@inproceedings{HauslerFischerWunderlichetal., author = {Hausler, Peter and Fischer, Johannes and Wunderlich, Lukas and Recum, Patrick and Peller, Sebastian and Hirsch, Thomas and Bierl, Rudolf}, title = {Miniaturisierte Sensoren basierend auf Oberfl{\"a}chenplasmonenresonanz, Chancen und Herausforderungen}, series = {DGaO-Proceedings 2021}, booktitle = {DGaO-Proceedings 2021}, publisher = {Dt. Gesellschaft f{\"u}r angewandte Optik}, address = {Erlangen-N{\"u}rnberg}, abstract = {Derzeit gibt es zahlreiche Bereiche, wie Umwelt Monitoring und zivile Infrastruktur in denen geeignete Sensoren f{\"u}r die {\"U}berwachung der Systeme fehlen. SPR-basierte Sensoren haben das Potential diese L{\"u}cke zu schließen. Um f{\"u}r den Einsatz in der Umwelt tauglich zu werden, m{\"u}ssen die Sensoren noch robuster werden. Hier wird eine m{\"o}gliche L{\"o}sung gezeigt.}, language = {de} } @article{JobstRecumEcijaArenasetal.2023, author = {Jobst, Simon and Recum, Patrick and {\´E}cija-Arenas, {\´A}ngela and Moser, Elisabeth and Bierl, Rudolf and Hirsch, Thomas}, title = {Semi-Selective Array for the Classification of Purines with Surface Plasmon Resonance Imaging and Deep Learning Data Analysis}, series = {ACS sensors}, volume = {8}, journal = {ACS sensors}, number = {9}, publisher = {American Chemical Society}, doi = {10.1021/acssensors.3c01114}, pages = {3530 -- 3537}, year = {2023}, abstract = {In process analytics or environmental monitoring, the real-time recording of the composition of complex samples over a long period of time presents a great challenge. Promising solutions are label-free techniques such as surface plasmon resonance (SPR) spectroscopy. They are, however, often limited due to poor reversibility of analyte binding. In this work, we introduce how SPR imaging in combination with a semi-selective functional surface and smart data analysis can identify small and chemically similar molecules. Our sensor uses individual functional spots made from different ratios of graphene oxide and reduced graphene oxide, which generate a unique signal pattern depending on the analyte due to different binding affinities. These patterns allow four purine bases to be distinguished after classification using a convolutional neural network (CNN) at concentrations as low as 50 μM. The validation and test set classification accuracies were constant across multiple measurements on multiple sensors using a standard CNN, which promises to serve as a future method for developing online sensors in complex mixtures.}, language = {en} }