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The ubiquitous presence of unintended plastics in the environment has been an issue in scientific studies and public debate. Recent studies on MP findings are focused mainly on aquatic systems, while little is known about MP in terrestrial ecosystems. Fermentation residues, sewage sludge and compost are secondary raw material fertilizers and represent a possible input path of plastics in soils. Soils are final sinks for micro plastics. In this context, samples were taken in a combined fermentation and composting plant in Germany to get real compost samples which were investigated. Existing regulations include requirements for total contents of plastics in combination with visual determination methods. In order to avoid possible underdeterminations, precise detection methods should be used in the future from a scientific point of view. For this reason, the use of thermoanalytical detection is an appropriate way. Spectroscopic methods such as Raman or FTIR are not suitable for determining the mass content of microplastic, as these output a particle number. In Germany, compost is a potential vector for MP in soil due to its use as fertilizer. Therefore, we show the application of TThermoExtractionDesorption-GasChromatography-MassSpectrometry (TED-GC-MS) as a fast, integral analytical technique, which in contrast to the spectroscopic methods does not measure the number of particles but a mass content. In a nitrogen atmosphere the sample is pyrolyzed to 600 °C and an excerpt of the pyrolysis gases is collected on a solid phase adsorber. Afterwards, the decomposition gases are desorbed and measured in a GC-MS system. Characteristic pyrolysis products of each polymer can be used to identify the polymer type and determine the mass contents in the present sample. For the first time the work represents a routine procedure for the determination of plastics in composts and fermentation residues. This current study will also give inside in various important aspects of sample preparation, which include a meaningful size fractionation, a necessary density separation regarding the removal of inorganic contents and at finally a homogenization.
The trafficking of illegal drugs by criminal networks at borders, harbors, or airports is an increasing issue in public health as these routes ensure the main supply of illegal drugs. The prevention of drug smuggling, including the installation of scanners and other analytical devices to detect ultra-small traces of drugs within a reasonable time frame, remains a challenge. The presented immunosensor is based on a monolithic affinity column with a large excess of immobilized hapten, which traps fluorescently labeled antibodies as long as the analyte cocaine is absent. In the presence of the drug, some binding sites of the antibody will be blocked, which leads to an immediate breakthrough of the labeled protein, detectable by highly sensitive laser-induced fluorescence with the help of a Peltier-cooled complementary metal-oxide-semiconductor (CMOS) camera. Liquid handling is performed with high-precision syringe pumps and microfluidic chip-based mixing devices and flow cells. The biosensor achieved limits of detection of 23 pM (7 ppt) of cocaine with a response time of 90 seconds and a total assay time below 3 minutes. With surface wipe sampling, the biosensor was able to detect 300 pg of cocaine. This immunosensor belongs to the most sensitive and fastest detectors for cocaine and offers near-continuous analyte measurement.
This paper summarizes the results of research aimed at assessing cracks in reinforced concrete structures using embedded ultrasonic sensors. The diffuse ultrasonic waves were considered to evaluate the health status of the tested structures. There are different algorithms used to detect cracks in the structure, but most studies have been performed on benchmark reinforced concrete (RC) structures and in laboratory conditions. Since there were difficulties with the validity of Damage detection in real structures in the presence of environmental changes and noises, the application of advanced signal processing methods was necessary. Therefore, the wavelet transform was applied to process ultrasonic signals acquired from multiple civil structures. It is shown that the ultrasonic sensors with an applied wavelet transform algorithm on collected signals can successfully detect cracks in the laboratory as well as in a real environment. Experimental results showed a perfect match for detecting damage and quasi-static load in the presence of environmental changes. The results were confirmed with other techniques. In addition, designing an extra filter for removing noises can be avoided by using the applied algorithms. The obtained results confirmed that diffuse ultrasonic sensor methodology with the proposed algorithm is useful and effective in Monitoring real RC structures, and it is better than traditional techniques.
A promising approach for the further improvement of fire detection techniques is the inclusion of fire gases in the characteristics used to detect a fire. A screening of sample materials representative for common first burning items and their gas release under different ignition conditions was performed to gain an overview of possible target gas candidates. Different fire scenarios were investigated to evaluate fire gases produced in early stages of fire development, their time of appearance and concentration development using the vitiated mass loss calorimeter and a heating plate setup in the single burning item test chamber.
The ubiquitous presence of unwanted plastics in the environment, especially microscale particles, has been an issue in scientific studies and public debate in the last years. It is well known that oxidative degradation and subsequent fragmentation, caused by UV-radiation, oxidative aging and abrasion lead to the decomposition of larger plastic products into microplastics (MP). Possible effects of these MP on ecosystems are still unclear. Recent studies on MP findings are focused mainly on aquatic systems, while little is known about MP in terrestrial ecosystems.
A possible source of MP input into the soil is compost from domestic bio-waste. Inappropriate waste separation causes plastic fragments in the bio-waste, some of which end up in the compost. In Germany compost is used as fertilizer in agriculture, hence MP could enter the soil by this pathway. So far, there have been only a few studies on this object. For this reason, analysis of compost as a sink and source of MP in ecosystems is of high interest.
To estimate and monitor the MP content in compost and soil, fast and harmonised analytical methods are essential, which not only measure the polymer type and number of particles, but also the mass content. The most common spectroscopic methods are very time-consuming, often require complex sample preparation steps and cannot determine mass contents. Therefore, we used ThermoExtractionDesorption-GasChromatography-MassSpectrometry (TED-GC-MS) as a fast, integral analytical technique. The sample is pyrolyzed to 600°C in a nitrogen atmosphere and an excerpt of the pyrolysis gases is collected on a solid phase adsorber. Afterwards, the decomposition gases are desorbed and measured in a GC-MS system. Characteristic pyrolysis products can be used to identify the polymer type and determine the mass contents. This method is well established for the analysis of MP in water filtrate samples.
In the present work we optimized the TED-GC-MS method for compost and compost/soil matrix and very common polymers, such as polyethylene, polypropylene, polyethylene-terephthalate and polystyrene (sample mass, detection limits, interfering signals, etc.). Additionally, specific pyrolysis products of polymers used for bio-waste bags, such as polylactide (PLA) and polybutylenadipat-terephthalat (PBAT) had to be identified and evaluated.
First measurements were carried out on model and real samples from prepared mixtures and composting plant. The samples were sterilized, fractionated, filtered and dried. In addition, half of the sample material was treated with hydrogen peroxide to investigate a possible effect on detection.
In structural parts under vibrational loading fatigue cracks can initiate and grow, which can lead to structural failure. Conventional non-destructive testing methods for crack detection provide just a snapshot of fatigue crack evolution, whereas crack luminescence coating realizes clear visibility of the entire crack formation. Fatigue causing cyclic tensile tests and examinations on special test bodies allowing control of the crack opening width demonstrate a high sensitivity of the coating.
Conventional methods of crack detection only provide a snapshot of the fatigue evolution at a specific location and in the moment of examination. The crack luminescence method realizes a clear visibility of the occurring cracks in loaded components during ongoing operation. Several different experiments show that due to the sensitive coating even the early stage of the crack formation can be detected what makes the crack luminescence helpful to determine the incipient crack opening behavior depending on load alternation. Due to the emitting of light under UV-radiation the crack gets clearly visible what makes continuous monitoring and automated crack detection possible. This can reduce costs and time needed for maintenance and inspection.
We analysed the hydrogen generation during the smouldering of polymeric materials, which are typically used in the household, in the Smoke Density Chamber coupled to a new developed hydrogen sensor to detect early stages of fires. The results of hydrogen generation were compared with the emission of carbon monoxide and smoke during the fire scenarios. Additionally, the results were compared with parameters used in traditional commercial detection systems. In this scenario, the hydrogen sensor showed encouraging results for the detection of fires in earlier phase compared to traditional detectors. Furthermore, we tested the new developed hydrogen sensor in a real room with different fire scenarios. We have also investigated interferences, e.g. steam and cigarette smoke. The hydrogen sensor could detect hydrogen generation in the earliest stage of fire, even before CO and smoke were developed in detectable amounts. Therefore, the hydrogen sensor can be applied for early fire detection in case of pyrolysis. The sensors are quite good for detecting pyrolysis gases. But when it comes to a fast ignition other techniques are more suitable for it. The sensors are best for combination with other techniques, such as smoke detectors.
Reliability analysis of the ultrasonic inspection system for the inspection of hollow railway axles
(2016)
Axles are safety critical train components that are subjected to significant cyclic loading during operation. If the crack is initiated in the axle, cyclic loading will lead to crack propagation. To maintain structural integrity, axles must be periodically inspected for fatigue cracks in the material. Deutsche Bahn uses mechanized ultrasonic inspection system to inspect hollow railway axles. The inspections are performed from the bore surface, using several conventional transducers with different incident angles, inspecting the axle along the entire length. As with the every safety critical system, the reliability of these inspections must be determined with regard to their flaw detection capabilities. Traditionally this is done according to the relevant standards for railway vehicles. To investigate the capability of the NDT system more thoroughly, we want to evaluate the capability of the inspection system to detect flaws by means of probability of detection (POD) curves. It will be shown that other parameters, beside the size of the crack, for example crack position in the axle, influence the detection of the crack. The influence of these parameters was evaluated using ultrasonic simulation. The evaluation served as an input for the manufacturing of the flaws in the real scale axle. Once these axles are inspected and the data evaluated, using data from both measurement and simulation, we will express the POD of the crack as function of influencing parameters using the multiparameter POD model.
This study aims at improving the detection of near-surface defects in magnetizable and conductive specimens by combining the measurements of eddy current, magnetic flux leakage and thermography testing. Different signal processing methods for data normalization are proposed to enable data fusion at the pixel level. These methods are applied to a test specimen which contains 10 variably-sized defects. We quantitatively evaluate the performances of a total of 29 detection methods with respect to false alarm reduction at a fixed level of true positive rate. We report that false positive rate could be reduced from 1.65% down to 0.28% by the best multi-sensor method compared to the best single-sensor performance on the smallest defect, when 50% found flaw pixels are required for successful detection.