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Optical gas sensors based on chiral-nematic liquid crystals (N* LCs) forming one-dimensional photonic crystals do not require electrical energy and have a considerable potential to supplement established types of sensors. A chiral-nematic phase with tunable selective reflection is induced in a nematic host LC by adding reactive chiral dopants. The selective chemical reaction between dopant and analyte is capable to vary the pitch length (the lattice constant) of the soft, self-assembled, one-dimensional photonic crystal. The progress of the ongoing chemical reaction can be observed even by naked eye because the color of the samples varies. In this work, we encapsulate the responsive N* LC in microscale polyvinylpyrrolidone (PVP) fibers via coaxial electrospinning. The sensor is, thus, given a solid form and has an improved stability against nonavoidable environmental influences. The reaction behavior of encapsulated and nonencapsulated N* LC toward a gaseous analyte is compared, systematically. Making use of the encapsulation is an important step to improve the applicability.
The simultaneous operation of multiple different semiconducting metal oxide (MOX) gas sensors is demanding for the readout circuitry. The challenge results from the strongly varying signal intensities of the various sensor types to the target gas. While some sensors change their resistance only slightly, other types can react with a resistive change over a range of several decades. Therefore, a suitable readout circuit has to be able to capture all these resistive variations, requiring it to have a very large dynamic range. This work presents a compact embedded system that provides a full, high range input interface (readout and heater management) for MOX sensor operation. The system is modular and consists of a central mainboard that holds up to eight sensor-modules, each capable of supporting up to two MOX sensors, therefore supporting a total maximum of 16 different sensors. Its wide input range is archived using the resistance-to-time measurement method. The system is solely built with commercial off-the-shelf components and tested over a range spanning from 100Ω to 5 GΩ (9.7 decades) with an average measurement error of 0.27% and a maximum error of 2.11%. The heater management uses a well-tested power-circuit and supports multiple modes of operation, hence enabling the system to be used in highly automated measurement applications. The experimental part of this work presents the results of an exemplary screening of 16 sensors, which was performed to evaluate the system’s performance.
Explorative experiments were done to figure out differences in the emission of volatile organic compounds (VOCs) of not infested trees and trees infested by Anoplophora glabripennis (Asian longhorn beetle, ALB), a quarantine pest. Therefore, VOCs from some native insect species, Anoplophora glabripennis infested Acer, stressed Acer, healthy Acer, Populus and Salix were obtained by enrichment on adsorbents. Qualitative analysis was done by thermal desorption gas chromatography coupled with a mass selective detector (TD-GC/MS). Altogether 169 substances were identified. 11 substances occur from ALB infested or mechanically damaged trees i.e. stressed trees, but not from healthy trees. (+)-Cyclosativene, (+)-α-longipinene, copaene and caryophyllene are detectable only from ALB-infested Acer not from mechanically damaged or healthy Acer. However, these substances are also emitted by healthy Salix. 2,4-Dimethyl-1-heptene is among all tree samples exclusively present in the ambience of ALB-infested trees. It´s rarely detectable from native insect species’ samples.
Kollaborative Industrieroboter werden für produzierende Unternehmen immer kosteneffizienter. Während diese Systeme für den menschlichen Mitarbeiter eine große Hilfe sein können, stellen sie gleichzeitig ein ernstes Gesundheitsrisiko dar, wenn die zwingend notwendigen Sicherheitsmaßnahmen nur unzureichend umgesetzt werden. Herkömmliche Sicherheitseinrichtungen wie Zäune oder Lichtvorhänge bieten einen guten Schutz, aber solch statische Schutzvorrichtungen sind in neuen, hochdynamischen Arbeitsszenarien problematisch.
Im Forschungsprojekt BeyondSPAI wurde ein Funktionsmuster eines Multisensorsystems zur Absicherung solcher dynamischer Arbeitsszenarien entworfen, implementiert und im Feld getestet. Kern des Systems ist eine robuste optische Materialklassifikation, die mit Hilfe eines intelligenten InGaAs-Kamerasystems Haut von anderen typischen Werkstückoberflächen (z.B. Holz, Metalle od. Kunststoffe) unterscheiden kann. Diese einzigartige Eigenschaft wird genutzt, um menschliche Mitarbeiter zuverlässig zu erkennen, so dass ein konventioneller Roboter in Folge als personenbewusster Cobot arbeiten kann.
Das System ist modular und kann leicht mit weiteren Sensoren verschiedenster Art erweitert werden. Es kann an verschiedene Marken von Industrierobotern angepasst werden und lässt sich schnell an bestehenden Robotersystemen integrieren. Die vier vom System bereitgestellten Sicherheitsausgänge können dazu verwendet werden - abhängig von der durchdrungenen Überwachungszone - entweder eine Warnung auszugeben, die Bewegung des Roboters auf eine sichere Geschwindigkeit zu verlangsamen, oder den Roboter sicher anzuhalten. Sobald alle Zonen wieder als „eindeutig frei von Personen“ identifiziert sind, kann der Roboter wieder beschleunigen, seine ursprüngliche Bewegung wiederaufnehmen und die Arbeit fortsetzen.
The application of Raman and infrared (IR) microspectroscopy is leading to hyperspectral data containing complementary information concerning the molecular composition of a sample. The classification of hyperspectral data from the individual spectroscopic approaches is already state-of-the-art in several fields of research. However, more complex structured samples and difficult measuring conditions might affect the accuracy of classification results negatively and could make a successful classification of the sample components challenging. This contribution presents a comprehensive comparison in supervised pixel classification of hyperspectral microscopic images, proving that a combined approach of Raman and IR microspectroscopy has a high potential to improve classification rates by a meaningful extension of the feature space. It shows that the complementary information in spatially co-registered hyperspectral images of polymer samples can be accessed using different feature extraction methods and, once fused on the feature-level, is in general more accurately classifiable in a pattern recognition task than the corresponding classification results for data derived from the individual spectroscopic approaches.