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This contribution provides an overview of the BAMline synchrotron radiation beamline, which specializes in hard X-ray spectroscopy techniques for materials research. The BAMline offers X-ray absorption spectroscopy (XAS), x-ray fluorescence spectroscopy (XRF), and tomography to study materials' electronic structure, chemical composition, and structure. Key capabilities include standard and dispersive XAS for electronic structure, micro-XRF for elemental mapping, coded aperture imaging, and depth-resolved grazing exit XAS. The BAMline enables in situ characterization during materials synthesis and functions for energy, catalysis, corrosion, biology, and cultural heritage applications.
Ongoing developments like the implementation of machine learning techniques for experiment optimization and data analysis will be discussed. For instance, Bayesian optimization is being used to improve beamline alignment and scanning. An outlook to the future, where the BAMline will continue pioneering dynamic and multi-scale characterization, aided by advanced data science methods, to provide unique insights into materials research, will be given.
The structure and composition of ancient gold objects retain information about their long history of manufacture, from the exploitation of the ore to the finishing touches, as well as evidence of their use, deposition, and degradation. By developing an efficient analytical strategy, it is possible to retrieve that information. This chapter sets the necessary foundation 131to explore fully the analytical results presented in the following chapters of this volume. The techniques employed in the analyses of the Egyptian jewellery are described and the analytical parameters provided. For more established techniques, only brief introductions are presented, while more recent developments are presented in greater detail.
Enhancing efficiency at bamline: employing data science and machine learning for x-ray research
(2023)
This talk discusses how data science and machine learning techniques are being applied at the BAM Federal Institute for Materials Research and Testing to enhance efficiency and automation at the BAMLine synchrotron facility. The methods presented include Gaussian processes and Bayesian optimization for beamline adjustment and optimization of X-ray measurements. These statistical techniques allow automated alignment of beamline components and active learning scanning to reduce measurement time.
Additional machine learning methods covered are neural networks for quantification of X-ray fluorescence (XRF) data and decoding coded apertures.
Laser breakdown spectroscopy (LIBS) is a common tool for applications in various fields of science and technology. Originally an atomic analysis technique, LIBS was later extended to molecular analysis due to the transient nature of the laser-induced plasma, which develops from a hot dissociation stage on a nanosecond to several microsecond scale to a relatively cold recombination stage on a scale of 10 to 100 microseconds after breakdown. Molecules formed during the recombination stage or incompletely dissociated after ablation can be efficiently detected, allowing the analysis of "difficult" elements or even molecular isotopes. However, with a small amount of ablated material and a short lifetime of the luminous plasma, analytical signals, especially molecular ones, can be very weak.
Several methods have been proposed for reheating the plasma and increasing its lifetime, for example, a two-pulse LIBS or a LIBS combined with microwave radiation or with an electric spark discharge. Here we propose another one, LIBS combined with a capacitively coupled RF discharge at 13.6 MHz. The advantages of this combination are an increase in the lifetime of atomic and molecular emission and operation in a low-pressure atmosphere, which significantly reduces pressure line broadening and allows high-resolution spectroscopy. Another major advantage is operating in a chemically controlled atmosphere that can predictably drive desired chemical reactions. In this presentation, we will show the first results obtained with RF-LIBS combination. These will include separate and joint characterization of LIBS and RF plasmas and evaluation of its potential for elemental and molecular analysis and for plasma enhanced chemical vapor deposition.
Many applications of LIBS require the measurement of plasma temperature and electron density, which in turn requires knowledge of the integrated line intensity and the shape of the spectral lines. While the integral intensity is preserved as light passes through the spectrometer, the shape emitted by an individual atom or ion is greatly distorted. This is due, firstly, to the transfer of light through the plasma (self-absorption), secondly, to the influence of the instrumental function of the spectrometer, and, thirdly, to the aberrations of the optical system. In addition, processing of spectral information, such as background removal, noise reduction, deconvolution, and line fitting, introduces additional errors in the reconstructed linewidth and line integral, which leads to erroneous temperature and electron density values.
This communication will be devoted to the general shortcomings of spectral data processing and the resulting inaccuracies in determining the plasma parameters. The analysis is based on the use of synthetic spectra generated by plasma with known temperature and particle density. The estimation of errors caused by inadequate processing of the spectral data is made by comparing the initial and determined plasma parameters. As a result, an improved data processing method will be proposed that takes into account the spectrum distortion by the instrumental function and integration on the pixel detector. The former is accounted for by convolution (instead of deconvolution) of the estimated line profile using a predetermined slit function, and the latter is achieved by piecewise integration of the line profile by the pixel detector, taking into account the pixel size and uniform or non-uniform pixel separation. Recommendations will be made for which analytic function best approximates the observed spectral lines and examples will be given for the application of this routine to calibration-free LIBS using both synthetic and experimental data.
In the LIBS literature, almost every second article reports the determination of the plasma temperature using the Boltzmann plot method or the determination of the electron density using the Stark line broadening relation. The first requires the measurement of the integrated intensities of the spectral lines, and the second requires the measurement of the linewidth, under the same assumption of optical thinness. It is taken for granted that this can be easily done either by working with the raw spectra or by fitting an appropriate function to the observed spectral lines. As a rule, reported data are not verified either by an alternative method (e.g., Thomson scattering) or by computer simulations using synthetic spectra.
However, the question of how to extract the necessary information from the raw spectral data is not as simple as it might seem. The quality of such an extraction will depend critically on the type of spectral instrument used, its resolution, and the noise superimposed on the data. The problem is that we do not see the spectrum emitted by the plasma, but the spectrum distorted by the measurement; an exaggerated example of such a distortion is shown in Fig. 1. The elimination of this distortion belongs to the class of inverse problems, the so-called ill-posed problems, whose successful solution crucially depends on the quality of the information available. When it comes to spectroscopy, quality of information primarily means high spectral resolution and low noise. Not all spectrometers used in LIBS can provide the quality needed to solve the inverse problem; this casts doubt on many published plasma measurements.
The current presentation will be devoted to general shortcomings in the processing of spectral data and inaccuracies in the determination of plasma parameters resulting from these shortcomings. The analysis is based on the use of synthetic spectra produced by plasma with known characteristics, i.e., temperature, species densities, and electron density. The estimation of errors caused by inadequate processing of spectral data is made by comparing the initial and reconstructed plasma parameters. Recipes will be given for which the analytic function best approximates the observed spectral lines, and how data processing errors affect accuracy of calibration-free LIBS will be discussed. These issues were only partially covered in previously published works, for example [1, 2, 3].
The aim of the project is to develop an adequate model of laser induced plasma for conditions expected in space missions, i.e., vacuum, or low-pressure CO2 atmosphere. Numerical modeling will help to find optimal experimental parameters for the laser ablation under artificial lunar or Martian environments and obtain both qualitative, in terms of a composition, and quantitative, in terms of an elemental abundance, information about interrogated samples based on spectral data generated by the model. The best operational conditions will be found at a low cost without conducting tedious and time-consuming optimization experiments. The modeling approaches will be supported by machine learning to accelerate the optimization.
The application of multivariate data analysis is essential in extracting the full potential of laser-induced XUV spectroscopy (LIXS) for high-precision elemental mapping. LIXS offers significant advantages over traditional laser-induced breakdown spectroscopy in UV-vis (LIBS), including higher precision and a wider dynamic range,[1,2] while making it possible to determine light elements like lithium and fluorine. However, it is challenged by the presence of unresolved transition arrays (UTAs) for heavier elements. These UTAs add considerable complexity to the spectral data, often concealing crucial information. In this study, we employ well-established multivariate data analysis techniques and intensive data preprocessing to unravel this contained information.
The refined analysis reveals a high level of detail, enabling the precise identification of inhomogeneities within material samples. Our approach has particular relevance for studying aging processes in lithium-ion batteries (LIBs), specifically in relation to varying cathode materials and fluorine-containing polymer binder content. By combining elemental distribution with structural information, this improved method can offer a more comprehensive understanding of sample inhomogeneities and aging processes in LIBs, contributing to the development of more reliable and sustainable battery technologies.
Laser-induced XUV spectroscopy (LIXS) is an emerging technique for elemental mapping. In comparison to conventional laser-induced breakdown spectroscopy in UV-vis (LIBS), it has a higher precision and wider dynamic range, and it is well suited for the quantification light elements like lithium and fluorine. Further it can spot oxidation states. The XUV spectra are produced at a very early stage of the plasma formation. Therefore, effects from plasma evolution on the reproducibility can be neglected. It has been shown, that high-precision elemental quantification in precursor materials for lithium-ion batteries (LIBs) can be performed using LIXS. Based on these results, LIXS mapping was used to investigate aging processes in LIBs. Different cathode materials with varying compositions of fluorine containing polymer binders were compared at different stages of aging. Due to effects comparable to X-ray photoelectron spectroscopy but in reverse, monitoring of changes in the oxidation state is envisioned, which makes information about the chemical environment of the observed elements accessible. The combination of elemental distribution and structural information leads to a better understanding of aging processes in LIBs, and the development of more sustainable and safe batteries.
Improved Data Processing for Accurate Plasma Diagnostics with Implications for Calibration-Free LIBS
(2023)
Many LIBS papers report the determination of plasma temperature using the Boltzmann plot method or the determination of electron density using the Stark line broadening relation. This requires measuring the integrated intensities of the spectral lines and the linewidth under the assumption of optical thinness. It is taken for granted that this can be easily done either by working with the raw spectra or by fitting the appropriate function to the observed spectral lines. However, extracting the necessary information from raw spectral data is not as easy as it might seem. The quality of such extraction will depend to a decisive extent on the type of spectral instrument used. The spectrum emitted by the plasma is distorted by the device; an example is shown in Fig. 1. The elimination of this distortion belongs to the class of inverse problems, the successful solution of which fundamentally depends on the quality of the available information. When it comes to spectroscopy, the quality of information primarily means high spectral resolution and low noise. Not all spectrometers used in LIBS can provide the quality needed to solve the inverse problem; this casts doubt on many published plasma measurements. This communication will be devoted to the general shortcomings of spectral data processing and the inaccuracies in determining the plasma parameters resulting from these shortcomings. The analysis is based on the use of synthetic spectra generated by plasma with known temperature, particle density and electron density. The estimation of errors caused by inadequate processing of spectral data is made by comparing the initial and measured plasma parameters from the spectra. Recommendations will be made for which analytic function best approximates the observed spectral lines, and how data processing errors affect the accuracy of calibration-free LIBS will be discussed. These issues were only partially covered in previously published works, for example [1, 2].
Introduction
Lithium-ion batteries (LIBs) are one key technology to overcome the climate crisis and energy transition challenges. Demands of electric vehicles on higher capacity and power drives research on innovative cathode and anode materials. These high energy-density LIBs are operated at higher voltages, leading to increased electrolyte decay and the current collectors' degradation. Even though this fundamental corrosion process significantly affects battery performance, insufficient research is being done on the aluminum current collector. Fast and convenient analytical methods are needed for monitoring the aging processes in LIBs.
Methods
In this work glow-discharge optical emission spectrometry (GD-OES) was used for depth profile analysis of aged cathode material. The measurements were performed in pulsed radio frequency mode. Under soft and controlled plasma conditions, high-resolution local determination (in depth) of the elemental composition is possible. Scanning electron microscopy (SEM) combined with a focused ion beam (FIB) cutting and energy dispersive X-ray spectroscopy (EDX) was used to confirm GD-OES results and obtain additional information on elemental distribution.
Results
The aging of coin cells manufactured with different cathode materials (LCO, LMO, NMC111, NMC424, NMC532, NMC622, and NMC811) was studied. GD-OES depth profiling of new and aged cathode materials was performed. Quantitative analysis was possible through calibration with synthetic standards and correction by sputter rate. Different amounts of aluminum deposit on the cathode surface were found for different materials. The deposit has its origin in the corrosion of the aluminum current collector. The results are compatible with results from FIB-EDX. However, GD-OES is a faster and less laborious analytical method. Therefore, it will accelerate research on corrosion processes in high energy-density batteries.
Innovative aspects
- Quantitative depth profiling of cathode material
-Monitoring of corrosion processes in high energy-density lithium-ion batteries
- Systematic investigation of the influence of different cathode materials
Per- and polyfluoroalkyl substances (PFAS) are a large group of organofluorine surfactants used in the formulations of thousands of consumer goods. The continuous use of PFAS in household products and the discharge of PFAS from industrial plants into the sewer system have been resulted in contaminated effluents and sewage sludge from wastewater treatment plants (WWTPs) which became an important pathway for PFAS into the environment. Because sewage sludge is often used as fertilizer its application on agricultural soils has been observed as significant input path for PFAS into our food chain. To produce high-quality phosphorus fertilizers for a circular economy from sewage sludge, PFAS and other pollutants (e.g. pesticides and pharmaceuticals) must be separated from sewage sludge. Normally, PFAS are analyzed using PFAS protocols typically with time-consuming extraction steps and LC-MS/MS target quantification. However, for screening of PFAS contaminations in wastewater-based fertilizers also the DGT technique can be used for the PFAS extraction. Afterwards, combustion ion chromatography (CIC) can be applied to analyze the “total” amount of PFAS on the DGT binding layer. The DGT method was less sensitive and only comparable to the extractable organic fluorine (EOF) method values of the fertilizers in samples with >150 µg/kg, because of different diffusion properties for various PFAS, but also kinetic exchange limitations. However, the DGT approach has the advantage that almost no sample preparation is necessary. Moreover, the PFAS adsorption on the DGT binding layer was investigated via surface sensitive spectroscopical methods, such as Fourier-transform infrared (FT-IR) and fluorine K-edge X-ray absorption near-edge structure (XANES) spectroscopy.
DICONDE (Digital Imaging and Communication in Non-Destructive Testing) is an open international standard for storing and exchanging industrial test data and process-related information. The DICONDE standard defines both the semantics for structured storage of data and the network-based communication between two endpoints. This allows many test processes to be mapped digitally and securely, while at the same time meeting normative requirements such as traceability to the tester and test object and reproducibility of test results.
The amount of plant-available phosphorus (P) in soil strongly influences the yield of plants in agriculture. Therefore, various simple chemical extraction methods have been developed to estimate the plant-available P pools in soil. More recently, several experiments with the DGT technique have shown that it has a much better correlation to plant-available P in soils than standard chemical extraction methods (e.g. calcium-acetate-lactate (CAL), Colwell, Olsen, water) when soils with different characteristics are considered. However, the DGT technique cannot give information on the plant-available P species in the soil. Therefore, we combined DGT with solution 31P nuclear magnetic resonance (NMR) spectroscopy. This was achieved by using a modified DGT device in which the diffusive layer had a larger pore size, the binding layer incorporated an adsorption material with a higher capacity, and the device had a larger exposure area. The spectroscopic investigation was undertaken after elution of the deployed DGT binding layer in a NaOH solution. Adsorption tests using solutions of known organic P compounds showed that a sufficient amount of these compounds could be adsorbed on the binding layer in order for them to be analyzed by solution 31P NMR spectroscopy. Furthermore, various intermediates of the hydrolysis of trimetaphosphate in soil could be also analyzed over time.
O3BET Quality Protocols
(2023)
Presentation of the process-oriented approach for the development of the quality protocolls (standard operation procedures and work instructions) for the O3BETs. O3BETs are innovative testing facilities for building envelopes which are developed in the course of the Metabuilding Labs EU Horizon 2020 project.
X-ray refraction is analogous to visible light deflection by matter; it occurs at boundaries between different media. The main difference between visible light and X-rays is that in the latter case deflection angles are very small, from a few seconds to a few minutes of arc (i.e., the refraction index n is near to 1). Importantly, deflection of X-rays is also sensitive to the orientation of the object boundaries. These features make X-ray refraction techniques extremely suitable to a) detect defects such as pores and microcracks and quantify their densities in bulk (not too heavy) materials, and b) evaluate porosity and particle properties such as orientation, size, and spatial distribution (by mapping). While X-ray refraction techniques cannot in general image single defects, they can detect objects with size above a few wavelengths of the radiation.
Such techniques, especially at the Synchrotron BESSY II, Berlin, Germany, can be used in-situ, i.e. when the specimen is subjected to temperatures or external loads.
The use of X-ray refraction analysis yields quantitative information, which can be directly input in kinetics, mechanical and damage models.
We hereby show the application of non-destructive X-ray refraction radiography (SXRR, 2D mapping also called topography) to problems in additive manufacturing:
1) Porosity analysis in PBF-LM-Ti64. Through the use of SXRR, we could not only map the (very sparse) porosity distribution between the layers and quantify it, but also classify, and thereby separate, the filled porosity (unmolten powder) from the keyhole and gas pores (Figure 1).
2) In-situ heat treatment of laser powder bed fusion PBF-LM-AlSi10Mg to monitor microstructure and porosity evolution as a function of temperature (Figure 2). By means of SXRR we indirectly observed the initial eutectic Si network break down into larger particles as a function of increasing temperature. We also could detect the thermally induced porosity (TIP). Such changes in the Si-phase morphology upon heating is currently only possible using scanning electron microscopy, but with a much smaller field-of-view. SXRR also allows observing the growth of some individual pores, usually studied via X-ray computed tomography, but again on much smaller fields-of-view.
Our results show the great potential of in-situ SXRR as a tool to gain in-depth knowledge of the defect distribution and the susceptibility of any material to thermally induced damage and/or microstructure evolution over statistically relevant volumes.
Great complexity characterizes Additive Manufacturing (AM) of metallic components via laser powder bed fusion (PBF-LB/M). Due to this, defects in the printed components (like cracks and pores) are still common. Monitoring methods are commercially used, but the relationship between process data and defect formation is not well understood yet. Furthermore, defects and deformations might develop with a temporal delay to the laser energy input. The component’s actual quality is consequently only determinable after the finished process.
To overcome this drawback, thermographic in-situ testing is introduced. The defocused process laser is utilized for nondestructive testing performed layer by layer throughout the build process. The results of the defect detection via infrared cameras are shown for a research PBF-LB/M machine.
This creates the basis for a shift from in-situ monitoring towards in-situ testing during the AM process. Defects are detected immediately inside the process chamber, and the actual component quality is determined.
Knowledge representation in the materials science and engineering (MSE) domain is a vast and multi-faceted challenge: Overlap, ambiguity, and inconsistency in terminology are common. Invariant and variant knowledge are difficult to align cross-domain. Generic top-level semantic terminology often is too abstract, while MSE domain terminology often is too specific.
The PMDco is designed in direct support of the FAIR principles to address immediate needs of the global experts community and their requirements. The illustrated findings show how the PMDco bridges semantic gaps between high-level, MSE-specific, and other science domain semantics, how the PMDco lowers development and integration thresholds, and how to fuel it from real-world data sources ranging from manually conducted experiments and simulations as well as continuously automated industrial applications.
LIBS ConSort: Development of a sensor-based sorting method for constuction and demolition waste
(2023)
Closed material cycles and unmixed material fractions are required to achieve high recovery and recycling rates in the building industry. In construction and demolition waste (CDW) recycling, the preference to date has been to apply simple but proven techniques to process large quantities of construction rubble in a short time. This is in contrast to the increasingly complex composite materials and structures in the mineral building materials industry. Manual sorting involves many risks and dangers for the executing staff and is merely based on obvious, visually detectable differences for separation. An automated, sensor-based sorting of these building materials could complement or replace this practice to improve processing speed, recycling rates, sorting quality, and prevailing health conditions. A joint project of partners from industry and research institutions approaches this task by investigating and testing the combination of laser-induced breakdown spectroscopy (LIBS) with near-infrared (NIR) spectroscopy and visual imaging. Joint processing of information (data fusion) is expected to significantly improve the sorting quality of various materials like concrete, main masonry building materials, organic components, etc., and may enable the detection and separation of impurities such as SO3-cotaining building materials (gypsum, aerated concrete, etc.) Focusing on Berlin as an example, the entire value chain will be analyzed to minimize economic / technological barriers and obstacles at the cluster level and to sustainably increase recovery and recycling rates. We present current advances and results about the test stand development combining LIBS with NIR spectroscopy and visual imaging. In the future, this laboratory prototype will serve as a fully automated measurement setup to allow real-time classification of CDW on a conveyor belt.
Electromagnetic stirring is known to promote material flow, reduce porosity, uniform elements distribution, and refine grain in laser beam welding (LBW), which enhances the applicability of LBW in various industries. In this study, a phase-field model of dendrite growth in AA5754 Al alloy electromagnetic stirring laser beam welding was established. The model considered the thermal electromagnetic Lorentz force resulting from the interaction between the electric field generated by the Seebeck effect and the magnetic field, as well as the temperature gradient and solidification rate of the solidification interface obtained from the computational fluid dynamics electromagnetic stirring LBW model. The variation rules of dendrite growth with different magnetic parameters and effects are analyzed. Comprehensively, the magnetic field promotes the solidification rate, thus promoting interfacial instability and a large magnetic flux density leads to a faster interface instability. The solidification rate as well as the temperature gradient affect the growth rate, and the accelerated growth caused by the so lidification rate with a high frequency and a large magnetic flux density effectively inhibits the slow growth caused by the temperature gradient. The thermal electromagnetic Lorentz force is the main factor for the branch increment at low frequencies, while both thermal electromagnetic Lorentz force and temperature gradient in crease the number of branches at high frequencies. The calculated average branch numbers considering various factors in the stable stage under different magnetic parameters were consistent with the results of the scanning electron microscope tests.
Information about an existing structure can be collected at certain costs to evaluate the reliability and condition as realistically as necessary. This information can be relevant or irrelevant, true or biased, precise or imprecise. The incorporation of relevant and quality-assessed measured information into reliability reassessment offers the chance to extend remaining lifetimes and support decision making about optimal actions or maintenance strategies. This paper shows recent developments in a national research project that aims to produce a guideline on the NDT-based, structure-specific modification of partial safety factors. The general methodology, results from recalculations according to the Eurocodes and metrologically solvable testing tasks relevant in the recalculation of the concrete bridges are shown and compared with the non-destructive testing methods applicable to concrete bridges. A case study is used to demonstrate that as-built drawings, in this case of the positions of tendons and shear reinforcement, can be verified using the radar method.
High energy X-ray synchrotron diffraction is used to investigate the elastic anisotropy of the nickel-based superalloy IN718 produced by laser powder bed fusion (PBF-LB). This material is characterized by a columnar grain morphology with some crystallographic texture. The material is subjected to elastic loading to determine the diffraction elastic constants (DECs). Furthermore, the single-crystal elastic constants (SCEC) are refined from these experiments using different micromechanical models. The results show that each micromechanical model predicts a specific set of SCEC that well describes the elastic anisotropy of PBF-LB/IN718.
Data file (RData) containing measurement data recorded during the production process of the Certified Reference Material BAM-A001 containing Polycyclic Aromatic Hydrocarbons (PAH) in olive oil. The data can be most conveniently openend using the Shiny-App eCerto which is accessible at https://www.bam.de/eCerto.
Hot Isostatic Pressing (HIP) is often introduced to tackle the porosity issue in additively manufactured (AM) materials. For instance, HIP post-processing is recommended to improve fatigue resistance of Laser powder bed fusion (PBF-LB) manufactured parts [1, 2]. Even though HIP cannot completely remove porosity, it significantly decreases the defect population and its average size below the critical threshold value leading to early crack initiation.
In the present study, in-situ investigation of HIP procedure of PBF-LB Ti-6Al-4V parts was carried out to gain further insights into the densification mechanism occurring during HIP. The in-situ observations at high pressure and high temperature are uniquely possible at the PSICHE beamline of the Soleil synchrotron (France), thanks to the Ultrafast Tomography on a Paris-Edinburgh Cell (UToPEC) and the combination of the fast phase-contrast tomography and energy-dispersive diffraction [3, 4]. A detailed methodology was developed to ensure that the correct pressure and temperature were maintained during the experiments.
The results allowed an estimation of the global dentification rate during HIP of PBF-LB Ti-Al-4V material, as well as a detailed quantitative characterization of the influence of pore size and shape on the densification process, thereby understanding the effectiveness of HIP process on different pore categories. After 20 mins, 75% of porosity can be considered as closed or has size below the resolution of the XCT reconstruction. We also observed that the smallest defects showed higher densification rate, while the defect shape did not have significant effect on such rate. The current development of in-situ HIP experiment allows experimental quantification and validation of the simulation work. Ultimately it paves the road to tailoring the HIP procedure for different materials depending on the porosity and microstructure.
The achievable spatial resolution of active thermographic testing is inherently limited by the diffusive nature of heat conduction in solids. This degradation of the achievable spatial resolution for a semi-infinite body acting on a defect signal can be approximated by spatial convolution with the Green’s function of the heat PDE. As the degradation in spatial resolution is dependent on the depth 𝐿, a common rule of thumb specifies that for proper detection, any defect should feature a spatial extension greater or equal to the depth it is located at. However, as the exact shape of a defect can have a large impact on its severity, at best a proper reconstruction of the defect shape should be performed, which therefore must also deal with the aforementioned adverse effects of heat conduction. One recent method to overcome the spatial resolution limit of thermographic testing is the photothermal super resolution reconstruction method. It is based on performing multiple active thermographic measurements on the same region of interest (ROI) with varying spatially structured heating and subsequent numerical reconstruction of the measured defect signals by solving a severely ill-posed inverse reconstruction problem relying on heavy regularization. By extending the experimental implementation of the method to make use of random-pixel patterns projected onto the ROI using a laser-coupled DLP-projector, defect reconstructions can now be performed within a reasonable time frame (~15 min per ROI) at high accuracy. Compared to conventional thermographic testing methods, the photothermal super resolution reconstruction stands out by resulting in a sparse representation of the defect structure of the ROI, making it especially well-suited to further automatic defect classification and quality assurance measures in an Industry 4.0 context.
In our (dramatically understaffed) X-ray scattering laboratory, developing a systematic, holistic methodology let us provide scattering and diffraction information for more than 2100 samples for 200+ projects led by 120+ collaborators. Combined with automated data correction pipelines, and our analysis and simulation software, this led to more than 40 papers in the last 5 years with just over 2 full-time staff members.
This year, our new, modular synthesis platform has made more than 1000 additional samples for us to analyse and catalogue. By virtue of the automation, the synthesis of these samples is automatically documented in excruciating detail, preparing them for upload and exploitation in large-scale materials databases. Having developed these proof-of-concepts, we find that materials research itself is changed dramatically by automating dull tasks in a laboratory.
This talk is intended to spark ideas and collaborations by providing an overview of: 1) the current improvements in our scattering laboratory methodology, 2) introducing our open, modular robotic platform that is used for systematic sample preparation, and 3) demonstrating the data structure of the synthesis logs and measurements. Finally, the remaining bottlenecks and points of attention across all three are highlighted.
In this talk, the importance of metadata is underscored by real-world examples.
Metadata is essential to alleviating the reproducibility crises in science. This imples that a wide range of metadata must be collected, with a heavy emphasis on the automated collection of such metadata. This must subsequently be organized in an intelligible, archival structure, when possible with units and uncertainties.
Such metadata can aid in improving the usage efficiency of instrumentation, as is demonstrated on the MOUSE instrument. This metadata can now be used to connect the various aspects of the holistic experimental procedure to gain better insights on the materials structure.
A second example shows the extraction and organization of such metadata from an automated materials development platform, collected during the synthesis of 1200 samples. These metadata from the synthesis can then be linked to the results from the analysis of these samples, to find direct correlations between the synthesis parameters and the final structure of the materials.
Laser powder bed fusion is one of the most promising additive manufacturing techniques for printing complex-shaped metal components. However, the formation of subsurface porosity poses a significant risk to the service lifetime of the printed parts. In-situ monitoring offers the possibility to detect porosity already during manufacturing. Thereby, process feedback control or a manual process interruption to cut financial losses is enabled.
Short-wave infrared thermography can monitor the thermal history of manufactured parts which is closely connected to the probability of porosity formation. Artificial intelligence methods are increasingly used for porosity prediction from the obtained large amounts of complex monitoring data. In this study, we aim to identify the potential and the challenges of deep-learning-assisted porosity prediction based on thermographic in-situ monitoring.
Therefore, the porosity prediction task is studied in detail using an exemplary dataset from the manufacturing of two Haynes282 cuboid components. Our trained 1D convolutional neural network model shows high performance (R² score of 0.90) for the prediction of local porosity in discrete sub-volumes with dimensions of (700 x 700 x 40) μm³.
It could be demonstrated that the regressor correctly predicts layer-wise porosity changes but presumably has limited capability to predict differences in local porosity. Furthermore, there is a need to study the significance of the used thermogram feature inputs to streamline the model and to adjust the monitoring hardware. Moreover, we identified multiple sources of data uncertainty resulting from the in-situ monitoring setup, the registration with the ground truth X-ray-computed tomography data and the used pre-processing workflow that might influence the model’s performance detrimentally.
The evaluation of technical components and materials in terms of condition and quality with the aid of non-destructive testing methods plays an outstanding role both in industrial serial production and in the individual assessment of components. The ultrasonic echo method is used for a wide variety of testing tasks, such as measuring the thickness of a component. At the Bundesanstalt für Materialforschung und -prüfung (BAM) in Berlin, test methods are developed and their performance is demonstrated. For this purpose, references such as materials, test procedures or data sets are generated, which are used to evaluate these developed test methods. An important component of these references are data sets that are created according to the BAM Data Policy and made available to the scientific community and users of test methods in practice on the basis of the FAIR principles (Findability, Accessibility, Interoperability, and Reuse).
This presentation provides a comprehensive overview of recent developments and the current status within the Semantic Interoperability work area, with a particular emphasis on the advancements related to the Platform MaterialDigital Core Ontology (PMDco). The presentation will delve into the collaborative and community-supported curation process that has been instrumental in shaping PMDco. Additionally, we will introduce the innovative Ontology Playground, showcasing its role in fostering experimentation and exploration within the realm of ontology development.
The development of more powerful and more efficient lithium-ion batteries (LIBs) is a key area in battery research, aiming to support the ever-increasing demand for energy storage systems. To better understand the causes and mechanisms of degradation, and thus the diminishing cycling performance and lifetime often observed in LIBs, in operando techniques are essential, because battery chemistry can be monitored non-invasively, in real time. Moreover, there is increasing interest in developing new battery chemistries. Beyond LIBs, sodium ion batteries (NIBs) have gained increasing interest in recent years, as they are a promising candidate to complement LIBs, owing to their improved sustainability and lower cost, while still maintaining high energy density.[1] Initial phases of NIB commercialisation have occurred in the past year. However, for the widespread commercialisation of NIBs, there are still challenges that need to be overcome in developing optimized electrode materials and electrolytes. For the development of such materials and greater understanding of sodium storage mechanisms, solid electrolyte interface (SEI) formation and stability, and degradation processes, in operando methodologies are crucial.
Among the techniques available for in operando analysis, nuclear magnetic resonance spectroscopy (NMR) and imaging (MRI) are becoming increasingly used to characterize the chemical composition of battery materials, study the growth and distribution of dendrites, and investigate battery storage and degradation mechanisms. In situ and in operando 1H, 7Li and 23Na NMR and MRI have recently been used to study LIBs and NIBs, identifying chemical changes in Li and Na species respectively, in metallic, quasimetallic and electrolytic environment as well as directly and indirectly studying dendrite formation in both systems.[2-4] The ability of NMR and MRI to probe battery systems across multiple environments can further be complemented by the enhanced spatial resolution of micro-computed X-ray tomography (μ-CT) which can provide insight into battery material microstructure and defect distribution.
Here, we report in operando 1H and 7Li NMR and MRI experiments that investigate LIB performance, and the identification of changes in the Li signal during charge cycling, as well as the observation of signals in both 1H and 7Li NMR spectra that we attribute to diminishing battery performance, capacity loss and degradation. Additionally, recent operando methodology are adapted and implemented to study Sn based anodes in NIBs. 23Na spectroscopy is performed to monitor the formation and evolution of peaks assigned to stages of Na insertion into Sn, while 1H MRI is used to indirectly visualize the volume expansion of Sn anodes during charge cycling. Battery operation and degradation is further explored in these NIBs, using μ-CT, where the anode is directly visualized to a higher resolution and the loss of electrolyte in the cell, during cycling is observed
With the continuous release of anthropogenic pollutants into the environment, substantial risks for the human health arise. Concerning are especially persistent substances (e.g., PFAS) as they accumulate in food chains which inevitably result in the transgression of negative impact threshold levels. Environmental Analytical Chemsitry interfaces all disciplines of Risk Assessment. Therefore it is the important tool to identify, monitor, and remediate environmental pollutants. Based on the example of PFAS, a workflow to tackle environmental pollutants in a retro- and pro-spective way is shown. Within the project, the worldwide situation of environmental pollutants will be illustrated for the example of PFAS. As the major discipline to confront the problem, analytical chemistry will be shown as a key tool for contesting PFAS and creating safe-by-design materials in the future.
The application of compact NMR instruments to hot flowing samples or exothermically reacting mixtures is limited by the temperature sensitivity of permanent magnets. Typically, such temperature effects directly influence the achievable magnetic field homogeneity and hence measurement quality. The internal-temperature control loop of the magnet and instruments is not designed for such temperature compensation. Passive insulation is restricted by the small dimensions within the magnet borehole.
Here, we present a design approach for active heat shielding with the aim of variable temperature control of NMR samples for benchtop NMR instruments using a compressed airstream which is variable in flow and temperature. Based on the system identification and surface temperature measurements through thermography, a model predictive control was set up to minimise any disturbance effect on the permanent magnet from the probe or sample temperature.
This methodology will facilitate the application of variable-temperature shielding and, therefore, extend the application of compact NMR instruments to flowing sample temperatures that differ from the magnet temperature.
The combination of non-target analysis (NTA) based on HPLC-ESI-MS with elemental fluorine speciation analysis based on HPLC-PARCI-MS for the identification and quantification of (unknown) organofluorines in environmental samples is associated with several advantages e.g., reduced non-target data treatment workflow and quantification.
PFASs compromise persistent, bioaccumulative, and toxic properties and are hence, environmental contaminants of emerging concern. Thus, procedures for identifying potential sources of the entrance of these compounds into the environment, identification of new organofluorine species, and closing mass balances need urgent development. The intrinsic fluorine tag appears in a sizable fraction of these and other xenobiotics, making elemental speciation desirable for quantitative NTA in these areas. Current non-target approaches based on ESI-HRMS suffer from a lack of data mining algorithms for identification of PFASs with low fluorine mass percentages. Furthermore, low ionization efficiencies of the compounds hamper detection limits.
Hence, the proposed combination of simultaneous HPLC-ESI-MS and HPLC-PARCI-MS via split-stream coupling is a promising approach for environmental PFAS monitoring. Furthermore, it could serve as an important analytical procedure to set up limiting values in compliance with the desired PFAS ban of the European Union.
Compact NMR Spectroscopy in the field: A Versatile PAT Tool for Production of Specialty Chemicals
(2023)
Companies of chemical industry find themselves more often in a rapidly changing environment, e.g., due to variability of raw material quality or energy costs and efficiency. Process optimization and new process concepts become more and more important. For example, flexible and modular chemical plants can produce various high-quality products using multi-purpose equipment with short downtimes between campaigns and reduce time to market for new products. Intensified continuous production plants allow for difficult to produce compounds like exothermic reactions with high heat dissipation.
Highly automated “chemical” process control along with real-time quality control are prerequisites to such concepts and thus should be based on “chemical” information. A commercially available benchtop NMR spectrometer was integrated to the full requirements of an automated chemical production environment such as explosion safety, field communication, and robust evaluation of sensor data. Field studies in modular and conventional production plant setups show promising results gaining process knowledge for further optimization. NMR appeared as preeminent online analytical method and allow using a modular data analysis approach, which can even serve as reliable reference method for further calibration-dependent PAT applications (e.g., NIR or Raman spectroscopy).
Based on experiences from earlier field studies an improved NMR analyzer enclosure setup was developed and built, including the option of a secondary method (e.g., optical spectroscopy). Integrated control systems allow for a flexible implementation based on the available automation infrastructure at the chemical plant or pilot plant setup. In the future, fully integrated and intelligently interconnecting “smart” PAT systems and processes have the potential speed up the setup of production equipment for chemicals and pharmaceuticals and therefore help to reduce the time-to-market.
Almost all building materials in civil engineering have an open porosity and interact with or are affected by the environmental conditions. Structures might suffer from effects such as moisture adsorption, carbonation, corrosion, penetration of salt ions and chemical substances, etc. In the hygroscopic range, these processes are mostly driven by diffusion. Due to the confinement of small pores (less than1 µm), the Knudsen effect reduces the molecular diffusion. This reduction can become more significant in case of temporal changing pore systems because of physisorption of water vapor, carbonation, or chemisorption.
In this study, unstabilised earth blocks and earth masonry are investigated. In a first step, the pore size distribution of the blocks is measured and sorption isotherms are recorded in experiments. Besides the ordinary physisorption, the involved clay minerals undergo swelling or shrinking due to chemisorption. The following two effects must be considered: first, the reduction of the available pore space by the adsorbed water layer. For this, the Hillerborg sorption theory is used, which is a combination of the well-known Brunauer-Emmett-Teller sorption theory and the Kelvin equation. This allows the computation of adsorbed water layers even in curved pore geometries. Second, the variation of the initial pore size distribution due to chemisorption needs to be modelled. Based on these two models, the effective diffusion coefficient can be predicted. For validation, arrays of relative humidity sensors were embedded into a free-standing earth masonry wall. This monitoring was carried out over more than a year to have a broad variety of environmental conditions and was located in Berlin, Germany.
The prediction of the effective diffusion coefficient can also be transferred to other processes and allows the investigation of materials having temporarily changing pore systems. Examples are the carbonation of cementitious materials, alkali silica reaction, calcium leaching of long-lasting structures, etc. These effects are prominent in the meso-pore range and might significantly alter the effective diffusion coefficient.
Digital Calibration Certificates: Transforming Efficiency and Safety in Hydrogen Refuelling Station
(2023)
Digital Certificates have emerged as a pivotal element in automation and digitalisation. This presentation highlights the added value of a digitalised metrology, its impact on the workflows on the calibration service providing and receiving side as well as their significance in enhancing the quality infrastructure. An overview of digital calibration certificates (DCC) for temperature sensors, including their structure and role in establishing trust in hydrogen refuelling stations (HRS), will be discussed. Additionally, it explores the impact of DCC on optimising efficiency in the hydrogen refuelling process.
Exploring the paradigm shift brought about by Industry 4.0, where machines possess the ability to autonomously interpret digital certificate data, leads to streamlined safety checks and reduced human intervention. We will discuss how automated verification of machine-readable certificates contributes to maintaining and elevating safety standards over human-readable certificates. Furthermore, we will take a deep dive into the application of DCCs in HRS, showcasing how they enhance operational efficiency, accuracy, and maintenance by enabling real-time monitoring and adjustment of process data.
By exploring the interdependent relationship between digital certificates, machine-readable environments, and HRS optimisation, this presentation will provide valuable insights into harnessing cutting-edge technologies to create a safer, more efficient, and technologically empowered hydrogen refuelling process.
Over the past century, a range of synthetic compounds have been produced to improve humanity’s quality of life. These include pharmaceuticals, plastics, and other chemical compounds that possess properties making them potentially harmful when released to the environment (e.g., ecological and health impacts to humans and animals). Per- and polyfluoroalkyl substances (PFAS) are a large group of chemicals used in the formulations of thousands of consumer goods, including aqueous film-forming foams used to suppress aviation fires in training scenarios, non-stick cookware, fast-food wrappers, water-repellent fabrics, medical equipment, and plastic and leather products. Because of the recent regulations and restrictions on the use of long chain (≥C8) PFAS a significant shift in the industry towards short (C4-C7) and ultrashort (C1-C3) chain alternatives has been recognized the last years. Due to the high polarity and water solubility of ultrashort PFAS, the potential for bioaccumulation is low. However, the high persistence of ultrashort-chain PFAS will result in environmental accumulation, especially in aquatic environments, leading to potential risks for aquatic organisms and increased human external exposure through drinking water. Ultrashort PFAS like trifluoroacetic acid (TFA) are low to moderately toxic to a range of organisms. In addition, ultrashort PFAS can penetrate natural and anthropogenic barriers and eventually reach drinking water sources. Because common drinking water treatment techniques do not sufficiently remove them, they may reach human consumption. In the project we are focusing on detecting and removing PFAS, especially ultrashort-chain PFAS from contaminated groundwater. We are designing passive sampling devices, which can collect and monitor the temporal profile of PFAS species in groundwater. This will allow us to analyze PFAS contaminations in German and Israeli groundwater using state-of-the-art and novel analytical techniques and understand the extent of contamination. In addition to quantification, PFAS contaminated groundwater will be treated via a two-stage process to produce PFAS-free drinking water. As ultrashort-chain PFAS are difficult to analyze with the current target (LC-MS/MS) and sum parameter (AOF, EOF) analysis methods, we additionally using gas chromatography – mass spectrometry (GC-MS). Therefore, an analytical method based on GC-MS is in development to analyze the volatile ultrashort-chain PFAS (TFA, PFPrA, TFMS, PFEtS, PFPrS, trifluoroethanol, pentafluoropropanol and hexafluoro isopropanol) directly in contaminated groundwater samples with the headspace technique and in eluates of organic solvents from the developed passive sampler after direct injection. Moreover, a two-stages process is designed to increase the low concentrations found in groundwater using novel membranes processes such as closed-circuit reverse osmosis (CCRO) and mixed matrix composite nanofiltration membranes (MMCM). Next, the rejected streams containing higher concentrations of PFAS will be treated by coagulation, and the remaining PFAS adsorbed onto carbonaceous nanomaterials (CNMs). The DEFEAT-PFAS project will result in the development of novel tools to detect, quantify, and remove PFAS, especially ultrashort-chain PFAS from contaminated groundwater, and will acquire a new understanding of the extent of these contaminations.
Per- and polyfluoroalkyl substances (PFAS) are chemicals which were developed to improve humanity’s quality of life. Due to their high chemical stability and resistance to degradation by heat or acids, PFAS were used in a variety of consumer products. The continuous use of PFAS in household products and the discharge of PFAS from industrial plants into the sewer system resulted in the contamination of effluents and sewage sludge from wastewater treatment plants (WWTPs) (Roesch et al. 2022). Since sewage sludge is often used as fertilizer, its application on agricultural soils has been observed as a significant entry path for PFAS into the environment, specifically in our food chain. In Germany the sewage sludge/biosolid application on agricultural land was banned with the amendment of the German Sewage Sludge Ordinance and by 2029 sewage sludge application will be totally prohibited. However, phosphorus (P) from sewage sludge should still be recycled in WWTPs of cities with a population larger than 50,000 residents. To produce high-quality P-fertilizers for a circular economy, PFAS and other pollutants (e.g. pesticides and pharmaceuticals) must be separated from sewage sludge. Due to the strong diversity of industrial PFAS usage it is not clear if a safe application of novel recycled P-fertilizers from WWTPs can be guaranteed. Therefore, we analyzed various sewage sludges and wastewater-based fertilizers. Sewage sludge (SL) samples from various WWTPs in Germany and Switzerland, six sewage sludge ashes (SSA) from Germany, six thermally treated SL and SSA samples with different additives (temperatures: 700-1050 °C), two pyrolyzed SL samples (temperature: 400 °C) and two struvite samples from Germany and Canada were analyzed. The goal was to quantify PFAS in sewage sludges and wastewater-based P-fertilizers with the sum parameter extractable organic fluorine (EOF) by combustion ion chromatography (CIC). The results were compared with data from classical LC-MS/MS target analysis as well as selected samples by HR-MS suspect screening. The EOF values of the SLs mainly range between 154 and 538 µg/kg except for one SL which showed an elevated EOF value of 7209 µg/kg due to high organofluorine contamination. For the SSA samples the EOF values were lower and values between LOQ (approx. 60 µg/kg) and 121 µg/kg could be detected. For the pyrolyzed SLs no EOF values above the LOQ were detected. Moreover, the two wastewater-based struvite fertilizers contain 96 and 112 µg/kg EOF, respectively. In contrast to the EOF values, the sum of PFAS target values were relatively low for all SLs. Additional applied PFAS HR-MS suspect screening aimed to tentatively identify PFAS that could contribute to the hitherto unknown part of the EOF value. The majority of the detected fluorinated compounds are legacy PFAS such as short- and long-chain perfluorocarboxylic acids (PFCA), perfluorosulfonic acids (PFSA), polyfluoroalkyl phosphate esters (PAPs) and perfluorophosphonic acids (PFPA). Moreover, fluorinated pesticides, pharmaceutical as well as aromatic compounds were also identified, which are all included in the EOF parameter. Our research revealed that the current PFAS limit of 100 µg/kg for the sum of PFOS + PFOA in the German Fertilizer Ordinance is no longer up to date. Since the number of known PFAS already exceeds 10,000, the ordinance limit should be updated accordingly. Recent regulations and restrictions on using long-chain PFAS (≥C8) have resulted in a significant shift in the industry towards (ultra-)short-chain alternatives, and other, partly unknown, emerging PFAS. Ultimately, also fluorinated pesticides and pharmaceuticals, which end up as ultrashort PFAS in the WWTPs, have to be considered as possible pollutants in fertilizers from wastewater, too.
Laser powder bed fusion (PBF-LB/M) is a metal-based additive manufacturing (AM) process that utilizes a laser to melt powder particles selectively and form the desired product through a layer-by-layer fabrication process. It allows access to a vast new design space and can produce parts with far greater geometrical complexity than traditional methods. However, PBF-LB/M induces inevitable microstructural defects, such as voids and pores, that can adversely affect the quality and performance of the manufactured components. Hence, knowledge of pore formation, types, and suppression is essential for successful future AM applications. This research investigates the formation of different types of pores and their quantification from X-ray micro-computed tomography images of a formerly reported sample. It also examines the influence of the volumetric energy density (VED) on the size, shape, and location of pores for 316L stainless steel parts produced by the PBF-LB/M process.
When reassessing existing concrete bridges, the challenge is often to obtain missing or incomplete information on the internal structure. In particular, the number and position of the existing reinforcement as well as the geometric dimensions of the components are of interest. Non-destructive testing methods, like radar or ultrasound, which work on the basis of the pulse-echo method, have been established for this purpose, as they only require access to the component from one side. The measurement data recorded on the structure require pre-processing to be able to reproduce the internal structure geometrically accurately. Besides different steps of data processing, the geometrical reconstruction of the measured data based on the Synthetic Aperture Focusing Technique (SAFT) is state of the art today. In this paper, the technical possibilities of the ultrasonic echo method are presented based on measurements in the laboratory and on a real bridge structure. The precision of the reconstruction and its limitations are shown. In addition to the state of the art SAFT technique, open questions and the latest research approaches, such as imaging by reverse time migration (RTM) including initial results are discussed.
The focus of the presentation focus will be on 3D imaging by means of X-ray Computed Tomography (XCT) at the lab and at synchrotron, and the non-destructive residual stress (RS) characterization by diffraction of different kind of materials in FB8.5 Micro-NDT BAM. For instance, the manufacturing defects and high RS are inherent of additively manufacturing techniques and affect structural integrity of the components. Using XCT the defects size and shape distribution as well as geometrical deviations can be characterized, allowing the further optimization of the manufacturing process. Diffraction-based RS analysis methods using neutron and synchrotron X-rays at large scale facilities offer the possibility to non-destructively spatially resolve both surface and bulk RS in complex components and track their changes following applied thermal or mechanical loads.
Solid wastewater-based fertilizers were screened for per- and polyfluoroalkyl substances (PFAS) by the extractable organic fluorine (EOF) sum parameter method. The EOF values for ten sewage sludges from Germany and Switzerland range from 154 to 7209 mg kg−1. For thermal treated sewage sludge and struvite the EOF were lower with values up to 121 mg kg−1. Moreover, the application of PFAS targeted
and suspect screening analysis of selected sewage sludge samples showed that only a small part of the EOF sum parameter values can be explained by the usually screened legacy PFAS. The hitherto unknown part of EOF sum parameter contains also fluorinated pesticides, pharmaceutical and aromatic compounds. Because these partly fluorinated compounds can degrade to (ultra-)short PFAS in wastewater treatment plants they should be considered as significant sources of organic fluorine in the environment. The combined results of sum parameter analysis and suspect screening reveal the need to update current regulations, such as the German fertilizer ordinance, to focus not solely on a few selected PFAS such as perfluorooctane sulfonic acid (PFOS) and perfluorooctanoic acid (PFOA) but
consider an additional sum parameter approach as a more holistic alternative. Moreover, diffusion gradient in thin-films (DGT) passive samplers were utilized as an alternative simplified extraction method for PFAS in solid wastewater-based fertilizers and subsequently quantified via combustion ion chromatography. However, the DGT method was less sensitive and only comparable to the EOF values
of the fertilizers in samples with >150 mg kg−1, because of different diffusion properties for various PFAS, but also kinetic exchange limitations.
A spatial heterodyne spectrometer (SHS) was combined with a flame atomic absorption (FAA) setup to reveal the analytical potential of SHS to be used as a tool for high-resolution atomic absorption studies. Exploiting the advances in modern computational power, the spectral information encoded in the interferograms was extracted and separated. Consequently, unseeable interferometric ingredients corresponding to narrow-band absorption lines could be recognized. Namely, a single SHS absorption interferogram simultaneously contains both illumination background and absorption information, which can be distinguished through a series of computational steps. In the examples given by this work, we demonstrate the construction of Na absorbance spectra from a single image. The described single-image approach can be used to investigate highly dynamic systems, whereby background collections can be obviated.
When reassessing existing concrete bridges, the challenge is often to obtain missing or incomplete information on the internal structure. In particular, the number and position of the existing reinforcement as well as the geometric dimensions of the components are of interest. Non-destructive testing methods, like radar or ultrasound, which work on the basis of the pulse-echo method, have been established for this purpose, as they only require access to the component from one side. The measurement data recorded on the structure require pre-processing to be able to reproduce the internal structure geometrically accurately. Besides different steps of data processing, the geometrical reconstruction of the measured data based on the Synthetic Aperture Focusing Technique (SAFT) is state of the art today. In this paper, the technical possibilities of the ultrasonic echo method are presented based on measurements in the laboratory and on a real bridge structure. The precision of the reconstruction and its limitations are shown. In addition to the state of the art SAFT technique, open questions and the latest research approaches, such as imaging by reverse time migration (RTM) including initial results are discussed.
Laser-induced breakdown spectroscopy (LIBS) is becoming a more mature technology every year with new variants such as laser ablation molecular isotopic spectrometry, reheating by various discharge techniques, and multiple pulse excitation schemes, in which sometimes lasers of different pulse lengths are used. However, lasers with inherent parameters like pulse length and repetition rate are still almost exclusively employed. Recent years have witnessed the advent of novel high-repetition-rate laser concepts for machining processes, like welding, milling, and engraving. Here, a comprehensive study of single-pulse LIBS spectra of a single aluminum target is presented to showcase the applicability of flexible high duty-cycle master oscillator power amplifier (MOPA) lasers. Although traditional flashlamp-pumped Fabry–Pérot lasers only permit a variation in the pulse energy and are operated at very low duty-cycles, MOPA lasers add repetition rate and pulse length as variable parameters. A thorough analysis of the temporal plasma behavior revealed the emission dynamic to closely match the excitation laser pulse pattern. An aluminum sample’s spectral response was shown to be significantly impacted by variations in both rate and length. Although the spectral emission strength of the elemental lines of Al, Sr, and Ca all peaked at slightly different parameter settings, the strongest impact was found on the relative abundance of molecular AlO bands. Unlike in previous laser ablation molecular isotopic spectrometry (LAMIS) publications, the latter could be readily detected with a good intensity and well-resolved spectral features without any temporal gating of the detector. This finding, together with the fact that MOPA lasers are both inexpensive and dependable, makes for a promising combination for future studies including the detection of diatomic band structures.
A major advantage in the reassessment of existing structures is the possibility of including measured data that describe the actual properties and the current condition of the structure to be reassessed. Currently, the incorporation of such measured information is mostly unregulated. However, the use of measurement results is vitally important, since a measured data-based improvement of the computation models level of approximation can lead at least to more meaningful results, possibly to extended remaining life times of the structure and in the best case to a saving of resources. Conversely, not appreciating well measurable and relevant information can be equated with a waste of resources. In this paper, a concept for the comparable use of non-destructively measured data as basic variables in probabilistic reliability assessments is outlined and examined using a typical prestressed concrete road bridge as a case-study. An essential requirement is the calculation of measurement uncertainties in order to evaluate the quality of the measurement results comparably. In conclusion, the example of ultrasonic and radar measurement data is used to demonstrate the effects that the incorporation of the measured information has on the reliability of the structure.