NOUVEAU - H2020
NOUVEAU - HORIZON-CL4-2021-RESILIENCE-01-12
(Projet PEPR PROTEC)
Septembre 2022 - Août 2025
Coordinateur du projet : Annie LE GAL LA SALLE (équipe ST2E)
Unite de Catalyse et Chimie du Solide (UCCS Lille)
The NOUVEAU project will develop solid oxide cells (SOCs) with innovative La- and PMG-free electrode materials, solid electrolyte and interconnects with an overall reduced amount of REE (30%), recycled Yt (50-70%) and Cr (20%). To this end, advanced coating methodologies and modelling will be employed in combination with sustainable-by-design and recycling approaches. Integrated models will be adapted and developed to predict physicochemical properties/toxicity endpoints in real life scenarios, including multiscale models; data-based modelling (SHF, SPF); user-ready modelling for industrial deployment (SSbD tools); standardisation and regulatory compliance (REACH updates).
By addressing resource and energy efficiency through material design and waste management, NOUVEAU will create opportunities for increased circularity of raw materials, lower climate impact and decreased criticality of solid oxide cells materials.
To validate the NOUVEAU projects objectives and their economic, commercial and environmental impact, a comprehensive set of assessment techniques will be used, including life-cycle analysis, cost analysis, and social and eco-efficiency life-cycle analysis. The assessment results will guide the projects efforts towards optimised resource efficiency and SOC upscaling with improved stability benchmarked against the reference state-of-the ones.
More specifically, NOUVEAU will benefit from the complementarity and scalability of the green inks development in combination with spray printing, slot die coating and convection and radiation drying. The NOUVEAU project draws on the complementary expertise of applied research centres and innovation driven companies, including Marion Technologies, Coatema, Fiaxell and QSAR Lab, in the field of materials design, SOC engineering and multi-scale modelling, including in silico methodologies (machine learning, artificial intelligence).