TY - JOUR A1 - Kruschwitz, Sabine A1 - Oesch, T. A1 - Mielentz, Frank A1 - Meinel, Dietmar A1 - Spyridis, P. T1 - Non-Destructive Multi-Method Assessment of Steel Fiber Orientation in Concrete N2 - Integration of fiber reinforcement in high-performance cementitious materials has become widely applied in many fields of construction. One of the most investigated advantages of steel Fiber reinforced concrete (SFRC) is the deceleration of crack growth and hence its improved sustainability. Additional benefits are associated with its structural properties, as fibers can significantly increase the ductility and the tensile strength of concrete. In some applications it is even possible to entirely replace the conventional reinforcement, leading to significant logistical and environmental benefits. Fiber reinforcement can, however, have critical disadvantages and even hinder the Performance of concrete, since it can induce an anisotropic material behavior of the mixture if the fibers are not appropriately oriented. For a safe use of SFRC in the future, reliable non-destructive testing (NDT) methods need to be identified to assess the fibers’ orientation in hardened concrete. In this study, ultrasonic material testing, electrical impedance testing, and X-ray computed tomography have been investigated for this purpose using specially produced samples with biased or random Fiber orientations. We demonstrate the capabilities of each of these NDT techniques for fiber orientation measurements and draw conclusions based on these results about the most promising areas for future research and development. KW - Spectral induced polarization KW - Steel fiber reiniforced concrete KW - Fiber orientation KW - Non-destructive testing KW - Micro-computed tomography KW - Ultrasound PY - 2022 UR - https://nbn-resolving.org/urn:nbn:de:kobv:b43-543520 DO - https://doi.org/10.3390/app12020697 VL - 12 IS - 2 SP - 1 EP - 14 PB - MDPI CY - Basel Switzerland AN - OPUS4-54352 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - JOUR A1 - Singh, S. A1 - Palani, I. A. A1 - Paul, C. P. A1 - Funk, Alexander A1 - Gokuldoss, P. K. T1 - Wire Arc Additive Manufacturing of NiTi 4D Structures: Influence of Interlayer Delay N2 - Shape memory alloy structures for actuator and vibration damper applications may be manufactured using wire arc additive manufacturing (W AAM), which is one of the additive manufacturing technologies. Multilayer deposition causes heat accumulation during W AAM, which rises the preheat temperature of the previously created layer. This leads to process instabilities, which result in deviations from the desired dimensions and mechanical properties changes. During W AAM deposition of the wall structure, a systematic research is carried out by adjusting the interlayer delay from 10 to 30 s. When the delay period is increased from 10 to 30 s, the breadth decreases by 45% and the height increases by 33%. Grain refinement occurs when the interlayer delay duration is increased, resulting in better hardness, phase transformation temperature, compressive strength, and shape recovery behavior. This study shows how the interlayer delay affects the behavior of W AAM-built nickel-titanium alloy (NiTi) structures in a variety of applications. KW - Wire are additive manufacturing KW - Shape memory alloy KW - Nitinol KW - Interlayer delay PY - 2022 DO - https://doi.org/10.1089/3dp.2021.0296 SN - 2329-7662 SP - 1 EP - 11 PB - Liebert CY - New Rochelle, NY AN - OPUS4-55795 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - JOUR A1 - Valdestilhas, Andre A1 - Bayerlein, Bernd A1 - Moreno Torres, Benjami A1 - Zia, Ghezal Ahmad Jan A1 - Muth, Thilo T1 - The Intersection Between Semantic Web and Materials Science N2 - The application and benefits of Semantic Web Technologies (SWT) for managing, sharing, and (re-)using of research data are demonstrated in implementations in the field of Materials Science and Engineering (MSE). However, a compilation and classification are needed to fully recognize the scattered published works with its unique added values. Here, the primary use of SWT at the interface with MSE is identified using specifically created categories. This overview highlights promising opportunities for the application of SWT to MSE, such as enhancing the quality of experimental processes, enriching data with contextual information in knowledge graphs, or using ontologies to perform specific queries on semantically structured data. While interdisciplinary work between the two fields is still in its early stages, a great need is identified to facilitate access for nonexperts and develop and provide user-friendly tools and workflows. The full potential of SWT can best be achieved in the long term by the broad acceptance and active participation of the MSE community. In perspective, these technological solutions will advance the field of MSE by making data FAIR. Data-driven approaches will benefit from these data structures and their connections to catalyze knowledge generation in MSE. KW - Linked open data KW - Materials science KW - Ontology KW - Semantic web PY - 2023 UR - https://nbn-resolving.org/urn:nbn:de:kobv:b43-575506 DO - https://doi.org/10.1002/aisy.202300051 PB - Wiley-VCH GmbH CY - Weinheim AN - OPUS4-57550 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - JOUR A1 - Zaytsev, D. A1 - Funk, Alexander T1 - On the crack evolutional in human dentin under uniaxial compression imaged by high resolution tomography N2 - An observation of the fracture process in front of the crack tip inside a dentin sample by means of ex-situ X-ray computed tomography after uniaxial compression at different deformation values was carried out in this work. This ex-situ approach allowed the microstructure and fracturing process of human dentin to be observed during loading. No cracks are observed up to the middle part of the irreversible deformation in the samples at least visible at 0.4μm resolution. First cracks appeared before the mechanical stress reached the compression strength. The growth of the cracks is realized by connecting the main cracks with satellite cracks that lie ahead of the main crack tip and parallel its trajectory. When under the stress load the deformation in the sample exceeds the deformation at the compression strength of dentin, an appearance of micro-cracks in front of the main cracks is observed. The micro-cracks are inclined (~60°) to the trajectory of the main cracks. The further growth of the main cracks is not realized due to the junction with the micro-cracks; we assume that the micro-cracks dissipate the energy of the main crack and suppressed its growth. These micro-cracks serve as additional stress accommodations, therefore the samples do not break apart after the compression test, as it is usually observed under bending and tension tests. KW - Dentin KW - Crack evolution KW - Compression strength KW - Mechanical properties KW - Microstructure KW - Ex-situ X-ray computed tomography PY - 2023 UR - https://nbn-resolving.org/urn:nbn:de:kobv:b43-594810 DO - https://doi.org/10.18149/MPM.5152023_5 SN - 1605-8119 VL - 51 IS - 5 SP - 38 EP - 51 PB - Advanced Study Center CY - St. Petersburg AN - OPUS4-59481 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - JOUR A1 - Jablonka, Kevin Maik A1 - Ai, Qianxiang A1 - Al-Feghali, Alexander A1 - Badhwar, Shruti A1 - Bocarsly, Joshua D. A1 - Bran, Andres M. A1 - Bringuier, Stefan A1 - Brinson, L. Catherine A1 - Choudhary, Kamal A1 - Circi, Defne A1 - Cox, Sam A1 - de Jong, Wibe A. A1 - Evans, Matthew L. A1 - Gastellu, Nicolas A1 - Genzling, Jerome A1 - Gil, María Victoria A1 - Gupta, Ankur K. A1 - Hong, Zhi A1 - Imran, Alishba A1 - Kruschwitz, Sabine A1 - Labarre, Anne A1 - Lála, Jakub A1 - Liu, Tao A1 - Ma, Steven A1 - Majumdar, Sauradeep A1 - Merz, Garrett W. A1 - Moitessier, Nicolas A1 - Moubarak, Elias A1 - Mouriño, Beatriz A1 - Pelkie, Brenden A1 - Pieler, Michael A1 - Ramos, Mayk Caldas A1 - Ranković, Bojana A1 - Rodriques, Samuel G. A1 - Sanders, Jacob N. A1 - Schwaller, Philippe A1 - Schwarting, Marcus A1 - Shi, Jiale A1 - Smit, Berend A1 - Smith, Ben E. A1 - Van Herck, Joren A1 - Völker, Christoph A1 - Ward, Logan A1 - Warren, Sean A1 - Weiser, Benjamin A1 - Zhang, Sylvester A1 - Zhang, Xiaoqi A1 - Zia, Ghezal Ahmad Jan A1 - Scourtas, Aristana A1 - Schmidt, K. J. A1 - Foster, Ian A1 - White, Andrew D. A1 - Blaiszik, Ben T1 - 14 examples of how LLMs can transform materials science and chemistry: a reflection on a large language model hackathon N2 - Large-language models (LLMs) such as GPT-4 caught the interest of many scientists. Recent studies suggested that these models could be useful in chemistry and materials science. To explore these possibilities, we organized a hackathon. This article chronicles the projects built as part of this hackathon. Participants employed LLMs for various applications, including predicting properties of molecules and materials, designing novel interfaces for tools, extracting knowledge from unstructured data, and developing new educational applications. The diverse topics and the fact that working prototypes could be generated in less than two days highlight that LLMs will profoundly impact the future of our fields. The rich collection of ideas and projects also indicates that the applications of LLMs are not limited to materials science and chemistry but offer potential benefits to a wide range of scientific disciplines. KW - Large Language model KW - Hackathon KW - Concrete KW - Prediction KW - Inverse Design KW - Orchestration PY - 2023 UR - https://nbn-resolving.org/urn:nbn:de:kobv:b43-589961 DO - https://doi.org/10.1039/d3dd00113j VL - 2 IS - 5 SP - 1233 EP - 1250 PB - Royal Society of Chemistry (RSC) AN - OPUS4-58996 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - JOUR A1 - Moreno Torres, Benjami A1 - Völker, Christoph A1 - Firdous, Rafia T1 - Concreting a sustainable future: A dataset of alkali-activated concrete and its properties N2 - This data article introduces a dataset comprising 1630 alkali-activated concrete (AAC) mixes, compiled from 106 literature sources. The dataset underwent extensive curation to address feature redundancy, transcription errors, and duplicate data, yielding refined data ready for further data-driven science in the field of AAC, where this effort constitutes a novelty. The carbon footprint associated with each material used in the AAC mixes, as well as the corresponding CO2 footprint of every mix, were approximated using two published articles. Serving as a foundation for future expansions and rigorous data applications, this dataset enables the characterization of AAC properties through machine learning algorithms or as a benchmark for performance comparison among different formulations. In summary, the dataset provides a resource for researchers focusing on AAC and related materials and offers insights into the environmental benefits of substituting traditional Portland concrete with AAC. KW - Multidisciplinary KW - Data Set KW - Alkali Activated Concrete KW - Data driven design PY - 2023 UR - https://nbn-resolving.org/urn:nbn:de:kobv:b43-589974 DO - https://doi.org/10.1016/j.dib.2023.109525 SN - 2352-3409 VL - 50 SP - 1 EP - 9 PB - Elsevier B.V. AN - OPUS4-58997 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - JOUR A1 - Völker, Christoph A1 - Moreno Torres, Benjami A1 - Rug, Tehseen A1 - Firdous, Rafia A1 - Zia, Ghezal Ahmad Jan A1 - Lüders, Stefan A1 - Lisdero Scaffino, Horacio A1 - Höpler, Michael A1 - Böhmer, Felix A1 - Pfaff, Matthias A1 - Stephan, Dietmar A1 - Kruschwitz, Sabine T1 - Data driven design of alkali-activated concrete using sequential learning N2 - This paper presents a novel approach for developing sustainable building materials through Sequential Learning. Data sets with a total of 1367 formulations of different types of alkali-activated building materials, including fly ash and blast furnace slag-based concrete and their respective compressive strength and CO2-footprint, were compiled from the literature to develop and evaluate this approach. Utilizing this data, a comprehensive computational study was undertaken to evaluate the efficacy of the proposed material design methodologies, simulating laboratory conditions reflective of real-world scenarios. The results indicate a significant reduction in development time and lower research costs enabled through predictions with machine learning. This work challenges common practices in data-driven materials development for building materials. Our results show, training data required for data-driven design may be much less than commonly suggested. Further, it is more important to establish a practical design framework than to choose more accurate models. This approach can be immediately implemented into practical applications and can be translated into significant advances in sustainable building materials development. KW - Sustainable building materials KW - Sequential learning KW - Data-driven materials design KW - Alkali-activated building materials PY - 2023 UR - https://nbn-resolving.org/urn:nbn:de:kobv:b43-584376 UR - https://www.sciencedirect.com/science/article/pii/S095965262302379X DO - https://doi.org/10.1016/j.jclepro.2023.138221 SN - 0959-6526 SN - 1879-1786 VL - 418 SP - 1 EP - 13 PB - Elsevier CY - Amsterdam AN - OPUS4-58437 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - JOUR A1 - Mellios, Nikolaos A1 - Kruschwitz, Sabine A1 - Spyrdis, Panagiotis T1 - Performance Prediction of Anchors in SFRC using Minimally Invasive and Non‐Destructive Techniques N2 - AbstractFastenings are crucial for the upgrade of existing structures, by either enhancing structural components or modernising and expanding structures and structural systems. Whilst concrete is prevailing in the existing building stock, new types of concrete composites are increasingly used to improve the sustainability and overall structural performance of built assets. The addition of short dispersed fibres is a material technology with several applications. This paper addresses the synergy of fastening elements, introducing localised loads in concrete substrates, and of fibres, particularly in terms of the fibre orientation and density in the load application area, which is a significant quality aspect. The possibility to forecast this synergy based on non‐destructive or minimally invasive testing methods is an efficient tool to increase confidence in the design load‐bearing performance of the anchorages. Initially, previous literature and own investigations on such techniques are presented. Furthermore, a methodology is presented on associating such information to predict a single anchor's resistance on the basis of sophisticated non‐linear analyses and testing with engineered fibres alignments. T2 - Annual Meeting of the European-Association-on-Quality-Control-of-Bridges-and-Structures (EUROSTRUCT) CY - Vienna, Austria DA - 25.09.2023 KW - Minimally-invasive evaluation KW - Fastenings KW - Anchorage to concrete KW - Steel fibre reinforced concrete KW - Non-destructive evaluation NDE KW - Non-destructive testing NDT PY - 2023 UR - https://nbn-resolving.org/urn:nbn:de:kobv:b43-609223 DO - https://doi.org/10.1002/cepa.2115 VL - 69 IS - 5 SP - 1455 EP - 1463 PB - Wiley AN - OPUS4-60922 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - JOUR A1 - Costabel, S. A1 - Hiller, Thomas A1 - Dlugosch, R. A1 - Kruschwitz, Sabine A1 - Müller Petke, M. T1 - Evaluation of single-sided nuclear magnetic resonance technology for usage in geosciences N2 - Because of its mobility and ability to investigate exposed surfaces, single-sided (SiS) nuclear magnetic resonance (NMR) technology enables new application fields in geosciences. To test and assess its corresponding potential, we compare longitudinal (T1) and transverse (T2) data measured by SiS NMR with those of conventional geoscientific laboratory NMR. We use reference sandstone samples covering a broad range of pore sizes. Our study demonstrates that the lower signal-to-noise ratio of SiS NMR data generally tends to slightly overestimated widths of relaxation time distributions and consequently pore size distributions. While SiS and conventional NMR produce very similar T1 relaxation data, unbiased SiS NMR results for T2 measurements can only be expected for fine material, i.e. clayey or silty sediments and soils with main relaxation times below 0.05s. This limit is given by the diffusion relaxation rate due to the gradient in the primary magnetic field associated with the SiS NMR. Above that limit, i.e. for coarse material, the relaxation data is strongly attenuated. If considering the diffusion relaxation time of 0.2 s in the numerical data inversion process, the information content >0.2s is blurred over a range larger than that of conventional NMR. However, our results show that principle range and magnitudes of the relaxation time distributions are reconstructed to some extent. Regarding these findings, SiS NMR can be helpful to solve geoscientific issues, e.g. to assess the hydro-mechanical properties of the walls of underground facilities or to provide local soil moisture data sets for calibrating indirect remote techniques on the regional scale. The greatest opportunity provided by the SiS NMR technology is the acquisition of profile relaxation data for rocks with significant bedding structures at the µm scale. With this unique feature, SiS NMR can support the understanding and modeling of hydraulic and diffusional anisotropy behavior of sedimentary rocks. KW - Single-sided NMR KW - Geosciences KW - Nuclear magnetic resonance PY - 2022 UR - https://nbn-resolving.org/urn:nbn:de:kobv:b43-561676 DO - https://doi.org/10.1088/1361-6501/ac9800 SN - 0957-0233 VL - 34 IS - 1 SP - 1 EP - 13 PB - IOP Publishing AN - OPUS4-56167 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - JOUR A1 - Völker, Tobias A1 - Mensing, Friedrich A1 - Kruschwitz, Sabine T1 - Estimation of cement content in concrete by spatially resolved laser induced breakdown spectroscopy N2 - The cement content in concrete significantly influences critical properties such as durability, permeability, strength, and workability. Traditional methods for estimating the cement content face limitations. These include the need for comprehensive chemical and solubility knowledge, extensive sample preparation, and their time-consuming and destructive nature. This study investigates the application of laser-induced breakdown spectroscopy (LIBS) as an alternative method. It involves probing concrete samples with high spatial resolution and analyzing the resultant spectra. The methodology is first tested on mesoscale concrete models to assess limitations and inherent errors. Subsequently, the methodology is applied to actual concrete samples with varying cement content and aggregate size distributions. The results demonstrate a promising accuracy, with an average relative error of approximately 8%. This paper offers a comprehensive evaluation of the method's advantages, limitations, and factors influencing its practical applicability in field conditions. KW - LIBS KW - Spectroscopy KW - Cement content KW - Elemental mapping PY - 2024 UR - https://nbn-resolving.org/urn:nbn:de:kobv:b43-620112 DO - https://doi.org/10.1016/j.cemconres.2024.107714 SN - 1873-3948 VL - 189 SP - 1 EP - 10 PB - Elsevier Ltd. AN - OPUS4-62011 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Völker, Christoph A1 - John, Elisabeth A1 - Firdous, Rafia A1 - Hirsch, Tamino A1 - Kaczmarek, Daria A1 - Ziesack, Kevin A1 - Buchwald, Anja A1 - Stephan, Dietmar A1 - Kruschwitz, Sabine ED - Ferrara, Liberato ED - Muciaccia, Giovanni ED - di Summa, Davide T1 - Beyond Theory: Pioneering AI-Driven Materials Design in the Sustainable Building Material Lab N2 - This work focuses on Artificial Intelligence (AI)-driven materials design, addressing the challenge of improving the sustainability of building materials amid complex formulations. These formulations involve various components, such as binders, additives, and recycled aggregates, necessitating a balance between environmental impact and performance. Traditional experimental methods often fall short in managing the complexity of material composition, hindering fast enough development of optimal solutions. Our research explores complex composition materials design through a comprehensive, comparative lab study between Data-Driven Design, using SLAMD - an open-source AI materials design tool, and traditional Design of Experiments (DOE). We aimed to develop a high-performance, alkali-activated material using secondary precursors, aiming for a compressive strength exceeding 100 MPa after 7-days. The findings reveal that AI-driven design outperforms DOE in development speed and material quality, successfully identif. T2 - 4 RILEM Spring Convention and Conference on advanced construction materials and processes for a carbon neutral society 2024 CY - Milano, Italy DA - 07.04.2024 KW - Secondary Raw Materials KW - Data-Driven Design KW - Sequential Learning KW - Design of Experiments KW - Alkali-Activated Binder PY - 2024 SN - 978-3-03170281-5 DO - https://doi.org/10.1007/978-3-031-70281-5_31 SN - 2211-0852 VL - 2 SP - 274 EP - 282 PB - Springer AN - OPUS4-61662 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Zia, Ghezal Ahmad Jan A1 - Völker, Christoph A1 - Moreno Torres, Benjami A1 - Kruschwitz, Sabine T1 - An Adaptive Upscaling Approach for Assessing Materials’ Circularity Potential with Non-destructive Testing (NDT) N2 - Advancing towards a circular economy necessitates the efficient reuse and maintenance of structural materials, which relies on accurate, non-damaging condition assessments. This paper introduces an innovative AI-driven adaptive sampling (AS) technique integrated with Non-Destructive Testing (NDT) to optimize this process. AS focuses on critical data points, reducing the amount of data needed for precise assessments—evidenced by our method requiring on average only 7 samples for Logistic Regression and 8 for Random Forest, contrasted with 29 for traditional sampling. By reducing the necessity for extensive data collection, our method not only streamlines the assessment process but also significantly contributes to the sustainability goals of the circular economy. These goals include resource efficiency, waste reduction, and material reuse. Efficient condition assessments promote infrastructure longevity, reducing the need for new materials and the associated environmental impact. The circular economy aims to create a sustainable system where resources are reused, and waste is minimized. This is achieved by extending the lifecycle of materials, reducing the environmental footprint, and promoting recycling and reuse. Longevity directly contributes to the circular economy by maximizing the utility and lifespan of existing materials and structures. Longer-lasting infrastructure means fewer resources are needed for repairs or replacements, leading to reduced material consumption and waste generation. This aligns with the circular economy's principles of sustainability and resource efficiency. This research not only advances the field of structural health monitoring but also aligns with the broader objective of enhancing sustainable construction practices within the circular economy framework. T2 - Rilem Spring Convention CY - Milano, Italy DA - 09.04.2024 KW - Adaptive Sampling KW - Random Sampling KW - Machine Learning KW - Non-Destructive Testing KW - Condition Assessment KW - Circular Economy PY - 2024 SN - 978-3-031-70277-8 DO - https://doi.org/10.1007/978-3-031-70277-8_38 SN - 2211-0844 VL - 55 SP - 330 EP - 338 PB - Springer Nature Switzerland CY - Switzerland AN - OPUS4-62458 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Kruschwitz, Sabine A1 - Völker, Christoph A1 - Zia, Ghezal Ahmad Jan A1 - Moreno Torres, Benjami A1 - Hartmann, Timo ED - Ferrara, L. ED - Muciaccia, G. ED - di Summa, D. T1 - REINCARNATE: Shaping a Sustainable Future in Construction Through Digital Innovation N2 - We introduce the REINCARNATE project, funded by the European Union’s Horizon Europe program, to boost circularity by merging digital innovations with practical applications and a focus on material reuse. The heart of REINCARNATE is the Circular Potential Information Model (CP-IM), a digital platform designed to assess and enhance the recyclability of construction materials, construction products, and buildings. The CP-IM integrates advanced technologies such as digital twins, AI, and robotics to revolutionize the handling of construction waste, turning it into valuable resources and cutting the environmental footprint of the sector. Among its features are digital tracing, material durability predictions, and CO2 reduction materials design. These are showcased in eleven European demonstration projects, highlighting the practical benefits of these technologies in reducing construction waste and CO2 emissions by up to 80% and 70% respectively. REINCARNATE aims to marry innovation with real-world application, providing the construction industry with strategies for sustainable and circular practices. T2 - 4 RILEM Spring Convention and Conference on advanced construction materials and processes for a carbon neutral society 2024 CY - Milano, Italy DA - 07.04.2024 KW - Digital construction KW - Construction sustainability KW - European project KW - Llife cycle KW - Recycled materials PY - 2024 SN - 978-3-031-70280-8 SN - 978-3-03170281-5 SN - 978-3-031-70283-9 DO - https://doi.org/10.1007/978-3-031-70281-5_32 SN - 2211-0844 SN - 2211-0852 N1 - Serientitel: RILEM Bookseries – Series title: RILEM Bookseries VL - 56 IS - 2 SP - 283 EP - 291 PB - Springer CY - Cham AN - OPUS4-61661 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - JOUR A1 - Klewe, Tim A1 - Strangfeld, Christoph A1 - Kruschwitz, Sabine T1 - Review of moisture measurements in civil engineering with ground penetrating radar – Applied methods and signal features N2 - When applying Ground Penetrating Radar (GPR) to assess the moisture content of building materials, different medium properties, dimensions, interfaces and other unknown influences may require specific strategies to achieve useful results. Hence, we present an overview of the various approaches to carry out moisture measurements with GPR in civil engineering (CE). We especially focus on the applied Signal features such as time, amplitude and frequency features and discuss their limitations. Since the majority of publications rely on one single feature when applying moisture measurements, we also hope to encourage the consideration of approaches that combine different signal features for further developments. KW - Ground Penetrating Radar KW - Moisture KW - Civil engineering KW - Signal features PY - 2021 UR - https://nbn-resolving.org/urn:nbn:de:kobv:b43-520684 DO - https://doi.org/10.1016/j.conbuildmat.2021.122250 VL - 278 SP - 122250 PB - Elsevier Ltd. AN - OPUS4-52068 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - JOUR A1 - Moreno Torres, Benjami A1 - Völker, Christoph A1 - Munsch, Sarah Mandy A1 - Hanke, T. A1 - Kruschwitz, Sabine ED - Tosti, F. T1 - An Ontology-Based Approach to Enable Data-Driven Research in the Field of NDT in Civil Engineering N2 - Although measurement data from the civil engineering sector are an important basis for scientific analyses in the field of non-destructive testing (NDT), there is still no uniform representation of these data. An analysis of data sets across different test objects or test types is therefore associated with a high manual effort. Ontologies and the semantic web are technologies already used in numerous intelligent systems such as material cyberinfrastructures or research databases. This contribution demonstrates the application of these technologies to the case of the 1H nuclear magnetic resonance relaxometry, which is commonly used to characterize water content and porosity distri-bution in solids. The methodology implemented for this purpose was developed specifically to be applied to materials science (MS) tests. The aim of this paper is to analyze such a methodology from the perspective of data interoperability using ontologies. Three benefits are expected from this ap-proach to the study of the implementation of interoperability in the NDT domain: First, expanding knowledge of how the intrinsic characteristics of the NDT domain determine the application of semantic technologies. Second, to determine which aspects of such an implementation can be improved and in what ways. Finally, the baselines of future research in the field of data integration for NDT are drawn. KW - Ontology Engineering KW - Interoperability KW - Data-integration KW - NMR relaxometry KW - materials informatics PY - 2021 UR - https://nbn-resolving.org/urn:nbn:de:kobv:b43-529716 DO - https://doi.org/10.3390/rs13122426 SN - 2072-4292 N1 - Geburtsname von Munsch, Sarah Mandy: Nagel, S. M. - Birth name of Munsch, Sarah Mandy: Nagel, S. M. VL - 13 IS - 12 SP - 2426 PB - Multidisciplinary Digital Publishing Institute (MDPI) CY - Basel, Switzerland AN - OPUS4-52971 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - JOUR A1 - Klewe, Tim A1 - Strangfeld, Christoph A1 - Ritzer, Tobias A1 - Kruschwitz, Sabine T1 - Classification of Practical Floor Moisture Damage Using GPR - Limits and Opportunities N2 - Machine learning in non-destructive testing (NDT) offers significant potential for efficient daily data analysis and uncovering previously unknown relationships in persistent problems. However, its successful application heavily depends on the availability of a diverse and well-labeled training dataset, which is often lacking, raising questions about the transferability of trained algorithms to new datasets. To examine this issue closely, the authors applied classifiers trained with laboratory Ground Penetrating Radar (GPR) data to categorize on-site moisture damage in layered building floors. The investigations were conducted at five different locations in Germany. For reference, cores were taken at each measurement point and labeled as (i) dry, (ii) with insulation damage, or (iii) with screed damage. Compared to the accuracies of 84 % to 90 % within the laboratory training data (504 B-Scans), the classifiers achieved a lower overall accuracy of 53 % for on-site data (72 B-Scans). This discrepancy is mainly attributable to a significantly higher dynamic of all signal features extracted from on-site measurements compared to laboratory training data. Nevertheless, this study highlights the promising sensitivity of GPR for identifying individual damage cases. In particular the results showing insulation damage, which cannot be detected by any other non-destructive method, revealed characteristic patterns. The accurate interpretation of such results still depends on trained personnel, whereby fully automated approaches would require a larger and diverse on-site data set. Until then, the findings of this work contribute to a more reliable analysis of moisture damage in building floors using GPR and offer practical insights into applying machine learning to non-destructive testing for civil engineering (NDT-CE). KW - GPR KW - Material moisture KW - Building floor KW - Machine Learning PY - 2024 UR - https://nbn-resolving.org/urn:nbn:de:kobv:b43-607932 DO - https://doi.org/10.1007/s10921-024-01111-7 SN - 0195-9298 VL - 43 IS - 3 SP - 1 EP - 16 PB - Springer Science and Business Media LLC AN - OPUS4-60793 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - JOUR A1 - Klewe, Tim A1 - Strangfeld, Christoph A1 - Ritzer, Tobias A1 - Kruschwitz, Sabine T1 - Combining Signal Features of Ground-Penetrating Radar to Classify Moisture Damage in Layered Building Floors N2 - To date, the destructive extraction and analysis of drilling cores is the main possibility to obtain depth information about damaging water ingress in building floors. The time- and costintensive procedure constitutes an additional burden for building insurances that already list piped water damage as their largest item. With its high sensitivity for water, a ground-penetrating radar (GPR) could provide important support to approach this problem in a non-destructive way. In this research, we study the influence of moisture damage on GPR signals at different floor constructions. For this purpose, a modular specimen with interchangeable layers is developed to vary the screed and insulation material, as well as the respective layer thickness. The obtained data set is then used to investigate suitable signal features to classify three scenarios: dry, damaged insulation, and damaged screed. It was found that analyzing statistical distributions of A-scan features inside one B-scan allows for accurate classification on unknown floor constructions. Combining the features with multivariate data analysis and machine learning was the key to achieve satisfying results. The developed method provides a basis for upcoming validations on real damage cases. KW - Radar KW - Material Moisture KW - Non-destructive testing KW - Signal Features KW - Civil Engineering KW - Machine Learning PY - 2021 UR - https://nbn-resolving.org/urn:nbn:de:kobv:b43-533606 DO - https://doi.org/10.3390/app11198820 VL - 11 IS - 19 SP - 8820 PB - MDPI AN - OPUS4-53360 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Bintz, Thilo A1 - Kruschwitz, Sabine T1 - Non-destructive Testing for the Determination of Durability-Relevant Material Properties of Clinker-Reduced Building Materials N2 - In the quest to combat climate change, the construction industry, heavily reliant on cement-based materials, faces scrutiny due to significant CO2 emissions, mainly from clinker production. To address this, there’s a need to strategically reduce clinker content in cement. However, these new formulations must meet the same requirements as the original ones, so their durability must be investigated. In this study, we determined material parameters that enable an assessment of moisture and ion transport. These methods offer advantages over conventional approaches, including non-destructiveness, reduced measurement time, simplified setup, enhanced resolution, and improvement of detection limits. Our investigation utilizes NDT techniques, using 1H NMR relaxometry for moisture transport and LIBS for ion transport in various clinker-reduced materials. The NMR tomograph provides spatial insights into internal moisture transport, correlated with weight change assessments for the capillary transport coefficient. Additionally, based on NMR relaxometry data the chloride diffusion coefficient is estimated. Chloride migration tests are performed, and results are evaluated using LIBS and indicator tests for the chloride migration coefficient. Our findings highlight NMR relaxometry and LIBS advantages over conventional methods, showcasing superior spatial resolution, non-destructiveness, and, in some cases, expedited results with independence from the formulation of the cement matrix. Thus, these new methods can be used to test the durability of new, more heterogeneous cementbased building. T2 - Rilem Spring Convention 2024 CY - Milano, Italy DA - 10.04.2024 KW - NMR relaxometry KW - LIBS KW - Clinker reduced cement KW - Durability parameters PY - 2025 SN - 978-3-031-70281-5 DO - https://doi.org/10.1007/978-3-031-70281-5_48 VL - 56 SP - 426 EP - 434 AN - OPUS4-61824 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Munsch, Sarah Mandy A1 - Telong, Melissa A1 - Grobla, Lili A1 - Schumacher, Katrin A1 - Völker, Christoph A1 - Yared, Kaleb A1 - Kruschwitz, Sabine ED - Ferrara, Liberato ED - Muciaccia, Giovanni ED - di Summa, Davide T1 - Study on the Predictability of Carbonation Resistance of Cementitous Materials Based on NMR Features and the Use of SLAMD N2 - This study explores the acceleration of material design in the concrete industry, focusing on improving carbonation resistance, a key factor in the durability of concrete structures. Traditional tests for carbonation resistance are lengthy, but with the construction industry aiming for sustainable production, finding a balance between carbonation resistance and CO2 footprint is crucial. Our research employs two innovative methods: 1. Applying the Sequential Learning App for Materials Discovery (SLAMD), an AI materials design framework, to an extensive dataset of real-world concrete compositions to selectively test materials that meet market demands: maximum durability, optimal eco-durability, and the best cost-durability trade-off. 2. Investigating 1H Nuclear Magnetic Resonance (NMR) relaxometry as a quick alternative for characterizing carbonation behavior, as it saves time compared to traditional tests and assesses the complete material’s pore space. Specific NMR features are then integrated into the material design model, with the model’s performance compared against traditional approaches. The results of our study are compelling, demonstrating that materials can be precisely tailored to meet specific requirements with minimal data points. This marks a significant stride in the concrete industry, indicating thatNMR-based, lowfidelity surrogate characterizations, combined with a focused, data-driven design approach, can substantially accelerate the development of durable, sustainable concrete mixtures. T2 - 4 RILEM Spring Convention and Conference on advanced construction materials and processes for a carbon neutral society 2024 CY - Milano, Italy DA - 07.04.2024 KW - Nuclear magnetic resonance KW - carbonation resistance KW - cement and concrete KW - SLAMD app KW - predictability PY - 2024 DO - https://doi.org/10.1007/978-3-031-70281-5_49 VL - 2 SP - 435 EP - 442 PB - Springer CY - Cham, Switzerland AN - OPUS4-61692 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - JOUR A1 - Fritzsche, Sven A1 - Jaenisch, Gerd-Rüdiger A1 - Pavasaryte, Lina A1 - Funk, Alexander T1 - XCT and DLW: Synergies of Two Techniques at Sub-Micrometer Resolution N2 - Direct Laser Writing (DLW) and X-ray computed tomography (XCT) both offer unique possibilities in their respective fields. DLW produces full three-dimensional (3D) polymer structures on the microscale with resolutions below 100 nm. The fabricated structures can be analysed by XCT or X-ray microscopy (XRM), which incorporates additional X-ray lenses, in three dimensions down to a minimal basic spatial resolution of about 500 nm or 50 nm, respectively. In this work, two different DLW structures are analysed via XCT. Internal defects are detected and analysed for the purpose of quality control. Defects and structures with sizes down to 1.5 µm are successfully analysed. A 3D reconstruction and internal, hidden features of the fabricated structures are shown and discussed. In a first-of-its-kind study, we demonstrate the detectability of a single-voxel line inside a fabricated structure that would not be detectable with SEM or light microscopy. Furthermore, the direct fabrication on a PET substrate is shown to overcome the high X-ray absorbance of commonly used glass substrates. Attenuation spectra of SZ2080 and glass substrates are compared to a fabrication route direct on a 170 µm PET foil. The practical aspects of XCT measurements for DLW structures on different substrates will be discussed. KW - Non-destructive testing KW - Two-photon polymerization KW - X-ray microscopy KW - XCT KW - 2PP KW - Direct laser writing PY - 2022 UR - https://nbn-resolving.org/urn:nbn:de:kobv:b43-560525 DO - https://doi.org/10.3390/app122010488 VL - 12 IS - 20 SP - 1 EP - 15 PB - MDPI CY - Basel AN - OPUS4-56052 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - JOUR A1 - Martin, T. A1 - Zimmermann, E. A1 - Klitzsch, N. A1 - Hördt, A. A1 - Huisman, J. A. A1 - Radic, T. A1 - Kruschwitz, Sabine T1 - Round-robin test of SIP laboratory measurements using electrical test networks N2 - This study presents the results of an interlaboratory test designed to evaluate the accuracy of spectral induced polarization (SIP) measurements using controlled electrical test networks. The study, conducted in Germany since 2006, involved 12 research institutes, six different impedance measurement devices and four types of electrical test networks specifically designed to evaluate phase shift errors in SIP measurements. The test networks, with impedances ranging from 100 to 150 kΩ, represent high-impedance samples with different phase characteristics, and pose the measurement challenges typical of such samples, including high contact impedances and parasitic capacitances. Four key findings emerged from the study: (1) Impedance measurements across all devices showed deviations within 1 per cent over a wide frequency range (0.001–1000 Hz); (2) phase errors remained below 1 mrad up to 100 Hz for most devices, but increased at higher frequencies due to parasitic capacitances and electromagnetic coupling effects; (3) lab-specific instruments have lower phase errors than field instruments when used in a laboratory environment, primarily due to the effects of long cables and too low input impedances of the field instruments; and (4) short cables and driven shielding technology effectively minimized parasitic capacitance and improved measurement accuracy. The study highlights the usefulness of test networks in assessing the accuracy of SIP measurements and raises awareness of the various factors influencing the quality of SIP data. KW - Induced polarization KW - Electrical properties KW - Electrical resistivity tomography (ERT) PY - 2025 UR - https://nbn-resolving.org/urn:nbn:de:kobv:b43-631522 DO - https://doi.org/10.1093/gji/ggaf153 SN - 0956-540X VL - 242 IS - 1 SP - 1 EP - 13 PB - Oxford University Press (OUP) AN - OPUS4-63152 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - JOUR A1 - Fricke, F. A1 - Brandalero, M. A1 - Liehr, Sascha A1 - Kern, Simon A1 - Meyer, Klas A1 - Kowarik, Stefan A1 - Hierzegger, R. A1 - Westerdick, S. A1 - Maiwald, Michael A1 - Hübner, M. T1 - Artificial Intelligence for Mass Spectrometry and Nuclear Magnetic Resonance Spectroscopy Using a Novel Data Augmentation Method N2 - 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. KW - Industry 4.0 KW - Cyber-Physical Systems KW - Artificial Neural Networks KW - Mass Spectrometry KW - Nuclear Magnetic Resonance Spectroscopy KW - Modular Production PY - 2021 UR - https://nbn-resolving.org/urn:nbn:de:kobv:b43-539412 UR - https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9638378 DO - https://doi.org/10.1109/TETC.2021.3131371 SN - 2168-6750 VL - 10 IS - 1 SP - 87 EP - 98 PB - IEEE AN - OPUS4-53941 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - JOUR A1 - Kern, Simon A1 - Liehr, Sascha A1 - Wander, Lukas A1 - Bornemann-Pfeiffer, Martin A1 - Müller, S. A1 - Maiwald, Michael A1 - Kowarik, Stefan T1 - Artificial neural networks for quantitative online NMR spectroscopy N2 - 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. KW - Online NMR spectroscopy KW - Real-time process monitoring KW - Artificial neural networks KW - Automation KW - Process industry PY - 2020 UR - https://nbn-resolving.org/urn:nbn:de:kobv:b43-507508 DO - https://doi.org/10.1007/s00216-020-02687-5 SN - 1618-2642 VL - 412 IS - 18 SP - 4447 EP - 4459 PB - Springer CY - Berlin AN - OPUS4-50750 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - JOUR A1 - Ulbricht, Alexander A1 - Gollwitzer, Christian A1 - Kupsch, Andreas A1 - Léonard, Fabien A1 - Müller, Bernd R. A1 - Oesch, Tyler A1 - Onel, Yener A1 - Thiede, Tobias A1 - Zscherpel, Uwe ED - Puente León, F. ED - Zagar, B. T1 - Moderne Methoden der CT-gestützten Strukturanalyse T1 - Modern techniques of CT based structure analysis N2 - Durch den großflächigen Einsatz der Computertomographie (CT) in unterschiedlichen Industriebereichen steigen auch die Anforderungen an die quantitative Bildanalyse. Subjektive Bildwahrnehmung muss durch objektive Algorithmen ersetzt werden. In diesem Artikel stellt die Bundesanstalt für Materialforschung und -prüfung (BAM), die seit den 1980er Jahren an der Entwicklung der industriellen CT beteiligt ist, anhand ausgewählter Beispiele den aktuellen Stand ihrer Analysemethoden an verschiedenen Anwendungsbeispielen der CT vor. N2 - The increasing use of computed tomography (CT) in various industrial sectors requires more sophisticated techniques of quantitative image analysis. Subjective image perception needs to be replaced by objective algorithms. The German Federal Institute for Materials Research and Testing (BAM) has been involved in the development of industrial CT since the 1980s. This paper summarizes the current status of quantitative 3D image analysis techniques based on selected examples. KW - Computed tomography KW - Computertomographie KW - Röntgen-Refraktion KW - X-Ray refraction KW - Schadensanalyse KW - Damage analysis KW - Normung KW - standardization PY - 2020 DO - https://doi.org/10.1515/teme-2019-0125 SN - 0171-8096 SN - 2196-7113 VL - 87 IS - 2 SP - 81 EP - 91 PB - de Gruyter CY - Berlin AN - OPUS4-50337 LA - deu AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - JOUR A1 - Plarre, Rüdiger A1 - Zocca, Andrea A1 - Spitzer, Andrea A1 - Benemann, Sigrid A1 - Gorbushina, Anna A1 - Li, Y. A1 - Waske, Anja A1 - Funk, Alexander A1 - Wilgig, Janka A1 - Günster, Jens T1 - Searching for biological feedstock material: 3D printing of wood particles from house borer and drywood termite frass N2 - Frass (fine powdery refuse or fragile perforated wood produced by the activity of boring insects) of larvae of the European house borer (EHB) and of drywood termites was tested as a natural and novel feedstock for 3D-printing of wood-based materials. Small particles produced by the drywood termite Incisitermes marginipennis and the EHB Hylotrupes bajulus during feeding in construction timber, were used. Frass is a powdery material of particularly consistent quality that is essentially biologically processed wood mixed with debris of wood and faeces. The filigree-like particles flow easily permitting the build-up of woodbased structures in a layer wise fashion using the Binder Jetting printing process. The Quality of powders produced by different insect species was compared along with the processing steps and properties of the printed parts. Drywood termite frass with a Hausner Ratio HR = 1.1 with ρBulk = 0.67 g/cm3 and ρTap = 0.74 g/cm3 was perfectly suited to deposition of uniformly packed layers in 3D printing. We suggest that a variety of naturally available feedstocks could be used in environmentally responsible approaches to scientific material sciences/additive manufacturing. KW - 3D printing KW - X-ray tomographic KW - SEM micrography KW - Drywood termite PY - 2021 UR - https://nbn-resolving.org/urn:nbn:de:kobv:b43-521517 DO - https://doi.org/10.1371/journal.pone.0246511 VL - 16 IS - 2 SP - e0246511 AN - OPUS4-52151 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - JOUR A1 - Munsch, Sarah Mandy A1 - Schmidt, Wolfram A1 - Lorenzoni, Renata A1 - Telong, Melissa A1 - Grobla, Lili A1 - Lauinger, Robert A1 - Kruschwitz, Sabine T1 - Investigation of the hydration of clinker‑reduced cementitious binders by 1H NMR relaxometry N2 - In this paper, we demonstrate the value of 1 H NMR relaxometry for studying the hydration of clinker-reduced, climate-friendly cementitious binders. Our study includes white cement (WC), ordinary Portland cement (OPC), and samples incorporating reactive agro-waste based ashes and non-reactive biochars as supplementary cementitous materials (SCM). NMR measurements were performed over a period of up to 120 h during hydration with an echo time of 50 μ s and a relatively large sample size of 20 mL. The results were compared to heat flow calorimetry (HFC) data, and a detailed comparison with literature data was performed for pure OPC and WC. The results show that time-resolved NMR measurements, especially the analysis of individual NMR signal components assigned to defined 1 H reservoirs, are effective for studying hydration processes. They offer insights into the evolution of the microstrucure and specific chemical phases. NMR provides valuable information and serves as a good complement to HFC. The comparison with data obtained with shorter echo times (40 μs or around 15–45 μs with solid echo sequence) on much smaller samples showed almost identical developments with respect to the T2 distributions. For the SCM samples, NMR results indicated partially accelerated hydration processes compared to classical OPC hydration. One SCM sample acted as a highly reactive pozzolan, showing a similar hydration process to OPC with the strongest effect observed when superplasticizer was added. Adding biochar delayed C-S-H gel pore formation but significantly increased capillary pores and even free water, likely due to the sponge-like structure. KW - Nuclear magnetic resonance relaxometry KW - Heat flow calorimetry KW - Cement KW - Hydration KW - Clinker reduction KW - Carbon emission KW - Kinetics PY - 2025 UR - https://nbn-resolving.org/urn:nbn:de:kobv:b43-630284 DO - https://doi.org/10.1617/s11527-025-02632-x SN - 1871-6873 VL - 58 IS - 137 SP - 1 EP - 23 PB - Springer Nature AN - OPUS4-63028 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - JOUR A1 - Völker, Tobias A1 - Blaschke, Jil A1 - Lierenfeld, Matthias A1 - Wilsch, Gerd A1 - Dalichow, Dirk A1 - Truffer, Philipp A1 - Kruschwitz, Sabine T1 - Aktuelle Anwendungsbeispiele der laserinduzierten Plasmaspektroskopie in der Bauwerksdiagnostik N2 - Die Zustandsbewertung und Instandhaltung von Betonbauwerken erfordert zuverlässige und effiziente Analysemethoden, um komplexe Schadensmechanismen frühzeitig erkennen und fundiert bewerten zu können. Klassische chemisch-analytische Verfahren in der Betonanalytik liefern zwar präzise Ergebnisse bezogen auf die Probenmasse, sind jedoch mit erheblichem Laboraufwand verbunden, in ihrer räumlichen Auflösung durch die Probenahme begrenzt und hinsichtlich der Ergebnisinterpretation mit Unsicherheiten behaftet. Die laserinduzierte Plasmaspektroskopie (LIBS) bietet hier ein hohes Potenzial als schnelle, bildgebende und weitgehend zerstörungsarme Alternative. In den vergangenen Jahren hat sich das Verfahren zunehmend in der Bauwerksdiagnostik etabliert und wird heute für ein breites Spektrum an Anwendungen eingesetzt. Der vorliegende Beitrag gibt einen Überblick über den aktuellen Stand der LIBS-Anwendungen und zeigt anhand ausgewählter Praxisbeispiele die Leistungsfähigkeit, den Mehrwert, die Grenzen und die zukünftigen Entwicklungsperspektiven des Verfahrens auf. KW - LIBS KW - Spektroskopie KW - Chemische Analyse KW - Infrastruktur KW - Bauwerksdiagnostik PY - 2026 DO - https://doi.org/10.1002/best.70078 SN - 1437-1006 SP - 1 EP - 13 AN - OPUS4-65445 LA - deu AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - JOUR A1 - Das, A. A1 - Ott, C. A1 - Pechimuthu, Dinesh A1 - Moosavi, Robabeh A1 - Stoica, M. A1 - Derlet, P. M. A1 - Maaß, Robert T1 - Shear-band cavitation determines the shape of the stress-strain curve of metallic glasses N2 - Metallic glasses are known to have a remarkably robust yield strength, admitting Weibull moduli as high as for crystalline engineering alloys. However, their postyielding behavior is strongly varying, with large scatter in both flow stress levels and strains at failure. Using x-ray tomography, we reveal how a strain-dependent internal evolution of shear-band cavities underlies this unpredictable postyielding response.We demonstrate how macroscopic strain softening coincides with the first detection of internal shear-band cavitation. Cavity growth during plastic flow is found to follow a power law, which yields a fractal dimension and a roughness exponent in excellent agreement with self-similar surface properties obtained after fracture. These findings demonstrate how internal microcracking coexists with shear-band plasticity along the plastic part of a stress-strain curve, rationalizing the large variability of plastic flow behavior seen for metallic glasses. KW - Shear-band cavitation KW - Metallic glasses PY - 2023 DO - https://doi.org/10.1103/PhysRevMaterials.7.023602 SN - 2475-9953 VL - 7 IS - 2 SP - 1 EP - 11 PB - American Physical Society AN - OPUS4-57042 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - JOUR A1 - Kruschwitz, Sabine A1 - Munsch, Sarah Mandy A1 - Telong, Melissa A1 - Schmidt, Wolfram A1 - Bintz, Thilo A1 - Fladt, Matthias A1 - Stelzner, Ludwig T1 - The NMR core analyzing TOMograph: A multi-functional tool for non-destructive testing of building materials N2 - NMR is becoming increasingly popular for the investigation of building materials as it is a non-invasive technology that does not require any sample preparation nor causes damage to the material. Depending on the specific application it can offer insights into properties like porosity and spatial saturation degree as well as pore structure. Moreover it enables the determination of moisture transport properties and the (re-)distribution of internal moisture into different reservoirs or chemical phases upon damage and curing. However, as yet most investigations were carried out using devices originally either designed for geophysical applications or the analysis of rather homogeneous small scale (< 10 mL) samples. This paper describes the capabilities of an NMR tomograph, which has been specifically optimized for the investigation of larger, heterogeneous building material samples (diameters of up to 72 mm, length of up to 700 mm) with a high flexibility due to interchangeable coils allowing for a high SNR and short echo times (50 - 80 m s). KW - Fire spalling KW - Moisture transport KW - Concrete KW - Cement hydration KW - Sensitivity KW - Supplementary cementitous materials KW - Frost salt attack PY - 2023 UR - https://nbn-resolving.org/urn:nbn:de:kobv:b43-573755 DO - https://doi.org/10.1016/j.mrl.2023.03.004 SN - 2097-0048 VL - 3 IS - 3 SP - 207 EP - 219 PB - Elsevier B.V. AN - OPUS4-57375 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - JOUR A1 - Lorenzoni, Renata A1 - Fritsch, Tobias A1 - Kruschwitz, Sabine A1 - Bruno, Giovanni A1 - Schmidt, Wolfram T1 - Mechanical analysis of cement-biochar composites using in-situ X-ray microtomography and digital volume correlation N2 - This study addresses biochar as a potential carbon-sequestering filler in cement and examines its effect on mechanical properties using X-ray computed tomography (XCT) and digital volume correlation (DVC). DVC was reliably used to measure global displacement and has proven to be an effective method for correcting displacement data obtained from mechanical tests conducted without traditional instrumentation, such as extensometer. This made it possible to measure strain and Young’s modulus accurately. The results demonstrate that while 5 vol% biochar replacement had minimal effect on mechanical properties, a 25 vol% biochar replacement caused a 35 % reduction in Young’s modulus and 40 % reduction in the ultimate compressive strength. Additionally, DVC detected strain concentrations and predicted material failure locations even when cracks could not be quantified using XCT alone. Moreover, the study reveals that biochar particles, due to their sharp geometry, increase internal shear strain during uniaxial compression, unlike round phases such as pores. KW - Cementitious materials KW - Biochar KW - Mechanical proprieties KW - In-situ X-ray microtomography KW - Digital volume correlation PY - 2025 UR - https://nbn-resolving.org/urn:nbn:de:kobv:b43-638065 DO - https://doi.org/10.1016/j.conbuildmat.2025.142511 SN - 1879-0526 VL - 490 PB - Elsevier Ltd. AN - OPUS4-63806 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - JOUR A1 - Kruschwitz, Sabine A1 - Halisch, M. A1 - Dlugosch, R. A1 - Prinz, Carsten T1 - Toward a better understanding of low-frequency electrical relaxation - An enhanced pore space characterization N2 - Relaxation phenomena observed in the electrical low-frequency range (approximately 1 mHz-10 kHz) of natural porous media like sandstones is often assumed to be directly related to the dominant (modal) pore throat sizes measured, for instance, with mercury intrusion porosimetry. Attempts to establish a universally valid relationship between pore size and peak Spectral Induced Polarization (SIP) relaxation time have failed, considering sandstones from very different origins and featuring great variations in textural and chemical compositions as well as in geometrical pore space properties. In addition working with characteristic relaxation times determined in Cole-Cole or Debye decomposition fits to build the relationship have not been successful. In particular, samples with narrow pore throats are often characterized by long SIP relaxation times corresponding to long “characteristic length scales” in these media, assuming that the diffusion coefficients along the electrical double layer were constant. Based on these observations, three different types of SIP relaxation can be distinguished. We present a new way of assessing complex pore spaces of very different sandstones in a multi-methodical approach to combine the benefits of mercury intrusion porosimetry, micro-computed tomography, and nuclear magnetic resonance. In this way, we achieve much deeper insight into the pore space due to the different resolutions and sensitivities of the applied methods to both pore constrictions (throats) and wide pores (pore bodies). We experimentally quantify pore aspect ratios and volume distributions within the two pore regions. We clearly observe systematic differences between three SIP relaxation types identified previously and can attribute the SIP peak relaxation times to measured characteristic length scales within our materials. We highlight selected results for a total of nine sandstones. It seems that SIP relaxation behavior depends on the size difference of the narrow pore throats to the wide pore bodies, which increases from SIP Type 1 to Type 3. KW - µ-CT KW - Spectral induced polarization KW - Nuclear magnetic resonance KW - Pore space PY - 2020 UR - https://nbn-resolving.org/urn:nbn:de:kobv:b43-509763 DO - https://doi.org/10.1190/GEO2019-0074.1 SN - 0016-8033 VL - 85 IS - 4 SP - MR257 EP - MR270 PB - Society of Exploration Geophysicists AN - OPUS4-50976 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - JOUR A1 - Dayani, Shahabeddin A1 - Markötter, Henning A1 - Schmidt, Anita A1 - Widjaja, Martinus Putra A1 - Bruno, Giovanni T1 - Multi-level X-ray computed tomography (XCT) investigations of commercial lithium-ion batteries from cell to particle level N2 - Adopting X-ray computed tomography (XCT) for ex-situ characterization of battery materials has gained interest in the past decade. The main goal of this paper is to demonstrate the effectiveness of several X-ray computer tomography techniques to study commercial batteries. General guidelines are provided to select the most suitable imaging equipment and parameters for investigations of lithium-ion batteries, spanning the length scales from cell to electrode, down to particle level. Relevantly, such parameters would also be suitable for operando experiments. Safety mechanisms and manufacturing inconsistencies at cell level as well as defects and inhomogeneity in cathode and anode is illustrated and quantified. Furthermore, relation of beam energy and sample-detector-distance on contrast retrieved from attenuation and phase shift is inspected using Synchrotron XCT. KW - Non-destructive testing KW - X-ray computed tomography KW - Synchrotron X-ray computed tomography KW - Lithium-ion battery PY - 2023 DO - https://doi.org/10.1016/j.est.2023.107453 SN - 2352-152X VL - 66 SP - 107453 PB - Elsevier Ltd. AN - OPUS4-57512 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - JOUR A1 - Zia, Ghezal Ahmad Jan A1 - Hanke, Thomas A1 - Skrotzki, Birgit A1 - Völker, Christoph A1 - Bayerlein, Bernd T1 - Enhancing Reproducibility in Precipitate Analysis: A FAIR Approach with Automated Dark-Field Transmission Electron Microscope Image Processing N2 - AbstractHigh-strength aluminum alloys used in aerospace and automotive applications obtain their strength through precipitation hardening. Achieving the desired mechanical properties requires precise control over the nanometer-sized precipitates. However, the microstructure of these alloys changes over time due to aging, leading to a deterioration in strength. Typically, the size, number, and distribution of precipitates for a quantitative assessment of microstructural changes are determined by manual analysis, which is subjective and time-consuming. In our work, we introduce a progressive and automatable approach that enables a more efficient, objective, and reproducible analysis of precipitates. The method involves several sequential steps using an image repository containing dark-field transmission electron microscopy (DF-TEM) images depicting various aging states of an aluminum alloy. During the process, precipitation contours are generated and quantitatively evaluated, and the results are comprehensibly transferred into semantic data structures. The use and deployment of Jupyter Notebooks, along with the beneficial implementation of Semantic Web technologies, significantly enhances the reproducibility and comparability of the findings. This work serves as an exemplar of FAIR image and research data management. KW - Industrial and Manufacturing Engineering KW - General Materials Science KW - Automated image analysis KW - FAIR research data management KW - Reproducibility KW - microstructural changes PY - 2024 UR - https://nbn-resolving.org/urn:nbn:de:kobv:b43-593905 DO - https://doi.org/10.1007/s40192-023-00331-5 SN - 2193-9772 SP - 1 EP - 15 PB - Springer Science and Business Media LLC CY - Heidelberg AN - OPUS4-59390 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - JOUR A1 - Bayerlein, Bernd A1 - Hanke, T. A1 - Muth, Thilo A1 - Riedel, Jens A1 - Schilling, Markus A1 - Schweizer, C. A1 - Skrotzki, Birgit A1 - Todor, A. A1 - Moreno Torres, Benjami A1 - Unger, Jörg F. A1 - Völker, Christoph A1 - Olbricht, Jürgen T1 - A Perspective on Digital Knowledge Representation in Materials Science and Engineering N2 - The amount of data generated worldwide is constantly increasing. These data come from a wide variety of sources and systems, are processed differently, have a multitude of formats, and are stored in an untraceable and unstructured manner, predominantly in natural language in data silos. This problem can be equally applied to the heterogeneous research data from materials science and engineering. In this domain, ways and solutions are increasingly being generated to smartly link material data together with their contextual information in a uniform and well-structured manner on platforms, thus making them discoverable, retrievable, and reusable for research and industry. Ontologies play a key role in this context. They enable the sustainable representation of expert knowledge and the semantically structured filling of databases with computer-processable data triples. In this perspective article, we present the project initiative Materials-open-Laboratory (Mat-o-Lab) that aims to provide a collaborative environment for domain experts to digitize their research results and processes and make them fit for data-driven materials research and development. The overarching challenge is to generate connection points to further link data from other domains to harness the promised potential of big materials data and harvest new knowledge. KW - Data infrastructures KW - Digital representations KW - Digital workflows KW - Knowledge graphs KW - Materials informatics KW - Ontologies KW - Vocabulary providers PY - 2022 UR - https://nbn-resolving.org/urn:nbn:de:kobv:b43-546729 DO - https://doi.org/10.1002/adem.202101176 SN - 1438-1656 SP - 1 EP - 14 PB - Wiley-VCH GmbH CY - Weinheim AN - OPUS4-54672 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - JOUR A1 - Karafiludis, Stephanos A1 - Standl, Jacob A1 - Ryll, Tom W. A1 - Schwab, Alexander A1 - Prinz, Carsten A1 - Wolf, Jakob B. A1 - Kruschwitz, Sabine A1 - Emmerling, Franziska A1 - Völker, Christoph A1 - Stawski, Tomasz M. T1 - High-Entropy Phosphate Synthesis: Advancements through Automation and Sequential Learning Optimization N2 - Transition metal phosphates (TMPs) are extensively explored for electrochemical and catalytical applications due to their structural versatility and chemical stability. Within this material class, novel high-entropy metal phosphates (HEMPs)─containing multiple transition metals combined into a single-phase structure─are particularly promising, as their compositional complexity can significantly enhance functional properties. However, the discovery of suitable HEMP compositions is hindered by the vast compositional design space and complex or very specific synthesis conditions. Here, we present a data-driven strategy combining automated wet-chemical synthesis with a Sequential Learning App for Materials Discovery (SLAMD) framework (Random Forest regression model) to efficiently explore and optimize HEMP compositions. Using a limited set of initial experiments, we identified multimetal compositions in a single-phase crystalline solid. The model successfully predicted a novel Co0.3Ni0.3Fe0.2Cd0.1Mn0.1 phosphate octahydrate phase, validated experimentally, demonstrating the effectiveness of the machine learning approach. This work highlights the potential of integrating automated synthesis platforms with data-driven algorithms to accelerate the discovery of high-entropy materials, offering an efficient design pathway to advanced functional materials. KW - Metal phosphates KW - High entropy KW - Sequential learning KW - Automated synthesis KW - MAP KW - Random forest KW - Machine learning PY - 2025 UR - https://nbn-resolving.org/urn:nbn:de:kobv:b43-641554 DO - https://doi.org/10.1021/acs.cgd.5c00549 SN - 1528-7483 VL - 25 IS - 19 SP - 7989 EP - 8001 PB - American Chemical Society (ACS) CY - Washington, DC AN - OPUS4-64155 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER -