Project acronym 3D-OA-HISTO
Project Development of 3D Histopathological Grading of Osteoarthritis
Researcher (PI) Simo Jaakko Saarakkala
Host Institution (HI) OULUN YLIOPISTO
Country Finland
Call Details Starting Grant (StG), LS7, ERC-2013-StG
Summary "Background: Osteoarthritis (OA) is a common musculoskeletal disease occurring worldwide. Despite extensive research, etiology of OA is still poorly understood. Histopathological grading (HPG) of 2D tissue sections is the gold standard reference method for determination of OA stage. However, traditional 2D-HPG is destructive and based only on subjective visual evaluation. These limitations induce bias to clinical in vitro OA diagnostics and basic research that both rely strongly on HPG.
Objectives: 1) To establish and validate the very first 3D-HPG of OA based on cutting-edge nano/micro-CT (Computed Tomography) technologies in vitro; 2) To use the established method to clarify the beginning phases of OA; and 3) To validate 3D-HPG of OA for in vivo use.
Methods: Several hundreds of human osteochondral samples from patients undergoing total knee arthroplasty will be collected. The samples will be imaged in vitro with nano/micro-CT and clinical high-end extremity CT devices using specific contrast-agents to quantify tissue constituents and structure in 3D in large volume. From this information, a novel 3D-HPG is developed with statistical classification algorithms. Finally, the developed novel 3D-HPG of OA will be applied clinically in vivo.
Significance: This is the very first study to establish 3D-HPG of OA pathology in vitro and in vivo. Furthermore, the developed technique hugely improves the understanding of the beginning phases of OA. Ultimately, the study will contribute for improving OA patients’ quality of life by slowing the disease progression, and for providing powerful tools to develop new OA therapies."
Summary
"Background: Osteoarthritis (OA) is a common musculoskeletal disease occurring worldwide. Despite extensive research, etiology of OA is still poorly understood. Histopathological grading (HPG) of 2D tissue sections is the gold standard reference method for determination of OA stage. However, traditional 2D-HPG is destructive and based only on subjective visual evaluation. These limitations induce bias to clinical in vitro OA diagnostics and basic research that both rely strongly on HPG.
Objectives: 1) To establish and validate the very first 3D-HPG of OA based on cutting-edge nano/micro-CT (Computed Tomography) technologies in vitro; 2) To use the established method to clarify the beginning phases of OA; and 3) To validate 3D-HPG of OA for in vivo use.
Methods: Several hundreds of human osteochondral samples from patients undergoing total knee arthroplasty will be collected. The samples will be imaged in vitro with nano/micro-CT and clinical high-end extremity CT devices using specific contrast-agents to quantify tissue constituents and structure in 3D in large volume. From this information, a novel 3D-HPG is developed with statistical classification algorithms. Finally, the developed novel 3D-HPG of OA will be applied clinically in vivo.
Significance: This is the very first study to establish 3D-HPG of OA pathology in vitro and in vivo. Furthermore, the developed technique hugely improves the understanding of the beginning phases of OA. Ultimately, the study will contribute for improving OA patients’ quality of life by slowing the disease progression, and for providing powerful tools to develop new OA therapies."
Max ERC Funding
1 500 000 €
Duration
Start date: 2014-02-01, End date: 2019-01-31
Project acronym 3Ps
Project 3Ps
Plastic-Antibodies, Plasmonics and Photovoltaic-Cells: on-site screening of cancer biomarkers made possible
Researcher (PI) Maria Goreti Ferreira Sales
Host Institution (HI) INSTITUTO SUPERIOR DE ENGENHARIA DO PORTO
Country Portugal
Call Details Starting Grant (StG), LS7, ERC-2012-StG_20111109
Summary This project presents a new concept for the detection, diagnosis and monitoring of cancer biomarker patterns in point-of-care. The device under development will make use of the selectivity of the plastic antibodies as sensing materials and the interference they will play on the normal operation of a photovoltaic cell.
Plastic antibodies will be designed by surface imprinting procedures. Self-assembled monolayer and molecular imprinting techniques will be merged in this process because they allow the self-assembly of nanostructured materials on a “bottom-up” nanofabrication approach. A dye-sensitized solar cell will be used as photovoltaic cell. It includes a liquid interface in the cell circuit, which allows the introduction of the sample (also in liquid phase) without disturbing the normal cell operation. Furthermore, it works well with rather low cost materials and requires mild and easy processing conditions. The cell will be equipped with plasmonic structures to enhance light absorption and cell efficiency.
The device under development will be easily operated by any clinician or patient. It will require ambient light and a regular multimeter. Eye detection will be also tried out.
Summary
This project presents a new concept for the detection, diagnosis and monitoring of cancer biomarker patterns in point-of-care. The device under development will make use of the selectivity of the plastic antibodies as sensing materials and the interference they will play on the normal operation of a photovoltaic cell.
Plastic antibodies will be designed by surface imprinting procedures. Self-assembled monolayer and molecular imprinting techniques will be merged in this process because they allow the self-assembly of nanostructured materials on a “bottom-up” nanofabrication approach. A dye-sensitized solar cell will be used as photovoltaic cell. It includes a liquid interface in the cell circuit, which allows the introduction of the sample (also in liquid phase) without disturbing the normal cell operation. Furthermore, it works well with rather low cost materials and requires mild and easy processing conditions. The cell will be equipped with plasmonic structures to enhance light absorption and cell efficiency.
The device under development will be easily operated by any clinician or patient. It will require ambient light and a regular multimeter. Eye detection will be also tried out.
Max ERC Funding
998 584 €
Duration
Start date: 2013-02-01, End date: 2018-01-31
Project acronym AI-PREVENT
Project A nationwide artificial intelligence risk assessment for primary prevention of cardiometabolic diseases
Researcher (PI) Andrea Ganna
Host Institution (HI) HELSINGIN YLIOPISTO
Country Finland
Call Details Starting Grant (StG), LS7, ERC-2020-STG
Summary Diabetes, stroke and coronary artery disease (cardiometabolic diseases) are the leading cause of death in Europe. Given that effective pharmacological and lifestyle interventions are available, it is important to identify high risk individuals at an early stage. Traditionally, this is done using clinical prediction models. However, the established models have substantial limitations: they are often used by doctors only when an underlying disease is already suspected, they are not developed on updated nationally-representative data and they require time-consuming clinical measurements. Thus, a substantial part of the population is not provided with risk assessment. I propose to revolutionize the existing approaches to primary prevention by providing risk assessment of cardiometabolic diseases before an individual even steps into the doctor’s office for a visit. To this end my project has three main objectives:
1) Development of artificial intelligence (AI) approaches to model health trajectories based on nationwide registry data on medications, diagnoses, familial risk and socio-demographic information to obtain accurate risk estimates for cardiometabolic disease. I will integrate high quality data from selected countries that have long traditions of registry data (Finland and Sweden, over 7.5 million individuals).
2) To identify health trajectories that maximize the clinical utility of genetic scores by integrating genetic and registry-based data on > 1 million people to identify subgroups of individuals for whom genetic information might improve risk prediction.
3) Validation of AI and genetic-based risk assessment as first-stage screening via a clinical study in 2800 individuals.
My project leverages the latest developments in AI and high-quality data of unprecedented scale to deliver a paradigm shift with important public health consequences by potentially changing the way cardiometabolic disease risk is assessed.
Summary
Diabetes, stroke and coronary artery disease (cardiometabolic diseases) are the leading cause of death in Europe. Given that effective pharmacological and lifestyle interventions are available, it is important to identify high risk individuals at an early stage. Traditionally, this is done using clinical prediction models. However, the established models have substantial limitations: they are often used by doctors only when an underlying disease is already suspected, they are not developed on updated nationally-representative data and they require time-consuming clinical measurements. Thus, a substantial part of the population is not provided with risk assessment. I propose to revolutionize the existing approaches to primary prevention by providing risk assessment of cardiometabolic diseases before an individual even steps into the doctor’s office for a visit. To this end my project has three main objectives:
1) Development of artificial intelligence (AI) approaches to model health trajectories based on nationwide registry data on medications, diagnoses, familial risk and socio-demographic information to obtain accurate risk estimates for cardiometabolic disease. I will integrate high quality data from selected countries that have long traditions of registry data (Finland and Sweden, over 7.5 million individuals).
2) To identify health trajectories that maximize the clinical utility of genetic scores by integrating genetic and registry-based data on > 1 million people to identify subgroups of individuals for whom genetic information might improve risk prediction.
3) Validation of AI and genetic-based risk assessment as first-stage screening via a clinical study in 2800 individuals.
My project leverages the latest developments in AI and high-quality data of unprecedented scale to deliver a paradigm shift with important public health consequences by potentially changing the way cardiometabolic disease risk is assessed.
Max ERC Funding
1 550 057 €
Duration
Start date: 2021-01-01, End date: 2025-12-31
Project acronym Bi3BoostFlowBat
Project Bioinspired, biphasic and bipolar flow batteries with boosters for sustainable large-scale energy storage
Researcher (PI) Pekka PELJO
Host Institution (HI) TURUN YLIOPISTO
Country Finland
Call Details Starting Grant (StG), PE8, ERC-2020-STG
Summary To satisfy our growing energy demand while reducing reliance on fossil fuels, a switch to renewable energy sources is vital. The intermittent nature of the latter means innovations in energy storage technology is a key grand challenge. Cost and sustainability issues currently limit the widespread use of electrochemical energy storage technologies, such as lithium ion and redox flow batteries. As the scale for energy storage is simply enormous, the only option is to look for abundant materials. However, compounds that fulfil the extensive requirements entailed at low cost has yet to be reported. While it is possible that the holy grail of energy storage will be found, for example by advanced computational tools and machine learning to design “perfect” abundant molecules, a more flexible, innovative solution to sustainable and cost-effective large-scale energy storage is required. Bi3BoostFlowBat will develop game changing strategies to widen the choice of compounds utilizable for batteries to simultaneously satisfy the requirements for low cost, optimal redox potentials, high solubility and stability in all conditions. The aim of this project is to develop cost-efficient batteries by using solid boosters and by eliminating cross over. Two approaches will be pursued for cross-over elimination 1) bio-inspired polymer batteries, where cross-over of solubilized polymers is prevented by size-exclusion membranes and 2) biphasic emulsion flow batteries, where redox species are transferred to oil phase droplets upon charge. Third research direction focuses on systems to maintain a pH gradient, to allow operation of differential pH systems to improve the cell voltages. Limits of different approaches will be explored by taking an electrochemical engineering approach to model the performance of different systems and by validating the models experimentally. This work will chart the route towards the future third generation battery technologies for the large-scale energy storage.
Summary
To satisfy our growing energy demand while reducing reliance on fossil fuels, a switch to renewable energy sources is vital. The intermittent nature of the latter means innovations in energy storage technology is a key grand challenge. Cost and sustainability issues currently limit the widespread use of electrochemical energy storage technologies, such as lithium ion and redox flow batteries. As the scale for energy storage is simply enormous, the only option is to look for abundant materials. However, compounds that fulfil the extensive requirements entailed at low cost has yet to be reported. While it is possible that the holy grail of energy storage will be found, for example by advanced computational tools and machine learning to design “perfect” abundant molecules, a more flexible, innovative solution to sustainable and cost-effective large-scale energy storage is required. Bi3BoostFlowBat will develop game changing strategies to widen the choice of compounds utilizable for batteries to simultaneously satisfy the requirements for low cost, optimal redox potentials, high solubility and stability in all conditions. The aim of this project is to develop cost-efficient batteries by using solid boosters and by eliminating cross over. Two approaches will be pursued for cross-over elimination 1) bio-inspired polymer batteries, where cross-over of solubilized polymers is prevented by size-exclusion membranes and 2) biphasic emulsion flow batteries, where redox species are transferred to oil phase droplets upon charge. Third research direction focuses on systems to maintain a pH gradient, to allow operation of differential pH systems to improve the cell voltages. Limits of different approaches will be explored by taking an electrochemical engineering approach to model the performance of different systems and by validating the models experimentally. This work will chart the route towards the future third generation battery technologies for the large-scale energy storage.
Max ERC Funding
1 499 880 €
Duration
Start date: 2021-01-01, End date: 2025-12-31
Project acronym Brain Health Toolbox
Project The Brain Health Toolbox: Facilitating personalized decision-making for effective dementia prevention
Researcher (PI) Alina Gabriela SOLOMON
Host Institution (HI) ITA-SUOMEN YLIOPISTO
Country Finland
Call Details Starting Grant (StG), LS7, ERC-2018-STG
Summary Preventing dementia and Alzheimer disease (AD) is a global priority. Previous single-intervention failures stress the critical need for a new multimodal preventive approach in these complex multifactorial conditions. The Brain Health Toolbox is designed to create a seamless continuum from accurate dementia prediction to effective prevention by i) developing the missing disease models and prediction tools for multimodal prevention; ii) testing them in actual multimodal prevention trials; and iii) bridging the gap between non-pharmacological and pharmacological approaches by designing a combined multimodal prevention trial based on a new European adaptive trial platform. Disease models and prediction tools will be multi-dimensional, i.e. a broad range of risk factors and biomarker types, including novel markers. An innovative machine learning method will be used for pattern identification and risk profiling to highlight most important contributors to an individual’s overall risk level. This is crucial for early identification of individuals with high dementia risk and/or high likelihood of specific brain pathologies, quantifying an individual’s prevention potential, and longitudinal risk and disease monitoring, also beyond trial duration. Three Toolbox test scenarios are considered: use for selecting target populations, assessing heterogeneity of intervention effects, and use as trial outcome. The project is based on a unique set-up aligning several new multimodal lifestyle trials aiming to adapt and test non-pharmacological interventions to different geographic, economic and cultural settings, with two reference libraries (observational - large datasets; and interventional - four recently completed pioneering multimodal lifestyle prevention trials). The Brain Health Toolbox covers the entire continuum from general populations to patients with preclinical/prodromal disease stages, and will provide tools for personalized decision-making for dementia prevention.
Summary
Preventing dementia and Alzheimer disease (AD) is a global priority. Previous single-intervention failures stress the critical need for a new multimodal preventive approach in these complex multifactorial conditions. The Brain Health Toolbox is designed to create a seamless continuum from accurate dementia prediction to effective prevention by i) developing the missing disease models and prediction tools for multimodal prevention; ii) testing them in actual multimodal prevention trials; and iii) bridging the gap between non-pharmacological and pharmacological approaches by designing a combined multimodal prevention trial based on a new European adaptive trial platform. Disease models and prediction tools will be multi-dimensional, i.e. a broad range of risk factors and biomarker types, including novel markers. An innovative machine learning method will be used for pattern identification and risk profiling to highlight most important contributors to an individual’s overall risk level. This is crucial for early identification of individuals with high dementia risk and/or high likelihood of specific brain pathologies, quantifying an individual’s prevention potential, and longitudinal risk and disease monitoring, also beyond trial duration. Three Toolbox test scenarios are considered: use for selecting target populations, assessing heterogeneity of intervention effects, and use as trial outcome. The project is based on a unique set-up aligning several new multimodal lifestyle trials aiming to adapt and test non-pharmacological interventions to different geographic, economic and cultural settings, with two reference libraries (observational - large datasets; and interventional - four recently completed pioneering multimodal lifestyle prevention trials). The Brain Health Toolbox covers the entire continuum from general populations to patients with preclinical/prodromal disease stages, and will provide tools for personalized decision-making for dementia prevention.
Max ERC Funding
1 498 268 €
Duration
Start date: 2019-02-01, End date: 2024-01-31
Project acronym CapBed
Project Engineered Capillary Beds for Successful Prevascularization of Tissue Engineering Constructs
Researcher (PI) Rogerio Pedro Lemos de Sousa Pirraco
Host Institution (HI) UNIVERSIDADE DO MINHO
Country Portugal
Call Details Starting Grant (StG), PE8, ERC-2018-STG
Summary The demand for donated organs vastly outnumbers the supply, leading each year to the death of thousands of people and the suffering of millions more. Engineered tissues and organs following Tissue Engineering approaches are a possible solution to this problem. However, a prevascularization solution to irrigate complex engineered tissues and assure their survival after transplantation is currently elusive. In the human body, complex organs and tissues irrigation is achieved by a network of blood vessels termed capillary bed which suggests such a structure is needed in engineered tissues. Previous approaches to engineer capillary beds reached different levels of success but none yielded a fully functional one due to the inability in simultaneously addressing key elements such as correct angiogenic cell populations, a suitable matrix and dynamic conditions that mimic blood flow.
CapBed aims at proposing a new technology to fabricate in vitro capillary beds that include a vascular axis that can be anastomosed with a patient circulation. Such capillary beds could be used as prime tools to prevascularize in vitro engineered tissues and provide fast perfusion of those after transplantation to a patient. Cutting edge techniques will be for the first time integrated in a disruptive approach to address the requirements listed above. Angiogenic cell sheets of human Adipose-derived Stromal Vascular fraction cells will provide the cell populations that integrate the capillaries and manage its intricate formation, as well as the collagen required to build the matrix that will hold the capillary beds. Innovative fabrication technologies such as 3D printing and laser photoablation will be used for the fabrication of the micropatterned matrix that will allow fluid flow through microfluidics. The resulting functional capillary beds can be used with virtually every tissue engineering strategy rendering the proposed strategy with massive economical, scientific and medical potential
Summary
The demand for donated organs vastly outnumbers the supply, leading each year to the death of thousands of people and the suffering of millions more. Engineered tissues and organs following Tissue Engineering approaches are a possible solution to this problem. However, a prevascularization solution to irrigate complex engineered tissues and assure their survival after transplantation is currently elusive. In the human body, complex organs and tissues irrigation is achieved by a network of blood vessels termed capillary bed which suggests such a structure is needed in engineered tissues. Previous approaches to engineer capillary beds reached different levels of success but none yielded a fully functional one due to the inability in simultaneously addressing key elements such as correct angiogenic cell populations, a suitable matrix and dynamic conditions that mimic blood flow.
CapBed aims at proposing a new technology to fabricate in vitro capillary beds that include a vascular axis that can be anastomosed with a patient circulation. Such capillary beds could be used as prime tools to prevascularize in vitro engineered tissues and provide fast perfusion of those after transplantation to a patient. Cutting edge techniques will be for the first time integrated in a disruptive approach to address the requirements listed above. Angiogenic cell sheets of human Adipose-derived Stromal Vascular fraction cells will provide the cell populations that integrate the capillaries and manage its intricate formation, as well as the collagen required to build the matrix that will hold the capillary beds. Innovative fabrication technologies such as 3D printing and laser photoablation will be used for the fabrication of the micropatterned matrix that will allow fluid flow through microfluidics. The resulting functional capillary beds can be used with virtually every tissue engineering strategy rendering the proposed strategy with massive economical, scientific and medical potential
Max ERC Funding
1 499 940 €
Duration
Start date: 2018-11-01, End date: 2023-10-31
Project acronym DrugComb
Project Informatics approaches for the rational selection of personalized cancer drug combinations
Researcher (PI) Jing TANG
Host Institution (HI) HELSINGIN YLIOPISTO
Country Finland
Call Details Starting Grant (StG), LS7, ERC-2016-STG
Summary Making cancer treatment more personalized and effective is one of the grand challenges in our health care system. However, many drugs have entered clinical trials but so far showed limited efficacy or induced rapid development of resistance. We critically need multi-targeted drug combinations, which shall selectively inhibit the cancer cells and block the emergence of drug resistance. This project will develop mathematical and computational tools to identify drug combinations that can be used to provide personalized and more effective therapeutic strategies that may prevent acquired resistance. Utilizing molecular profiling and pharmacological screening data from patient-derived leukaemia and ovarian cancer samples, I will develop model-based clustering methods for identification of patient subgroups that are differentially responsive to first-line chemotherapy. For patients resistant to chemotherapy, I will develop network modelling approaches to predict the most potential drug combinations by understanding the underlying drug target interactions. The drug combination prediction will be made for each patient and will be validated using a preclinical drug testing platform on patient samples. I will explore the drug combination screen data to identify significant synergy at the therapeutically relevant doses. The drug combination hits will be mapped into signalling networks to infer their mechanisms. Drug combinations with selective efficacy in individual patient samples or in sample subgroups will be further translated into in treatment options by clinical collaborators. This will lead to novel and personalized strategies to treat cancer patients.
Summary
Making cancer treatment more personalized and effective is one of the grand challenges in our health care system. However, many drugs have entered clinical trials but so far showed limited efficacy or induced rapid development of resistance. We critically need multi-targeted drug combinations, which shall selectively inhibit the cancer cells and block the emergence of drug resistance. This project will develop mathematical and computational tools to identify drug combinations that can be used to provide personalized and more effective therapeutic strategies that may prevent acquired resistance. Utilizing molecular profiling and pharmacological screening data from patient-derived leukaemia and ovarian cancer samples, I will develop model-based clustering methods for identification of patient subgroups that are differentially responsive to first-line chemotherapy. For patients resistant to chemotherapy, I will develop network modelling approaches to predict the most potential drug combinations by understanding the underlying drug target interactions. The drug combination prediction will be made for each patient and will be validated using a preclinical drug testing platform on patient samples. I will explore the drug combination screen data to identify significant synergy at the therapeutically relevant doses. The drug combination hits will be mapped into signalling networks to infer their mechanisms. Drug combinations with selective efficacy in individual patient samples or in sample subgroups will be further translated into in treatment options by clinical collaborators. This will lead to novel and personalized strategies to treat cancer patients.
Max ERC Funding
1 500 000 €
Duration
Start date: 2017-06-01, End date: 2022-05-31
Project acronym DynaOmics
Project From longitudinal proteomics to dynamic individualized diagnostics
Researcher (PI) Laura Linnea Maria Elo-Uhlgren
Host Institution (HI) TURUN YLIOPISTO
Country Finland
Call Details Starting Grant (StG), LS7, ERC-2015-STG
Summary Longitudinal omics data hold great promise to improve biomarker detection and enable dynamic individualized predictions. Recent technological advances have made proteomics an increasingly attractive option but clinical longitudinal proteomic datasets are still rare and computational tools for their analysis underdeveloped. The objective of this proposal is to create a roadmap to detect clinically feasible protein markers using longitudinal data and effective computational tools. A biomedical focus is on early detection of Type 1 diabetes (T1D). Specific objectives are:
1) Novel biomarker detector using longitudinal data. DynaOmics introduces novel types of multi-level dynamic markers that are undetectable in conventional single-time cross-sectional studies (e.g. within-individual changes in abundance or associations), develops optimization methods for their robust and reproducible detection within and across individuals, and validates their utility in well-defined samples.
2) Individualized disease risk prediction dynamically. DynaOmics develops dynamic individualized predictive models using the multi-level longitudinal proteome features and novel statistical and machine learning methods that have previously not been used in this context, including joint models of longitudinal and time-to-event data, and one-class classification type techniques.
3) Dynamic prediction of T1D. DynaOmics builds a predictive model of dynamic T1D risk to assist early detection of the disease, which is crucial for developing future therapeutic and preventive strategies. T1D typically involves a relatively long symptom-free period before clinical diagnosis but current tools to predict early T1D risk have restricted power.
The objectives involve innovative and unconventional approaches and address major unmet challenges in the field, having high potential to open new avenues for diagnosis and treatment of complex diseases and fundamentally novel insights towards precision medicine.
Summary
Longitudinal omics data hold great promise to improve biomarker detection and enable dynamic individualized predictions. Recent technological advances have made proteomics an increasingly attractive option but clinical longitudinal proteomic datasets are still rare and computational tools for their analysis underdeveloped. The objective of this proposal is to create a roadmap to detect clinically feasible protein markers using longitudinal data and effective computational tools. A biomedical focus is on early detection of Type 1 diabetes (T1D). Specific objectives are:
1) Novel biomarker detector using longitudinal data. DynaOmics introduces novel types of multi-level dynamic markers that are undetectable in conventional single-time cross-sectional studies (e.g. within-individual changes in abundance or associations), develops optimization methods for their robust and reproducible detection within and across individuals, and validates their utility in well-defined samples.
2) Individualized disease risk prediction dynamically. DynaOmics develops dynamic individualized predictive models using the multi-level longitudinal proteome features and novel statistical and machine learning methods that have previously not been used in this context, including joint models of longitudinal and time-to-event data, and one-class classification type techniques.
3) Dynamic prediction of T1D. DynaOmics builds a predictive model of dynamic T1D risk to assist early detection of the disease, which is crucial for developing future therapeutic and preventive strategies. T1D typically involves a relatively long symptom-free period before clinical diagnosis but current tools to predict early T1D risk have restricted power.
The objectives involve innovative and unconventional approaches and address major unmet challenges in the field, having high potential to open new avenues for diagnosis and treatment of complex diseases and fundamentally novel insights towards precision medicine.
Max ERC Funding
1 499 869 €
Duration
Start date: 2016-06-01, End date: 2022-05-31
Project acronym E-CONTROL
Project "Electric-Field Control of Magnetic Domain Wall Motion and Fast Magnetic Switching: Magnetoelectrics at Micro, Nano, and Atomic Length Scales"
Researcher (PI) Sebastiaan Van Dijken
Host Institution (HI) AALTO KORKEAKOULUSAATIO SR
Country Finland
Call Details Starting Grant (StG), PE3, ERC-2012-StG_20111012
Summary "The aim of the proposed research is to study electric-field induced magnetic phenomena in thin-film ferromagnetic-ferroelectric heterostructures. In particular, the project addresses ferroic order competition and magnetoelectric coupling dynamics at micro, nano, and atomic length scales.
The first part of the project focuses on the dynamics of coupled ferromagnetic-ferroelectric domains and electric-field induced magnetic domain wall motion at sub-nanosecond time scales. For simultaneous imaging of both ferroic domain responses to ultra-short electric-field pulses, the construction of a time-resolved polarization microscope is proposed. The second part relates to finite-size scaling of ferroic domain correlations in continuous films and electric-field control of magnetic effects in patterned nanostructures. Here, the aim is to elucidate the competition between magnetoelectric coupling at ferromagnetic-ferroelectric interfaces and the relevant energy scales within the bulk of ferroic materials. Moreover, electric-field induced domain wall motion in magnetic nanowires is pursued as a viable low-power alternative to current-driven spin-torque effects. Finally, the third part of E-CONTROL aims at visualization of magnetoelectric coupling effects with atomic precision. For this frontier study, the development of in situ transmission electron microscopy (TEM) techniques is proposed. The new measurement method enables the application of local electric fields on cross-sectional specimen during TEM analysis and this is bound to provide unique insights in strain-mediated and charge-modulated coupling mechanisms between ferromagnetic and ferroelectric thin films."
Summary
"The aim of the proposed research is to study electric-field induced magnetic phenomena in thin-film ferromagnetic-ferroelectric heterostructures. In particular, the project addresses ferroic order competition and magnetoelectric coupling dynamics at micro, nano, and atomic length scales.
The first part of the project focuses on the dynamics of coupled ferromagnetic-ferroelectric domains and electric-field induced magnetic domain wall motion at sub-nanosecond time scales. For simultaneous imaging of both ferroic domain responses to ultra-short electric-field pulses, the construction of a time-resolved polarization microscope is proposed. The second part relates to finite-size scaling of ferroic domain correlations in continuous films and electric-field control of magnetic effects in patterned nanostructures. Here, the aim is to elucidate the competition between magnetoelectric coupling at ferromagnetic-ferroelectric interfaces and the relevant energy scales within the bulk of ferroic materials. Moreover, electric-field induced domain wall motion in magnetic nanowires is pursued as a viable low-power alternative to current-driven spin-torque effects. Finally, the third part of E-CONTROL aims at visualization of magnetoelectric coupling effects with atomic precision. For this frontier study, the development of in situ transmission electron microscopy (TEM) techniques is proposed. The new measurement method enables the application of local electric fields on cross-sectional specimen during TEM analysis and this is bound to provide unique insights in strain-mediated and charge-modulated coupling mechanisms between ferromagnetic and ferroelectric thin films."
Max ERC Funding
1 499 465 €
Duration
Start date: 2012-10-01, End date: 2017-09-30
Project acronym ELASTIC-TURBULENCE
Project Purely-elastic flow instabilities and transition to elastic turbulence in microscale flows of complex fluids
Researcher (PI) Manuel Antonio Moreira Alves
Host Institution (HI) UNIVERSIDADE DO PORTO
Country Portugal
Call Details Starting Grant (StG), PE8, ERC-2012-StG_20111012
Summary Flows of complex fluids, such as many biological fluids and most synthetic fluids, are common in our daily life and are very important from an industrial perspective. Because of their inherent nonlinearity, the flow of complex viscoelastic fluids often leads to counterintuitive and complex behaviour and, above critical conditions, can prompt flow instabilities even under low Reynolds number conditions which are entirely absent in the corresponding Newtonian fluid flows.
The primary goal of this project is to substantially expand the frontiers of our current knowledge regarding the mechanisms that lead to the development of such purely-elastic flow instabilities, and ultimately to understand the transition to so-called “elastic turbulence”, a turbulent-like phenomenon which can arise even under inertialess flow conditions. This is an extremely challenging problem, and to significantly advance our knowledge in such important flows these instabilities will be investigated in a combined manner encompassing experiments, theory and numerical simulations. Such a holistic approach will enable us to understand the underlying mechanisms of those instabilities and to develop accurate criteria for their prediction far in advance of what we could achieve with either approach separately. A deep understanding of the mechanisms generating elastic instabilities and subsequent transition to elastic turbulence is crucial from a fundamental point of view and for many important practical applications involving engineered complex fluids, such as the design of microfluidic mixers for efficient operation under inertialess flow conditions, or the development of highly efficient micron-sized energy management and mass transfer systems.
This research proposal will create a solid basis for the establishment of an internationally-leading research group led by the PI studying flow instabilities and elastic turbulence in complex fluid flows.
Summary
Flows of complex fluids, such as many biological fluids and most synthetic fluids, are common in our daily life and are very important from an industrial perspective. Because of their inherent nonlinearity, the flow of complex viscoelastic fluids often leads to counterintuitive and complex behaviour and, above critical conditions, can prompt flow instabilities even under low Reynolds number conditions which are entirely absent in the corresponding Newtonian fluid flows.
The primary goal of this project is to substantially expand the frontiers of our current knowledge regarding the mechanisms that lead to the development of such purely-elastic flow instabilities, and ultimately to understand the transition to so-called “elastic turbulence”, a turbulent-like phenomenon which can arise even under inertialess flow conditions. This is an extremely challenging problem, and to significantly advance our knowledge in such important flows these instabilities will be investigated in a combined manner encompassing experiments, theory and numerical simulations. Such a holistic approach will enable us to understand the underlying mechanisms of those instabilities and to develop accurate criteria for their prediction far in advance of what we could achieve with either approach separately. A deep understanding of the mechanisms generating elastic instabilities and subsequent transition to elastic turbulence is crucial from a fundamental point of view and for many important practical applications involving engineered complex fluids, such as the design of microfluidic mixers for efficient operation under inertialess flow conditions, or the development of highly efficient micron-sized energy management and mass transfer systems.
This research proposal will create a solid basis for the establishment of an internationally-leading research group led by the PI studying flow instabilities and elastic turbulence in complex fluid flows.
Max ERC Funding
994 110 €
Duration
Start date: 2012-10-01, End date: 2018-01-31
