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Funded Projects › HORIZON

URANUS · Understanding degradation mechanisms of metallic materials in extreme environments using element-resolved electrochemistry

HORIZONStatus: SIGNED1 January 202631 December 2030EU funding €1,499,760Call ERC-2025-STG

Evaluating and fabricating sustainable materials for the energy sector is crucial in addressing the current energy crisis and climate change. Despite safety and sustainability concerns, especially as the early generations of nuclear power plants (NPP) near the end of their operational lifespans, NPPs produce almost one-fifth of the world’s electrical energy. However, controlling material degradation and reprocessing issues in NPPs remains surprisingly understudied due to the extreme operating conditions. Up to 95% of uranium from spent nuclear fuel can be recycled through reprocessing in concentrated nitric acid at temperatures exceeding 100°C, although the underlying mechanisms and the role of alloying elements in the fuel container are not yet well understood.Electrochemical methods have been applied in these extreme electrolytes to investigate materials degradation mechanisms providing in situ global responses. However, these methods are limited as they rely on assumptions developed in aqueous media and do not provide direct, element-specific information, necessitating complementary ex situ surface characterizations. To bridge the gap, the URANUS project will develop a novel element-resolved electrochemistry setup for extreme electrolytes, leveraging the unique expertise of the PI in this field.Element-by-element degradation mechanisms of metallic materials in concentrated nitric acid will be elucidated for the first time, tracking the fate of each alloying element. Other non-aqueous electrolytes including molten salt systems will also be investigated with this approach. This will open new avenues for controlling the corrosion of materials in the energy sector. The unique element-resolved database generated from this project will serve as input parameters for training machine learning models aimed at efficiently discovering optimal alloys in specific environments, thereby reducing the consumption of raw and scarce materials during the materials design process.

Consortium · 2 organisations

coordinator

SORBONNE UNIVERSITE

FR · €1,499,760

thirdParty

CENTRE NATIONAL DE LA RECHERCHE SCIENTIFIQUE CNRS

FR

Research fields

View the official record on CORDIS →

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