TY - CONF A1 - de Camargo, Andrea Simone Stucchi T1 - Optical and Spectroscopic Properties of Glasses N2 - Optical glasses have given us the eyes to unveil the microscopic and the macroscopic worlds through microscope and telescope lenses, they have revolutionized the way we communicate through fiber optics and portable device screens, as well as given us new perspectives for sustainable energy harvesting conversion and generation. To meet the demands of ever-growing markets, the compositional design of new glasses requires full characterization of thermal, mechanical, electric, and optical properties. Particularly, the characterization of optical properties is not only relevant for optical/luminescent applications but is also essential for the tailored design of some medical and dental materials such as restorative resins, implants and prothesis. In this seminar we will introduce the fundaments on the optical properties of materials, with particular focus on glasses, and the techniques to characterize them. T2 - 3rd Sao Carlos School on Glasses and Glass-Ceramics CY - Sao Carlos, Brazil DA - 10.03.2025 KW - Optical properties KW - Glasses PY - 2025 AN - OPUS4-63773 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - de Camargo, Andrea Simone Stucchi T1 - Structure and photophysics of RE ion doped gallium phosphofluoride glasses containing silver nanostructures: Effects of heat treatment and femtosecond direct laser writing. N2 - Gallium fluoride phosphate glasses are interesting hosts for emissive rare-earth (RE) dopants. Despite featuring low refractive index, they present high energy radiation resistance and a wide optical transmission range (350 to 1700 nm) enabling observation of important RE3+ emissions in the visible to near-infrared spectral range. In a previous, NMR-based structural study of the system xGa(PO3)3–(40-x)GaF3–20BaF2–20ZnF2–20SrF2 (x = 5 - 25 mol%), we verified that the network structure of these glasses is dominated by P-O-Ga linkages with no P-O-P linkages and that Ga is mainly six-coordinated in a mixed fluoride/phosphate environment. For Eu3+ doped samples, the photophysical properties strongly suggest changes in the ion´s ligand distribution toward a fluoride-dominated environment at low P/F, which translates into improved radiative emissions. To extend the studies to other RE doped glasses, and glasses containing Ag nanostructures (and their influence on RE emission), we selected the composition 25Ga(PO3)3 – 20ZnF2 – 30BaF2 - (25 – x - y)SrF2 – xAgNO3 - yNdF3, where x = 0, 1, 3, 5, 10 mol% and y = 0 or 1 mol%. The glasses were synthesized through the conventional melt-quenching technique and fully characterized from the thermal, structural, and microstructural viewpoints. By appropriate heat-treatment, the presence of both Ag nanoclusters (Ag-NCs, < 10 nm) and larger nanoparticles was confirmed by TEM in agreement with the observation of broad emission bands in the visible spectrum. Excited state lifetime measurements evidence the presence of non-radiative energy transfer processes between the Ag species to Nd3+ ions but there is no direct evidence of plasmon enhancement effects. In samples singly doped with silver, direct laser writing experiments using a femtosecond laser were also performed which induced localized growth of Ag-NCs associated to a variation in the refractive index. Preliminary results indicate the potential of the DLW technique for tailoring optical glass properties. T2 - 27th International Congress on Glass 2025 CY - Kolkata, India DA - 20.01.2025 KW - Gallium phosphofluoride glass KW - Structure-property correlation KW - Femtosecond direct laser writting KW - Neodymium PY - 2025 AN - OPUS4-63774 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Heuser, Lina T1 - Composition-structure-property relationships of ternary borate glasses N2 - Since the pioneer work of Krogh-Moe [1] in the 1960’s the structure clarification of borate glasses is of interest for many generations. In the present contribution the composition–structure–property relationships of Me2O–ZnO–B2O3, Me = Li (LZB), Na (NZB), K (KZB), Rb (RZB), CaO–ZnO–B2O3 (CaZB), and Li2O–PbO–B2O3 (LPbB) glasses are investigated. Non-toxic, highly polarizable zinc oxide potentially substitutes harmful lead oxide in glasses for applications e.g., in photovoltaics and microelectronics. Glass structure was analyzed using Raman and infrared spectroscopy, and the glass properties were measured using dilatometry (Tg, viscosity data, coefficient of thermal expansion) and rotational viscometry (high-temperature viscosity) [2,3]. The spectroscopic results show that alkali and calcium ions largely balance out the negative charges of BO4-units, while zinc and lead ions mainly stabilize the non-bridging oxygen atoms (NBO). The type of network modifier ion directs formation of the BO4-containing groups and the number of BO4-units (N4). Thus, i.) BO4-containing pentaborate was assigned in LZB, NZB and CaZB glasses, and ii.) diborate in KZB and RZB glasses. Simultaneous presence of pentaborate and diborate was only detected in LPbB. The combination of modifier’s properties, i.e., lower Lewis’s basicity (Rb+ > Li+), higher field strength (Rb+ < Li+), lower polarizability (Rb+ > Li+), and steric effects with increasing size (Rb+ > Li+) [4], leads to slightly increasing N4 in the series RZB (0.36) ≈ KZB < NZB (0.37) < LZB ≈ CaZB (0.39). This increase in N4 results in an increase in the atomic packing density 0.519 (RZB) – 0.584 (LZB) and thus in the Young’s Modulus 38.1 GPa (RZB) – 92.5 GPa (LZB) and in the glass transition temperature (Tg) 444 °C (RZB) – 467 °C (LZB) in the alkali series. In reverse order the coefficient of thermal expansion decreases from 12·10-6 K-1 (RZB) – 8.54·10-6 K-1 (LZB) with increasing N4. Ca2+ in CaZB enhances the viscosity (Tg: 580 °C) compared to Li+ in isocompositional LZB due to higher coordination. On the other hand, Pb2+ in LPbB decreases the viscosity (Tg: 399 °C) compared to Zn2+ in LZB with similar molar fractions. T2 - 98th Glass-Technology Conference of the German Society of Glass Technology (DGG) CY - Goslar, Germany DA - 26.05.2025 KW - Borate glasses KW - Structure-property relationship PY - 2025 AN - OPUS4-63771 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - JOUR A1 - Xie, Zhuocheng A1 - Atila, Achraf A1 - Guénolé, Julien A1 - Korte-Kerzel, Sandra A1 - Al-Samman, Talal A1 - Kerzel, Ulrich T1 - Predicting grain boundary segregation in magnesium alloys: An atomistically informed machine learning approach N2 - Grain boundary (GB) segregation substantially influences the mechanical properties and performance of magnesium (Mg). Atomic-scale modeling, typically using ab-initio or semi-empirical approaches, has mainly focused on GB segregation at highly symmetric GBs in Mg alloys, often failing to capture the diversity of local atomic environments and segregation energies, resulting in inaccurate structure-property predictions. This study employs atomistic simulations and machine learning models to systematically investigate the segregation behavior of common solute elements in polycrystalline Mg at both 0 K and finite temperatures. The machine learning models accurately predict segregation thermodynamics by incorporating energetic and structural descriptors. We found that segregation energy and vibrational free energy follow skew-normal distributions, with hydrostatic stress, an indicator of excess free volume, emerging as an important factor influencing segregation tendency. The local atomic environment’s flexibility, quantified by flexibility volume, is also crucial in predicting GB segregation. Comparing the grain boundary solute concentrations calculated via the Langmuir-McLean isotherm with experimental data, we identified a pronounced segregation tendency for Nd, highlighting its potential for GB engineering in Mg alloys. This work demonstrates the powerful synergy of atomistic simulations and machine learning, paving the way for designing advanced lightweight Mg alloys with tailored properties. KW - Machine learning KW - Grain boundary segregation KW - Magnesium alloys KW - Atomistic simulation PY - 2025 UR - https://nbn-resolving.org/urn:nbn:de:kobv:b43-638499 DO - https://doi.org/10.1016/j.jma.2025.03.021 SN - 2213-9567 VL - 13 IS - 6 SP - 2636 EP - 2650 PB - Elsevier B.V. AN - OPUS4-63849 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Ruehle, Bastian T1 - Workflow generation, management, and semantic description for Self-Driving Labs N2 - The software backend that controls the robotic hardware and runs the synthesis workflows is a very important component of any Self-Driving Lab (SDL). On the one hand, it has to deal with orchestrating and managing complex and task-specific hardware through low-level communication protocols and plan and use the available resources as efficiently as possible while executing (parallelized) workflows, on the other hand, it is the interface the users use to communicate with this highly complex platform, and as such, it needs to be as helpful and user-friendly as possible. This includes the AI-aided experimental design in which the system helps the user to decide which experiment to run next, providing automated data analysis from characterization measurements, and offering easy to understand tools and graphical user interfaces for generating the workflows that are executed on the platform. Lastly, the specificity of the workflows and their dependence on the hardware and software of the SDLs necessitates a common description or ontology for making them easily interchangeable and interoperable between different platforms and labs. In this contribution, we present several key aspects of “Minerva-OS”, the central backend that orchestrates the syntheses workflows of our SDL for Nano- and Advanced Materials Syntheses [1]. One key feature is the resource management or “traffic control” for scheduling and executing parallel reactions in a multi-threaded environment. Another is the interface with data analysis algorithms from in-line, at-line, and off-line measurements. Here, we will give examples of how automatic image segmentation of electron microscopy images with the help of AI [2] can be used for reducing the “data analysis bottleneck” from an off-line measurement. We will also discuss, compare, and show benchmarks of various machine learning (ML) algorithms that are currently implemented in the backend and can be used for ML-guided, closed-loop material optimization in our SDL. Lastly, we will show our recent efforts [3] in making the workflow generation on SDLs more user-friendly by using large language models to generate executable workflows automatically from synthesis procedures given in natural language and user-friendly graphical user interfaces based on node editors that also allow for knowledge graph extraction from the workflows. In this context, we are currently also working on an ontology for representing the process steps of the workflows, which will greatly facilitate the semantic description and interoperability of workflows between different SDL hardware and software platforms. T2 - Accelerate 2025 CY - Toronto, Canada DA - 11.08.2025 KW - Nanomaterials KW - Advanced Materials KW - Workflows KW - Machine Learning KW - SDL PY - 2025 AN - OPUS4-63936 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - JOUR A1 - Lemiasheuski, Anton A1 - Wehrkamp, F. A1 - Bajer, Evgenia A1 - Sonnenburg, Elke A1 - Göbel, Artur A1 - Porohovoj, Ilja A1 - Bettge, Dirk A1 - Pfennig, Anja T1 - Practical application of an automated 3D metallography system for the reconstruction and microstructural analysis of porosity in a sintered steel N2 - In the 3D microstructural analysis of a digital twin of porosity in the sintered steel Astaloy CrA, pore shape, average pore size as well as pore distribution will be analyzed. Porosity plays a major role in powder-metallurgical materials since it greatly impacts the mechanical properties of these materials and therefore represents a key parameter in their characterization. Based on the robot-assisted automated serial sectioning and imaging (RASI) system of the Federal Institute for Materials Research and Testing (BAM, Bundesanstalt für Materialforschung und Prüfung) in Berlin, the technique of metallographic serial sectioning will be used to image the microstructure and reconstruct a digital 3D twin from the stack of images obtained. Compared with an individual 2D microsection, the quantitative microstructural analysis of this 3D twin will enable more accurate conclusions on the shape, size and distribution of pores. This paper will detail the key steps in 3D microstructural analysis, including the metallographic preparation routine, the imaging technique, image alignment as well as the segmentation of pores. After the methodology has been described, the results of the quantitative microstructural analysis will be presented and the validity of quantitative parameters of 3D and 2D images will be compared and discussed. The analysis of more than 10,000 pores revealed a correlation between pore shape and pore size. It was also found that a 2D representation of the material surface is insufficient for a precise quantitative characterization of porosity. N2 - In der 3D-Gefügeanalyse eines digitalen Zwillings von Poren in einem Sinterstahl des Typs Astaloy CrA werden sowohl die Porenform als auch die durchschnittliche Porengröße und -verteilung analysiert. Die Porosität spielt in pulvermetallurgischen Werkstoffen eine große Rolle, da sie erheblich die mechanischen Eigenschaften beeinflusst und daher bei der Charakterisierung dieser Werkstoffe ein wichtiger Parameter ist. Basierend auf der Verwendung des Robot-Assisted Automated Serial-Sectioning and Imaging (RASI)-Systems der Bundesanstalt für Materialforschung und Prüfung (BAM) in Berlin, wird das metallographische Serienschnittverfahren genutzt, um das Gefüge aufzunehmen und aus dem Bildstapel einen digitalen 3D-Zwilling zu rekonstruieren. Verglichen zu einem 2D-Einzelschliff ermöglicht die quantitative Gefügeanalyse dieses 3D-Zwillings präzisere Aussagen zu Porenform, -größe und -verteilung. Diese Arbeit beschreibt die wesentlichen Schritte, die für eine 3D-Gefügeanalyse nötig sind, darunter die metallografische Präparationsroutine, das Bildgebungs-Verfahren, das Alignment der Bilder sowie die Segmentierung der Poren. Im Anschluss an die methodische Darstellung werden die Ergebnisse der quantitativen Gefügeanalyse präsentiert und ein Vergleich zwischen der Aussagekraft der quantitativen Parameter von 3D- und 2D-Abbildungen diskutiert. Bei der Analyse von über 10.000 Poren konnte eine Korrelation zwischen der Form und der Porengröße aufgezeigt werden. Weiterhin konnte aufgezeigt werden, dass eine 2D-Abbildung der Werkstoffoberfläche nicht ausreichend für eine eindeutige quantitative Beschreibung der Porosität ist. KW - 3D Metallographie KW - RASI KW - Porenstruktur PY - 2025 DO - https://doi.org/10.1515/pm-2025-0049 SN - 2195-8599 VL - 62 IS - 8 SP - 516 EP - 535 PB - De Gruyter Brill AN - OPUS4-63930 LA - mul AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Bettge, Dirk T1 - Fraktographie mit Hilfe von klassischen Methoden und Machine Learning – bisherige Erfahrungen und Anwendung auf weitere Fragestellungen N2 - In der Schadensanalyse und bei der Bewertung von mechanischen Experimenten liefert die Fraktographie Informationen über die vorliegenden Bruchmerkmale und den Bruchmechanismus eines Bauteils oder einer Probe. Im Rahmen des DGM AK Fraktographie werden an metallischen Werkstoffen Vergleichsexperimente durchgeführt und die Ergebnisse in einer Datenbank zur Verfügung gestellt. In einem Vorhaben wurde die Analyse von Bruchmerkmalen mittels Topographie-Daten und Machine Learning erprobt. Die gewonnenen Erfahrungen könnten auf weitere Anwendungsfälle wie z.B. Schweißverbindungen angewendet werden. T2 - Erfahrungsaustausch Werkstoff- und Bauteilprüfung CY - Halle, Germany DA - 04.09.2025 KW - Fraktographie KW - Machine Learning KW - Schadensanalyse KW - Schweißverbindungen PY - 2025 AN - OPUS4-64032 LA - deu AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - JOUR A1 - Colombo, Marta A1 - Mostoni, Silvia A1 - Fredi, Giulia A1 - Rodricks, Carol A1 - Kalinka, Gerhard A1 - Riva, Massimiliano A1 - Vassallo, Andrea A1 - Di Credico, Barbara A1 - Scotti, Roberto A1 - Zappalorto, Michele A1 - D'Arienzo, Massimiliano T1 - Interfacial Chemistry Behind Damage Monitoring in Glass Fiber‐Reinforced Composites: Attempts and Perspectives N2 - Glass Fiber Reinforced Polymers (GFRPs) are widely used in structural applications but degrade over time due to internal damage. Structural Health Monitoring (SHM) enables early damage detection, improving reliability and reducing maintenance costs. Traditional SHM methods are often invasive and expensive. An emerging solution involves the embedding of carbon‐based filler like carbon nanotubes and reduced graphene oxide into GFRPs, forming conductive networks that detect damage through resistance changes. However, poor adhesion among GF, filler, and matrix can reduce mechanical performance. Therefore, tailoring GF and filler surface chemistry is essential to enhance durability and enable effective self‐sensing properties. This review summarizes the most recent efforts in modifying GF with carbon‐based filler to design GFRP with improved sensing ability and mechanical performance. After a brief introduction on the role of SHM solutions in early damage detection, an overview of the common GF and filler used in GFRPs will be provided. Then, the most relevant GF modification strategies exploited to incorporate carbon‐based filler in GFRPs will be described, focusing on the chemical grafting approach, which allows a careful optimization of the fiber/matrix interface. Last, a concise summary of the key mechanical and electrical tests to evaluate interfacial adhesion and self‐sensing will be supplied. KW - Review KW - Interface KW - Micromechanics KW - Polymer matrix composites KW - Glass fibre reinforced composites PY - 2025 UR - https://nbn-resolving.org/urn:nbn:de:kobv:b43-639934 DO - https://doi.org/10.1002/pc.70332 SN - 0272-8397 SP - 1 EP - 30 PB - Wiley AN - OPUS4-63993 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Schilling, Markus T1 - Data-Driven Materials Science: Reproducibility and Standardization N2 - Advancing development and digitalization in materials science requires to focus on quality assurance, interoperability, and compliance with FAIR principles. Semantic technologies offer effective solutions for these challenges by enabling the storage, processing, and contextualization of data in machine-actionable and human-readable formats – essential for robust data management. This presentation highlights the PMD Core Ontology 3.0 (PMDco 3.0), developed specifically for the field of materials science and engineering, and its implementation within generic knowledge representation frameworks. Demonstrators such as standardized mechanical testing, material processing workflows, and the Orowan Demonstrator exemplify the ontology’s practical applications. The use of graph patterns, able to be compiled into rule-based semantic shapes, supports a unified and automated approach to managing heterogeneous experimental data across domains. T2 - Persson Group Seminar CY - Berkeley, CA, USA DA - 23.06.2025 KW - Semantic Data KW - Data Integration KW - Digitalization KW - Data Interoperability KW - PMD Core Ontology KW - Graph Patterns PY - 2025 AN - OPUS4-63484 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Moschetti, Michael T1 - Capabilities and Applications of the Robot-Assisted Serial-Sectioning and Imaging (RASI) System N2 - The Robot-Assisted Serial-sectioning and Imaging (RASI) system at BAM provides automated, high-resolution 3D microstructural characterization for diverse materials. Integrating robotics with precision sectioning, etching, and optical microscopy, RASI reconstructs large volumes (approaching 15 × 15 × 15 mm³) with sub-micron detail. This presentation showcases RASI’s versatility through case studies on cast irons, sintered and additively manufactured steels, and ceramic-metallic packages. We demonstrate how RASI reveals true 3D architectures of features like graphite networks, pores, melt pools, and defects, often missed by 2D analysis. These quantitative datasets elucidate process-microstructure-property relationships and provide crucial 'ground truth' for validating computational models and developing digital twins. Ongoing RASI enhancements will also be highlighted. T2 - The 7th International Congress on 3D Materials Science (3DMS 2025) CY - Anaheim, CA, USA DA - 15.06.2025 KW - 3D microstructural characterization KW - Deep learning image segmentation KW - High-throughput materials analysis KW - Advanced materials’ development PY - 2025 AN - OPUS4-63693 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER -