Data-Gouv-ML/pcri-informations-sur-les-projets-laureats huggingface dataset:
Automated high resolution water sampler for environmental monitoring (AIRWAVES)

Framework: Horizon 2020 — Excellent Science
Programme: European Research Council (ERC)
Theme: European research council
Destination: Proof of concept grants
Action type: ERC actions
Countries involved: Germany
Call: ERC Proof of Concept Grant (ERC-2018-PoC, 2018)
Start date: 2019-01-01
End date: 2022-06-30
Duration (months): 42.0
Total project cost (EUR): 149711.25
EU funding (EUR): 149711.0
Participants: 1
Project status: CLOSED

A new automated water sampling technology was developed under the ERC Consolidator Grant STEEPclim with the potential to revolutionize environmental monitoring worldwide. A changing climate and growing scarcity of water strongly increase the need of reliable standardized and highly automated environmental monitoring, thus creating a growing market for our innovative solution. Our first prototype successfully operated under field conditions. Now we seek funding to further develop this device and explore commercialization pathways. Today, rain water, river discharge and climate are monitored routinely with high temporal resolution using quality sensors, but no adequate automated technology for obtaining representative samples for laboratory grade analysis is available for weather services, hydromet offices, chemical industry or research institutions. So far taking, preserving and analyzing samples from natural waters is meticulous, labor intensive and expensive. Isotope signatures in water are ideal tracers of processes in the water cycle. Stable isotope analysis of precipitation can identify changing atmospheric circulation patterns and the origin of groundwater. They can also be used for the reconstruction of paleoclimate from ancient waters locked in geological archives. The analysis of fruits, food and drink products, of drugs, explosives and human remains is used to identify their regional provenance. For this purpose a robust understanding of the modern distribution of isotopes in space and time is indispensable. The autonomous and robust sampler introduced here is designed to fulfill the high demands on sampling and storage for isotope analysis. It is portable, has low power consumption and can be accessed remotely for maintenance and to adapt the sampling protocol strategy. The obtained water samples are not restricted to isotope analysis but can be used for any type of environmental water analysis.

Keywords: water cycle,hydrological monitoring,isotope hydrology,isotope geochemistry,palaeoclimatology,geochemistry
FISH-LIVER-HARVESTER (FLH) Project - an automated fish liver harvesting machine (DeLiver)

Framework: Horizon 2020 — Industrial Leadership
Programme: Innovation in SMEs
Action type: SME instrument
Countries involved: Faroe Islands
Call: SME instrument (H2020-SMEInst-2018-2020-1, 2018)
Start date: 2018-08-01
End date: 2019-06-30
Duration (months): 11.0
Total project cost (EUR): 71429.0
EU funding (EUR): 50000.0
Participants: 1
Project status: CLOSED

The application is for developing a new machine, Fish Liver Harvester (for harvesting fish livers from codfish) from a TRL (Technological Readiness Level) 6-7 to TRL level 8-9. The working principle of the machine is utilizing the differences in specific gravity of the livers and the rest of the guts, separating them in a flow of sea water running through the machine. The livers are floating in sea water, getting the livers to seek upwards, while the rest of the guts are seeking downwards. The separation is performed by a rotating separator, synchronized with conveyor carriers on the conveyor belt in the machine, and driven by the conveyor chain, separating the livers from the guts over a sharp edge. The livers are flowing with the seawater out of the machine, while the conveyor carriers are bringing the rest of the guts out at the other end of the machine. The actual machine is a Prototype 2 machine construction, which is based on the experiences made with a Prototype 1 machine, which was tested onboard two Faroese trawlers, Sjagaklettur and Hamranes. The experiences made with Prototype 1, feedback from the crew onboard the trawler Hamranes, and good cooperation with the Faroese Food Authorities were used in the design of a easier cleanable, more hygienic Prototype 2. Prototype 2 was installed on board trawler Hamranes in December 2016 and is still being tested on board. The feedback from the crew on Hamranes has been positive and a number of trawlers have already indicated purchasing interest. The intention is to optimize the machine and the yield and bring it ready for commercialization. The machine can make it possible to utilize a mostly wasted resource, that can be used both for food and feed production. The machine was developed for deep sea trawlers, but can also be adapted to longlining fishing boats and other fishing boats. There is also a demand for a land-based system, separating livers from guts in land-based manual gutting operations.

Keywords: sustainable fishery,high sea trawler,fishing,fish liver
Resource-efficient Machine for the Recycling of coal dust into High-calorie Briquettes (INSTABRIQ)

Framework: Horizon 2020 — Industrial Leadership
Programme: Innovation in SMEs
Action type: SME instrument
Countries involved: Austria
Call: SME instrument (H2020-SMEInst-2018-2020-1, 2018)
Start date: 2018-11-01
End date: 2019-02-28
Duration (months): 4.0
Total project cost (EUR): 71429.0
EU funding (EUR): 50000.0
Participants: 1
Project status: CLOSED

Coke, a highly-calorific low-sulphur fuel obtained from bituminous coal, is vital in the production of steel by steelworks. During this process, up to 0.5 kg of coke dust/tonne of coke is expelled. The release of coke dust can present respiratory health hazards to humans, whilst accumulating in the environment due to minimal recycling, often as a consequence of the presence of metal contaminants. Un-contaminated coke dust can currently be turned into coke briquettes with the aid of binding agents or adhesives. Binding agents and adhesives, however, contribute to inefficient combustion and release of environmentally-harmful SO2 and NOx gases, which decrease the efficiency of CCS technologies (i.e. CCS technology can be up to 99 % efficient in the absence of SO2 and NOx). Thus, minimising the amounts of sulphur and nitrogen in coal can dramatically reduce the percentage of CO2 emitted into the environment. We, the C.A. Group, have developed INSTABRIQ, a new type of briquetting machine, which can compress any type of coal dust to create low-sulphur (as low as 0.65 %) “clean coal” briquettes, without using adhesives, thus also affording a high heat capacity. INSTABRIQ briquettes have the same structural integrity as adhesive-containing solutions. As an experienced metals recycling company, we are also uniquely positioned to remove metal contaminants from coke dust, thus increasing the portion of coal, which can be recycled to produce briquettes. INSTABRIQ is a unique and value-added tool for coal-reliant industries, facilitating virgin-raw-material- and CCS-technology-efficiency. Following this project, we expect to commercialise our machine by 2021 to create briquettes primarily for reuse in the steel industry and secondarily for households. INSTABRIQ represents the first coal-based venture for the C.A. Group and will allow us to further diversify our revenue streams in addition to new intellectual property generation.

Keywords: eco friendly,high calorific,briquette,coal,coke,dust,recycling,steelworks,briquetting machine,carbon capture and sequestration technologies
Intelligent digital tools for screening of brain connectivity and dementia risk estimation in people affected by mild cognitive impairment. (AI-Mind)

Framework: Horizon 2020 — Societal Challenges
Programme: Health, demographic change and wellbeing
Theme: Methods and data
Action type: Research and Innovation actions
Countries involved: Spain,Estonia,Italy,Italy,Norway,Finland,Norway,Switzerland,Netherlands,Luxembourg
Call: Digital diagnostics – developing tools for supporting clinical decisions by integrating various diagnostic data (H2020-SC1-2020-Single-Stage-RTD, 2020)
Start date: 2021-03-01
End date: 2026-02-28
Duration (months): 60.0
Total project cost (EUR): 13998862.5
EU funding (EUR): 13998862.5
Participants: 17
Project status: SIGNED

More than 10 million Europeans show signs of mild cognitive impairment (MCI), a condition intermediate between normal brain ageing and dementia. The evolution of MCI differs from person to person; some remain stable or return to normal, but 50% progress to dementia within five years. Current practice lacks the necessary screening tools to identify those 50% at risk. The patient’s journey typically takes many years of inefficient clinical follow-ups before a conclusive diagnosis is finally reached. AI-Mind will radically shorten this journey to 1 week through a digital solution that is able to provide a fast and accurate (>95%) prediction for the individual dementia risk. Our AI-Mind platform service, can be easily integrated into existing clinical practices and contains 2 new artificial-intelligence-based tools. The AI-Mind Connector identifies dysfunctional brain networks. The AI-Mind Predictor assesses demen-tia risk using data from the Connector, advanced cognitive tests, genetic biomarkers and important textual variables. Our aim is to set up a European clinical network that will upload patient data to the AI-Mind European cloud platform. The consortium comprises excellent researchers in neuroscience and computer science, from 5 clinical cen-tres, who closely collaborate with 3 SMEs contributing unique technologies, an established data govern-ance body, and Alzheimer Europe. Together, they plan to deliver a medical device of class 2b that can reach TRL7 by the end of the project. AI-Mind represents a major step forward in the risk assessment of dementia. Clinicians will promptly advise therapies to delay the onset of disease, and patients will enjoy independent lives for longer. By offering a globally accessible, cheap and precise tool for dementia pre-diction, AI-Mind will improve the health care system and boost innovation by shifting the R&D of phar-maceutical organisations and other companies to preventive diagnostic methods and therapies for dementia.

Keywords: mild cognitive impairment,dementia,screening,brain connectivity,multimodal risk assessment,machine learning,artificial intelligence,deep learning
Collapse Of Atmospheric Turbulence (COAT)

Framework: Horizon 2020 — Excellent Science
Programme: European Research Council (ERC)
Theme: European research council
Destination: Consolidator grants
Action type: ERC actions
Panel: Earth system science
Panel description: Physical geography, geology, geophysics, atmospheric sciences, oceanography, climatology, cryology, ecology, global environmental change, biogeochemical cycles, natural resources management
Countries involved: Netherlands
Call: ERC Consolidator Grant (ERC-2014-CoG, 2014)
Start date: 2016-01-01
End date: 2020-12-31
Duration (months): 60.0
Total project cost (EUR): 1659580.0
EU funding (EUR): 1659580.0
Participants: 1
Project status: CLOSED

This project aims to predict the cessation of continuous turbulence in the evening boundary layer. The interaction between the lower atmosphere and the surface is studied in detail, as this plays a crucial role in the dynamics. Present generation forecasting models are incapable to predict whether or not turbulence will survive or collapse under cold conditions. In nature, both situations frequently occur and lead to completely different temperature signatures. As such, significant forecast errors are made, particularly in arctic regions and winter conditions. Therefore, prediction of turbulence collapse is highly relevant for weather and climate prediction. Key innovation lies in our hypothesis. The collapse of turbulence is explained from a maximum sustainable heat flux hypothesis which foresees in an enforcing positive feedback between the atmosphere and the underlying surface. A comprehensive theory for the transition between the main two nocturnal regimes would be ground-breaking in meteorological literature. We propose an integrated approach, which combines in-depth theoretical work, simulation with models of various hierarchy (DNS, LES, RANS), and observational analysis. Such comprehensive methodology is new with respect to the problem at hand. An innovative element is the usage of Direct Numerical Simulation in combination with dynamical surface interactions. This advanced technique fully resolves turbulent motions up to their smallest scale without the need to rely on subgrid closure assumptions. From a 10-year dataset (200m mast at Cabauw, Netherlands) nights are classified according to their turbulence characteristics. Multi-night composites are used as benchmark-cases to guide realistic numerical modelling. In the validation phase, generality of the results with respect to both climate and surface characteristics is assessed by comparison with the FLUXNET data-consortium, which operates on a long-term basis over 240 sites across the globe.

Keywords: nocturnal boundary layers,turbulence,land atmosphere interaction
Understanding the Role of Automatic Partner Attitudes in Relationship Functioning (AutoRelationPun)

Framework: Horizon 2020 — Excellent Science
Programme: Marie-Sklodowska-Curie Actions
Theme: Marie Skłodowska-Curie
Destination: Individual Fellowships (IF)-Global Fellowships
Action type: Marie Skłodowska-Curie actions
Panel: Social Sciences and Humanities
Countries involved: United States,Netherlands
Call: Individual Fellowships (H2020-MSCA-IF-2020, 2020)
Start date: 2022-09-01
End date: 2025-08-31
Duration (months): 36.0
Total project cost (EUR): 253052.16
EU funding (EUR): 253052.16
Participants: 2
Project status: SIGNED

Romantic relationships are essential for promoting health and reducing mortality risks. However, remaining satisfied with a long-term romantic partner is notoriously difficult, as illustrated by the fact that divorce rates hover around 30-50% in most industrialized societies. Although research shows that how partners respond to one another defines well-functioning relationships, the origins of such crucial behavior remain largely unknown. How can we explain why partners behave the way they do? We argue that studying automatic partner attitudes, as assessed by implicit measures, is key to better understand and promote the functioning and the well-being of relationships. Because they are less sensitive to the positive illusions that are ubiquitous in relationships, we posit that automatic partner attitudes reflect the pattern of affective experiences that people encounter with their partner and, thus, are the primary source of behavior toward that partner. We test this crucial role of automatic partner attitudes for relationship functioning in two lines of research, using a combination of fine-grained longitudinal, observational and experimental methods. In the line of research A, we aim to identify how these attitudes form and affect behavior in dyadic interactions. Sampling real-life experiences from romantic couples over different time spans will allow us to understand the cyclical relationship between automatic partner attitudes and behavior in daily life. In the line of research B, we aim to intervene on these attitudes to improve relationship functioning and well-being. Experimentally enhancing automatic partner attitudes and the ability to regulate them will allow us to identify causal paths and develop interventions. By taking an inter-disciplinary approach, the proposed project will invigorate research in relationship science, social cognition and attitudes, but also offer applicable strategies that can efficiently help couples and reliably benefit society.

Keywords: social psychology,romantic relationships,automatic attitudes
Segmented Chainring for Sequential Shifting in Bicycles (SEGSEQ)

Framework: Horizon 2020 — Industrial Leadership
Programme: Innovation in SMEs
Action type: SME instrument
Countries involved: Spain
Call: SME instrument (H2020-EIC-SMEInst-2018-2020-3, 2020)
Start date: 2020-09-01
End date: 2023-01-31
Duration (months): 29.0
Total project cost (EUR): 1739250.0
EU funding (EUR): 1217475.0
Participants: 1
Project status: CLOSED

Gear shifting in bicycles allows to comfortably ride on uphill or downhill terrain. Regarding gearing technology, there is a long-standing challenge to get the maximum gear range (large variation between the easiest and hardest gears in order to access a wider set of terrains) with minimum steps between gears (for improved efficiency on each terrain). However, little changes have been observed in bicycle transmissions in the last 60 years. Even the recent introduction of electronic derailleurs did not meet market expectations regarding the possibility of achieving sequential or automatic shifting. SEGSEQ presents a segmented chainring actuation system that provides smooth and reliable shifting in any situation. The disruptive technological proposal of SEGSEQ introduces several characteristics that cannot be found currently in any product of a market very much monopolized by 2 companies (Shimano and Sram). The system provides performance gains in all metrics: 30% more usable gears, 15% wider gear-range, 15% closer gear-steps, 6% less weight and the possibility to operate shifts in sequential form and even in automatic form using cadence, pedalling power and/or heart-rate feedback. After multiple iterations and refinements of the SEGSEQ concept, application for several patents and the favorable timing for using complementary solutions whose patent protection expires in 2019-2020, the aim of the present project is to enter the 2,400 M€ groupset market and become the leading technological reference. Due to the strong competitors, it is daring for conventional investors to finance a promising concept that has not been thoroughly tested by the market. However, it is believed that the disruptive nature of SEGSEQ has the potential to become the European contender to lead the groupset market. In this context, ZUMA is looking for 1.7 M€ for product development (1.2 M€ EIC grant + 0.5 M€ via other sources) and 3.5 M€ for market deployment (via EIC equity).

Keywords: bicycle,transmission,electronic derailleur,sequential shifting,automatic shifting
Advanced glasses, Composites And Ceramics for High growth Industries (CoACH)

Framework: Horizon 2020 — Excellent Science
Programme: Marie-Sklodowska-Curie Actions
Theme: Marie Skłodowska-Curie
Destination: Innovative Training Networks (ITN)-European Training Networks
Action type: Marie Skłodowska-Curie actions
Panel: Information Science and Engineering
Countries involved: Germany,Italy,Italy,Italy,Germany,Italy,Finland,Sweden,France,France,Czechia,United Kingdom,United Kingdom,United Kingdom
Call: Marie Skłodowska-Curie Innovative Training Networks (ITN-ETN) (H2020-MSCA-ITN-2014, 2014)
Start date: 2015-01-01
End date: 2018-12-31
Duration (months): 48.0
Total project cost (EUR): 3882899.52
EU funding (EUR): 3882899.52
Participants: 15
Project status: CLOSED

The aim of CoACH (Advanced glasses, Composites And Ceramics for High growth Industries) is to offer a multidisciplinary training in the field of high-tech GLASSES, CERAMICS and COMPOSITES based on effective and proven industry-academia cooperation. Our scientific goals are to develop advanced knowledge on glass and ceramic based materials and to develop innovative, cost-competitive, and environmentally acceptable materials and processing technologies. The inter/multi-disciplinary and -sectorial characteristic is guaranteed by the presence of 5 academic partners and 10 companies having top class expertise in glass, ceramic and composite science and technology, modelling, design, characterization and commercialization. Advanced materials fall within the KEY ENABLING TECHNOLOGIES (KETs) and are themselves an emerging supra-disciplinary field; expertise on these new materials brings competitiveness in the strategic thematic areas of: HEALTH-innovative glass and composite for biomedical applications, ENERGY-innovative glass, ceramic and composite materials for energy harvesting/scavenging, solid oxide electrolysis cells and oil, gas and petrochemical industries, ICT-new glass fibre sensors embedded in smart coatings for harsh environment, ENVIRONMENT-new and low cost glass, ceramic and composite materials from waste. The originality of the research programme is to be seen in the supra-disciplinary approach to new glass- and ceramic- based materials and their applications: recruited researchers will benefit from a complete set of equipment and expertise enabling them to develop advanced knowledge in KETs and strategic thematic areas for the EU and to convert it into products for economic and social benefit. The effective research methodology used by the partners and the mutual exploitation of their complementary competences have been successfully experienced in the past in long term common research cooperation and in on-going common projects, including a Marie Curie ITN

Keywords: glass,glass ceramics,advanced ceramics and composites,biomaterials,materials for energy and ict,environment friendly materials,inter sectorial training
Development of a novel method for a simultaneous production of 2-butene and degradable polyesters from biomass (SE)

Framework: Horizon 2020 — Excellent Science
Programme: Marie-Sklodowska-Curie Actions
Theme: Marie Skłodowska-Curie
Destination: Individual Fellowships (IF)-Global Fellowships
Action type: Marie Skłodowska-Curie actions
Panel: Chemistry
Countries involved: Sweden,United States
Call: Individual Fellowships (H2020-MSCA-IF-2020, 2020)
Start date: 2022-03-01
End date: 2025-02-28
Duration (months): 36.0
Total project cost (EUR): 261192.0
EU funding (EUR): 261192.0
Participants: 2
Project status: SIGNED

A high dependence of chemical industry on crude oil raises multiple concerns due to its limited supply and an environmental and politico-economic impact. Thus, the development of more sustainable and renewable systems for the production of fuels and materials is highly desirable. Biomass is an available and renewable source of an organic matter. Production of bio-based fuels and chemicals results in a carbon-neutral cycle. Lignocellulosic biomass or woody biomass is a non-edible source of biomass. It makes lignocellulose an especially attractive source for the production of chemicals, fuels and materials, since it does not directly compete with the production of food. Lignocellulose is in essence a mixture of three classes of polymers: cellulose (38–50 wt%), hemicellulose (23–32 wt%) and lignin (15–40 wt%). Cellulose is a polysaccharide with a regular, semi-crystalline structure; hemicellulose is an amorphous irregular polysaccharide comprising of xylose, galactose, arabinose, mannose, etc. Among these three components lignin stands out as being the largest source of naturally occurring aromatics. Lignin has a complex and inhomogeneous structure. That is why unlike carbohydrate part of biomass (cellulose and hemicellulose), lignin finds a limited application in the chemical industry. In the current project we aim to develop an integrated process for the simultaneous production of degradable aromatic polyesters and 2-butene from lignin. This will be achieved in a three-step process. First, we will implement a reductive catalytic fractionation of wood to generate iso-eugenol and propenyl syringol from lignin . We envision to further transform these products into biphenolic compounds and butene via a metathesis reaction. In the final step we will convert bisphenols into polyesters via a condensation polymerization with maleic and succinic anhydrides.

Keywords: lignin,catalysis,polyesters,light olefins,metathesis,catalytic fractionation of woody biomass,bio based materials