Techniques like speckle holography and shearography are rarely applied due to the complexity of instrument setup and lack of automated result analysis, despite their potential. By simulating speckle interferometric outcomes, we seek to address these challenges, enabling more efficient measurement processes and paving the way for automated defect recognition. This research focuses on developing a simulation code for speckle interferometric results derived from finite element analyses. The aim is to improve the parameter settings of speckle interferometry measurements and create specific datasets, which will be used to develop machine learning-based methods for automation in series production.

This study investigates the effects of mechanical strain on the surface roughness of copper conductors, focusing on the electrolyte-refined copper (Cu-ETP, CW004A) used in H07V-U 1.5 mm2 single-core cables. For the first time, the surface roughness evolution is characterized using the power spectral density (PSD) function, enabling a detailed roughness analysis across different spatial length scales. Conductors were subjected to mechanical stress, with measurements taken at multiple stages of service life. The study confirms the results from other studies that surface roughness increases significantly in the early stages of loading, with a plateau observed in 50 % - 75 % of cycles to failure. Micro crack formation and material extrusion are identified as key mechanisms driving roughness growth, especially at small length scales, with a shift towards larger length scales as strain intensifies. The increasing Hurst exponent suggests a transformation from a random to a more persistent and correlated surface. The results underscore the potential of power spectral density analysis in understanding surface behavior in copper conductors.

Staphylococcus aureus (S. aureus) is one of the bacterial species capable of forming multilayered biofilms on implants. Such biofilms formed on implanted medical devices often require the removal of the implant in order to avoid sepsis or, in the worst case, even the death of the patient. To address the problem of unwanted S. aureus biofilm formation, its first step, i.e., adhesion, must be understood and prevented. Thus, the development of adhesion-reducing surface coatings for implant materials is of utmost importance. In this work, we used single-cell force spectroscopy to analyze the adhesion of the biofilm-forming S. aureus strain SA113 on naive and protein-coated silicon surfaces (SiO2). In addition to the wild type, we used the SA113 ΔdltA knockout mutant to further investigate the effect of d-alanylation of lipoteichoic acids of the cell wall. In order to examine how the surface charge affects adhesion, we coated silanized SiO2 surfaces with amphiphilic class II hydrophobins. The naturally occurring hydrophobin HFBI was used as well as the HFBI variant D40Q/D43N, which is less negatively charged at physiological pH due to the exchange of two acidic aspartate residues. These two types of hydrophobin-coated surfaces resemble each other in roughness and wettability but differ only in charge. By measurement of the forces with which each S. aureus strain binds to hydrophobin-coated surfaces, we show that the adhesion of S. aureus at surfaces can be influenced by the charges exposed by the target surfaces. Therefore, in addition to hydrogen bonding, electrostatic interactions between the cell and the hydrophilic surface govern adhesion on these surfaces. Moreover, we found that for both HFBI coatings, the adhesion strength of S. aureus is reduced by nearly a factor of 30 compared to silanized SiO2 surfaces. Therefore, hydrophobin coatings are of great interest for further use in the field of biomedical surface coating.

This work explores the combination of direct numerical simulations (DNSs) and experimental approaches for studying technical emulsification processes. Although emulsions have long been used in a variety of industries and many important research papers have been published over the years, quantifying and predicting the dispersion of droplets in another liquid remains challenging because of the complex multiphase nature and microscopic droplet scales. This study focuses on water-in-gasoline emulsions, which have the potential to improve efficiency and reduce emissions in combustion-based power generation. Experimental data from two different emulsion injection systems are complemented with DNS to gain insight into emulsification and the resulting droplet size distribution. In situ shadow imaging is used to acquire the experimental droplet size distributions, whereas DNS is performed via the geometric volume of fluid (VoF) method with the open-source code PARIS. The results indicate consistent agreement between the experimental and simulation results. Additionally, a corresponding trend of increasing droplet size is observed as the volume fraction of the dispersed phase increases. Furthermore, a detailed analysis of various probability density functions for modeling droplet size distributions (DSDs) reveals that the gamma distribution is the most appropriate. Overall, this work demonstrates that DNS can be successfully combined with experiments to increase the understanding of emulsification processes.

The Active Radar Interferometer (AcRaIn) represents a novel approach in secondary radar technology, aimed at environments with high reflective clutter, such as pipes and tunnels. This study introduces a compact design minimizing peripheral components and leveraging commercial semiconductor technologies operating in the 24 GHz ISM band. A heterodyne principle was adopted to enhance unambiguity and phase coherence without requiring synchronization or separate communication channels. Experimental validation involved free-space and pipe measurements, demonstrating functionality over distances up to 150 m. The radar system effectively reduced interference and achieved high precision in both straight and bent pipe scenarios, with deviations below 1.25% compared to manual measurements. By processing signals at intermediate frequencies, advantages such as improved efficiency, isolation, and system flexibility were achieved. Notably, the integration of amplitude modulation suppressed passive clutter, enabling clearer signal differentiation. Key challenges identified include optimizing signal processing and addressing logarithmic signal attenuation for better precision. These findings underscore AcRaIn’s potential for pipeline monitoring and similar applications.