5 Werkstofftechnik
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Paper des Monats
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Zirconium vanadate (ZrV2O7) is a well-known negative thermal expansion (NTE) material which exhibits significant isotropic contraction over a broad temperature range (~150°C < T < 800°C). The linear thermal expansion coefficient of ZrV2O7 is −7.1×10-6 K-. Therefore, it can be used to create composites with controllable expansion coefficients and prevent destruction by thermal shock.
Material characterization, leading to application, requires pure, homogenous samples of high crystallinity via a reliable synthesis route. While there is a selection of described syntheses in the literature, it still needs to be addressed which synthesis route leads to truly pure and homogenous samples. Here, we study the influence of the synthesis methods (solid-state, sol-gel, solvothermal) and their parameters on the sample's purity, crystallinity, and homogeneity. The reproducibility of results and data obtained with scanning electron microscopy (SEM), X-ray diffraction (XRD), differential scanning calorimetry, and thermogravimetric analysis (DSC/TGA) were analyzed extensively. The sol-gel method proves superior to the solid-state method and produces higher-quality samples over varying parameters. Sample purity also plays an important role in NTE micro and macro-scale characterizations that explain the impact of porosity versus structural changes.
Moreover, we implement ab-initio-based vibrational computations with partially treated anharmonicity (quasi-harmonic approximation, temperature-dependent effective harmonic potentials) in combination with experimental methods to follow and rationalize the negative thermal expansion in this material, including the influence of the local structure disorder, microstructure, and defects. Khosrovani et al. and Korthuis et al., in a series of diffraction experiments, attributed the thermal contraction of ZrV2O7 to the transverse thermal motion of oxygen atoms in V-O-V linkages. In addition to previous explanations, we hypothesize that local disorder develops in ZrV2O7 crystals during heating.
We are working on the experimental ZrV2O7 development and discuss difficulties one might face in the process as well as high-quality sample significance in further investigation. The obtained samples are currently used in the ongoing research of structure analysis and the negative thermal expansion mechanism.
Zirconium vanadate (ZrV2O7) is a well-known negative thermal expansion (NTE) material which exhibits significant isotropic contraction over a broad temperature range (~150°C < T < 800°C). The linear thermal expansion coefficient of ZrV2O7 is −7.1×10-6 K-. Therefore, it can be used to create composites with controllable expansion coefficients and prevent destruction by thermal shock.
Material characterization, leading to application, requires pure, homogenous samples of high crystallinity via a reliable synthesis route. While there is a selection of described syntheses in the literature, it still needs to be addressed which synthesis route leads to truly pure and homogenous samples. Here, we study the influence of the synthesis methods (solid-state, sol-gel, solvothermal) and their parameters on the sample's purity, crystallinity, and homogeneity. The reproducibility of results and data obtained with scanning electron microscopy (SEM), X-ray diffraction (XRD), differential scanning calorimetry, and thermogravimetric analysis (DSC/TGA) were analyzed extensively. The sol-gel method proves superior to the solid-state method and produces higher-quality samples over varying parameters. Sample purity also plays an important role in NTE micro and macro-scale characterizations that explain the impact of porosity versus structural changes.
Moreover, we implement ab-initio-based vibrational computations with partially treated anharmonicity (quasi-harmonic approximation, temperature-dependent effective harmonic potentials) in combination with experimental methods to follow and rationalize the negative thermal expansion in this material, including the influence of the local structure disorder, microstructure, and defects. Khosrovani et al. and Korthuis et al., in a series of diffraction experiments, attributed the thermal contraction of ZrV2O7 to the transverse thermal motion of oxygen atoms in V-O-V linkages. In addition to previous explanations, we hypothesize that local disorder develops in ZrV2O7 crystals during heating.
We are working on the experimental ZrV2O7 development and discuss difficulties one might face in the process as well as high-quality sample significance in further investigation. The obtained samples are currently used in the ongoing research of structure analysis and the negative thermal expansion mechanism.
The system Na2O.B2O3-SiO2 (NBS) is the basis of many industrial glass applications and therefore one of the most studied systems at all. Glass formation is possible over a wide compositional range, but the system also contains ranges of pronounced phase separation and crystallization tendency. Despite its importance, experimental data are limited to few compositional areas. The general understanding and modelling of glass formation, phase separation, and crystallization in this system would therefore be easier if small step melt series could be studied. The efficient melting of such glass series is now possible with the new robotic glass melting system at the Federal Institute for Materials Research and Testing (BAM, Division Glasses). Using three exemplary joins within this NBS system, the small step changes of glass transition temperature (Tg), crystallization behavior as well as glass density (Roh) was studied. Additionally, experimental Tg and Roh data were compared with their modeled counterparts using SciGlass and a newly developed DFT model, respectively.
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.
Suitable material solutions are of key importance in designing and producing components for engineering systems – either for functional or structural applications. Materials data are generated, transferred, and introduced at each step along the complete life cycle of a component. A reliable
materials data space is therefore crucial in the digital transformation of an industrial branch.
A great challenge in establishing a materials data space lies in the complexity and diversity of materials science and engineering. It must be able to handle data from different knowledge areas over several magnitudes of length scale.
The Platform MaterialDigital (PMD) is expected to network a large number of repositories of materials data, allowing the direct contact of different stakeholders as materials producers, testing labs, designers and end users. Following the FAIR principles, it will promote the semantic
interoperability across the frontiers of materials classes. In the frame of a large joint initiative, PMD works intensively together with currently near 20 research consortia in promoting this exchange (www.material-digital.de).
In this presentation we will describe the status of our Platform MaterialDigital. We will also present in more detail the activities of GlasDigital, one of the joint projects mentioned above dealing with the digitalization of glass design and manufacturing.
(https://www.bam.de/Content/EN/Projects/GlasDigital/glasdigital.html)
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.
A Round Robin study has been carried out to estimate the impact of the human element in small-angle scattering data analysis. Four corrected datasets were provided to participants ready for analysis. All datasets were measured on samples containing spherical scatterers, with two datasets in dilute dispersions, and two from powders.
Most of the 46 participants correctly identified the number of populations in the dilute dispersions, with half of the population mean entries within 1.5 % and half of the population width entries within 40 %, respectively. Due to the added complexity of the structure factor, much fewer people submitted answers on the powder datasets.
For those that did, half of the entries for the means and widths were within 44 % and 86 % respectively. This Round Robin experiment highlights several causes for the discrepancies, for which solutions are proposed.
A round-robin study has been carried out to estimate the impact of the human element in small-angle scattering data analysis. Four corrected datasets were provided to participants ready for analysis. All datasets were measured on samples containing spherical scatterers, with two datasets in dilute dispersions and two from powders. Most of the 46 participants correctly identified the number of populations in the dilute dispersions, with half of the population mean entries within 1.5% and half of the population width entries within 40%. Due to the added complexity of the structure factor, far fewer people submitted answers on the powder datasets. For those that did, half of the entries for the means and widths were within 44 and 86%, respectively. This round-robin experiment highlights several causes for the discrepancies, for which solutions are proposed.