CE09 - Nano-objets et nanomatériaux fonctionnels, interfaces

Nanostructure evolution in oxide materials at high temperature investigated with advanced X-ray scattering and machine learning based data analysis – NanOX-ML

Submission summary

The NanOX-ML project focuses on the understanding of the coupling between phase transitions, local compositional fluctuations and microstrain distributions in dense nanostructured oxide polycrystals subjected to very high temperature thermal loading. During their elaboration, or during their use, these refractory materials have to withstand very high temperatures and sometimes repeated and severe thermal cycles. Most of them are made of polycationic oxides resulting in the formation of solid solutions. Phase separation processes, for instance like, but not restricted, spinodal decomposition, occur during thermal treatments, as a result of the coupling between local variations of composition at the nanometer scale and strain relaxation mechanisms. These transformations may significantly affect their structural integrity (creeping, cracking, etc.) during their lifetime. The approach proposed in this project is based on x-ray diffraction (XRD) performed at synchrotron radiation facilities. Indeed, these X-ray sources are the only existing sources allowing to analyze quantitatively, in situ, at high temperature, and in the conditions of synthesis or use, the evolutions at the nanometer scale of dense oxide polycrystals. The increasing efficiency of modern synchrotron radiations poses new challenges, not only in terms of the huge data throughput making human-based approaches unfeasible, but also regarding the development of smarter data collection schemes. At the core of the NanOX-ML are the development of machine learning (ML) codes able to solve this issues, via the development of new algorithms enabling, on the one hand, the development of agile and versatile data collection procedures and, on the other hand, the ability to analysis data in real time.
Following the HoTMiX project co-funded by the ANR and the DFG, which will end in November 2023 and which aimed at dissociating the coupling between local deformation, anisotropic thermal expansion and phase transition, this new NanOX-ML project is focused on the influence of local compositional fluctuations at the nanoscale. The use of different XRD approaches, namely with a monochromatic beam or with a polychromatic microbeam, coupled with the development of efficient ML algorithm we allow us to tackle this problem. Through the development of smart data acquisition procedures and on-the-fly data analysis procedures, our goal is also to provide the scientific community with tools and methodologies that can be directly transposed to different cases.

Project coordination

René GUINEBRETIERE (Institut de Recherche sur les Céramiques)

The author of this summary is the project coordinator, who is responsible for the content of this summary. The ANR declines any responsibility as for its contents.


NEEL Institut Néel
IRCER Institut de Recherche sur les Céramiques
PIMM Procédés et Ingénierie en Mécanique et Matériaux
MEM Modélisation et Exploration des Matériaux

Help of the ANR 513,804 euros
Beginning and duration of the scientific project: - 48 Months

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