Ni based superalloys. The combination of these three elements provides high melting temperatures, substantial creep resistance, and exceptional phase stability. Recent alloys in this field have successfully addressed the primary challenge of inadequate oxidation resistance known as "pesting" in air environment. Furthermore, it is recognized that water vapor, constituting approximately 10 vol.% of relevant turbine environments, causes otherwise protective oxides such as SiO2 to react, forming volatile Si(OH)4. However, the exact mechanisms remain inadequately understood. This study evaluates the high-temperature oxidation behavior of both multi phase (MoSS, T1, T2, A15) (Mo9Si8B and Mo12,5Si8,5B27,5Ti2Fe (at.%)) and two phase (MoSS, T1/D88) (Mo20Si52,8Ti and Mo21Si34Ti0,5B) Mo Si (B) (Ti) (Fe) alloys under dry and water vapor containing (wet) atmosphere. To ensure reliable oxidation protection at lower temperatures in wet atmosphere, Si and Yb silicate Environmental Barrier Coatings (EBCs) are utilized. The goal is to acquire comprehensive knowledge about the oxidation behavior of Mo Si (B) (Ti) (Fe) alloys with and without coating systems in complex atmospheres. Oxidation tests are conducted at 1200 °C in both dry and wet atmospheres for up to 100 h. Techniques such as thermogravimetric analysis (TGA), scanning electron microscopy (SEM), X ray diffraction (XRD), and transmission electron microscopy (TEM) including electron energy loss spectroscopy (EELS) are employed to assess the oxidation kinetics and to characterize the microstructural development of the oxide layers. The results from these investigations highlight that an increase in Ti content in general enhances the oxidation resistance in the Mo Si (B) (Ti) (Fe) system, manifesting itself in reduced oxide layer thicknesses and lower specific mass changes. This enhancement is closely linked to a finer and more homogeneous microstructure, which promotes the oxidation stability via shorter diffusion paths. However, a higher Ti content in coated alloys leads to thicker oxide layers and higher specific mass changes, particularly in the Mo20Si52,8Ti alloy as compared to the Mo12,5Si8,5B27,5Ti2Fe alloy. The use of a wet atmosphere results in increased specific mass changes and thicker oxide layers across all alloys, primarily due to the formation of volatile oxides such as MoO3, which accelerate the oxidation rate. Coated alloys demonstrate exceptional oxidation resistance with minimal specific mass changes and oxide layer thicknesses, especially evident for the Mo12,5Si8,5B27,5Ti2Fe alloy. Additionally, the investigation of the oxide layer on the Mo20Si52,8Ti alloy confirms the significance of a nearly pore-free SiO2 layer for effective oxidation resistance. The supplementary application of Yb silicate layers effectively minimizes the impact of water vapor and shows only a slightly worsened oxidation behavior under wet conditions, underscoring the protective nature of the coatings. These results emphasize the necessity of an effective coating system to ensure satisfactory oxidation resistance in wet atmospheres.

Rescue emergencies are generally quite strenuous and challenging because they deal with human lives in a situation where it is difficult to apprehend precisely the health status of a distressed patient due to personal and work limitations. Often the rescue personnel have to deal with high levels of mental and physical stress, trauma, and emotional strain during rescue operations, especially when dealing with injured and vulnerable victims. This certainly impacts their decision-making abilities and overall well-being. Time, the main constraint in such rescue situations, also plays a vital role in decision-making. Therefore, it is necessary to recognize relevant situations i.e. health complications of the rescue patients on site, and to take appropriate first aid measures. The situation may change during the further course of initial treatment, with rescue workers judging this primarily by the visible condition of the emergency patient, the data from medical equipment (e.g., ventilator, ECG), and the mission description from the control room. Such changes are required to be responded to immediately, for example by resuscitation or appropriate medication. That's why, waiting for results from medical tests like MRI and ECG which are time-consuming and not suitable for emergency cases is not considered suitable for rescue cases. Considering these technical constraints, the doctoral thesis focuses on employing artificial intelligence (AI) models in two aspects that can expedite and improve the rescue process. Firstly, for the diagnosis of health complications in rescue situations and secondly to identify the correct medications as a part of initial treatment. A major part of this research is focused on advanced data analysis techniques that were used to extract information from 12 years of rescue records of 273,283 cases in the German city of Siegen-Wittgenstein. The initial data received from the rescue station was raw and in many cases contained incomprehensible information for which Natural Language Processing (NLP) techniques were applied to extract and interpolate relevant attributes. Subsequently, a detailed method for creating various AI models to promptly detect six key complications— Cardiovascular, Respiratory, Psychiatric, Neurological, Metabolic, and Abdominal—was conducted and is detailed in this dissertation. To develop the detection models for each complication, Artificial Intelligence(AI) algorithms like machine learning including both classical and deep learning approaches were used. To train these models attributes like patients' medical history, health diagnoses including neurological assessment, vital signs, initial impression of the rescue personnel, administered medications, and other treatment paths were used. During the course of development, one primary objective was to identify the model achieving the greatest accuracy and precision. Based on this research, Extreme Gradient Boosting (XGB) and Random Forest (RF) algorithms were found as the most promising, showcasing accuracy rates ranging from 80\% to 96\%. After recognizing health complications, further research was done to find out if AI can also be implemented to determine possible medications based on detected complications and patients' health vitals. The result achieved from it also was impressive with accuracy close to 80\%. AI models are further tested by deploying them into various accelerators, such as ARM processors, FPGAs and microcontrollers, to evaluate their performance based on inference time. The overall focus of this research is to overcome the rescue challenges in real-time by recognizing rescue situations and improving the quality of care and efficiency of rescue personnel.

Quantum networks promise to provide secure communication and better computational power than classical computer networks. An important resource for such networks is multipartite entanglement. Challenges in real-world applications include imperfections and noise. This thesis contributes to the characterisation of multipartite entanglement and the design of quantum networks, taking the effects of noise into account. Currently, one open problem is the characterisation of multipartite entangled states. The first part of this thesis focuses on a family of such multipartite entangled states which can be described by the graphical formalism of graph and hypergraph states. We use the graphical properties to develop easy-to-compute criteria for assessing the equivalence of entangled states under local unitary transformations. We also extend our analysis to the hypergraph state formalism for continuous variable systems and develop graphical transformation rules characterising multipartite continuous variable entanglement. Additionally, we address how to deal with noise by identifying robust graph state structures that yield high-fidelity post-measurement states. Subsequently, we develop methods for reducing the number of input states in purification protocols of hypergraph states, another option to deal with noise. The second part of this thesis focuses on quantum network architectures. We analyse the probabilistic nature of entanglement distribution across network segments, deriving expressions for the average waiting time required to establish entangled links in simple repeater chains. We also show how to achieve maximal key rates in networks with imperfect quantum channels. Finally, we show that the performance of a simple repeater chain can be improved by adding memory qubits to the repeater stations.