By advancing breakthrough research on LOHC technologies, UnLOHCked aims at developing a radically disruptive, versatile and scalable LOHC-dehydrogenation plant. Firstly, highly active and stable catalysts without critical raw materials will be developed for reducing LOHC dehydrogenation at moderate temperatures. Secondly, an SOFC-system will be developed to be thermally integrated with the dehydrogenation process. The heat demand of the dehydrogenation unit will be fully covered by the fuel cell, while generating electric power. The surplus of hydrogen is exported. These innovative systems fully integrated will allow significant increase of overall efficiency (>50%) to hydrogen and electric power production from LOHC. Three industry partners, HERAEUS, HYGEAR and FRAMATOME, will collaborate with four universities and research centres, the University of Bilbao (Spain), CEA, CNRS-Lyon and North-West University of South Africa to develop scalable prototype system at TRL 5, validating the performance of the technology during at least 500 h. The ambition is to demonstrate the feasibility of a fully CO2-free dehydrogenation process for large-scale production of hydrogen (100-1,000 t H2/d) and electricity with competitive prices (hydrogen carrier delivery cost <2.5€/kg). Thus converting CO2-free LOHC to electricity and hydrogen instead of using NG or LPG as heat source. The UnLOHCked approach is clean & circular: it decreases energy consumption, does not use noble metals while generating CO2-free hydrogen and electricity. Techno-economic studies will demonstrate the potential of the technology to both supply hydrogen and renewable electricity to decarbonise the EU economy and to open-up hydrogen transportation by LOHC. FRAMATOME, HYGEAR AND HERAEUS will support the consortium preparing for fast market entry after the project.

As Internet of Things (IoT) and IoT-Edge-Cloud continuum technologies advance, physical environments are becoming increasingly equipped with sensors, fuelling the development of smart space ecosystems. Massive quantities of data produced by IoT devices revolutionize the way such ecosystems operate via the exploitation of AI models/services. This has led to the emergence of the so-called Artificial Intelligence of Things (AIoT) systems. In general, designing techniques to promote robustness, efficiency and continual operation of AIoT systems requires realistic and trustworthy data at scale. However, such data is not always easy to obtain due to the cost of smart space construction, the inconvenience of long-term device tracking, the sensor/knowledge data gaps in diverse scenarios of a smart space, and the restrictions imposed on sensitive data sharing. Furthermore, an efficient AIoT system operation requires trustworthy AI services, as well as novel approaches for speeding up their inference across the IoT-Edge/Cloud continuum. PANDORA aims to devise and implement a comprehensive framework enabling the delivery of trustworthy datasets of smart space ecosystems, as well as the deployment and green operation of AIoT systems in such spaces. PANDORA spans two phases: (1) prior to AIoT system deployment; (2) post AIoT system deployment and operation. Phase 1 proposes and combines a series of novel techniques such as synthetic data generation, quantification of uncertainties, and data summarization for the delivery of trustworthy datasets, as well as explainable AI and domain-informed model training/testing in smart space ecosystems. Phase 2 defines novel AIaaS and CaaS techniques for the robust, explainable, green and continual operation of AIoT systems deployed in such spaces. The trustworthiness and applicability of the PANDORA framework will be tested through five pilot cases hosting AIoT applications in smart buildings, factories and critical infrastructures.

The security of supply of VVER nuclear fuel has become and will remain of vital importance in countries operating VVER-type reactors, and due to the strongly interconnected European grid network, this is also instrumental for Europe as a whole. The aim of SAVE project is to strengthen VVER fuel security of supply in Europe and Ukraine by qualifying a reliable and safer sovereign VVER-440 fuel design, by developing a fast-track licensing path and improving European capabilities for VVER-440 fuel design qualification. Through a strong European collaboration, a large qualification campaign will be performed within European test facilities to assess the performance of the new, safer and sovereign VVER-440 fuel assembly design. SAVE will prepare in-reactor qualification with mutualised LFAs (Lead Fuel Assemblies) programs, which will significantly accelerate design readiness for fuel reloads. SAVE will also define the plans to enable fully European manufacturing route and address the needs on Core Monitoring Systems, to initiate the next steps of development. To contribute to these objectives and increase the security of supply in Europe, the SAVE project is gathering 17 partners from 8 European countries, within a consortium composed of experienced and highly qualified European actors in nuclear fuel and VVER technologies, including nuclear utilities, TSOs, research entities, leading industrial actors and universities.

The objective of this proposal is to investigate the feasibility and viability for existing Nuclear Power Plants (NPPs) to generate large amounts of hydrogen for supporting the decarbonisation goals of the EU- as the EU aims to be climate-neutral by 2050. Moreover, this feasibility study aims to prepare the realization of nuclear hydrogen generation projects in the short term (2025). Thereby, three major fields will be investigated: 1) Investigation of the technical, economic and operational feasibility 2) Development of configuration for a pilot project 3) Selection of pilot plant site(s) for implementation phase (2025) The superior goal of the NPHyCo project is to prepare a definite nuclear powered hydrogen cogeneration project with a short implementation horizon. This will be achieved by a comprehensive assessment of the technical feasibility and the commercial reasonability. The investigations will be focused on existing H2 generation technologies and existing and willing NPP to be able to implement a NPHyCo project within a 3-year time period after completion of this project. The NPHyCo project aims to gain knowledge concerning the coupling of NPP plants and newly built H2 plants. The project will propose an optimal degree of integration, to maximise mutual benefits, based on mutual integrity of both plants. The results will be summarized in practical decision matrixes, guidelines, and checklists. These results can be used by NPP operators, H2 generation EPCs and all other stakeholders, such as investors, safety authorities, notified bodies and H2 consumers for their decision-making processes.

ENTENTE "European Database for Multiscale Modelling of Radiation Damage" aims to design a new European experimental/modelling materials database to collect and store pedigree data on radiation damage of RPV steels, according to FAIR (Findability, Accessibility, Interoperability, and Reusability) principles. The project can be seen as three interconnected blocks: DATABASE Design - Multi-disciplinary teams (materials scientists, engineers, software developers) for the definition of new effective data formats suitable for microstructural and modelling data, and interfaces needed to ensure interoperability. - Interface the SOTERIA platform with the ENTENTE database so that experimental data and metadata could be retrieved and post processed in order to correctly parametrize modelling tools ADVANCED experiments/models - Microstructural characterization, linked with appropriate models, by means of advanced (S)TEM techniques, APT, -XRD and in-situ TEM for mapping the radiation induced defects and associated strain-stress fields - In-depth analysis of segregation and structural, chemical nature and strength of grain boundaries to study hardening and non-hardening embrittlement INNOVATIVE data analysis and hybrid models - Simulation tools that enable the description of radiation damage up to space and time scales that are comparable with those reached in experiments on RPV steels. Accelerated physically informed fracture laws with a reasonable predicting capability on heterogeneous microstructures. - First application of ICME (Integrated Computational Materials Engineering) approaches to enable virtual studies of alternative neutron embrittlement scenarios -Machine learning and artificial neural networks approaches to support atomistic modeling as well as to predict hardening and/or embrittlement Target data will be those generated during previous EURATOM projects (LONGLIFE, PERFORM, SOTERIA, TAREG, PHARE) on RPV steels.