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Modeling is an important tool for understanding a physical phenomenon. It helps to interpret results of experiments and optimize experimental parameters for obtaining a desirable result. Modeling laser induced plasma is beneficial for many scientific and industrial fields, e.g., analytical chemistry, pulsed laser deposition, plasma enhanced chemical vapor deposition, laser welding, additive manufacturing etc. In this presentation, a personal experience in development of a physical model of laser induced plasma will be given in a chronological sequence starting from early 2000th and until now.
Over the time, the model evolved from its simple analytical form that described plasma emission spectra to its current numerical form that describes plasma dynamics, chemistry, and interaction with a substrate surface. Several examples will be given for the application of the model to practical problems such as spectroscopic chemical analysis, plasma enhanced chemical vapor deposition, and surface modification by laser ablation.
The applications of composite overwrapped pressure vessels (COPVs) in extreme conditions, such as storing hydrogen gases at very high pressure, impose new requirements related to the system's integrity and safety. The development of a structural health monitoring (SHM) system that allows for continuous monitoring of the COPVs provides rich information about the structural integrity of the component. Furthermore, the collected data can be used for different purposes such as increasing the periodic inspection intervals, providing a remaining lifetime prognosis, and also ensuring optimal operating conditions. Ultimately this information can be complementary to the development of the envisioned digital twin of the monitored COPVs. Guided waves (GWs) are preferred to be used in continuous SHM given their ability to travel in complex structures for long distances. However, obtained GW signals are complex and require advanced processing techniques. Machine learning (ML) is increasingly utilized as the main part of the processing pipeline to automatically detect anomalies in the system's integrity. Hence, in this study, we are scrutinizing the potential of using ML to provide continuous monitoring of COPVs based on ultrasonic GW data. Data is collected from a network of sensors consisting of fifteen Piezoelectric (PZT) wafers that were surface mounted on the COPV. Two ML algorithms are used in the automated evaluation procedure (i) a long short-term memory (LSTM) autoencoder for anomaly detection (defects/impact), and (ii) a convolutional neural network (CNN) model for feature extraction and classification of the artificial damage sizes and locations. Additional data augmentation steps are introduced such as modification and addition of random noise to original signals to enhance the model's robustness to uncertainties. Overall, it was shown that the ML algorithms used were able to detect and classify the simulated damage with high accuracy.
Natural and synthetic estrogens are key endocrine-disrupting chemicals. Despite occurring at ultra-trace levels (below ng L-1), it is believed that they are contributing to an increase in feminized fish and other endocrine disruptive effects, and hence, their inclusion in the Watch list was not unexpected. One of the main sources ofestrogens to surface waters is wastewater effluent. Once in surface waters, they can partition into different compartments, i.e., water and suspended particulate matter. For this reason, there is an urgent need for a methodology to monitor estrogen levels below the environmental quality standards (EQS) set by the Water Framework Directive requirements.
In this study, a precise and accurate gas chromatography-mass spectrometry method (GC-MS/MS) for the analysis of estrone (E1), 17β-estradiol (17β-E2), 17α-estradiol (17α- E2), 17-alpha-ethinylestradiol (EE2), and estriol (E3) in whole water samples with ng L-1 limit of quantification (LOQ) was developed and validated in accordance with CEN/TS 16800:2020 guidelines.
Statistic process control as well as process capability demand for calibrated determination of layer thicknesses in various industries, e.g. automotive, aerospace, microelectronics manufacturing. Calibration requires well know and well characterized samples. A calibration laboratory accredited according to DIN EN ISO 17025 has the objective to distribute standards traceable to SI units to industrial laboratories for quality control of manufacturing of various products. Especially, the thickness determination of thin metallic coatings e.g. from galvanic processes or layer deposition using X-Ray Fluorescence can be significantly improved by customized calibration samples. This is essential as the measurement uncertainty directly correlates to the capability performing reliable control of processes with high yield. For calibration laboratories, the validation of results using round robins and the direct comparison to national metrology institutes is a prerequisite to demonstrate the competence to perform calibration services.
In this paper a strategy to obtain traceability and validation for thin alloy layers as well as first results are presented. The combined use of the accredited method for determination of mass per area from measurement of mass and area combined with standard free X-Ray Fluorescence as well as chemical analysis of dissolved samples with thin layers is deployed for material systems as NixZn1-x as well as NixP1-x. The obtained results are compared to reference free X-Ray Fluorescence at the BESSY II laboratories of Physikalisch-Technische Bundesanstalt. An excellent agreement of the obtained measured values as mass per unit area and alloy concentrations from the different applied methods within the measurement uncertainty was observed for NixP1-x showing the successful performed traceability of the calibration samples to SI units in combination with a validation of results by national metrology institutes and the round robin approach.
The presence of microplastic (MP) particles in aquatic environments raised concern About possible enrichment of organic and inorganic pollutants due to their specific surface and chemical properties. In particular the role of metals within this context is still poorly understood. Therefore, the aim of this work was to develop a fully validated acid digestion protocol for metal analysis in different polymers, which is a prerequisite to study such interactions.
The proposed digestion protocol was validated using six different certified reference materials in the microplastic size range consisting of polyethylene, polypropylene, acrylonitrile butadiene styrene and polyvinyl chloride. As ICP-MS/MS enabled time-efficient, sensitive and robust analysis of 56 metals in one measurement, the method was suitable to provide mass fractions for a multitude of other elements beside the certified ones (As, Cd, Cr, Hg, Pb, Sb, Sn and Zn). Three different microwaves, different acid mixtures as well as different temperatures in combination with different hold times were tested for optimization purposes.
With the exception of Cr in acrylonitrile butadiene styrene, recovery rates obtained using the optimized protocol for all six certified reference materials fell within a range from 95.9% ± 2.7% to 112% ± 7%. Subsequent optimization further enhanced both precision and recoveries ranging from 103% ± 5% to 107 ± 4% (U; k = 2 (n = 3)) for all certified metals (incl. Cr) in acrylonitrile butadiene styrene. The results clearly show the analytical challenges that come along with metal analysis in chemically resistant plastics. Addressing specific analysis Tools for different sorption scenarios and processes as well as the underlying kinetics was beyond this study’s scope. However, the future application of the two recommended thoroughly validated total acid digestion protocols as a first step in the direction of harmonization of metal analysis in/on MP will enhance the significance and comparability of the generated data. It will contribute to a better understanding of the role of MP as vector for trace metals in the environment.
Attenuation of ultrasonic signals in concrete has the potential to carry much information about the microstructure of the material. In this work a series of concrete specimens of varying porosities and pore size distributions were internally imaged with x-ray computed tomography (CT), and then subsequently examined with throughtransmission ultrasound. The CT images were used to quantify both capillary porosity of cement paste as well as internal interfaces that are likely to produce elastic wave scattering. Ultrasound signals were represented as a diffusion process such that absorption and scattering attenuation could be isolated. As implemented, the diffusion model was a poor predictor of capillary porosity, while diffusivity was a reasonable predictor of scattering interfaces. Diffusivity was found to scale extremely well with small scale porosity, which made it a good predictor of compressive strength.
Reliability assessment of Structural Health Monitoring (SHM) systems
poses new challenges pushing the research community to address many questions which are still open. For guided wave-based SHM it is not possible to evaluate the system performance without taking into account the target structure and applied system parameters. This range of variables would result in countless measurements.
Factors like environmental conditions, structural dependencies and wave characteristics demand novel solutions for performance analysis of SHM systems compared to those relying on classical non-destructive evaluation. Such novel approaches typically require model-assisted investigations which may not only help to explain and understand performance assessment results but also enable complete studies without costly experiments. Within this contribution, a multi input multi output approach using a sparse transducer array permanently installed on a composite structure to excite and sense guided waves is considered. Firstly, the method and the analysis of path-based performance assessment are presented considering an open-access dataset from the Open Guided Wave platform. Then, a performance analysis of a guided wave-based SHM system using Probability of Detection is presented. To explain some unexpected results, the model-assisted investigations are used to understand the physical phenomena of wave propagation in the test specimen including the interaction with damage. Finally, issues and future steps in SHM systems’ performance assessment and their development are discussed.
The presentation provides an overview and introduction to the need and drivers for the digital transformation of the quality infrastructure (QI). It illustrates the tools and processes that are the fundation of a digital QI and how the initiatve QI-Digital aims at developing accordant solutions. Special emphasis is given to the pilot projects at BAM.
Engineered nanomaterials (NM) with their large surface-to-volume ratios and their for some materials observed size-dependent functional properties are of increasing relevance for current and future developments in various fields such as medical and pharmaceutical industry, computing and electronics or food and consumer products. The performance and safety of NM are determined by the sum of their intrinsic physicochemical properties. Especially, the particle surface chemistry, which is largely controlled by the chemical nature and density of functional groups (FG) and ligands, is an important key driver for NM performance, stability, and processibility as well as the interaction of NM with the environment. Thus, methods for FG quantification can foster the sustainable development of functional and safe(r) NM.
Aiming at the development of simple, versatile, and multimodal tools for the quantification of many bioanalytically relevant FG and ligands, we investigated and compared various analytical methods commonly used for FG quantification. This includes electrochemical titration methods, dye-based optical assays, and other instrumental analytical techniques such as nuclear magnetic resonance and thermal analysis methods.
The potential of our multimodal approach for FG quantification was demonstrated for commercial and custom-made polymeric and silica particles of varying FG, used as optical pH sensors. In the future, our strategy can contribute to establish multi-method characterization strategies to provide a more detailed picture of the structure-properties relationship.