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.

Wherever moving bodies are in contact with each other, the respective materials have to show certain friction and wear (tribological) performance. These materials have to fulfil additional functionality and meet ecological, health and safety regulations too. In order to bring novel materials into products, extensive materials characterisation is required. i-TRIBOMAT aims at establishing the world first open test bed of tribological materials characterisation to support industrial innovations among European manufacturing industries and SMEs by upscaling materials to the mechanical components level. i-TRIBOMAT open test bed enables user-driven versatile characterisation of materials at reduced costs by also shortening the time-to-market ca. 5 times. i-TRIBOMAT will realize a unique bundle of shared tribological infrastructure and expertise consisting of >100 tribometers, materials characterisation equipment and additional tools for modelling, protocols, tribo-analytics, design of experiments and online monitoring. i-TRIBOMAT will establish an IT-platform for materials and tribological data harmonisation, management, analytics, sharing and mining. i-TRIBOMAT on its collaboration interface will supply lab-to-field upscaling tools by combining testing with computation, e.g., using artificial intelligence methods, virtual work rooms and surrogate models for various stakeholders, like EUMAT-platform. i-TRIBOMAT services will be validated by three industrially relevant use cases: energy efficiency (transportation), renewable energy (wind turbine) and manufacturing (seals) represented by large-, medium- and small-sized companies. i-TRIBOMAT will be THE European Single Entry Point offering intelligent Tribological Materials Characterisation to predict the durability of materials in use or novel for a wide field of industrial applications. i-TRIBOMAT is expected to cover ≥6.4 % of the dedicated market with a turnover of 9.6 M€ and EBITA objective of 600 k€/year by 2027.

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.