Nanophysics is one of the most important research areas for advancing nano- and quantum technological applications. In particular, enhancing sensitivity and achieving miniaturization require a deep understanding of structural and physical properties at the atomic scale.
One focus is on the analysis of single molecules and their unique properties, particularly regarding the emission of light. A central aspect of our research is the development and understanding of single-photon emitters. These quantum light sources are crucial for the realization of future technologies in quantum communication and quantum information processing.
In modern nanotechnology, the identification of biomolecules at the nanoscale plays a crucial role. A promising method for studying these molecules is scanning tunneling microscopy (STM). This technique allows for imaging the surface of materials with atomic resolution. Recently, we demonstrated the identification of the different components in sugars and peptides on surfaces. This method opens up new possibilities for exploring biomolecules, which are of great importance for biotechnological and medical applications.
A novel Electrospray Ion Beam Deposition (ESIBD) system is being developed for controlled deposition of complex biomolecules under ultra-high vacuum conditions. This programmable system enables atomic-scale investigation of organic molecules with quantum technology applications using scanning tunneling microscopy (STM). Key innovations include modular chamber design, RF-operation with rectangular voltage waves for enhanced mass filtering, and Python-based control software. The system facilitates precise deposition of non-volatile molecules like peptides and photosynthesis building blocks onto clean surfaces, advancing research in quantum communication and single photon sources.
Using artificial intelligence (AI), the analysis of images captured by imaging methods can be significantly optimized. In particular, AI enables faster and less biased evaluation of the measurement data, which can lead to more precise results. An important aspect of this is training the algorithms with synthetic data, thereby overcoming the limitation of the few available measurement data. Overall, the combination of AI and synthetic data helps to significantly accelerate the analysis processes.
Batteries are essential energy storage devices whose efficiency and lifetime are greatly influenced by the structure of their electrodes. Surface analysis of the electrodes provides a deeper understanding of the capacity losses and aging processes, as well as the impact of impurities. With surface-sensitive techniques such as XPS (X-ray Photoelectron Spectroscopy) and SEM (Scanning Electron Microscopy), changes in chemical composition and structure can be measured.