Formation of cobalt oxide clusters on oxygen modified graphene

Aim of this thesis is to study and characterize the formation of cobalt oxide clusters on graphene. The performed experiments consist of three different steps. First, a monolayer graphene film is grown with temperature programmed growth and chemical vapor deposition on an Iridium (111) single crystal surface. Second, the graphene film is exposed to oxygen radicals, which bind to the film and form clusters. In the last step, cobalt oxide was deposited onto the O-functionalized graphene film to form cobalt oxide clusters atop the oxygen clusters.\\ The evolution of the experiment is studied with x-ray photoelectron spectroscopy. The recorded data of the C 1s spectrum from the O-functionalization experiment are coherent with recently published literature on the same topic. The Ir 4f$_{7/2}$ suggests that 6.5\% of the carbon atoms are bound in an epoxy group. Furthermore the Ir 4f$_{7/2}$ spectra show exclude intercalataion of oxygen or cobalt oxide. Subsequent deposition of cobalt oxide on the O-functionalized graphene film leads to cobalt oxide in rocksalt structure containing Co$^{2+}$ ions. The ratio of cobalt oxide and the epoxy group is 1:1 which suggest the formation of cobalt oxide cluster onto the oxygen radicals. Nonetheless, further scanning tunneling microscopy experiments have to be performed to describe the stability, structure, size and coverage of these clusters. This is briefly discussed in the outlook.Many problems in our modern society would be solved, if we could mimic the chemical processes in nature. In the photosynthesis of plants light is absorbed and converted into chemical energy in the form of organic molecules through a complex chemical process. An important step of this chemical reaction is the splitting of water (H20) into hydrogen (H2) and oxygen (O2). Since there is plenty of water on earth, an efficient way to mimic nature's photocatalytic water could provide us with fuel (H2) and stop the greenhouse effect by using the energy of the sun. The splitting of water requires however a very efficient catalysts. Catalysts, are well known for their usage in cars, where they convert toxic gases(e.g. CO and NO) into less toxic ones (CO2 and N2). Catalysts that help us with the splitting of water are, however, made of rare and expensive metals like Platinum. Hence, the production of those is not efficient. Scientist therefore search for new improved and cheaper alternatives. As catalysts often have a complex structure, they are difficult to characterize, simplified model catalysts are therefore used instead. Such a model system should contain several ordered collections of cobalt oxide particles. The recently discovered material graphene brings along these properties. Grown on a metal it forms a mesh, which can be imagined as a landscape consisting of alternating hills and valleys and it is possible to deposit cobalt oxide atoms only in the valleys of the mesh. The deposition of ordered cobalt oxide clusters and their structure was investigated in my work and it contributes to the understanding of cobalt oxide catalyst that might be used as photocatalysts for the splitting of water in the future

Deep convolutional neural networks for cyclic sensor data

Predictive maintenance plays a critical role in ensuring the uninterrupted operation of industrial systems and mitigating the potential risks associated with system failures. This study focuses on sensor-based condition monitoring and explores the application of deep learning techniques using a hydraulic system testbed dataset. Our investigation involves comparing the performance of three models: a baseline model employing conventional methods, a single CNN model with early sensor fusion, and a two-lane CNN model (2L-CNN) with late sensor fusion. The baseline model achieves an impressive test error rate of 1% by employing late sensor fusion, where feature extraction is performed individually for each sensor. However, the CNN model encounters challenges due to the diverse sensor characteristics, resulting in an error rate of 20.5%. To further investigate this issue, we conduct separate training for each sensor and observe variations in accuracy. Additionally, we evaluate the performance of the 2L-CNN model, which demonstrates significant improvement by reducing the error rate by 33% when considering the combination of the least and most optimal sensors. This study underscores the importance of effectively addressing the complexities posed by multi-sensor systems in sensor-based condition monitoring.Comment: 4 pages, 3 figures, submitted to the IEEE Sensors Conferenc

On measuring the acoustic state changes in lipid membranes using fluorescent probes

Ultrasound is increasingly being used to modulate the properties of biological membranes for applications in drug delivery and neuromodulation. While various studies have investigated the mechanical aspect of the interaction such as acoustic absorption and membrane deformation, it is not clear how these effects transduce into biological functions, for example, changes in the permeability or the enzymatic activity of the membrane. A critical aspect of the activity of an enzyme is the thermal fluctuations of its solvation or hydration shell. Thermal fluctuations are also known to be directly related to membrane permeability. Here solvation shell changes of lipid membranes subject to an acoustic impulse were investigated using a fluorescence probe, Laurdan. Laurdan was embedded in multi-lamellar lipid vesicles in water, which were exposed to broadband pressure impulses of the order of 1MPa peak amplitude and 10{\mu}s pulse duration. An instrument was developed to monitor changes in the emission spectrum of the dye at two wavelengths with sub-microsecond temporal resolution. The experiments show that changes in the emission spectrum, and hence the fluctuations of the solvation shell, are related to the changes in the thermodynamic state of the membrane and correlated with the compression and rarefaction of the incident sound wave. The results suggest that acoustic fields affect the state of a lipid membrane and therefore can potentially modulate the kinetics of channels and proteins embedded in the membrane