The main objective of the Sea4Volt project is the development of a novel low temperature Anion Exchange Membrane (AEM) electrolyser concept, able to operate efficiently, selectively, and durably with a direct seawater feed under a slight pH-gradient. Reaching this will require identifying and developing new suitable materials (catalysts, membrane, coatings, porous transport layers, bipolar plates, sealings), as well as novel electrolyser design options. The Sea4Volt will develop and demonstrate a direct seawater electrolyser prototype with novel materials/components and membrane/ionomers to reach effective high-performing and corrosion-resistant seawater electrolysis system. Results of in-operation tests will be published in public deliverables, workshops, and conferences, making it possible for the partners outside of Sea4Volt consortium to exploit leading to a wider impact throughout the European electrolyzer and fuel cell industry. The choice of the newly emerged AEM technology proposed in this project, on one hand, emphasises the extensive innovative technological impact exhibited in the implementation of novel non-CRM materials, PFAS-free anion exchange membranes and ionomers, new electrode designs and protective coatings. On the other hand, the intrinsic cost-effectiveness of the AEM technology, embedded in utilization of low-cost materials, is expected to provide further cost reductions to such an offshore electrolyser system, and will result to anticipated lower cost of green hydrogen production. The technology enabling the generation of green hydrogen directly from seawater holds immense societal-wide impacts. Shift towards green hydrogen production could also stimulate economic growth through the creation of new industries and job opportunities, particularly in regions with abundant seawater resources. Sea4Volt is also being targeted in the areas characterised with deficit of fresh water especially in underdeveloped regions around the globe.

AgRimate focuses on transforming pruning tasks for small-scale farmers by using Augmented Reality (AR) and Robotics technologies, enriched with Artificial Intelligence (AI). Tackling the significant challenges and labour-intensive aspects of pruning in high-value crops like olive groves and vineyards, AgRimate introduces an innovative and scalable solution. This solution relies on an artificial intelligence module capable of learning from expert human knowledge to address tree pruning challenges. This module extends its functionality to the solution through various tools, from a learning tool that adapts to the user's knowledge to an Augmented Reality solution providing real-time guidance during the pruning process with the human at the centre. It seamlessly integrates with two different robotic solutions, either assisting the user with exoskeletons or autonomously performing tasks with a highly advanced robot. Additionally, AgRimate incorporates a comprehensive assessment tool designed to evaluate the solution's impact, not only on farmers but also on rural communities. This holistic approach aims not only to enhance productivity and resource efficiency but also to improve social inclusiveness and working conditions within the agricultural sector. By forging a consortium of leading research institutions, SMEs, and agricultural stakeholders from across Europe, AgRimate is at the forefront of cultivating sustainable, technologically advanced, and socially responsible farming practices. Ultimately, AgRimate stands as a beacon of innovation in agricultural technology, keenly aligned with the EU's broader goals of digital transformation, environmental sustainability, and socio-economic equity, showcasing a path forward for future farming that prioritizes both yield and community wellbeing.

During the last years, EU manufacturing has faced production flexibility challenges by deploying, among others, novel hybrid manufacturing systems, involving collaborative robots and mobile manipulators combined with flexible grippers, vision systems, sophisticated tasks/actions planning solutions and flexible integration platforms. Despite the importance of AI enabled flexible robotic systems, several aspects settle back their wider adoption, and impact on the objectives of the green deal: •Limited cognition/ intelligence: existing solutions support non-trivial tasks but cannot act autonomously. •Insufficient perception and diagnostics: In a circular economy, there is an increased need for understanding the state of products or parts that are being handled, after they have been used. •Decision making is restricted: Current decision-making focuses on process or line level, not taking into account optimization at value chain level or per individual product. •Small scale adaptation of AI due to small number of available data and training needed, to support tailored solutions in high variability context. •Lack of use of explicitized knowledge in AI and robotics. Lifecycle data and knowledge is not used across the value chain to improve decision making after a product’s first life. •Complexity in robot programming and interaction which requires the involvement of skilled engineers, does not provide flexibility in execution, Thus, ROB4GREEN aims to develop easy to use and deploy AI driven collaborative robotic systems, that can reason and adapt to a variety of strategies for processing products after their first life, both hardware and behavior wise, improving existing skills and generating new ones, working autonomously combining data and knowledge. Such systems will be validated at scale and in major industries, showcasing optimization ranging from cell to the whole value chain, towards achieving significant impact on the objectives of the green deal.

Modern industrial companies aim to extend their products with services as fundamental value-added activities. The key potential of the concept of Product Service System (PSS), besides radical improvements in the use of products, is a reduction of environmental footprint of products and services. The overall footprint of PSS is still insufficiently investigated. The services within PSS are an important, insufficiently used source of (digitalized) data on the product and its use. It is likely that digital means facilitating provision of consistent “track and trace” information on the origin, composition and entire life cycle not only of a product but of all services offered and used around the product, will offer important contribution towards achievement of full circularity for manufacturing. The key idea of PSS-Pass is to investigate how extension of DPP to Digital Product Service System Passport (DPSSP) can be effectively achieved and how it will allow for improved circularity of the manufacturing industry. The overarching hypothesis is that LCA underpinned by Machine Learning (ML) methods and informed by dynamic data paves the way to more accurate LCA while supporting PSS life cycle decision making. The collected and sharable data from DPSSP will allow to effectively apply ML as well as Digital Twin (DT) for more reliable decision-making processes concerning circularity of PSS. The project will provide Methodological Framework for definition, development and update of DPSSP, Digital Environment for DPSSP built on existing interoperability architectures, set of ontologies for improved interoperability at DPSSP Environment, novel DT-based Simulation Framework, for modelling standardized and interoperable DTs for PSS lifecycle analysis, and AI based method/tool to forecasts the environmental impact of PSS. The PSS-Pass solutions will be tested and evaluated within 3 pilots in diverse sectors: home appliances, complex equipment, and textile industry.

A fundamental limitation with current approaches aiming to bioprint tissues and organs is an inability to generate constructs with truly biomimetic composition and structure, resulting in the development of engineered tissues that cannot execute their specific function in vivo. This is perhaps unsurprising, as many tissues and organs continue to mature postnatally, often taking many years to attain the compositional and structural complexity that is integral to their function. A potential solution to this challenge is to engineer tissues that are more representative of an earlier stage of development, using bioprinting to not only generate such constructs, but to also provide them with guiding structures and biochemical cues that supports their maturation into fully functional tissues or organs within damaged or diseased in vivo environments. It has recently been demonstrated that such developmental processes are better recapitulated in microtissues or organoids formed from self-organizing (multi)cellular aggregates, motivating their use as biological building blocks for the engineering of larger scale tissues and organ. The main goal of micro2MACRO (m2M) is to develop a new bioprinting platform capable of spatially patterning numerous cellular aggregates or microtissues into scaled-up, personalised durable load-bearing grafts and guiding their (re)modelling into fully functional tissues in vivo within damaged or diseased environments. This will be achieved using a converged bioprinting approach capable of rapidly depositing cells and microtissues into guiding scaffold structures with high spatial resolution in a rapid, reliable, reproducible and quantifiable manner. These guiding structures will then function to direction the fusion and remodelling of cellular aggregates and microtissues into structurally organised tissues in vitro and in vivo, as well as providing medium-term (3-5 years) mechanical support to the regenerating tissue.