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Die Integration und Wiederverwendung von Wissen und Daten aus der Herstellung, Bearbeitung und Charakterisierung von Materialien wird im Zuge der digitalen Transformation in der Materialwissenschaft und Werkstofftechnologie in verschiedenen Projekten behandelt. Dabei liegt der Fokus auf der Interoperabilität von Daten und Anwendungen, die nach den FAIR-Prinzipien erstellt und veröffentlicht werden. Zur Umsetzung eines komplexen Datenmanagements und der Digitalisierung im Bereich der Materialwissenschaften gewinnen Ontologien zunehmend an Bedeutung. Sie ermöglichen sowohl menschenlesbare als auch maschinenverständliche und -interpretierbare Wissensrepräsentationen durch semantische Konzeptualisierungen.
Im Rahmen des Projektes Plattform MaterialDigital (PMD, materialdigital.de), welches in dieser Präsentation vorgestellt wird, werden Ontologien verschiedener Ebenen entwickelt (verbindende mid-level sowie Domänen-Ontologien). Die PMD-Kernontologie (PMD Core Ontology - PMDco) ist eine Ontologie der mittleren Ebene, die Verbindungen zwischen spezifischeren MSE-Anwendungsontologien und domänenneutralen Konzepten herstellt, die in bereits etablierten Ontologien höherer Ebenen (top-level Ontology) verwendet werden. Sie stellt somit einen umfassenden Satz von durch Konsensbildung in der Gemeinschaft (geteiltes Vokabular) entstanden Bausteinen grundlegender Konzepte aus der Materialwissenschaft und Werkstofftechnik (MSE) dar. In dieser Präsentation wird die PMDco adressiert.
Weiterhin wird eine auf die PMDco bezogene normenkonforme ontologische Repräsentation zur Speicherung und Weiterverarbeitung von Zugversuchsdaten – die Tensile Test Ontology (TTO) – präsentiert, die in enger Zusammenarbeit mit dem zugehörigen DIN-Standardisierungsgremium NA 062-01-42 AA erstellt wurde. Dies umfasst den Weg von der Entwicklung einer Ontologie nach Norm, der Konvertierung von Daten aus Standardtests in das interoperable RDF-Format bis hin zur Verbindung von Ontologie und Daten. Auf Grundlage der dabei entwickelten Struktur und Vorgehensweise können weitere Materialcharakterisierungsmethoden semantisch beschrieben werden, woraus sich in diesem Zusammenhang Kooperationsmöglichkeiten ergeben.
Inspired by the ability to track atomic-scale dynamics with x-ray photon correlation spectroscopy (XPCS)1 and recent results of long-term atomistic simulations on material transport2, we reveal here an unprecedented spectrum of short- and long-term relaxation dynamics. Tracked along a 300 000 s long isotherm at 0.98Tg, a Zr-based bulk metallic glass exhibits temporal fluctuations that persist throughout the entire isotherm, demonstrating a continuous heterogeneous dynamics at the probed length scale. In concert with microsecond molecular dynamic simulations, we identify intermittent cluster dynamics as the origin for temporal signatures in the corresponding intensity cross-correlations. Despite temporally heterogeneous aging, the Kohlrausch-Williams-Watts functions is well suited to capture the average intermediate relaxation time regime, but at very long time scales an asymptotic power-law better describes the data. This indicates anomalous diffusion and gives overall strong evidence for temporal fractional diffusion in metallic glasses. We discuss these results in terms of the underlying structural fast and slow relaxation modes and their manifestation in the temporal form of the structural decorrelations.
Interlaboratory comparisons (ILCs) are an important tool for the validation of characterisation methods, and, herwith, a prerequisite for standardisation. The "Versailles Project on Advanced Materials and Standards" offers a framework for performing such ILCs: (i) TiO2 nanoparticles using ToF-SIMS, and (ii) the chemical composition of functionalized graphene using XPS. The results of both ILCs underline the importance of sample preparation for the measured results.
The BAMline at the synchrotron X-ray source BESSY II (Berlin, Germany) is supporting researchers especially in materials science [1]. As a non-destructive characterization method, synchrotron X-ray imaging, especially tomography (SXCT) with hard X-Rays, plays an important role in structural 3D characterization. The imaging capabilities allow for in-situ and operando experiments. In this presentation the equipment, data handling pipeline as well as various examples from material science are presented.
In series-connected lithium-ion cells are susceptible to an electrical safety hazard called over-discharge. Here the behavior of copper dissolution and deposition during over-discharge is presented, which leads to irreversible loss of capacity and internal short circuits. The concentration of dissolved and deposited copper is quantified. Also, a non-uniform distribution pattern of copper deposition on the cathode is shown.
The second example deals with an Al alloy 7017 customized for a 3d-printing process by means of laser-based powder bed fusion (PBF-LB) technique. These advanced alloys have a significantly higher modulus of elasticity than conventional Al alloys, making them attractive for applications requiring high stiffness. SXCT during In-situ tensile tests confirmed that fracture initiation strongly depends on defects created during printing. However, the cracks are deflected from decohesion around inclusions/precipitates embedded in the Al matrix, increasing ductility.
Low temperature cofired ceramic (LTCC) multilayer housings offer 3D-circuits for a wide range of applications in telecommunications, microsystems and sensor technology. Such housings are produced by combining structured and metallized ceramic layers using tape-casting and multilayer technology. The characterization of the integrity, deformation, defects, and positioning of the internal metal features was carried out using in-situ SXCT at up to 950°C.
A high-precision nail penetration tool was developed to characterize the mechanically induced thermal runaway (TR) of lithium-ion battery (LIB) cells in a dynamic temperature range down to -190 °C. To investigate safety-specific low-temperature transport conditions, the damaged cells were tomographically imaged during thawing.
The presentation is divided in two parts. In the first part the significance of mechanical impacts as ignition source for hydrogen containing atmospheres is shown and recent results of a research project evaluating ignition probabilities under different conditions is presented. In the second part future challenges in the field of hydrogen safety are discussed. Finally, the German National Hydrogen Safety Alliance is introduced.
Standardised methods need validation. The main validation parameters like trueness, repeatability and intermediate precision and reproducibility are presented. Furthermore, different methods for the validation are disussed: (certified) reference materials, representative testing materials and interlaboratory comparisons. At last, the need of proficiency testing is stressed.
Sunlight is an almost unavoidable environmental cue and plays a fundamental role in the biology of pro- and eukaryotic organisms. To cope with sunlight-associated stresses e.g., high temperatures, UV radiation with associated DNA damage, accumulation of reactive oxygen species (ROS), desiccation and osmotic stresses, it is important for organisms to accurately sense and respond to changes in light. The benefits of light are obvious for green organisms such as cyanobacteria, algae and plants which use light as an energy source (photosynthesis). Less apparent are other light-dependent processes such as light-driven DNA repair by photolyases (photoreactivation) or ion pumping by microbial opsins. Fungi that can share light-flooded habitats with phototrophs may profit from their excess photosynthetic products. Rock-inhabiting Dothideomycetes and Eurotiomycetes including Knufia petricola possess many proteins for absorbing UV/blue, green, red and far-red light, produce the black 1,8 dihydroxynaphthalene (DHN) melanin and orange-red carotenoids, and may live in multispecies biofilms. Here, we are addressing the question to which extent constitutive pigment formation (melanin and carotenoids) and responses mediated by the stress-activated mitogen-activated protein (MAP) kinase contribute to the observed light (UV-B) tolerance of K. petricola.
Die additive Fertigung mittels Schweißverfahren bietet große ökonomische Vorteile für eine ressourceneffiziente Bauteilherstellung. Offene Fragen bezüglich Homogenität, Anisotropie der Schweißgefüge und den damit verbundenen Bauteileigenschaften stehen einer wirtschaftlichen Verarbeitung oftmals im Wege. Finale Bauteilgeometrie und Oberflächengüte erfordern meist komplementäre subtraktive Fertigungsschritte. Werkstoffe für hochbelastbare Komponenten sind oftmals schwer spanbar. In einem Vorhaben der BAM und des ISAF wurde untersucht, wie die Modifikation der AM-Schweißzusätze und das ultraschallunterstützte Fräsen (US) die Zerspanungssituation verbessern. Der vorliegende Artikel stellt wesentliche Zusammenhänge zwischen Legierung, Gefüge und Zerspanung zweier schwer spanbarer Hochleistungslegierungen (FeNi und CoCr) dar. Großes Potenzial zeigte neben dem US die Modifikation mit Zr und Hf bei Zulegierung in das Schweißgut mittels Beschichtung von Massivdrähten bzw. Herstellung von Fülldrähten.
Die additive Fertigung mittels Schweißverfahren bietet große ökonomische Vorteile für eine ressourceneffiziente Bauteilherstellung. Offene Fragen bezüglich Homogenität, Anisotropie der Schweißgefüge und den damit verbundenen Bauteileigenschaften stehen einer wirtschaftlichen Verarbeitung oftmals im Wege. Finale Bauteilgeometrie und Oberflächengüte erfordern meist komplementäre subtraktive Fertigungsschritte. Werkstoffe für hochbelastbare Komponenten sind oftmals schwer spanbar. In einem Vorhaben der BAM und des ISAF wurde untersucht, wie die Modifikation der AM-Schweißzusätze und das ultraschallunterstützte Fräsen (US) die Zerspanungssituation verbessern. Der vorliegende Artikel stellt wesentliche Zusammenhänge zwischen Legierung, Gefüge und Zerspanung zweier schwer spanbarer Hochleistungslegierungen (FeNi und CoCr) dar. Großes Potenzial zeigte neben dem US die Modifikation mit Zr und Hf bei Zulegierung in das Schweißgut mittels Beschichtung von Massivdrähten bzw. Herstellung von Fülldrähten.
Gläser zeichnen sich durch eine breite und kontinuierlich abstimmbare chemische Zusammensetzung sowie einzigartige Formgebungstechniken aus, was sie oft zur Schlüsselkomponente moderner Hochtechnologien macht. Die Glasentwicklung ist jedoch oft noch zu kosten-, zeit- und energieintensiv. Der Einsatz von robotergestützten Schmelzsystemen, eingebettet in eine Ontologie-basierte digitale Umgebung, soll diese Probleme in Zukunft überwinden. Im Rahmen der BMBF Forschungsinitiative MaterialDigital unternimmt das Verbundprojekt GlasDigital „Datengetriebener Workflow für die beschleunigte Entwicklung von Glas“ erste Schritte in diese Richtung. Das Projektkonsortium, an dem das Fraunhofer ISC in Würzburg, die Friedrich-Schiller-Universität Jena (OSIM), die Technische Universität Clausthal (INW) und die Bundesanstalt für Materialforschung und -prüfung (BAM, Fachgruppe Glas) beteiligt sind, will alle wesentlichen Basiskomponenten für eine beschleunigte datengetriebene Glasentwicklung zusammenführen. Zu diesem Zweck wird ein robotergestütztes Hochdurchsatz-Glasschmelzsystem mit neuartigen Inline-Sensoren zur Prozessüberwachung, auf maschinellem Lernen (ML) basierenden adaptiven Algorithmen zur Prozessüberwachung und -optimierung, neuartigen Werkzeugen für die Hochdurchsatz-Glasanalyse sowie ML-basierten Algorithmen zum Glasdesign, Data Mining sowie Eigenschafts- und Prozessmodellierung ausgestattet. Der Vortrag gibt einen Überblick darüber, wie all diese Komponenten miteinander verzahnt sind, und veranschaulicht ihre Nutzbarkeit anhand einiger Beispiele.
lasses stand out by their wide and continuously tunable chemical composition and large variety of unique shaping techniques making them a key component of modern high technologies. Glass development, however, is still often too cost-, time- and energy-intensive. The use of robotic melting systems embedded in an ontology-based digital environment is intended to overcome these problems in future. As part of the German research initiative MaterialDigital, the joint project GlasDigital takes first steps in this direction. The project consortium involves the Fraunhofer ISC in Würzburg, the Friedrich Schiller University Jena (OSIM), the Clausthal University of Technology (INW), and the Federal Institute for Materials Research and Testing (BAM, Division Glasses) and aims to combine all main basic components required for accelerated data driven glass development. For this purpose, a robotic high throughput glass melting system is equipped with novel inline sensors for process monitoring, machine learning (ML)-based, adaptive algorithms for process monitoring and optimization, novel tools for high throughput glass analysis and ML-based algorithms for glass design, including software tools for data mining as well as property and process modelling. The talk gives an overview how all these tools are interconnected and illustrates their usability with some examples.
Glasses stand out by their wide and continuously tunable chemical composition and large variety of unique shaping techniques making them a key component of modern high technologies. Glass development, however, is still often too cost-, time- and energy-intensive. The use of robotic melting systems embedded in an ontology-based digital environment is intended to overcome these problems in future. As part of the German research initiative MaterialDigital, the joint project GlasDigital takes first steps in this direction. The project consortium involves the Fraunhofer ISC in Würzburg, the Friedrich Schiller University Jena (OSIM), the Clausthal University of Technology (INW), and the Federal Institute for Materials Research and Testing (BAM, Division Glasses) and aims to combine all main basic components required for accelerated data driven glass development. For this purpose, a robotic high throughput glass melting system is equipped with novel inline sensors for process monitoring, machine learning (ML)-based, adaptive algorithms for process monitoring and optimization, novel tools for high throughput glass analysis and ML-based algorithms for glass design, including software tools for data mining as well as property and process modelling. The talk gives an overview how all these tools are interconnected and illustrates their usability with some examples.
Im Projekt GlasDigital sollen digitale Werkzeuge für die Hochdurchsatzentwicklung neuartiger Glaswerkstoffe erarbeitet werden. Dies soll durch die Kombination robotischer Syntheseverfahren mit selbstlernenden Maschinen und ihre Einbindung in eine Ontologie-basierte digitale Infrastruktur realisiert werden.
Rotschlamm fällt in sehr großen Mengen bei der Herstellung von Aluminiumoxid an und wird fast ausschließlich deponiert. Es wird eine neue Technologie vorgestellt bei der das im Rotschlamm enthaltene Eisenoxid mittels Wasserstoffplasma reduziert wird. Es wird ein Reaktionsmechanismus über Titanomagnetit und Hercynit vorgeschlagen.
A chemical engineer by training, Brian drifted towards physics and now focuses on a broad spectrum of activities with the aim to improve scientific reproducibility. This includes studies on holistically improving data quality, data collection efficiency and traceability, as well as concomitant laboratory automation for the preparation of consistent, well-documented sample series. The need for pragmatism led to an inexpensive, flexible laboratory automation platform that can be implemented in a modest amount of time. This talk presents that effort.
Der Vortrag gibt einen Überblick zu ultrakurzen Laserpulsen, der Ultrakurzpulslaser-Materialbearbeitung und der dabei ggf. auftretenden unerwünschten Röntgenstrahlung. Es wird die Laserbearbeitung von technischen und biologischen Materialien diskutiert. Schutzmöglichkeiten gegenüber der Röntgenstrahlung werden erörtert.
Der Vortrag gibt einen Überblick über die vielfältigen Möglichkeiten der Oberflächenfunktionalisierung mittels Mikro- und Nanostrukturierung durch Ultrakurzpuls-Lasermaterialbeabeitung. Dies schließt eine Diskussion des Phänomens der Laser-induzierten periodischen Oberflächenstrukturen (LIPSS, engl.: Laser-induced Periodic Surface Structures, Ripples), sowie deren Klassifikation und Bildungsmechanismen mit ein. Ein Schwerpunkt der Präsentation liegt auf der Diskussion verschiedener Anwendungsmöglichkeiten der LIPSS in Bereichen der Optik, Fluidik, Tribologie und Medizin, sowie auf einem Ausblick auf die industrielle Skalierbarkeit der LIPSS-Technologie.
The integration of advanced simulation models into industrial applications, especially in civil engineering, is a challenge that requires to develop new concepts. In particular, this is related to the validation of models using open access and FAIR data as well as objective methods for identifying model parameters and model comparison with the integration of both the models and their calibration into reproducible workflow systems.
The challenges are illustratively discussed for a variety of applications ranging from bridge monitoring over the structural behavior under blast loading up to the additive manufacturing of concrete. An important basis for model validation is the existence of machine-readable data structures for experimental data, e.g. based on semantic representations. In addition, platforms to share this structured data in a searchable format in addition with reproducible scientific workflows must be established.
Based on these tools, objective methods for model parameter estimation and validation of the models are a critical step in ensuring their accuracy and usefulness in industrial applications. Bayesian inference is one approach that can be used to optimize simulation parameters, improving the accuracy of the model while being able to estimate the uncertainty. Integrating these procedures in a real-time application with a bidirectional information exchange between the physical systems and the virtual simulation model leads to the concept of digital twins.
For complex simulation models, the computational efficiency is a significant barrier both for real-time applications and in the context of model calibration with potentially millions of required forward-model evaluations. As a consequence, reduced order modeling approaches such as the proper generalized decomposition method can be used to build an efficient to evaluate model - speeding up the online computation but requiring a precomputation in an offline phase.
Finally, a perspective for integrating material and structural design into a joint design optimization is given. The integration of advanced simulation models into industrial applications is a rapidly evolving field with significant potential for innovation and progress. From additive manufacturing of concrete to digital twins and reduced order modeling, there are many tools and approaches that can be used to improve industrial processes and drive progress in a variety of industries.
Obwohl der Leichtbau mit carbonfaserverstärkten Kunststoffen (CFK) einen wichtigen Beitrag zum Klimaschutz und zur Ressourceneffizienz liefert, stellt der wachsende Abfallstrom von aktuell 62 kt/a eine große Herausforderung dar. Weil die Produktion von Carbonfasern (CF) überwiegend auf fossilen Rohstoffen basiert und sehr energieintensiv ist, werden nachhaltige Recyclinglösungen dringend benötigt. Hier wird ein Überblick über aktuelle Recyclingprozesse gegeben und deren Limitierungen diskutiert. Darüber hinaus wird eine Möglichkeit zur sicheren chemischen Nutzung von CF-haltigen Reststoffen aufgezeigt, die nicht zur Wiederverwertung geeignet sind. Das Projekt CF Pyro untersucht deren Einsatz als Sekundärrohstoff in der Pyrometallurgie und setzt besondere Schwerpunkte bei der Reaktivität von CF, der Prozessstabilität und der Vermeidung von Emissionen gesundheitsschädlicher WHO-Fasern. Abschließend wird eine, auf den experimentellen Ergebnissen und im Dialog mit Expertinnen und Experten aus Wirtschaft, Wissenschaft und Politik entwickelten, Technologiebewertung vorgestellt.
Lecture-like introduction in the topic. Fundamental aspects are discussed: Burning of polymers, fire risks, pyrolysis, combustion in the flame, physical chemistry of ignition, flame spread and steady state burning, flame retardancy mechanisms and groups of flame retardants, fire hazards (smoke, toxic products) and environmental concerns, fire testing.
Dem AVK-Arbeitskreis Wasserstoff-Drucktanks werden ausgehend von Zielen und Aufgaben der BAM sowie der Fachkompetenz auf dem Gebiet der Sicherheit von Composite-Druckbehältern über die prüftechnische Ausstattung, die angewendeten und weiterentwickelten Prüf- und Untersuchungsmethoden sowie zu Beispielen von Forschungsvorhaben wie DELFIN, COLIBRI und SH2APED berichtet.
Eurachem is an international network of national organisations having the objective of establishing a system for the international traceability of chemical measurements and the promotion of good quality practices. It also provides a forum for discussing common problems and exchanging information on quality assurance issues in analytical chemistry. The presentation will explain how Eurachem is organised, the requirements for participation and the opportunities for collaboration and networking. In particular, it will discuss why Eurachem offers an interesting network for young scientists to exchange experiences and further their own education.
Metallic glasses (MGs) are disordered solids that exhibit a range of outstanding mechanical, thermomechanical, and functional properties. Whilst being a promising class of structural materials, well-defined and exploitable structure-property relationships are still lacking. This offsets them strongly from the crystalline counterparts, for which length-scale based property determination has been key for decades.
In recent years, both atomistic simulations and experiments have nurtured the view of heterogeneities that manifest themselves either as a structural partitioning into well-relaxed percolated network components and more frustrated domains in atomistic simulations, or as spatially-resolved property fluctuations revealed with atomic force microscopy. These signatures depend sensitively on the processing history and likely reflect emerging medium-range order fluctuations at the scale of 1-10 nanometers.
Here we demonstrate and discuss the emergence of spatially resolved property fluctuations at length scales that are one to two orders of magnitude larger. Such long-range decorrelation length scales are hard to reconcile in a monolithic glass but may offer the perspective of experimentally easy-to-access length-scale based structure-property relationships. Whilst long-range property fluctuations can be seen in both the plastic and elastic response, we focus here on high-throughput elastic nanoindentation mapping across the surface of a Zr-based model glass. After a deconvolution of surface topography and curvature effects, the spatially-resolved elastic response reveals an elastic microstructure with a correlation length of ca. 150-170 nm. Analytical scanning-transmission electron microscopy (STEM) is used to link the elastic property fluctuations to the chemistry and structure of the MG. In concert, nano-elastic mapping and STEM suggests that structural variations in the glass are responsible for the unexpectedly large length scales. We discuss these findings in terms of the materials processing history and the perspective of exploiting nanoindentation-based spatial mapping to uncover structural length scales in atomically disordered solids.
Low Transformation Temperature (LTT)-Schweißzusätze sind ein innovativer Ansatz, um Eigenspannungen in Schweißnähten zu reduzieren. Dieser Vortrag behandelt neben den Einfluss der LTT-Schweißzusätze auch deren Naht-Ausführungen. Die unterschiedlichen LTT-Nahtgeometrien haben einen signifikanten Einfluss auf den Eigenspannungsabbau in der Schweißnaht und der Wärmeeinflusszone (WEZ).
Digital Transformation in Materials Science: Insights From the Platform MaterialDigital (PMD)
(2024)
The digital era has led to a significant increase in innovation in scientific research across diverse fields and sectors. Evolution of data-driven methodologies lead to a number of paradigm shifts how data, information, and knowledge is produced, understood, and analyzed. High profile paradigm shifts in the field of materials science (MS) include exploitative usage of computational tools, machine learning algorithms, and high-performance computing, which unlock novel avenues for investigating materials. In these presentations, we highlight prototype solutions developed in the context of the Platform MaterialDigital (PMD) project that addresses digitalization challenges. As part of the Material Digital Initiative, the PMD supports the establishment of a virtual materials data space and a systematic handling of hierarchical processes and materials data using a developed ontological framework as high priority work items. In particular, the mid-level ontology PMDco and its augmentation through application-specific ontologies are illustrated. As part of the conclusion, a discussion encompasses the evolutionary path of the ontological framework, taking into account standardization efforts and the integration of modern AI methodologies such as natural language processing (NLP). Moreover, demonstrators illustrated in these presentations highlight: The integration and interconnection of tools, such as digital workflows and ontologies, Semantic integration of diverse data as proof of concept for semantic interoperability, Improved reproducibility in image processing and analysis, and Seamless data acquisition pipelines supported by an ontological framework. In this context, concepts regarding the application of modern research data management tools, such as electronic laboratory notebooks (ELN) and laboratory information management systems (LIMS), are presented and elaborated on. Furthermore, the growing relevance of a standardized adoption of such technologies in the future landscape of digital initiatives is addressed. This is supposed to provide an additional basis for discussion with respect to possible collaborations.
The digital era has led to a significant increase in innovation in scientific research across diverse fields and sectors. Evolution of data-driven methodologies lead to a number of paradigm shifts how data, information, and knowledge is produced, understood, and analyzed. High profile paradigm shifts in the field of materials science (MS) include exploitative usage of computational tools, machine learning algorithms, and high-performance computing, which unlock novel avenues for investigating materials. In these presentations, we highlight prototype solutions developed in the context of the Platform MaterialDigital (PMD) project that addresses digitalization challenges. As part of the Material Digital Initiative, the PMD supports the establishment of a virtual materials data space and a systematic handling of hierarchical processes and materials data using a developed ontological framework as high priority work items. In particular, the mid-level ontology PMDco and its augmentation through application-specific ontologies are illustrated. As part of the conclusion, a discussion encompasses the evolutionary path of the ontological framework, taking into account standardization efforts and the integration of modern AI methodologies such as natural language processing (NLP). Moreover, demonstrators illustrated in these presentations highlight: The integration and interconnection of tools, such as digital workflows and ontologies, Semantic integration of diverse data as proof of concept for semantic interoperability, Improved reproducibility in image processing and analysis, and Seamless data acquisition pipelines supported by an ontological framework. In this context, concepts regarding the application of modern research data management tools, such as electronic laboratory notebooks (ELN) and laboratory information management systems (LIMS), are presented and elaborated on. Furthermore, the growing relevance of a standardized adoption of such technologies in the future landscape of digital initiatives is addressed. This is supposed to provide an additional basis for discussion with respect to possible collaborations.
Glasses stand out by their wide and continuously tunable chemical composition and large variety of unique shaping techniques making them a key component of modern high technologies. Glass development, however, is still often too cost-, time- and energy-intensive. The use of robotic melting systems embedded in an ontology-based digital environment is intended to overcome these problems in future.
For this purpose, a robotic high throughput glass melting system is equipped with novel inline sensors for process monitoring, machine learning (ML)-based, adaptive algorithms for process monitoring and optimization, novel tools for high throughput glass analysis and ML-based algorithms for glass design. This includes software tools for data mining as well as property and process modelling. The presentation provides an overview of how all these tools merge into a digital infrastructure and illustrates their usability using examples.
All infrastructural parts were developed by a consortium consisting of the Fraunhofer ISC in Würzburg, the Friedrich-Schiller-University Jena (OSIM), the Clausthal University of Technology (INW) and the Federal Institute for Materials Research and Testing (BAM, Division Glasses) as part of a joint project of the German research initiative MaterialDigital.
Bonds and local atomic environments are crucial descriptors of material properties. They have been used to create design rules and heuristics and as features in machine learning of materials properties. Implementations and algorithms (e.g., ChemEnv and LobsterEnv) for identifying local atomic environments based on geometrical characteristics and quantum-chemical bonding analysis are nowadays available. Fully automatic workflows and analysis tools have been developed to use quantum-chemical bonding analysis on a large scale. The lecture will demonstrate how our tools, that assess local atomic environments and perform automatic bonding analysis, help to develop new machine learning models and a new intuitive understanding of materials. Furthermore, the general trend toward automation in density functional-based materials science and some of our recent contributions will be discussed.
Bonds and local atomic environments are crucial descriptors of material properties. They have been used to create design rules and heuristics and as features in machine learning of materials properties. Implementations and algorithms (e.g., ChemEnv and LobsterEnv) for identifying local atomic environments based on geometrical characteristics and quantum-chemical bonding analysis are nowadays available. Fully automatic workflows and analysis tools have been developed to use quantum-chemical bonding analysis on a large scale. The lecture will demonstrate how our tools, that assess local atomic environments and perform automatic bonding analysis, help to develop new machine learning models and a new intuitive understanding of materials.[5,6] Furthermore, the general trend toward automation in density functional-based materials science and some of our recent contributions will be discussed.
Bonds and local atomic environments are crucial descriptors of material properties. They have been used to create design rules and heuristics and as features in machine learning of materials properties. Implementations and algorithms (e.g., ChemEnv and LobsterEnv) for identifying local atomic environments based on geometrical characteristics and quantum-chemical bonding analysis are nowadays available. Fully automatic workflows and analysis tools have been developed to use quantum-chemical bonding analysis on a large scale. The lecture will demonstrate how our tools, that assess local atomic environments and perform automatic bonding analysis, help to develop new machine learning models and a new intuitive understanding of materials. Furthermore, the general trend toward automation in computational materials science and some of our recent contributions will be discussed.
Following the new paradigm of materials development, design and optimization, digitalization is the main goal in materials sciences (MS) which imposes a huge challenge. In this respect, the quality assurance of processes and output data as well as the interoperability between applications following FAIR (findability, accessibility, interoperability, reusability) principles are to be ensured. For storage, processing, and querying of data in contextualized form, Semantic Web Technologies (SWT) are used since they allow for machine-actionable and human-readable knowledge representations needed for data management, retrieval, and (re)use.
In this respect, the motivation for digital transformation in materials sciences stemming from the need to handle the ever-increasing volume and complexity of data will be elaborated on. By embracing digital tools and methodologies, researchers can enhance the efficiency, accuracy, and reproducibility of their work. The benefits of digital transformation in materials sciences are manifold, including improved data management, enhanced collaboration, and accelerated innovation. Being a core component of this transformation, ensuring data reliability and reproducibility is critical for the advancement of the field, enabling researchers to build on each other's work with confidence. Implementing FAIR data principles facilitates this by making data more accessible and usable across different platforms and studies. Furthermore, Semantic Web technologies (SWT) and ontologies play a crucial role in achieving these goals. Ontologies, typically consisting of the T-Box (terminological component) and A-Box (assertional component), provide a structured framework for representing knowledge. This presentation will outline the path of ontology creation and the formal transformation procedure, highlighting the various ontology levels that organize data into meaningful hierarchies.
Real-world use cases presented, such as the Tensile Test Ontology (TTO) and the Orowan Demonstrator, illustrate the practical applications of these technologies. These examples will demonstrate how ontologies can be leveraged to standardize data and facilitate interoperability between different systems and research groups. Finally, in this presentation, Ontopanel is introduced, a tool designed to aid in the creation and management of ontologies. Ontopanel simplifies the process of developing and maintaining ontologies, making it accessible to researchers and practitioners in the field. By integrating these technologies and principles, the materials science community can move towards a more digital, interconnected, and efficient future making the knowledge and education on these topics very valuable.
The presentations discusses a use case for the optimization of concrete structures where structural and material design are integrated in a computational workflow. The workflow is based on both physics-based and data-based models and experimental data is used to calibrate/train these models with a specific focus on the integration of ucertainties.
Quenching a metallic liquid sufficiently fast can give rise to an amorphous solid, typically referred to as a metallic glass. This out-of-equilibrium material has a long suite of remarkable mechanical and physical properties but suffers from property deterioration via structural relaxation. As a function of time, relaxation may indeed constitute significant threads to safe applications. Consequently, relaxation of glasses has a long history across different amorphous materials and typical characterization methods promote a picture of gradually evolving and smooth relaxation, as for example obtained from mechanical spectroscopy. However, the true structural dynamics and underlying mechanisms remain far from understood and have hampered a physically informed atomic-scale picture of transport and physical aging of glasses.
Here we exploit the ability to track atomic-scale dynamics with x-ray photon correlation spectroscopy (XPCS) and resolve an unprecedented spectrum of short- and long-term relaxation time scales in metallic glasses. Conducted across temperatures and under the application of stress, the results reveal anything else than smooth aging and gradual energy minimization. In fact, temporal fluctuations persist throughout isothermal conditions over several hundred thousand of seconds, demonstrating heterogeneous dynamics at the atomic scale. In concert with microsecond molecular dynamic simulations, we identify possible mechanisms of correlated atomic-scale dynamics that can underly the temporal fluctuations and structural decorrelations. Despite temporally heterogeneous, the Kohlrausch-Williams-Watts functions is well suited to capture the average intermediate relaxation time regime, but at very long time scales an asymptotic power-law emerges. This indicates anomalous diffusion and gives overall strong evidence for temporal fractional diffusion in metallic glasses. We discuss these results in terms of the structural fast and slow relaxation modes as well as a true microstructure in metallic glasses.
Übersicht über die Umsetzung des EU Green New Deal, des Circular Economy Action Plan sowie der Sustainable Product Initiative. Ebenfalls erfolgt eine Zusammenfassung der Auswirkungen der Änderungen im EU-Produktrecht auf die Revision des geltenden Rechts und das Zusammenspiel zwischen Rechtssetzung und Normung.
The variability and heterogeneity in their structural composition is significant for nearly all synthetic and natural polymers. Beside molar mass also functionality type distribution, copolymer composition distribution and architectural features are responsible for changing the material properties.
A wide range of analytical techniques are available to get insight into these parameters. However, in most cases it is essential to combine different sophisticated techniques to get closer to actual structures and to avoid statistical averages.
Different liquid chromatographic separation modes and their coupling in a two-dimensional way (SEC, LCCC, HILIC, 2D-LC) in combination with suitable detection techniques (e.g. UV, RI, LS, IR, MALDI/ESI-MS) as well as techniques like BET, DCS, SEM/TEM were applied to make structural features visible.
A few aspects are discussed for structural different polylactides from various synthesis routes and their behavior in LC-MS. Moreover, some analytical results for technical lignins valorized by mechanochemical treatment or modified by functional polymers are presented.