INN-PRESSME aims at developing and implementing a sustainable OITB to support European companies to scale up their nano-enabled biomaterials and processes from TRL 4-5 to 7. We will focus on (nano)cellulose, bioplastics and natural fibres, combined with nanotechnology approaches to tailor biobased materials with properties and functionalities (barrier, antibacterial properties, improved corrosion or chemical resistance, etc.) that equal or outperform their fossil counterparts at competitive prices. INNPRESSME gathers 16 pilot lines, organized in routes and processes for feedstock conversion (PLA, PHA, fibre-based, cellulose-based), formulation and transformation and processing of bio-based material to high added value products. INN-PRESSME is a joint collaborative net of 27 partners from 10 EU countries from North (Fi, Nor, Swe, UK, Be), South (Sp, It, Fr) and central Europe (Ge, Po). All technical and market oriented services are provided by specific partners, include equipment and methods for processing, fast and accurate/reliable characterization, scaling up, nanosafety, eco-design and circular economy assessment (recyclability and biodegradability), product certification, digitization of the OITB, sustainable OITB´ business plan, technology transfer, support for funding, networking activities. The value proposition will be validated for 3 targeted mass markets (packaging, energy/transport, and consumer goods sectors) with 9 test cases co-developed with Early Adopters (7 IND, 1 SME) and with additional test cases through open calls. All this will secure the smooth transition of the new material concepts to the market, for a cumulated turnover of about 56M€ by 2029. Additionally, for maximizing the impacts on SMEs, the OITB will rely on a strong network of European clusters and initiatives and on an efficient promotional campaigns including towards the ECCP members or regional initiatives funded by ERDF. These services will be available through single entry point.

RealNano is an ambitious 36-month project that will develop rapid real-time nano-characterization materials tools & methodologies based on Spectroscopic Ellipsometry, Raman Spectroscopy, Imaging Photoluminescence and Laser Beam Induced Current Mapping that will be integrated to in-line R2R (Roll-to-Roll) Printing and OVPD (Organic Vapor Phase Deposition) Pilot-to-Production Lines (PPLs) for characterization of Organic & Printed Electronics (OE) nano- materials, layers, devices and products during their manufacturing. It will bring Digital Intelligence to manufacturing by combining, fast and non-destructive characterization tools and methodologies (high speed resolution at nanoscale, capable for multiple integration, advanced data management and analysis), to provide robust information and quality control on the nanomaterial properties and products quality, reliable manufacturing, without affecting the process. Objectives: O1. Develop rapid and real-time nanoscale, multi- modal & scale characterization tools/methodologies for OEs O2. Integrate the non-destructive nano-characterization tools in in-line R2R printing and OVPD PPLs O3. Develop characterization Protocols and Data Management for interoperability across industries O4. Demonstrate the tools in industrial OE processes for improvement of quality and reliability of products O5. Validation of OE product quality and manufacturability on commercial applications O6. Effective Transfer of results to industry by Open Innovation (Dissemination, Training, Networking/Clustering) and Management RealNano will revolutionize industrial manufacturing by strongly improving the speed of the characterization procedures in terms of performance (non-destructive with nm-scale resolution in ms time) and reliability (measure/analyze OE nanolayers over large areas). The in-line implementation to PPLs will achieve significant reduction of process time and resources needed to manufacture high quality OE products.

STARLight is a major opportunity to establish Europe at the forefront of 300mm Silicon Photonics (SiPho) innovation and thus “Solidify the European photonics ecosystem and accelerate its industrial deployment”. The main STARLight vision is to contribute to European sovereignty relying on the achievement of an efficient and resilient ecosystem and a sustainable society by driving excellence and innovation in the implementation of 300mm silicon photonics manufacturing capacity in Europe (STMicroelectronics, France). The main STARLight goals are: -To offer Europe a realistic solution ensuring its independency on the sourcing of SiPho and other needed optical module compounds (EIC, laser), based on a true European end-to-end value chain, tailored to the global market. -To give Europe the opportunity to move forward with industrial and academic players by joining in the risk-taking necessary for the growth dynamics of SiPho optical modules in Europe, addressing applications such as Data Centers including Hyperscalers, Artificial Intelligence, Telecom, Automotive Lidar, Inter-Satellites communication. -To expand the European photonics technology platform family in terms of accessibility, upscaling and high yield, packaging assembly and providing modelling tools to converge on a mainstream simulation flow. -To be ready for future applications with 200 GBaud (400 Gbps PAM4/lane) by leveraging leading-edge SiPho technology innovations using the integration of heterogeneous non-silicon materials. -To demonstrate the benefit of SiPho optical modules sustainability, providing a better energy-saving, reliability, limited usage of copper and aluminium materials, and reducing the usage of polluting material and water. The STARLight project makes it possible to promote a concept of collaboration, which is based on an ecosystem bringing together the various actors of the supply chain ranging from the technologies, the circuits providers to the manufacturer of modules and systems.

The Engines ITD will work towards radical engine architectures and new engine technologies to power the aircraft of the future. The objective is to increase fuel and energy efficiency of the engine and reduce environmental impact, regardless of whether the engine is powering a large airliner or just a small utility aircraft, meaning more thrust while burning less fuel and emitting less CO2, NOx and noise.

Storm water runoff typically contains and transports a wide range of pollutants, resulting in negative environmental effects with potential threats to ecosystems and health. Hundreds of runoff treatment ponds intended to moderate these impacts are likely to be delivering sub-optimal (and perhaps actually below legally required) levels of improvement in water quality due to poor understanding of flow patterns and the effects of vegetation. This proposal will generate a unique dataset to describe the influence of different types and configurations of vegetation on the pond's fundamental flow - and treatment - characteristics. We will also deliver a validated set of vegetative resistance and mixing parameters that are essential if 3D numerical modelling tools are to be used with confidence. These tools will ensure that future pond designs meet all their water quality and ecosystem services objectives for current legislation and the increasingly stringent EU regulatory framework anticipated over the next decade. Stormwater ponds take run-off from urban areas, highways and agricultural land, providing detention and attenuation of peak storm discharges and improving water quality. Stormwater ponds are able to provide protection to downstream drainage components and receiving waters by holding or treating run-off at or near the source and provide additional nature conservation and amenity benefits. Within the Highways Agency Asset Inventory System alone there are currently over 800 stormwater ponds. Pond performance (pollutant treatment efficiency) is directly related to hydraulic residence time, a function of the internal flow field, which in turn is controlled by the pond geometry and the distribution and type of vegetation present. The prediction of water quality improvements within drainage features is gaining importance with stormwater professionals. However, performance prediction is complex since water quality processes are functions of the pond hydraulic residence time. Current evaluations employ the nominal retention time which assumes plug flow through the pond, as the design consideration. It is accepted that the nominal retention time (pond volume/discharge) provides a poor estimate of the actual mean (or median) residence time, with overestimates of treatment times of 100% or more not being uncommon. However, it is still in use, even 'the norm'. In wastewater treatment wetlands, treatment is good since a high degree of engineering is adopted in creating an efficient, often linear, shape with uniform, dense, vegetation. In contrast, stormwater ponds must fit into existing water courses or urban environments. Together with the additional requirements for biodiversity and ecological function, this leads to pond layouts that may be less than ideal from a hydraulic perspective. Vegetation can have either a positive or negative role in water quality treatment within stormwater ponds. It provides the appropriate environment for the support of biofilms and the colonisation by algae, enhancing treatment, yet variable spatial distribution influences the spread of the hydraulic residence time. This proposal seeks to better understand and quantify the physical, vegetation-driven, flow mechanisms occurring within a stormwater pond and to develop a robust physically based modelling tool. The research proposed here will deliver improved understanding of the effects of vegetation (type: emergent, floating and submerged; physical characteristics: porosity and spatial distribution) on flow patterns and residence time distributions within stormwater ponds. The validated numerical modelling approach will permit the assessment of short circuiting, a measure of poor performance, and provide estimates for vegetation contact times, sediment deposition regions and rates. This will provide a tool for predicting the treatment efficiency of vegetated stormwater ponds.