Chemische Charakterisierung und Spurenanalytik
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Eingeladener Vortrag
- nein (12)
Ultrasound echo is a widely used NDT technique for determining the internal geometry of structures. Reverse-time migration (RTM) has been recently introduced to NDT applications, as an imaging method for ultrasound data, to overcome some of the limitations (e.g. imaging steeply dipping reflector) experienced by the Synthetic Aperture Focusing Technique (SAFT), the most commonly used imaging algorithm for these measurements.
The standard implementation of RTM also experiences some drawbacks caused by its imaging condition, which is based on the zero-lag of the cross-correlation between source and receiver wavefields and generates high-amplitude low-frequency artifacts. Three alternative imaging conditions, developed for seismic data applications, were tested for their ability to provide better images than the standard cross-correlation: illumination compensation, deconvolution and wavefield decomposition. A polyamide specimen was chosen for the simulation of a synthetic experiment and for real data acquisition. The migrations of both synthetic and real data were performed with the software Madagascar. The illumination imaging condition was able to reduce the low-frequency noise and had a good performance in terms of computing time. The deconvolution improved the resolution in the synthetic tests, but did not showed such benefit for the real experiments. Finally, as for the wavefield decomposition, although it presented some advantages in terms of attenuating the low-frequency noise and some unwanted reflections, it was not able to image the internal structure of the polyamide as well as the cross-correlation did. Suggestions on how to improve the cost-effectiveness of the implementation of the deconvolution and wavefield decomposition were presented, as well as possible investigations that could be carried out in the future, in order to obtain better results with those two imaging conditions.
Analyse des Tausalzeintrages in Fahrbahndeckenbetone im Kontext der Alkali-Kieselsäure-Reaktion
(2018)
In den letzten Jahren sind im deutschen Bundesautobahnnetz (BAB-Netz) vermehrt AKR-Schäden an Betonfahrbahndecken aufgetreten, die zum Teil zu einer Halbierung ihrer geplanten Nutzungsdauer von 30 Jahren führte. Ursächlich hierfür ist die Verwendung alkaliempfindlicher Gesteinskörnung, die bei gleichzeitiger Anwesenheit von Wasser infolge der Exposition der Fahrbahndecke und dem alkalischen Milieu durch den Einsatz alkalireicher Portlandzemente bei der Betonherstellung zu einer Alkali-Kieselsäure-Reaktion (AKR) führt. Zusätzlich wird der AKR-Schädigungsprozess in Betonfahrbahndecken durch den externen Tausalzeintrag (primär NaCl) im Winter begünstigt. Vor diesem Hintergrund kommt der Ermittlung des Tausalzeintrags in den Fahrbahndeckenbeton eine große Bedeutung zu. Die Analyse des Tausalzeintrags erfolgte bisher ausschließlich nasschemisch an gemahlenen Bohrkernsegmenten. Nachteilig ist hierbei die fehlende differenzierte Betrachtung des Natriumgehaltes im Zementstein und in der Gesteinskörnung. Der alternative Einsatz von LIBS (Laser-induced breakdown spectroscopy) eröffnet in diesem Kontext neue Möglichkeiten. So wird in diesem Beitrag an Hand von Bohrkernen aus einem AKR-geschädigten BAB-Abschnitt exemplarisch die Vorgehensweise bei der LIBS-Analyse zur Ermittlung der Na- und interagierenden Cl-Verteilung an vertikalen Schnittflächen des Bohrkerns aufgezeigt. Da der Tausalzeintrag primär über den Zementstein erfolgt, wurde der verfälschende Na-Grundgehalt der Gesteinskörnung mittels Zementsteinkriteriums (Nutzung unterschiedlichen Ca-Gehalts in Zementstein und Gesteinskörnung) eliminiert. Vergleichend durchgeführte Cl-Mappings mit Mikroröntgenfluoreszenzanalyse (MRFA) belegen die Güte der durchgeführten LIBS-Messungen.
Aber auch bei der Verifizierung der Übertragbarkeit der Ergebnisse der zum Ausschluss reaktiver Gesteinskörnung bei Neubau und Erneuerung im BAB-Netz eingesetzten Performanceprüfungen mit externem Alkalieintrag auf Praxisverhältnisse hat sich das LIBSVerfahren bewährt. So wurde festgestellt, dass die mit NaCl-Lösung beaufschlagten Laborprüfkörper aus einem repräsentativen Fahrbahndeckenbeton bei der Klimawechsellagerung (KWL) über ihre gesamte Höhe von 10 cm einen Eintrag von Na und Cl erfahren. Weiterhin konnte im Gegensatz zu bisherigen Annahmen erstmals mit LIBS und Nasschemie gezeigt werden, dass die Frost-Tauwechsel-Phase bei der KWL zu keinem erhöhten Tausalzeintrag führt.
Nach 1850 wurde zur weiteren Herabsetzung der Einbrenntemperatur von Glasmalfarben Borax (Na2B4O7 · 10 H2O) zugesetzt. Das Verhältnis war nun 1 Teil SiO2, 3 Teile PbO und 0,5 Teile Borax. Der analytische Nachweis von Bor in eingebrannten Malschichten war bisher jedoch nicht möglich.Daher wurden in Laborversuchen Glasmalfarben mit unterschiedlichem Gehalt an Borax auf Modellgläser aufgetragen, eingebrannt und anschließend unter simulierten Umweltbedingungen zeitraffend im Klimaschrank bewittert.
Mit Hilfe von elektronenmikroskopischen Untersuchungen können Malschichten charakterisiert werden und somit Hinweise auf mögliche Schadensursachen liefern. Erste Versuche zum Nachweis von Bor erfolgten mit Hilfe von LIBS-Messungen (Laser Induced Breakdown Spectroscopy) an im Labor hergestellten Glasmalfarben mit unterschiedlichem Boraxgehalt.
Nach 1850 wurde zur weiteren Herabsetzung der Einbrenntemperatur von Glasmalfarben Borax (Na2B4O7 · 10 H2O) zugesetzt. Das Verhältnis war nun 1 Teil SiO2, 3 Teile PbO und 0,5 Teile Borax. Der analytische Nachweis von Bor in eingebrannten Malschichten war bisher jedoch nicht möglich.
In Laborversuchen wurden Glasmalfarben mit unterschiedlichem Gehalt an Borax auf Modellgläser aufgetragen, eingebrannt und anschließend unter simulierten Umweltbedingungen zeitraffend im Klimaschrank bewittert. Mit Hilfe von elektronenmikroskopischen Untersuchungen können Malschichten charakterisiert werden und somit Hinweise auf mögliche Schadensursachen liefern. Der Nachweis von Bor erfolgte mit Hilfe von LIBS-Messungen (Laser Induced Breakdown Spectroscopy) an im Labor hergestellten Glasmalfarben mit unterschiedlichem Boraxgehalt.
Mass Spectrometry (MS) and Nuclear Magnetic Resonance Spectroscopy (NMR) are valuable analytical and quality control methods for most industrial chemical processes as they provide information on the concentrations of individual compounds and by-products. These processes are traditionally carried out manually and by a specialist, which takes a substantial amount of time and prevents their utilization for real-time closed-loop process control. This paper presents recent advances from two projects that use Artificial Neural Networks (ANNs) to address the challenges of automation and performance-efficient realizations of MS and NMR. In the first part, a complete toolchain has been realized to develop simulated spectra and train ANNs to identify compounds in MS. In the second part, a limited number of experimental NMR spectra have been augmented by simulated spectra, to train an ANN with better prediction performance and speed than state-of-the-art analysis. These results suggest that, in the context of the digital transformation of the process industry, we are now on the threshold of a strongly simplified use of MS and MRS and the accompanying data evaluation by machine-supported procedures, and can utilize both methods much wider for reaction and process monitoring or quality control.
Industry 4.0 is all about interconnectivity, sensor-enhanced process control, and data-driven systems. Process analytical technology (PAT) such as online nuclear magnetic resonance (NMR) spectroscopy is gaining in importance, as it increasingly contributes to automation and digitalization in production. In many cases up to now, however, a classical evaluation of process data and their transformation into knowledge is not possible or not economical due to the insufficiently large datasets available. When developing an automated method applicable in process control, sometimes only the basic data of a limited number of batch tests from typical product and process development campaigns are available. However, these datasets are not large enough for training machine-supported procedures. In this work, to overcome this limitation, a new procedure was developed, which allows physically motivated multiplication of the available reference data in order to obtain a sufficiently large dataset for training machine learning algorithms. The underlying example chemical synthesis was measured and analyzed with both application-relevant low-field NMR and high-field NMR spectroscopy as reference method. Artificial neural networks (ANNs) have the potential to infer valuable process information already from relatively limited input data. However, in order to predict the concentration at complex conditions (many reactants and wide concentration ranges), larger ANNs and, therefore, a larger Training dataset are required. We demonstrate that a moderately complex problem with four reactants can be addressed using ANNs in combination with the presented PAT method (low-field NMR) and with the proposed approach to generate meaningful training data.
Poor air quality, particularly in urban areas, causes various diseases and degrades living standards. Air Quality could be affected by emissions of odor, Volatile Organic Compounds (VOCs), and other gases. Therefore, assessment and monitoring of odorous air quality using sensitive, simple, rapid, accurate and portable tools is very important for public health. This study aimed to characterize odor emissions to detect malfunctions in facilities and to prevent air pollution and olfactory nuisance in the environment. A gas chromatographic method, in conjunction with sensorial analysis were performed for odorous air samples analysis collected from neighborhood of Meknes city (Morocco). Advanced multivariate statistical approaches, such as Principal Components Analysis (PCA), Discriminant Function Analysis (DFA), Support Vector Machines (SVMs), and Hierarchical Cluster Analysis (HCA), were used to describe samples similarities. The electronic nose (e-nose) data processing exhibits a satisfactory discrimination between the odorous air samples. Twenty-four VOCs with known molecular formulas were identified with Thermal Desorption-Gas Chromatography-Mass Spectrometry (TD-GC-MS). A validated Partial Least Square (PLS) model foresees good calibration between e-nose measurement and TD-GCMS analysis. The finding indicates that TD-GC–MS approach in conjunction with e-nose unit could be suitable tool for environmental measurement-based odor emissions.
With increasing demand and environmental concerns, researchers are exploring new materials that can perform as well or better than traditional materials while reducing environmental impact. The BAMline, a real-life sample materials research beamline, provides unique insights into materials’ electronic and chemical structure at different time and length scales. The beamline specializes in x-ray absorption spectroscopy, x-ray fluorescence spectroscopy, and tomography experiments. This enables real-time optimization of material properties and performance for various applications, such as energy transfer, energy storage, catalysis, and corrosion resistance. This paper gives an overview of the analytical methods and sample environments of the BAMline, which cover non-destructive testing experiments in materials science, chemistry, biology, medicine, and cultural heritage. We also present our own synthesis methods, processes, and equipment developed specifically for the BAMline, and we give examples of synthesized materials and their potential applications. Finally, this article discusses the future perspectives of the BAMline and its potential for further advances in sustainable materials research.
Intelligent sensor systems, certified reference materials and instrumental analytical-chemical methods contribute to safety and functionality in hydrogen technologies.
This article gives a brief overview of SensRef activities in the Competence Centre H2Safety@BAM on the issues: Analytical methods for the determination of hydrogen purity, certified reference materials as measurement standards with regard to gas quality (primary calibrators) of BAM, test methods for gas sensor systems to detect hydrogen in air as well as the application of fibre-optic sensor systems to monitor the expansion and ageing behaviour of composite containers in hydrogen technologies.