Knowledge-based improvements of Li-ion battery cost, performance, recyclabiKnowledge-based improvements of Li-ion battery cost, performance, recyclability and safety are needed to enable electric vehicles to rapidly gain market share and reduce CO2 emissions. SPIDER’s advanced, low-cost (75 €/kWh by 2030) battery technology is predicted to bring energy density to ~ 450 Wh/kg by 2030 and power density to 800 W/kg. It operates at a lower, and thus safer, voltage, which enables the use of novel, highly conductive and intrinsically safe liquid electrolytes. Safety concerns will be further eliminated (or strongly reduced), as thermal energy dissipation will be reduced to 4 kW/kg, and thermal runaway temperature increased to over 200°C. Moreover, SPIDER overcomes one of the main Li-ion ageing mechanisms for silicon based anodes: notably, the loss of cyclable lithium, which should increase lifetime to 2000 cycles by 2022 for first life applications with further usefulness up to 5000 cycles in second life (stationary energy storage). In addition, SPIDER’s classic cell manufacturing process with liquid electrolyte will be readily transferable to industry, unlike solid electrolyte designs, which still require the development of complex manufacturing processes. Finally, SPIDER batteries will be designed to be 60% recyclable by weight, and a dedicated recycling process will be developed and evaluated during SPIDER. In addition, SPIDER materials significantly reduce the use of critical raw materials. Finally, four SPIDER partners are identified by the European Battery Alliance as central and strategic for the creation of the needed European battery value chain: SGL, NANO, VMI & SOLVAY. In conclusion, SPIDER proposes a real breakthrough in battery chemistry that can be readily adopted within a sustainable, circular economy by a competitive, European battery value chain to avoid foreign market dependence and to capture the emerging 250 billion € battery market in Europe.

The answer to the current Raw Material supply challenge faced today in Europe, lies in technological innovations that increase the efficiency of resource utilization and allow the exploitation of yet untapped resources such as industrial waste streams and metallurgical by-products. One of the key industrial residues which is currently not or poorly valorised is Bauxite Residue (BR, more commonly known as “red mud”) from alumina refineries. Bauxite residue reuse solutions do exist as stand-alone but pooling them together in an integrated manner is the only way to render bauxite residue reuse viable from an economical point of view and acceptable for the industry The RemovAl project will combine, optimize and scale-up developed processing technologies for extracting base and critical metals from such industrial residues and valorising the remaining processing residues in the construction sector. In term of technological aspects, RemovAl will process several by-products from the aluminium sector and from other metallurgical sectors in Europe (SiO2 by-products, SPL, fly ash,and others). The different waste streams will be combined to allow for optimal and viable processing in different technological pilot nodes. The technologies and pilots in most cases have already been developed in previous or ongoing projects and through RemovAl they will be pooled together and utilized in a European industrial symbiosis network. In term of societal or non-technological aspects, RemovAl will gather key sectors like the non-ferrous metal and cement sectors in order to secure a true industrial symbiosis through a top-down approach considering also legislation and standardisation at European level in order to facilitate the implementation of the most promising technical solutions.

The goal of BrainCom is to develop a new generation of neuroprosthetic devices for large-scale and high density recording and stimulation of the human cortex, suitable to explore and repair high-level cognitive functions. Since one of the most invalidating neurospychological conditions is arguably the impossibility to communicate with others, BrainCom primarily focuses on the restoration of speech and communication in aphasic patients suffering from upper spinal cord, brainstem or brain damage. To target broadly distributed neural systems as the language network, BrainCom proposes to use novel electronic technologies based on nanomaterials to design ultra-flexible cortical and intracortical implants adapted to large-scale high-density recording and stimulation. The main challenge of the project is to achieve flexible contact of broad cortical areas for stimulation and neural activity decoding with unprecedented spatial and temporal resolution. Critically, the development of such novel neuroprosthetic devices will permit significant advances to the basic understanding of the dynamics and neural information processing in cortical speech networks and the development of speech rehabilitation solutions using innovative brain-computer interfaces. Beyond this application, BrainCom innovations will enable the study and repair of other high-level cognitive functions such as learning and memory as well as other clinical applications such as epilepsy monitoring using closed-loop paradigms. BrainCom will be carried out by a consortium assembled to foster the emergence of a new community in Europe acting towards the development of neural speech prostheses. Thanks to its high interdisciplinarity involving technology, engineering, biology, clinical sciences, and ethics, BrainCom will contribute advances to all levels of the value chain: from technology and engineering to basic and language neuroscience, and from preclinical research in animals to clinical studies in humans.

Preventive conservation (PC) has emerged as an important approach for the long-term preservation of sensitive cultural heritage (CH), notably for mobile artefacts, those displayed or stored in harsh environments and for small and medium-sized museums. SensMat aims to develop and implement effective, low cost (<20 – 30€ for basic platform), eco-innovative and user-friendly sensors, models and decision-making tools, as well as recommendations and guidelines to enable prediction and prevention of degradation of artefacts as a function of environmental conditions. SensMat is user-driven (inclusion of 19 museums in the project plus survey of 100 more), and the consortium has solid existing results and a strong capacity to mature the sensors, models and decision-making solutions to TRL 7 during the project. Based on multiscale modelling, data management systems, collaborative platforms and sensor communication networks (IoT), museums stakeholders will be informed in real-time of possible dangers to their artefacts, thus reducing degradation risks and costly conservation treatment. Demonstration of the platform in 10 representative case studies in museums, historical buildings, storage sites and workshops will prepare rapid uptake after the project. Knowledge transfer, training, and recommendations of best practices will facilitate standardization, strategy implementation, new policy definition, and wide-scale adoption of the new solution by cultural heritage sites immediately after the project.

The IOTWINS project will deliver large-scale industrial test-beds leveraging and combining data related to the manufacturing and facility management optimization domains, coming from diverse sources, such as data APIs, historical data, embedded sensors, and Open Data sources. The goal is to build a reference architecture for the development and deployment of distributed and edge-enabled digital twins of production plants and processes. Digital Twins collect data from manufacturing, maintenance, operations, facilities and operating environments, and use them to create a model of each specific asset, system, or process. These models are then used to detect and diagnose anomalies, to determine an optimal set of actions that maximize key performance metrics. IOTWINS proposes a hierarchical organization of digital twins modeling manufacturing production plants and facility management deployment environments at increasing accuracy levels: • IoT twins: featuring lightweight models of specific components performing big-data stream processing and local control for quality management operations (low latency and high reliability); • Edge twins: deployed at plant gateways and/or at emerging Multi-access Edge Computing nodes, providing higher level control knobs and orchestrating IoT sensors and actuators in a production locality, thus fostering local optimizations and interoperability; • Cloud twins: performing time-consuming and typically off-line parallel simulation and deep-learning, feeding the edge twin with pre-elaborated predictive models to be efficiently executed in the premises of the production plant for monitoring/control/tuning purposes