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AbstractGallium fluoride phosphate glasses feature low refractive index, high energy radiation resistance, wide transmission range, and favorable emission characteristics of rare‐earth dopants. For the development of optimized glass compositions, a fundamental understanding of these properties in terms of glass structure is sought. We report nuclear magnetic resonance (NMR) structural studies of glasses in the system xGa(PO3)3–(40 − x)GaF3–20BaF2–20ZnF2–20SrF2 (x = 5, 10, 15, 20, and 25 mol%). 31P NMR results with 71Ga recoupling show that the network structure is dominated by P–O–Ga linkages, and no P–O–P linkages exist. 71Ga NMR results show that Ga is mainly six‐coordinated featuring a mixed fluoride/phosphate coordination. Quantitative estimates of this ligand distribution around gallium were obtained by 71Ga{31P} spin echo double resonance (REDOR) measurements. Photophysical properties suggest changes in the Eu(III) ligand distribution toward a fluoride‐dominated environment at low P/F ratio while the glass network is largely sustained by bridging oxygen atoms via P–O–Ga linkages.
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.
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.
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)
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.
The manufacture of sintered glasses and glass-ceramics, glass matrix composites, and glass-bounded ceramics or pastes is often affected by un-expected gas bubble formation also named foaming. Against this background, in this presentation the main aspects and possible reasons of foaming are shown for completely different glass powders: a barium silicate glass powders used as SOFC sealants, and bioactive glass powders using different powder milling procedures.
Sintering and foaming were measured by means of heating microscopy backed up by XRD, differential thermal analysis (DTA), vacuum hot extraction (VHE), optical and electron microscopy, and infrared spectroscopy, and time-of-flight secondary ion mass spectrometry (ToF-SIMS).
Different densification was reached followed by significant foaming starting partly immediately, partly at higher temperature. Foaming increased significantly as milling progressed. For moderately milled glass powders, subsequent storage in air could also promote foaming. Although the milling atmosphere significantly affects the foaming of uniaxially pressed powder compacts sintered in air.
VHE studies show that foaming is driven by carbon gases and carbonates were detected by Infrared spectroscopy to provide the major foaming source. Carbonates could be detected even after heating to 750 °C, which hints on a thermally very stable species or mechanical trapping or encapsulating of CO2. Otherwise, dark gray compact colors for milling in isopropanol indicate the presence of residual carbon as well. Its significant contribution to foaming, however, could not be proved and might be limited by the diffusivity of oxygen needed for carbon oxidation to carbon gas.
Silicate glass fracture surface energy calculated from crystal structure and bond-energy data
(2023)
We present a novel method to predict the fracture surface energy, γ, of isochemically crystallizing silicate glasses using readily available crystallographic structure data of their crystalline counterpart and tabled diatomic chemical bond energies, D0. The method assumes that γ equals the fracture surface energy of the most likely cleavage plane of the crystal. Calculated values were in excellent agreement with those calculated from glass density, network connectivity and D0 data in earlier work. This finding demonstrates a remarkable equivalence between crystal cleavage planes and glass fracture surfaces.
AbstractViscous healing of cracks induced by the Vickers indentation in a soda lime magnesium silicate, a soda borosilicate, and a soda aluminosilicate glass (NAS) was studied by laser scanning microscopy. Plots of the crack length, width, and depth normalized to the initial crack length versus time over viscosity merge into single master curves of each of these quantities for each glass. Despite glass properties do not differ strikingly from each other, however, these master curves strongly differ among the glasses. This finding was attributed to a different interplay of various crack healing phenomena. Lateral cracks were found to be responsible for the bulging of the sample surface around the Vickers imprint, which in turn promotes radial crack widening as the main cause of healing delay. The most rapid healing of lateral cracks was observed in NAS in which bulging and crack widening were least pronounced.