Data-Gouv-ML/pcri-informations-sur-les-projets-laureats huggingface dataset:
Advanced Zeolite Catalysis for Sustainable Biorefinery to Produce Value-added Chemicals (ZEOBIOCHEM)

Framework: Horizon 2020 — Excellent Science
Programme: Marie-Sklodowska-Curie Actions
Theme: Marie Skłodowska-Curie
Destination: Research and innovation staff exchange
Action type: Marie Skłodowska-Curie actions
Panel: Information Science and Engineering
Countries involved: Spain,Hungary,China,Thailand,Hungary,Denmark,United Kingdom,Japan,United Kingdom,United Kingdom
Call: Research and Innovation Staff Exchange (H2020-MSCA-RISE-2019, 2019)
Start date: 2019-12-01
End date: 2025-09-30
Duration (months): 70.0
Total project cost (EUR): 1205200.0
EU funding (EUR): 874000.0
Participants: 16
Project status: SIGNED

The EU's long-term strategy, ‘A Clean Planet for All’, is underpinned by a bioeconomy, with biorefineries playing an important role. Aiming to contribute to this strategy, the ZEOBIOCHEM project will develop novel functionalised hierarchical zeolites for highly-efficient biorefinery catalysis, supporting the EU’s grand vision to achieve a climate neutral Europe by 2050. The project will be carried out by an internationally-leading multidisciplinary consortium with a complementary expertise in materials chemistry, heterogeneous catalysis, process intensification, biomass conversion and life cycle sustainability assessment. The research and innovation (R&I) activities will be performed collaboratively beyond the state-of-the-art by world-leading groups in relevant fields from the UK, Spain, Denmark, Hungary, China, Japan and Thailand. These activities will be facilitated by the international and intersectoral staff exchanges involving 200+ person-months, to enable the development of the next-generation biorefinery based on highly-efficient zeolite catalysts. Specifically, the project will develop hierarchical zeolites with optimised functionality (Brønsted, Lewis acidity and/or noble metal catalysts) for the biorefinery cases, such as bio-oil and sugar platforms upgrading and green solvent synthesis. To deliver the project outputs successfully, multidisciplinary expertise is essential. Thus, a high-quality and cross-disciplinary knowledge sharing and networking activities between the project partners will be established to address the R&I activities. The project includes extensive training, knowledge transfer and networking activities which will (i) ensure the development of our researchers with multidisciplinary skills for the future innovation, (ii) enhance the career development of the participants and (iii) maximise the opportunity to establish new networks and strengthen the existing networks, towards the sustainable development of the ZEOBIOCHEM partnership.

Keywords: hierarchical zeolites,catalysis,biorefinery,process intensification,life cycle assessment
Single Molecule Analysis in Nanoscale Reaction Chambers (SIMONANO2)

Framework: Horizon 2020 — Excellent Science
Programme: European Research Council (ERC)
Theme: European research council
Destination: Consolidator grants
Action type: ERC actions
Panel: Physical and analytical chemical sciences
Panel description: Analytical chemistry, chemical theory, physical chemistry/chemical physics
Countries involved: Sweden
Call: ERC CONSOLIDATOR GRANTS (ERC-2020-COG, 2020)
Start date: 2021-02-01
End date: 2026-01-31
Duration (months): 60.0
Total project cost (EUR): 1999550.0
EU funding (EUR): 1999550.0
Participants: 1
Project status: SIGNED

Single Molecule Analysis in Nanoscale Reaction Chambers Imagine that you would measure the average eye color of the population in Sweden. Clearly it would not say much about the colors of the eyes of the inhabitants. To obtain this information, one must of course study them individually. The same holds true for complex biological molecules, especially proteins, which may exist in many different configurations that cannot be resolved in an ensemble measurement. Heterogeneities in biomolecular structure and function limit our understanding of biology. To advance further it is vital that we study biomolecules individually. For proteins this is highly challenging since it must be done in a non-invasive manner under conditions similar to their native environment. The SIMONANO project aims to develop a new platform for single molecule analysis which provides essential advantages. Proteins will be controllably loaded into solid nanoscale chambers, thereby eliminating the need of field gradient forces or surface immobilization. Furthermore, the proteins are entrapped at physiological conditions and small ligands can still access them quickly. Most importantly, the content is regulated on the single molecule level, i.e. proteins can be controllably loaded one at a time and different types of proteins can be introduced sequentially. Advanced (but established) fluorescence microscopy techniques will be used to detect the proteins and analyze their reactions. The possibility to reliably entrap any desired number of proteins under physiological conditions and study their reactions will provide great scientific advancements in the life sciences. Once developed in this project, the nanoscale reaction chambers can become a tool used by biologists worldwide, which will advance our understanding of life on the molecular level. This will in turn lead to new applications in biotechnology and medicine.

Keywords: nanopores,polymer brushes,electrokinetics
Finding Endometriosis using Machine Learning (FEMaLe)

Framework: Horizon 2020 — Societal Challenges
Programme: Health, demographic change and wellbeing
Theme: Active ageing and self-management of health
Action type: Research and Innovation actions
Countries involved: Hungary,Sweden,Norway,Serbia,Türkiye,Latvia,France,Denmark,United Kingdom,United Kingdom,United Kingdom,Denmark
Call: Personalised early risk prediction, prevention and intervention based on Artificial Intelligence and Big Data technologies (H2020-SC1-DTH-2020-1, 2020)
Start date: 2021-01-01
End date: 2024-12-31
Duration (months): 48.0
Total project cost (EUR): 5944134.5
EU funding (EUR): 5944134.5
Participants: 17
Project status: SIGNED

The framework 'P4 Medicine' (predictive, preventative, personalized, participatory) was developed to detect and prevent disease through close monitoring, deep statistical analysis, biomarker testing, and patient health coaching to best use the limited healthcare resources and produce maximum benefit for all patients. However, we have seen only few feasible examples over the past 10 years. The Finding Endometriosis using Machine Learning (FEMaLe) project will revitalise the concept to develop and demonstrate the Scalable Multi-Omics Platform (SMOP) that converts multi-omic person population datasets into a personalised predictive model to improve intervention along the continuum of care for people with endometriosis. We will design, validate and implement a comprehensive model for the detection and management of people with endometriosis to facilitate shared decision making between the patient and the healthcare provider, enable the delivery of precision medicine, and drive new discoveries in endometriosis treatment to deliver novel therapies and improve quality of life for patients. We will rely on participatory processes, advanced computer sciences, state-of-the-art technologies, and patient-shared data to deliver: 1) mobile health app for people with endometriosis, 2) three clinical decision support (CDS) tools for targeted healthcare providers (risk stratification tool for general practitioners, multi-marker signature tool for gynaecologists, and non-invasive diagnostic tool for radiologist), and 3) computer vision-based software tool for real time augmented reality guided surgery of endometriosis. Health maintenance organisations (HMO) expect to be able to reduce overall cost of treatment by at least 20%, while improving patient outcomes, using CDS tools. The SMOP will be based on open protocol, embedded in all ethical and legal frameworks, to enable tailored and personalised usage to improve the lives of patients across Europe beyond the project period.

Keywords: advanced predictive models,complex multifactorial disease,shared decision making
Cloud-based Rapid Elastic MAnufacturing (CREMA)

Framework: Horizon 2020 — Industrial Leadership
Programme: Information and Communication Technologies
Action type: Research and Innovation actions
Countries involved: Austria,Germany,Spain,Germany,Germany,Germany,Spain,Germany,Belgium,Netherlands,United Kingdom,United Kingdom,United Kingdom,United Kingdom,United Kingdom,United Kingdom
Call: Process optimisation of manufacturing assets (H2020-FoF-2014, 2014)
Start date: 2015-01-01
End date: 2017-12-31
Duration (months): 36.0
Total project cost (EUR): 5332095.0
EU funding (EUR): 5324720.0
Participants: 23
Project status: CLOSED

Due to the proliferation of ICT Technologies, manufacturing industry is undergoing substantial transformation in terms of HW but also in terms of Cyber-Physical Production Systems and the SW and services used within production environments. In parallel, the manufacturing processes of the future are changing and need to be highly flexible and dynamic in order to satisfy customer demands for, e.g. large series production, mass customization, or changing orders. To keep pace with the needs of the manufacturing industry of the future, in Manufacturing 4.0 companies need to flexibly react to these demands and be able to offer production capacities in a rapid way. Thus companies looking for manufacturing capacity need to be supported by the means to find these capacities, configure them, and integrate them into their own manufacturing processes. To achieve this, one obvious approach is to port successful concepts from the field of Everything-as-a-Service (XaaS) and Cloud computing to manufacturing to mirror agile collaboration through flexible and scalable manufacturing processes: • Leasing and releasing manufacturing assets in an on-demand, utility-like fashion • Rapid elasticity through scaling leased assets up and down if necessary • Pay-per-use through metered service Applying these principles, Cloud manufacturing can move manufacturing processes from production-oriented to service-oriented networks by modelling single manufacturing assets as services in a similar way as SaaS or PaaS solutions. By modelling all process steps and manufacturing assets as services it is possible to realize cross-organization manufacturing orchestrations and integrate distributed resources and ultimately manufacture products more efficiently. While the theoretical foundations for Cloud manufacturing are manifest there are no proven tools and technologies exist in the market - CREMA aims to change this fact by providing Cloud-based Rapid Elastic Manufacturing based on SaaS and Cloud model

Keywords: cloud manufacturing,elastic processes
Atomic layer deposition of two-dimensional transition metal dichalcogenide nanolayers (ALDof 2DTMDs)

Framework: Horizon 2020 — Excellent Science
Programme: European Research Council (ERC)
Theme: European research council
Destination: Consolidator grants
Action type: ERC actions
Panel: Synthetic Chemistry and Materials
Panel description: New materials and new synthetic approaches, structure-properties relations, solid state chemistry, molecular architecture, organic chemistry
Countries involved: Netherlands
Call: ERC Consolidator Grant (ERC-2014-CoG, 2014)
Start date: 2015-08-01
End date: 2020-12-31
Duration (months): 65.0
Total project cost (EUR): 1968708.75
EU funding (EUR): 1968708.75
Participants: 1
Project status: CLOSED

Two-dimensional transition metal dichalcogenides (2D-TMDs) are an exciting class of new materials. Their ultrathin body, optical band gap and unusual spin and valley polarization physics make them very promising candidates for a vast new range of (opto-)electronic applications. So far, most experimental work on 2D-TMDs has been performed on exfoliated flakes made by the ‘Scotch tape’ technique. The major next challenge is the large-area synthesis of 2D-TMDs by a technique that ultimately can be used for commercial device fabrication. Building upon pure 2D-TMDs, even more functionalities can be gained from 2D-TMD alloys and heterostructures. Theoretical work on these derivates reveals exciting new phenomena, but experimentally this field is largely unexplored due to synthesis technique limitations. The goal of this proposal is to combine atomic layer deposition with plasma chemistry to create a novel surface-controlled, industry-compatible synthesis technique that will make large area 2D-TMDs, 2D-TMD alloys and 2D-TMD heterostructures a reality. This innovative approach will enable systematic layer dependent studies, likely revealing exciting new properties, and provide integration pathways for a multitude of applications. Atomistic simulations will guide the process development and, together with in- and ex-situ analysis, increase the understanding of the surface chemistry involved. State-of-the-art high resolution transmission electron microscopy will be used to study the alloying process and the formation of heterostructures. Luminescence spectroscopy and electrical characterization will reveal the potential of the synthesized materials for (opto)-electronic applications. The synergy between the excellent background of the PI in 2D materials for nanoelectronics and the group’s leading expertise in ALD and plasma science is unique and provides an ideal stepping stone to develop the synthesis of large-area 2D-TMDs and derivatives.

Keywords: 2d materials,atomic layer deposition,nanoelectronics,plasma chemistry
Market study for Solenco Power Box, a zero-carbon small-scale local energy storage product. Potential application as missing link in residential PV uptake (residential and commercial buildings) (SOLENCO)

Framework: Horizon 2020 — Societal Challenges
Programme: Secure, clean and efficient energy
Action type: SME instrument
Countries involved: Belgium
Call: Stimulating the innovation potential of SMEs for a low carbon energy system (H2020-SMEINST-1-2015, 2015)
Start date: 2015-07-01
End date: 2015-10-31
Duration (months): 4.0
Total project cost (EUR): 71429.0
EU funding (EUR): 50000.0
Participants: 1
Project status: CLOSED

The SOLENCO POWER BOX TM (SPB) eliminates the uncertainty in supply and cost of energy for family homes and commercial buildings by using small-scale energy storage based on hydrogen technology, in combination with renewable energy sources (wind and solar PV). The SPBTM is a Decentralized Power Production & Storage Device and enables a shift in the energy model from central distribution to local generation and distribution while stabilizing the grid and hence enabling higher penetration (up to 100%) solar PV energy. The product enables EU households to be 100% independent from the grid and 100% independent from price fluctuations in fossil fuels. It is the missing link for mass uptake of residential PV solar and your insurance against rising energy prices. The goal of Phase 1 of the SME instrument is to define, prioritize, and validate the application, value proposition and target market(s) for the product to draft an elaborated Business and Financial Plan. The objective is to use this Business and Financial Plan to convince potential investors to invest additional equity capital in the SPBTM to fund the mass-production and mass-commercialization of the product.

Keywords: zero carbon,small scale energy storage,business model,business plan,decentralized power production and storage device
An intelligent inspection system for improved and efficient power line cable maintenance (Intel-Line)

Framework: Horizon 2020 — Societal Challenges
Action type: Innovation actions
Countries involved: Türkiye,France,United Kingdom,United Kingdom
Call: Fast Track to Innovation Pilot (H2020-FTIPilot-2015-1, 2015)
Start date: 2016-10-01
End date: 2019-09-30
Duration (months): 36.0
Total project cost (EUR): 2529881.25
EU funding (EUR): 1980823.88
Participants: 4
Project status: CLOSED

Our prime objective is to generate in excess of €22million profits for the participating companies by addressing a European need for the safe maintenance and inspection of power lines. Intel-Line is an intelligent automatic, inspection system using advanced ultrasonics and incorporating wireless transmission and energy harvesting to provide a ‘fit and forget’ condition monitoring and failure alarm system. In an industry worth €210Bn and employing 1.1 million the supply of energy is vital, and with over 2million Km of overhead cable in Europe, any failure or interruption of power can cause catastrophic damage to the surroundings and to the industrial infrastructure and output. Current inspection techniques are expensive, time consuming and hazardous. Intel-Line will enable maintenance to be scheduled, reducing unnecessary maintenance costs and unexpected breakdowns. This will increase the availability of the power lines reducing the downtime. Nesne, a Turkish manufacturer of innovative electronic components is seeking investment of €2m in order to enhance and upgrade our current prototype system, scale up production, produce training materials and provide ‘in-service’ demonstrations to prospective clients. MCI will enhance the software/firmware and upgrade the signal processing, PI are a specialist NDT company who will use, market and distribute Intel-Line. BIC will augment our technology to rapidly progress to commercialisation. Using Intel-Line could save EU Power line distributors in excess of €150 million/yr. End users of Intel-Line will see an ROI within 6 months. The project participants will cumulatively increase their turnover by in excess of €40 million creating at least 40 new jobs. Our ultimate ambition is for Intel-Line to be fitted to cables for new erections and retro fitted during cable repair and maintenance providing the ultimate fit and forget system to guarantee the electricity supply.

Keywords: ultrasound energy harvesting power cables networks inspection automatic non destructive testing ndt wireless transmission condition monitoring structural health maintenance
New level of theoretical precision for LHC Run 2 and beyond (NNLOforLHC2)

Framework: Horizon 2020 — Excellent Science
Programme: European Research Council (ERC)
Theme: European research council
Destination: Consolidator grants
Action type: ERC actions
Panel: Fundamental constituents of matter
Panel description: Particle, nuclear, plasma, atomic, molecular, gas, and optical physics
Countries involved: United Kingdom
Call: ERC Consolidator Grant (ERC-2015-CoG, 2015)
Start date: 2016-10-01
End date: 2022-09-30
Duration (months): 72.0
Total project cost (EUR): 1713983.0
EU funding (EUR): 1713983.0
Participants: 1
Project status: CLOSED

LHC Run 2 will operate at significantly higher centre of mass energy and could offer an unprecedented insight into the largely unexplored region of TeV physics. The usefulness of this wealth of forthcoming high-quality data would, however, be strongly dependent on the availability of theoretical predictions with matching accuracy. The current state of the art for hadron collider calculations is NNLO and calculations for the most interesting 2-to-2 processes have already been performed. The utility of these theoretical results is, however, limited: they are either incomplete or have been applied to a limited number of observables. Moreover, their flexibility is limited which prevents their widespread use in experimental analyses. The goal of this proposal is to turn high-precision NNLO results into a LHC mainstream. We propose the following multi-prong approach, which addresses all main shortcoming of currently available 2-to-2 NNLO results: First, create a library of partonic events for the processes with top quarks and jets. A user-friendly interface will allow direct analysis by the user. Such a system would be novel, especially at NNLO, and once operational could be extended to any 2-to-2 NNLO process. Second, develop matching of NNLO calculations for processes with coloured partons to parton showers. This is an outstanding problem, which naturally fits the existing fixed order/shower expertise in the Cavendish. Its solution will unleash the potential of NNLO calculations and will make them truly useful for experimental analyses. Third, work on specific phenomenological analyses that make use, and promote, the precision results developed by the group. Last but not least, pursue novel techniques for computing 2-to-3 two-loop amplitudes, which is the bottleneck for advancing beyond the current frontier in LHC processes; use this to compute the 3 jet NNLO cross-section at the LHC. Such a result will be a first-ever and a great achievement for the LHC program.

Keywords: hadron colliders,nnlo calculations,phenomenology,standard model,physics beyond the standard model
Inherited disfunctions of brain microcirculation (V-EPC)

Framework: Horizon 2020 — Excellent Science
Programme: European Research Council (ERC)
Theme: European research council
Destination: Advanced grants
Action type: ERC actions
Panel: Physiology in Health, Disease and Ageing
Panel description: Organ and tissue physiology, comparative physiology, physiology of ageing, pathophysiology, interorgan and tissue communication, endocrinology, nutrition, metabolism, interaction with the microbiome, non-communicable diseases including cancer (and except disorders of the nervous system and immunity-related diseases)
Countries involved: Italy
Call: ERC Advanced Grant (ERC-2016-ADG, 2016)
Start date: 2017-09-01
End date: 2022-08-31
Duration (months): 60.0
Total project cost (EUR): 2451250.0
EU funding (EUR): 2451250.0
Participants: 1
Project status: CLOSED

Cerebral cavernous malformations (CCM) is a disease characterized by focal capillary-venous cavernomas located in the central nervous system. These malformations are fragile and bleed leading to seizures, paralysis and cerebral hemorrhages. The familial form of CCM is due to loss of function mutations of anyone of three genes called Ccm1, Ccm 2 and Ccm 3. Open skull surgery is the only possible therapy since effective medical treatments are still missing. In mouse models of CCM and human patients’ specimens, endothelial cells (ECs) lining the cavernomas co-express endothelial, mesenchymal and stem cell markers. These features combined to high cell proliferation are reminiscent of tumor initiating cells. In preliminary work, we isolated a rare population of highly proliferative ECs (V-EPC) intermingled with the normal vascular endothelium in vivo and co-expressing endothelial and stem cell markers. Our working hypothesis is that CCM originates from the clonal expansion of these V-EPCs that are more sensitive than mature endothelium to inactivation of Ccm genes. This possibility is supported by the observation that V-EPCs present a comparable gene expression profile and selective markers than the ECs lining CCM cavernomas. The project is divided in three related objectives: 1) To define the nature and functions of V-EPCs in normal vessels; 2) to test the capacity of V-EPCs to trigger the formation of CCM by undergoing clonal expansion in vivo; 3) to test whether targeting V-EPCs or inducing V-EPC maturation by pharmacological tools reduce or prevent the formation of CCM. If successful this work will introduce novel concepts in vascular biology such as: 1) the presence of resident V-EPCs that are differentially sensitive than mature endothelium to the same genetic mutation and angiogenic stimuli; 2) the identification of specific signaling pathways in V-EPC that explain their high proliferative potential; 3) the identification of V-EPC as therapeutic targets for CCM.