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We describe the miniaturization of the Surface Plasmon Resonance (SPR) technology which mainly finds its applications in pharmaceutical screening and biotechnology so far. SPR spectroscopy is a label-free, non-destructive and highly sensitive measurement principle detecting changes in the refractive index in striking distance to a gold surface. A transfer of this technology to a miniaturized sensor will broaden the range of possible applications. A promising feature which is included in the miniaturized system is the angle-dependent recording of the SPR signals without moving parts. Commercial SPR assays are mainly working with a small number of sensing spots. In contrast, the SPR imaging system shown here will allow to use an array of many sensing spots. In combination with chemical receptors designed as an artificial nose, the simultaneous detection of many analytes is envisioned for future applications.
Die Oberflächenplasmonenresonanzspektroskopie (SPR) wird aufgrund ihrer Vorzüge der zerstörungs- und markierungsfreien Messung, sowie der hohen Sensitivität sehr erfolgreich im Labormaßstab eingesetzt. Aufgrund der hohen Kosten geschieht dies bislang hauptsächlich in der Bioanalytik und dem Wirkstoffscreening. Gezeigt wird ein Ansatz, mit dem diese Technologie auf ein ultrakompaktes, kostengünstiges Sensorsystem übertragen wird, welches in der Lage ist, hoch-sensitiv Änderungen im Brechungsindex von 10-6 Einheiten (RIU) zu detektieren. Für Wasser bewirkt eine Temperaturschwankung von 1 K bereits eine Änderung von 1 mRIU. Daher wird durch ein integriertes Referenzsystem, sowie die Miniaturisierung erreicht, dass das System gegenüber Temperaturschwankungen, weitestgehend stabil ist. In dem MOEMS wird anstatt der konventionellen 1-Punkt bzw. 2-Punkt Messung ein bildgebendes Verfahren angewendet. Die Sensorfläche von 15 x 15 mm wird dabei mit einem handelsüblichen 4 Mio. Pixel CMOS Sensor ausgewertet. Um verlässliche Datenpunkte zu erhalten benötigt man eine Fläche von 10 x 10 Pixel. Demzufolge können auf dem Sensor theoretisch bis zu 40.000 Sensorspots parallel ausgewertet werden.
Surface Plasmon Resonance (SPR) is a well-known and established technology in bioanalysis and pharmaceutical sciences. Due to the expensive instrumentation and the need of trained people, it is mainly limited to applications in laboratories. However, there are some areas like environmental monitoring, chemical processing and civil infrastructure, which urgently need new sensor technologies. SPR has the potential to serve these fields. In order to be qualified for a use in these areas SPR has to overcome some hurdles. The instrumentation has to be robust, small in size and cheap. A device, which fits these needs, will be a micro-opto-electro-mechanical system (MOEMS) with integrated intelligent algorithms. In this book chapter, examples of miniaturized SPR devices are introduced, the limitations which have to be overcome as well as the possibilities for future applications are proposed. Due to the manifold advantages of this technology and the dropping prices for imaging sensors, Surface Plasmon Resonance imaging (SPRi) might become one of the leading technologies for SPR smart sensor systems.
The choice of wireless technology for a pest monitoring setup is crucial for energy efficiency and reliability. We analysed 48 different modules with regards to bitwise energy consumption theoretically and evaluated the best modules in real-life scenarios. It was found that the choice of module can be inferred from a thorough market analysis and Narrowband Internet of things (NB-IoT) and Long Range (LoRa) are the most promising candidates for deployment in industrial environments. Regarding the tested site NB-IoT showed a coverage of over 97 % with good signal quality. For LoRa a calculation of the coverage percentage is not reasonable, as it strongly depends on the amount and location of the gate-ways. It was determined that one gateway can cover more than 10,000 m(exp 2). The results indicate that NB-IoT is suitable for wireless transmission in industrial environments and that LoRa with an individual gateway setup is ideal as a backup solution.
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.
Reliable and fast detection of pest in the food processing and pharmaceutical industry is crucial to ensure hygienically safe, pure and healthy products. This is why intelligent traps, that are able to detect insects automatically are needed.
For this, a capacitive PCB sensor is developed and tested, which could be a simplistic, low power solution to the problem. This includes design, footprint generation and simulation as well as testing different types of capacitve sensing against noise immunity and sensitivity to Blattella germanica, the German cockroach. It is shown that a single specimen can very well and reliably be detected by the developed sensor.
