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Nanostructure evolution in oxide materials (NanOX-ML)

Nanostructure evolution in oxide materials (NanOX-ML)

Dietmar Gruber (ORCID: 0000-0002-6801-4324)
  • Grant DOI 10.55776/I6728
  • Funding program Principal Investigator Projects International
  • Status ongoing
  • Start April 1, 2024
  • End March 31, 2028
  • Funding amount € 478,027
  • Project website

Disciplines

Computer Sciences (30%); Physics, Astronomy (40%); Materials Engineering (30%)

Keywords

    ESRF Beamline, Nanostructure, Machine learning, Refractories, High temperature testing, X-ray

Abstract

In the vast majority of cases, refractories are coarse ceramic materials and are indispensable for many things in everyday life. For example, they are required for the production of steel, glass and cement to make industrial high-temperature processes possible in the first place. Refractories are used for the lining of furnaces. In use, temperatures of 1600C and sometimes even higher are reached. The basic components for refractories are almost exclusively thermally stable oxides, typical examples are periclase (MgO) and corundum (Al 2O3). The durability of refractories is essential for environmental and climate protection and for the economic profitability of the processes due to the considerable amount of CO2 emitted during their production (often in the order of 1.5kg CO2 per kg of product). In addition, the refractories play an important role in the quality of the products manufactured. During use, i.e. at high temperatures, diffusion processes, structural changes and different thermal expansion of the components occur in the refractories. This causes mechanical stresses in the structure, which influence their macroscopic behaviour. The project quantifies the changes in the microstructure and their effects as well as the macroscopic mechanical behaviour at temperatures of up to 1600C. To this end, X-Ray Diffraction (XRD) experiments will be carried out at the D2AM beamline of the ESRF (European Synchrotron Radiation Facility) in Grenoble. However, Synchrotron beam time is an expensive resource and, in typical setups, a large amount of beam time is spent on irradiating uninformative parts of the crystal. We will devise novel Machine Learning (ML) algorithms that are sensitive to exploration costs and, hence, minimize the exploration of uninformative parts of the crystal. This will further have a direct impact on resource usage, wasting less time and cost. Real-time optimization of XRD experiments presents new challenges for ML algorithms, partially due to data characteristics: The observed data (i.e., the diffraction patterns of the crystal) is represented in the Fourier space, while the experimental parameters guide the exploration in the real space (i.e., crystal coordinates). The macroscopic material behaviour will be investigated in a specialised laboratory at Montanuniversität Leoben. Here, in addition to the time-dependent deformation at high temperatures under compressive loads, the fracture energy will also be determined. The insight into the development of the nanostructure should provide information on the further development of refractory building materials. The aim is to develop recommendations for reducing the CO2 footprint of refractories. The findings of this project will also impact the run time and cost of thousands of experiments at the European Synchrotron Radiation Facility and other XRD facilities worldwide.

Research institution(s)
  • Technische Universität Wien - 45%
  • Montanuniversität Leoben - 55%
Project participants
  • Thomas Gärtner, Technische Universität Wien , associated research partner
International project participants
  • Olivier Castelnau, CNRS - France
  • Maxime Dupraz, Centre national de la recherche scientifique (CNRS) - France
  • Jean-Sébastien Micha, Commissariat a l´energie atomique et aux energies alternatives - France
  • Marc Huger, Université de Limoges - France
  • René Guinebretière, Université de Limoges - France

Research Output

  • 7 Citations
  • 3 Publications
Publications
  • 2024
    Title Logical Distillation of Graph Neural Networks
    DOI 10.24963/kr.2024/86
    Type Conference Proceeding Abstract
    Author Pluska A
    Pages 920-930
  • 2025
    Title Debiasing Implicit Feedback Recommenders via Sliced Wasserstein Distance-based Regularization
    DOI 10.1145/3705328.3759320
    Type Conference Proceeding Abstract
    Author Escobedo G
    Pages 1153-1158
  • 2025
    Title Interactive Knowledge-Based Kernel PCA for Solvent Selection
    DOI 10.1021/acssuschemeng.4c07974
    Type Journal Article
    Author Boobier S
    Journal ACS Sustainable Chemistry & Engineering
    Pages 4349-4368
    Link Publication

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