Plasmonic detection of biomarkers
The aim of this research line is to develop highly sensitive biosensors based on plasmonic sensing, primarily for biomedical applications and to mimic biological processes and conditions. In addition, we use the method for food quality control and for measuring environmental conditions.
A major challenge for sensors based on propagating surface plasmons is to achieve adequate selectivity and sensitivity for single (bio)molecules. Beyond a certain limit, state-of-the-art instruments and data processing methods are not suitable for optimizing sensitivity. The limitations lie in the technological components and the light-matter interaction based on pure propagating plasmons on flat metal surfaces. Therefore, the aim of this research is to firstly improve the performance of plasmonic devices by utilizing hybrid plasmons generated from nanostructured surfaces, and secondly to improve the analytical sensitivity and sensor resolution by using multivariate analysis, which significantly improves the analytical performance of biosensors by extracting relevant information while bypassing interfering factors such as nonlinear responses or experimental errors and noise.
Machine learning algorithms are also used in the design process of plasmonic nanostructures to optimize multiperiodic and aperiodic arrays. The simulation and design process is supported by the University of Kassel, Computational Materials and Photonics Group.