TY - CHAP A1 - Roth, Carina A1 - Hausler, Peter A1 - Bierl, Rudolf T1 - Einbindung eines 2D-Photodetektors in einen hochintegrierten SPR-Imaging-Sensor T2 - 7. MikroSystemTechnik Kongress "MEMS, Mikroelektronik, Systeme", 23.-25. Oktober 2017, München N2 - Die Oberflächenplasmonenresonanzspektroskopie (SPR) ist eine hochempfindliche Messmethode, die es erlaubt, Gase und Flüssigkeiten zerstörungs- und markierungsfrei in Echtzeit zu analysieren. Bisher vornehmlich im Labormaßstab in der Bioanalytik und dem Wirkstoffscreening eingesetzt, soll diese Technologie nun miniaturisiert und für weitere Anwendungsgebiete zugänglich gemacht werden. Dazu wird ein kompakter Micro-Opto-Electro-Mechanical Systems Sensor (MOEMS) entwickelt, der mit Hilfe des SPR Imaging Änderungen der chemischen Zusammensetzung verschiedener Flüssigkeiten inline messen kann. N2 - Surface plasmon resonance spectroscopy (SPR) is a highly sensitive measurement method which allows the analysis of gases and liquids in real-time in a non-destructive and marking-free manner. Presently SPR is predominantly used for pharmaceutical screening and biotechnical analysis. Now this technology is to be miniaturized and made accessible for further application areas. For this purpose, a compact micro-opto-electro-mechanical system sensor (MOEMS) is developed, which can measure changes in the chemical composition of different fluids using SPR imaging. T2 - Integration of a 2D-photodetector in a SPR-Imaging-Sensor KW - Bildsensor KW - Bioanalytik KW - chemische Zusammensetzung KW - Fotodetektor KW - Labormaßstab KW - Messmethode KW - mikrooptoelektromechanisches System KW - Oberflächenplasmon KW - Oberflächenplasmonenresonanzspektroskopie KW - pharmazeutisches Screening KW - Spektroskopie KW - Wirkstoff Y1 - 2017 SN - 978-3-8007-4491-6 SP - 848 EP - 850 PB - VDE-Verlag CY - München ER - TY - CHAP A1 - Bauer, Lukas A1 - Vitzthumecker, Thomas A1 - Bierl, Rudolf A1 - Ehrnsperger, Matthias T1 - Machine-learning-based detection and severity estimation of drought stress in plants using hyperspectral imaging data T2 - Remote Sensing for Agriculture, Ecosystems, and Hydrology XXVII N2 - Growing food demand due to population growth, coupled with increasingly frequent and severe droughts caused by climate change make water increasingly scarce. To address this, accurate assessment of plant water demand is essential for precise drought treatment and water conservation. Hyperspectral imaging (HSI) captures hypercubes, a combination of spectral and spatial data and offers promising capabilities for detection of plant stresses. However, most reported approaches only use selected spectral bands or indices, neglecting the full hypercube information. This is assumed to limit the detection accuracy. To overcome these limitations, we aim to develop a measurement pipeline to generate a comprehensive dataset comprising hypercubes of plants under varying drought stress levels along with selected physiological, environmental, and illumination data. This dataset will be used to train suitable data-driven models that enable improved drought stress detection as well as the non-invasive determination of physiological parameters based on HSI data. Y1 - 2025 U6 - https://doi.org/10.1117/12.3072011 PB - SPIE ER - TY - INPR A1 - Fischer, Johannes A1 - Hirsch, Thomas A1 - Reitmeier, Torsten A1 - Bierl, Rudolf T1 - Real-time hardware-based processing of high-precision detector signals for surface plasmon resonance spectroscopy N2 - Surface plasmon resonance (SPR) is limited by small-signal detectability and drift when subtraction occurs in software after digitization. We introduce an SPR detector that performs on-detector amplification and analog differential readout, eliminating moving parts and software-heavy correction. The hardware-native subtraction boosts the usable ADC range and suppresses illumination and environmental noise. In fixed-angle refractive-index steps (NaCl), the platform resolves Δn_min ≈ 1.8 × 10⁻⁷ RIU compared to 4.6–7.2 × 10⁻⁶ RIU on a commercial comparator and improves small-signal SNR by up to ∼5,000-fold, while remaining competitive at high signal levels. In a model IgG–BSA assay, the detector’s low noise floor clarifies early binding and equilibrium transitions. By generating inherently clean raw signals, this hardware-native approach dramatically enhances sensitivity and long-term stability for label-free biosensing and inline process analytics while rendering AI-based or complex post-processing entirely unnecessary. The concept generalizes across platforms and opens a compact route to robust, high-fidelity SPR in complex environments, with a clear path toward multi-wavelength and arrayed detectors for high-throughput chemical monitoring. KW - Drift-free SPR KW - Hardware-native sensing KW - Noise suppression KW - On-detector amplification KW - Small-signal detection KW - Surface plasmon resonance Y1 - 2025 U6 - https://doi.org/10.2139/ssrn.5971170 ER -