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- 8.1 Sensorik, mess- und prüftechnische Verfahren (55) (entfernen)
Paper des Monats
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Contamination of natural bodies of water or soil with oils and lubricants (or generally, hydrocarbon derivatives such as petrol, fuels, and others) is a commonly found phenomenon around the world due to the extensive production, transfer, and use of fossil fuels. In this work, we develop a simple system for the on-field detection of total petroleum hydrocarbons (TPHs) in water and soil. The test is based on the measurement of the fluorescence signal emitted by the molecular rotor 2-[ethyl[4-[2-(4-nitrophenyl)ethenyl]phenyl]amino]ethanol (4-DNS-OH). This dye is embedded in a hydrophobic polymeric matrix (polyvinylidene fluoride), avoiding interactions with water and providing a robust support for use in a test strip fashion. Together with the strips, an embedded optical system was designed for fluorescence signal read-out, featuring a Bluetooth low-energy connection to a commercial tablet device for data processing and analysis. This system works for the detection and quantification of TPHs in water and soil through a simple extraction protocol using a cycloalkane solvent with a limit of detection of 6 ppm. Assays in surface and sea waters were conclusive, proving the feasibility of the method for in-the-field operation.
With the reduction of large oil spills because of stricter regulations and safety measures, the question of how to manage smaller oil spills arises. Few on-site analytical tools are available for first responders or other law enforcement personnel to rapidly test for crude oil in the early management of localized polluted areas. The approach reported here relies on well-described computer-assisted multivariate data analysis of the intrinsic fluorescence fingerprints of crude oils to build a multivariate model for the rapid classification of crude oils and the prediction of their properties. Thanks to a dedicated robust portable reader, the method allowed classification and accurate prediction of various properties of crude oil samples like density (according to API, the American Petroleum Institute and viscosity as well as composition parameters such as volume fractions of paraffins or aromatics. In this way, autonomous operation in on-site or in-the-field applications becomes possible based on the direct (undiluted and untreated) measurement of samples and a rapid, tablet-operated readout system to yield a robust and simple analytical test with superior performance. Testing in real-life scenarios allowed the successful classification and prediction of a number of oil spill samples as well as weathered samples that closely resemble samples collected by first responders.
Implementation and validation of robot-enabled embedded sensors for structural health monitoring
(2024)
In the past decades, structural health monitoring (SHM) has matured into a viable supplement to regular inspections, facilitating the execution of repair and maintenance work in the early stages of structural damage. With the advent of wireless technologies and advancements in information and communication technologies, civil infrastructure has been increasingly instrumented with wireless sensor nodes to record, analyze, and communicate data relevant to SHM. A promising method for SHM is to embed sensors directly into concrete for recording SHM data from inside structural elements. In this paper, a sensor system for embedment into concrete is proposed, able to assess SHM data recorded from concrete. Power is supplied to the sensors on-demand by quadruped robots, which also collect the SHM data via radio-frequency identification (RFID), providing an automated and efficient SHM process. In laboratory experiments, the capability of the sensor system of automatically collecting the SHM data using quadruped robots is validated. In summary, the integration of RFID technology and robot-based inspection presented in this study demonstrates a vital approach to evolve current SHM practices towards more digitalized and automated SHM.
Remote gas sensors mounted on mobile robots enable the mapping of gas distributions in large or hardly accessible areas. A challenging task, however, is the generation of threedimensional distribution maps from these gas measurements.
Suitable reconstruction algorithms can be adapted, for instance, from the field of computed tomography (CT), but both their performance and strategies for selecting optimal measuring poses must be evaluated. For this purpose simulations are used, since, in contrast to field tests, they allow repeatable conditions. Although several simulation tools exist, they lack realistic models of remote gas sensors. Recently, we introduced a model for a Tunable Diode Laser Absorption Spectroscopy (TDLAS) gas sensor taking into account the conical shape of its laser beam. However, the novel model has not yet been validated with experiments. In this paper, we compare our model with a real sensor device and show that the assumptions made hold.
Remote gas sensors mounted on mobile robots enable the mapping of gas distributions in large or hardly accessible areas. A challenging task, however, is the generation of threedimensional distribution maps from these gas measurements.
Suitable reconstruction algorithms can be adapted, for instance, from the field of computed tomography (CT), but both their performance and strategies for selecting optimal measuring poses must be evaluated. For this purpose simulations are used, since, in contrast to field tests, they allow repeatable conditions. Although several simulation tools exist, they lack realistic models of remote gas sensors. Recently, we introduced a model for a Tunable Diode Laser Absorption Spectroscopy (TDLAS) gas sensor taking into account the conical shape of its laser beam. However, the novel model has not yet been validated with experiments. In this paper, we compare our model with a real sensor device and show that the assumptions made hold.