AQUAlity is a multidisciplinary and cross-sectoral European Training Network, that consist in eighteen participants (7 universities, 3 research institutes and 8 companies) and aims to generate and promote highly skilled scientists with the potential to face the present and future challenges concerning the protection of water resources from contaminants of emerging concern (CECs). AQUAlity will enroll fifteen early-stage researchers (ESRs) to conduct cutting-edge research on multidisciplinary aspects of novel hybrid technologies for the removal of CECs from aqueous systems. Moreover, ESRs will be trained to develop their creativity, critical and autonomous thinking, and entrepreneurial skills, thus boosting their scientific skills and innovation capacity in the field of water treatment technologies. This goal will be attained via a structured training-through-research programme, consisting of original individual research projects (performed both at the beneficiary organization and through intersectoral secondments) and education on technical and transferable skills (performed both at local level and with network-wide events). The overall research goal of AQUAlity is to develop highly innovative hybrid green technologies for CECs removal from aqueous systems by unconventional solar advanced oxidation processes (AOP) in combination with high flux ceramic nanofiltration membranes. Hence, ESRs will conduct frontier research in the field of AOP (organic photocatalysis, sensitized photocatalysts, photo-Fenton), materials development, nanofiltration technology, and will develop innovative hybrid photochemical/membranes systems to be tested on different waters to prove their potential for the production of high-quality water. Advanced analytical tools for the determination of CECs, their degradation products and water safeness will be developed. Thanks to the presence of industrial participants, AQUAlity aims for the commercial exploitation of part of the research results.

This proposal describes the third core project of the Graphene Flagship. It forms the fourth phase of the FET flagship and is characterized by a continued transition towards higher technology readiness levels, without jeopardizing our strong commitment to fundamental research. Compared to the second core project, this phase includes a substantial increase in the market-motivated technological spearhead projects, which account for about 30% of the overall budget. The broader fundamental and applied research themes are pursued by 15 work packages and supported by four work packages on innovation, industrialization, dissemination and management. The consortium that is involved in this project includes over 150 academic and industrial partners in over 20 European countries.

AMBROSIA aims to provide the foundations for a multi-sensing future-proof Point of Care Unit for sepsis diagnosis offered by a CMOS compatible toolkit and enhanced by on-chip photonic neural network technology to provide an accurate and rapid diagnosis. AMBROSIA will be investing in the established ultra-small-footprint and elevated sensitivity of integrated plasmo-photonic sensors reinforced by the well-known on-chip slow-light effect and micro-transfer printed lasers and photodiodes on Si3N4, as well as the functional processing and classification portfolio of integrated photonic neural network engines, towards painting the landscape of the next-coming disruption in sensor evolution, tailoring them in System-in-Package prototype assemblies, with the sensors being cheap disposable pluggable modules that can rapidly and accurately diagnose sepsis at the bedside in clinical environments. AMBROSIA targets to demonstrate a Point of Care Unit incorporating: i) a switchable sensor area array, with each sensor area facilitating a pluggable, 8-channel label-free plasmo-photonic sensor for sepsis diagnosis with a sensitivity over 130.000nm/RIU and a Limit of Detection below 10-8 RIU for each interferometric sensor, ii) an embedded Si3N4 photonic neural network processing and classifying at the same time the data from at least 7 biomarkers with zero-power providing in the first minutes an accurate and rapid diagnosis for sepsis, iii) Micro-transfer printed lasers and photodetectors on chip that will drastically decrease costs of both the sensing and neural network modules, and render the sensor arrays disposable.

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