Project acronym ALUNIF
Project Algorithms and Lower Bounds: A Unified Approach
Researcher (PI) Rahul Santhanam
Host Institution (HI) THE CHANCELLOR, MASTERS AND SCHOLARS OF THE UNIVERSITY OF OXFORD
Country United Kingdom
Call Details Consolidator Grant (CoG), PE6, ERC-2013-CoG
Summary One of the fundamental goals of theoretical computer science is to
understand the possibilities and limits of efficient computation. This
quest has two dimensions. The
theory of algorithms focuses on finding efficient solutions to
problems, while computational complexity theory aims to understand when
and why problems are hard to solve. These two areas have different
philosophies and use different sets of techniques. However, in recent
years there have been indications of deep and mysterious connections
between them.
In this project, we propose to explore and develop the connections between
algorithmic analysis and complexity lower bounds in a systematic way.
On the one hand, we plan to use complexity lower bound techniques as inspiration
to design new and improved algorithms for Satisfiability and other
NP-complete problems, as well as to analyze existing algorithms better.
On the other hand, we plan to strengthen implications yielding circuit
lower bounds from non-trivial algorithms for Satisfiability, and to derive
new circuit lower bounds using these stronger implications.
This project has potential for massive impact in both the areas of algorithms
and computational complexity. Improved algorithms for Satisfiability could lead
to improved SAT solvers, and the new analytical tools would lead to a better
understanding of existing heuristics. Complexity lower bound questions are
fundamental
but notoriously difficult, and new lower bounds would open the way to
unconditionally secure cryptographic protocols and derandomization of
probabilistic algorithms. More broadly, this project aims to initiate greater
dialogue between the two areas, with an exchange of ideas and techniques
which leads to accelerated progress in both, as well as a deeper understanding
of the nature of efficient computation.
Summary
One of the fundamental goals of theoretical computer science is to
understand the possibilities and limits of efficient computation. This
quest has two dimensions. The
theory of algorithms focuses on finding efficient solutions to
problems, while computational complexity theory aims to understand when
and why problems are hard to solve. These two areas have different
philosophies and use different sets of techniques. However, in recent
years there have been indications of deep and mysterious connections
between them.
In this project, we propose to explore and develop the connections between
algorithmic analysis and complexity lower bounds in a systematic way.
On the one hand, we plan to use complexity lower bound techniques as inspiration
to design new and improved algorithms for Satisfiability and other
NP-complete problems, as well as to analyze existing algorithms better.
On the other hand, we plan to strengthen implications yielding circuit
lower bounds from non-trivial algorithms for Satisfiability, and to derive
new circuit lower bounds using these stronger implications.
This project has potential for massive impact in both the areas of algorithms
and computational complexity. Improved algorithms for Satisfiability could lead
to improved SAT solvers, and the new analytical tools would lead to a better
understanding of existing heuristics. Complexity lower bound questions are
fundamental
but notoriously difficult, and new lower bounds would open the way to
unconditionally secure cryptographic protocols and derandomization of
probabilistic algorithms. More broadly, this project aims to initiate greater
dialogue between the two areas, with an exchange of ideas and techniques
which leads to accelerated progress in both, as well as a deeper understanding
of the nature of efficient computation.
Max ERC Funding
1 274 496 €
Duration
Start date: 2014-03-01, End date: 2019-02-28
Project acronym ANTI-ATOM
Project Many-body theory of antimatter interactions with atoms, molecules and condensed matter
Researcher (PI) Dermot GREEN
Host Institution (HI) THE QUEEN'S UNIVERSITY OF BELFAST
Country United Kingdom
Call Details Starting Grant (StG), PE2, ERC-2018-STG
Summary The ability of positrons to annihilate with electrons, producing characteristic gamma rays, gives them important use in medicine via positron-emission tomography (PET), diagnostics of industrially-important materials, and in elucidating astrophysical phenomena. Moreover, the fundamental interactions of positrons and positronium (Ps) with atoms, molecules and condensed matter are currently under intensive study in numerous international laboratories, to illuminate collision phenomena and perform precision tests of fundamental laws.
Proper interpretation and development of these costly and difficult experiments requires accurate calculations of low-energy positron and Ps interactions with normal matter. These systems, however, involve strong correlations, e.g., polarisation of the atom and virtual-Ps formation (where an atomic electron tunnels to the positron): they significantly effect positron- and Ps-atom/molecule interactions, e.g., enhancing annihilation rates by many orders of magnitude, and making the accurate description of these systems a challenging many-body problem. Current theoretical capability lags severely behind that of experiment. Major theoretical and computational developments are required to bridge the gap.
One powerful method, which accounts for the correlations in a natural, transparent and systematic way, is many-body theory (MBT). Building on my expertise in the field, I propose to develop new MBT to deliver unique and unrivalled capability in theory and computation of low-energy positron and Ps interactions with atoms, molecules, and condensed matter. The ambitious programme will provide the basic understanding required to interpret and develop the fundamental experiments, antimatter-based materials science techniques, and wider technologies, e.g., (PET), and more broadly, potentially revolutionary and generally applicable computational methodologies that promise to define a new level of high-precision in atomic-MBT calculations.
Summary
The ability of positrons to annihilate with electrons, producing characteristic gamma rays, gives them important use in medicine via positron-emission tomography (PET), diagnostics of industrially-important materials, and in elucidating astrophysical phenomena. Moreover, the fundamental interactions of positrons and positronium (Ps) with atoms, molecules and condensed matter are currently under intensive study in numerous international laboratories, to illuminate collision phenomena and perform precision tests of fundamental laws.
Proper interpretation and development of these costly and difficult experiments requires accurate calculations of low-energy positron and Ps interactions with normal matter. These systems, however, involve strong correlations, e.g., polarisation of the atom and virtual-Ps formation (where an atomic electron tunnels to the positron): they significantly effect positron- and Ps-atom/molecule interactions, e.g., enhancing annihilation rates by many orders of magnitude, and making the accurate description of these systems a challenging many-body problem. Current theoretical capability lags severely behind that of experiment. Major theoretical and computational developments are required to bridge the gap.
One powerful method, which accounts for the correlations in a natural, transparent and systematic way, is many-body theory (MBT). Building on my expertise in the field, I propose to develop new MBT to deliver unique and unrivalled capability in theory and computation of low-energy positron and Ps interactions with atoms, molecules, and condensed matter. The ambitious programme will provide the basic understanding required to interpret and develop the fundamental experiments, antimatter-based materials science techniques, and wider technologies, e.g., (PET), and more broadly, potentially revolutionary and generally applicable computational methodologies that promise to define a new level of high-precision in atomic-MBT calculations.
Max ERC Funding
1 318 419 €
Duration
Start date: 2019-02-01, End date: 2024-01-31
Project acronym BATNMR
Project Development and Application of New NMR Methods for Studying Interphases and Interfaces in Batteries
Researcher (PI) Clare GREY
Host Institution (HI) THE CHANCELLOR MASTERS AND SCHOLARSOF THE UNIVERSITY OF CAMBRIDGE
Country United Kingdom
Call Details Advanced Grant (AdG), PE4, ERC-2018-ADG
Summary The development of longer lasting, higher energy density and cheaper rechargeable batteries represents one of the major technological challenges of our society, batteries representing the limiting components in the shift from gasoline-powered to electric vehicles. They are also required to enable the use of more (typically intermittent) renewable energy, to balance demand with generation. This proposal seeks to develop and apply new NMR metrologies to determine the structure and dynamics of the multiple electrode-electrolyte interfaces and interphases that are present in these batteries, and how they evolve during battery cycling. New dynamic nuclear polarization (DNP) techniques will be exploited to extract structural information about the interface between the battery electrode and the passivating layers that grow on the electrode materials (the solid electrolyte interphase, SEI) and that are inherent to the stability of the batteries. The role of the SEI (and ceramic interfaces) in controlling lithium metal dendrite growth will be determined in liquid based and all solid state batteries.
New DNP approaches will be developed that are compatible with the heterogeneous and reactive species that are present in conventional, all-solid state, Li-air and redox flow batteries. Method development will run in parallel with the use of DNP approaches to determine the structures of the various battery interfaces and interphases, testing the stability of conventional biradicals in these harsh oxidizing and reducing conditions, modifying the experimental approaches where appropriate. The final result will be a significantly improved understanding of the structures of these phases and how they evolve on cycling, coupled with strategies for designing improved SEI structures. The nature of the interface between a lithium metal dendrite and ceramic composite will be determined, providing much needed insight into how these (unwanted) dendrites grow in all solid state batteries. DNP approaches coupled with electron spin resonance will be use, where possible in situ, to determine the reaction mechanisms of organic molecules such as quinones in organic-based redox flow batteries in order to help prevent degradation of the electrochemically active species.
This proposal involves NMR method development specifically designed to explore a variety of battery chemistries. Thus, this proposal is interdisciplinary, containing both a strong emphasis on materials characterization, electrochemistry and electronic structures of materials, interfaces and nanoparticles, and on analytical and physical chemistry. Some of the methodology will be applicable to other materials and systems including (for example) other electrochemical technologies such as fuel cells and solar fuels and the study of catalysts (to probe surface structure).
Summary
The development of longer lasting, higher energy density and cheaper rechargeable batteries represents one of the major technological challenges of our society, batteries representing the limiting components in the shift from gasoline-powered to electric vehicles. They are also required to enable the use of more (typically intermittent) renewable energy, to balance demand with generation. This proposal seeks to develop and apply new NMR metrologies to determine the structure and dynamics of the multiple electrode-electrolyte interfaces and interphases that are present in these batteries, and how they evolve during battery cycling. New dynamic nuclear polarization (DNP) techniques will be exploited to extract structural information about the interface between the battery electrode and the passivating layers that grow on the electrode materials (the solid electrolyte interphase, SEI) and that are inherent to the stability of the batteries. The role of the SEI (and ceramic interfaces) in controlling lithium metal dendrite growth will be determined in liquid based and all solid state batteries.
New DNP approaches will be developed that are compatible with the heterogeneous and reactive species that are present in conventional, all-solid state, Li-air and redox flow batteries. Method development will run in parallel with the use of DNP approaches to determine the structures of the various battery interfaces and interphases, testing the stability of conventional biradicals in these harsh oxidizing and reducing conditions, modifying the experimental approaches where appropriate. The final result will be a significantly improved understanding of the structures of these phases and how they evolve on cycling, coupled with strategies for designing improved SEI structures. The nature of the interface between a lithium metal dendrite and ceramic composite will be determined, providing much needed insight into how these (unwanted) dendrites grow in all solid state batteries. DNP approaches coupled with electron spin resonance will be use, where possible in situ, to determine the reaction mechanisms of organic molecules such as quinones in organic-based redox flow batteries in order to help prevent degradation of the electrochemically active species.
This proposal involves NMR method development specifically designed to explore a variety of battery chemistries. Thus, this proposal is interdisciplinary, containing both a strong emphasis on materials characterization, electrochemistry and electronic structures of materials, interfaces and nanoparticles, and on analytical and physical chemistry. Some of the methodology will be applicable to other materials and systems including (for example) other electrochemical technologies such as fuel cells and solar fuels and the study of catalysts (to probe surface structure).
Max ERC Funding
3 498 219 €
Duration
Start date: 2019-10-01, End date: 2024-09-30
Project acronym BAYES-KNOWLEDGE
Project Effective Bayesian Modelling with Knowledge before Data
Researcher (PI) Norman Fenton
Host Institution (HI) QUEEN MARY UNIVERSITY OF LONDON
Country United Kingdom
Call Details Advanced Grant (AdG), PE6, ERC-2013-ADG
Summary This project aims to improve evidence-based decision-making. What makes it radical is that it plans to do this in situations (common for critical risk assessment problems) where there is little or even no data, and hence where traditional statistics cannot be used. To address this problem Bayesian analysis, which enables domain experts to supplement observed data with subjective probabilities, is normally used. As real-world problems typically involve multiple uncertain variables, Bayesian analysis is extended using a technique called Bayesian networks (BNs). But, despite many great benefits, BNs have been under-exploited, especially in areas where they offer the greatest potential for improvements (law, medicine and systems engineering). This is mainly because of widespread resistance to relying on subjective knowledge. To address this problem much current research assumes sufficient data are available to make the expert’s input minimal or even redundant; with such data it may be possible to ‘learn’ the underlying BN model. But this approach offers nothing when there is limited or no data. Even when ‘big’ data are available the resulting models may be superficially objective but fundamentally flawed as they fail to capture the underlying causal structure that only expert knowledge can provide.
Our solution is to develop a method to systemize the way expert driven causal BN models can be built and used effectively either in the absence of data or as a means of determining what future data is really required. The method involves a new way of framing problems and extensions to BN theory, notation and tools. Working with relevant domain experts, along with cognitive psychologists, our methods will be developed and tested experimentally on real-world critical decision-problems in medicine, law, forensics, and transport. As the work complements current data-driven approaches, it will lead to improved BN modelling both when there is extensive data as well as none.
Summary
This project aims to improve evidence-based decision-making. What makes it radical is that it plans to do this in situations (common for critical risk assessment problems) where there is little or even no data, and hence where traditional statistics cannot be used. To address this problem Bayesian analysis, which enables domain experts to supplement observed data with subjective probabilities, is normally used. As real-world problems typically involve multiple uncertain variables, Bayesian analysis is extended using a technique called Bayesian networks (BNs). But, despite many great benefits, BNs have been under-exploited, especially in areas where they offer the greatest potential for improvements (law, medicine and systems engineering). This is mainly because of widespread resistance to relying on subjective knowledge. To address this problem much current research assumes sufficient data are available to make the expert’s input minimal or even redundant; with such data it may be possible to ‘learn’ the underlying BN model. But this approach offers nothing when there is limited or no data. Even when ‘big’ data are available the resulting models may be superficially objective but fundamentally flawed as they fail to capture the underlying causal structure that only expert knowledge can provide.
Our solution is to develop a method to systemize the way expert driven causal BN models can be built and used effectively either in the absence of data or as a means of determining what future data is really required. The method involves a new way of framing problems and extensions to BN theory, notation and tools. Working with relevant domain experts, along with cognitive psychologists, our methods will be developed and tested experimentally on real-world critical decision-problems in medicine, law, forensics, and transport. As the work complements current data-driven approaches, it will lead to improved BN modelling both when there is extensive data as well as none.
Max ERC Funding
1 572 562 €
Duration
Start date: 2014-04-01, End date: 2018-03-31
Project acronym BIGBAYES
Project Rich, Structured and Efficient Learning of Big Bayesian Models
Researcher (PI) Yee Whye Teh
Host Institution (HI) THE CHANCELLOR, MASTERS AND SCHOLARS OF THE UNIVERSITY OF OXFORD
Country United Kingdom
Call Details Consolidator Grant (CoG), PE6, ERC-2013-CoG
Summary As datasets grow ever larger in scale, complexity and variety, there is an increasing need for powerful machine learning and statistical techniques that are capable of learning from such data. Bayesian nonparametrics is a promising approach to data analysis that is increasingly popular in machine learning and statistics. Bayesian nonparametric models are highly flexible models with infinite-dimensional parameter spaces that can be used to directly parameterise and learn about functions, densities, conditional distributions etc, and have been successfully applied to regression, survival analysis, language modelling, time series analysis, and visual scene analysis among others. However, to successfully use Bayesian nonparametric models to analyse the high-dimensional and structured datasets now commonly encountered in the age of Big Data, we will have to overcome a number of challenges. Namely, we need to develop Bayesian nonparametric models that can learn rich representations from structured data, and we need computational methodologies that can scale effectively to the large and complex models of the future. We will ground our developments in relevant applications, particularly to natural language processing (learning distributed representations for language modelling and compositional semantics) and genetics (modelling genetic variations arising from population, genealogical and spatial structures).
Summary
As datasets grow ever larger in scale, complexity and variety, there is an increasing need for powerful machine learning and statistical techniques that are capable of learning from such data. Bayesian nonparametrics is a promising approach to data analysis that is increasingly popular in machine learning and statistics. Bayesian nonparametric models are highly flexible models with infinite-dimensional parameter spaces that can be used to directly parameterise and learn about functions, densities, conditional distributions etc, and have been successfully applied to regression, survival analysis, language modelling, time series analysis, and visual scene analysis among others. However, to successfully use Bayesian nonparametric models to analyse the high-dimensional and structured datasets now commonly encountered in the age of Big Data, we will have to overcome a number of challenges. Namely, we need to develop Bayesian nonparametric models that can learn rich representations from structured data, and we need computational methodologies that can scale effectively to the large and complex models of the future. We will ground our developments in relevant applications, particularly to natural language processing (learning distributed representations for language modelling and compositional semantics) and genetics (modelling genetic variations arising from population, genealogical and spatial structures).
Max ERC Funding
1 918 092 €
Duration
Start date: 2014-05-01, End date: 2019-04-30
Project acronym BiocatSusChem
Project Biocatalysis for Sustainable Chemistry – Understanding Oxidation/Reduction of Small Molecules by Redox Metalloenzymes via a Suite of Steady State and Transient Infrared Electrochemical Methods
Researcher (PI) Kylie VINCENT
Host Institution (HI) THE CHANCELLOR, MASTERS AND SCHOLARS OF THE UNIVERSITY OF OXFORD
Country United Kingdom
Call Details Consolidator Grant (CoG), PE4, ERC-2018-COG
Summary Many significant global challenges in catalysis for energy and sustainable chemistry have already been solved in nature. Metalloenzymes within microorganisms catalyse the transformation of carbon dioxide into simple carbon building blocks or fuels, the reduction of dinitrogen to ammonia under ambient conditions and the production and utilisation of dihydrogen. Catalytic sites for these reactions are necessarily based on metals that are abundant in the environment, including iron, nickel and molybdenum. However, attempts to generate biomimetic catalysts have largely failed to reproduce the high activity, stability and selectivity of enzymes. Proton and electron transfer and substrate binding are all finely choreographed, and we do not yet understand how this is achieved. This project develops a suite of new experimental infrared (IR) spectroscopy tools to probe and understand mechanisms of redox metalloenzymes in situ during electrochemically-controlled steady state turnover, and during electron-transfer-triggered transient studies. The ability of IR spectroscopy to report on the nature and strength of chemical bonds makes it ideally suited to follow the activation and transformation of small molecule reactants at metalloenzyme catalytic sites, binding of inhibitors, and protonation of specific sites. By extending to the far-IR, or introducing mid-IR-active probe amino acids, redox and structural changes in biological electron relay chains also become accessible. Taking as models the enzymes nitrogenase, hydrogenase, carbon monoxide dehydrogenase and formate dehydrogenase, the project sets out to establish a unified understanding of central concepts in small molecule activation in biology. It will reveal precise ways in which chemical events are coordinated inside complex multicentre metalloenzymes, propelling a new generation of bio-inspired catalysts and uncovering new chemistry of enzymes.
Summary
Many significant global challenges in catalysis for energy and sustainable chemistry have already been solved in nature. Metalloenzymes within microorganisms catalyse the transformation of carbon dioxide into simple carbon building blocks or fuels, the reduction of dinitrogen to ammonia under ambient conditions and the production and utilisation of dihydrogen. Catalytic sites for these reactions are necessarily based on metals that are abundant in the environment, including iron, nickel and molybdenum. However, attempts to generate biomimetic catalysts have largely failed to reproduce the high activity, stability and selectivity of enzymes. Proton and electron transfer and substrate binding are all finely choreographed, and we do not yet understand how this is achieved. This project develops a suite of new experimental infrared (IR) spectroscopy tools to probe and understand mechanisms of redox metalloenzymes in situ during electrochemically-controlled steady state turnover, and during electron-transfer-triggered transient studies. The ability of IR spectroscopy to report on the nature and strength of chemical bonds makes it ideally suited to follow the activation and transformation of small molecule reactants at metalloenzyme catalytic sites, binding of inhibitors, and protonation of specific sites. By extending to the far-IR, or introducing mid-IR-active probe amino acids, redox and structural changes in biological electron relay chains also become accessible. Taking as models the enzymes nitrogenase, hydrogenase, carbon monoxide dehydrogenase and formate dehydrogenase, the project sets out to establish a unified understanding of central concepts in small molecule activation in biology. It will reveal precise ways in which chemical events are coordinated inside complex multicentre metalloenzymes, propelling a new generation of bio-inspired catalysts and uncovering new chemistry of enzymes.
Max ERC Funding
1 997 286 €
Duration
Start date: 2019-03-01, End date: 2024-02-29
Project acronym ChemNav
Project Magnetic sensing by molecules, birds, and devices
Researcher (PI) Peter John Hore
Host Institution (HI) THE CHANCELLOR, MASTERS AND SCHOLARS OF THE UNIVERSITY OF OXFORD
Country United Kingdom
Call Details Advanced Grant (AdG), PE4, ERC-2013-ADG
Summary The sensory mechanisms that allow birds to perceive the direction of the Earth’s magnetic field for the purpose of navigation are only now beginning to be understood. One of the two leading hypotheses is founded on magnetically sensitive photochemical reactions in the retina. It is thought that transient photo-induced radical pairs in cryptochrome, a blue-light photoreceptor protein, act as the primary magnetic sensor. Experimental and theoretical support for this mechanism has been accumulating over the last few years, qualifying chemical magnetoreception for a place in the emerging field of Quantum Biology.
In this proposal, we aim to determine the detailed principles of efficient chemical sensing of weak magnetic fields, to elucidate the biophysics of animal compass magnetoreception, and to explore the possibilities of magnetic sensing technologies inspired by the coherent dynamics of entangled electron spins in cryptochrome-based radical pairs.
We will:
(a) Establish the fundamental structural, kinetic, dynamic and magnetic properties that allow efficient chemical sensing of Earth-strength magnetic fields in cryptochromes.
(b) Devise new, sensitive forms of optical spectroscopy for this purpose.
(c) Design, construct and iteratively refine non-natural proteins (maquettes) as versatile model systems for testing and optimising molecular magnetoreceptors.
(d) Characterise the spin dynamics and magnetic sensitivity of maquette magnetoreceptors using specialised magnetic resonance and optical spectroscopic techniques.
(e) Develop efficient and accurate methods for simulating the coherent spin dynamics of realistic radical pairs in order to interpret experimental data, guide the implementation of new experiments, test concepts of magnetoreceptor function, and guide the design of efficient sensors.
(f) Explore the feasibility of electronically addressable, organic semiconductor sensors inspired by radical pair magnetoreception.
Summary
The sensory mechanisms that allow birds to perceive the direction of the Earth’s magnetic field for the purpose of navigation are only now beginning to be understood. One of the two leading hypotheses is founded on magnetically sensitive photochemical reactions in the retina. It is thought that transient photo-induced radical pairs in cryptochrome, a blue-light photoreceptor protein, act as the primary magnetic sensor. Experimental and theoretical support for this mechanism has been accumulating over the last few years, qualifying chemical magnetoreception for a place in the emerging field of Quantum Biology.
In this proposal, we aim to determine the detailed principles of efficient chemical sensing of weak magnetic fields, to elucidate the biophysics of animal compass magnetoreception, and to explore the possibilities of magnetic sensing technologies inspired by the coherent dynamics of entangled electron spins in cryptochrome-based radical pairs.
We will:
(a) Establish the fundamental structural, kinetic, dynamic and magnetic properties that allow efficient chemical sensing of Earth-strength magnetic fields in cryptochromes.
(b) Devise new, sensitive forms of optical spectroscopy for this purpose.
(c) Design, construct and iteratively refine non-natural proteins (maquettes) as versatile model systems for testing and optimising molecular magnetoreceptors.
(d) Characterise the spin dynamics and magnetic sensitivity of maquette magnetoreceptors using specialised magnetic resonance and optical spectroscopic techniques.
(e) Develop efficient and accurate methods for simulating the coherent spin dynamics of realistic radical pairs in order to interpret experimental data, guide the implementation of new experiments, test concepts of magnetoreceptor function, and guide the design of efficient sensors.
(f) Explore the feasibility of electronically addressable, organic semiconductor sensors inspired by radical pair magnetoreception.
Max ERC Funding
2 997 062 €
Duration
Start date: 2013-12-01, End date: 2018-11-30
Project acronym COLORTTH
Project The Higgs: A colored View from the Top at ATLAS
Researcher (PI) Reinhild Fatima Yvonne Peters
Host Institution (HI) THE UNIVERSITY OF MANCHESTER
Country United Kingdom
Call Details Starting Grant (StG), PE2, ERC-2013-StG
Summary "With the ground-breaking discovery of a new, Higgs-like boson on July 4th, 2012, by the CMS and ATLAS collaborations at CERN, a new era of particle physics has begun. The discovery is the first step in answering an unsolved problem in particle physics, the question how fundamental bosons and fermions acquire their mass. One of the major goals in collider physics in the next few years will be the deeper insight into the nature of the new particle, its connection to the known fundamental particles and possible extensions beyond the standard model (SM) of particle physics.
My project aims at a particular interesting field to study, the relation of the new particle with the heaviest known elementary particle, the top quark. I aim to develop new, innovative techniques and beyond state-of-the-art methods to extract the Yukawa coupling between the top quark and the Higgs boson, which is expected to be of the order of one - much higher than that of any other quark. I will analyse the only process where the top-Higgs Yukawa coupling can be measured, in associated production of top quark pairs and a Higgs boson. The Higgs boson mainly decays into a pair of b-quarks. This is one of the most challenging channels at the LHC, as huge background processes from gluon splitting contribute. In particular, I will develop and study color flow variables, which provide a unique, powerful technique to distinguish color singlet Higgs bosons from the main background, color octet gluons.
The ultimate goal of the project is the first measurement of the top-Higgs Yukawa coupling and its confrontation with SM and beyond SM Higgs boson models, resulting in an unprecedented insight into the fundamental laws of nature.
The LHC will soon reach a new energy frontier of 13 TeV starting in 2014. This new environment will provide never seen opportunities to study hints of new physics and precisely measure properties of the newly found particle. This sets the stage for the project."
Summary
"With the ground-breaking discovery of a new, Higgs-like boson on July 4th, 2012, by the CMS and ATLAS collaborations at CERN, a new era of particle physics has begun. The discovery is the first step in answering an unsolved problem in particle physics, the question how fundamental bosons and fermions acquire their mass. One of the major goals in collider physics in the next few years will be the deeper insight into the nature of the new particle, its connection to the known fundamental particles and possible extensions beyond the standard model (SM) of particle physics.
My project aims at a particular interesting field to study, the relation of the new particle with the heaviest known elementary particle, the top quark. I aim to develop new, innovative techniques and beyond state-of-the-art methods to extract the Yukawa coupling between the top quark and the Higgs boson, which is expected to be of the order of one - much higher than that of any other quark. I will analyse the only process where the top-Higgs Yukawa coupling can be measured, in associated production of top quark pairs and a Higgs boson. The Higgs boson mainly decays into a pair of b-quarks. This is one of the most challenging channels at the LHC, as huge background processes from gluon splitting contribute. In particular, I will develop and study color flow variables, which provide a unique, powerful technique to distinguish color singlet Higgs bosons from the main background, color octet gluons.
The ultimate goal of the project is the first measurement of the top-Higgs Yukawa coupling and its confrontation with SM and beyond SM Higgs boson models, resulting in an unprecedented insight into the fundamental laws of nature.
The LHC will soon reach a new energy frontier of 13 TeV starting in 2014. This new environment will provide never seen opportunities to study hints of new physics and precisely measure properties of the newly found particle. This sets the stage for the project."
Max ERC Funding
1 163 755 €
Duration
Start date: 2014-02-01, End date: 2019-01-31
Project acronym CONTROL
Project Laser control over crystal nucleation
Researcher (PI) Klaas Wijnne
Host Institution (HI) UNIVERSITY OF GLASGOW
Country United Kingdom
Call Details Advanced Grant (AdG), PE4, ERC-2018-ADG
Summary The CONTROL programme I propose here is a five-year programme of frontier research to develop a novel platform for the manipulation of phase transitions, crystal nucleation, and polymorph control based on a novel optical-tweezing technique and plasmonics. About 20 years ago, it was shown that lasers can nucleate crystals in super-saturated solution and might even be able to select the polymorph that crystallises. However, no theoretical model was found explaining the results and little progress was made.
In a recent publication (Nat. Chem. 10, 506 (2018)), we showed that laser-induced nucleation can be understood in terms of the harnessing of concentration fluctuations near a liquid–liquid critical point using optical tweezing. This breakthrough opens the way to a research programme with risky, ambitious, and ground-breaking long-term aims: full control over crystal nucleation including chirality and polymorphism.
New optical and microscopic techniques will be developed to allow laser manipulation on a massively parallel scale and chiral nucleation using twisted light. Systematically characterising and manipulating the phase behaviour of mixtures, will allow the use of the optical-tweezing effect to effectively control the crystallisation of small molecules, peptides, proteins, and polymers. Exploiting nanostructures will allow parallelisation on a vast scale and fine control over chirality and polymorph selection through plasmonic tweezing. Even partial success in the five years of the programme will lead to fundamental new insights and technological breakthroughs. These breakthroughs will be exploited for future commercial applications towards the end of the project.
Summary
The CONTROL programme I propose here is a five-year programme of frontier research to develop a novel platform for the manipulation of phase transitions, crystal nucleation, and polymorph control based on a novel optical-tweezing technique and plasmonics. About 20 years ago, it was shown that lasers can nucleate crystals in super-saturated solution and might even be able to select the polymorph that crystallises. However, no theoretical model was found explaining the results and little progress was made.
In a recent publication (Nat. Chem. 10, 506 (2018)), we showed that laser-induced nucleation can be understood in terms of the harnessing of concentration fluctuations near a liquid–liquid critical point using optical tweezing. This breakthrough opens the way to a research programme with risky, ambitious, and ground-breaking long-term aims: full control over crystal nucleation including chirality and polymorphism.
New optical and microscopic techniques will be developed to allow laser manipulation on a massively parallel scale and chiral nucleation using twisted light. Systematically characterising and manipulating the phase behaviour of mixtures, will allow the use of the optical-tweezing effect to effectively control the crystallisation of small molecules, peptides, proteins, and polymers. Exploiting nanostructures will allow parallelisation on a vast scale and fine control over chirality and polymorph selection through plasmonic tweezing. Even partial success in the five years of the programme will lead to fundamental new insights and technological breakthroughs. These breakthroughs will be exploited for future commercial applications towards the end of the project.
Max ERC Funding
2 488 162 €
Duration
Start date: 2019-09-01, End date: 2024-08-31
Project acronym DeCO-HVP
Project Decouple Electrochemical Reduction of Carbon Dioxide to High Value Products
Researcher (PI) Kathryn Ellen TOGHILL
Host Institution (HI) UNIVERSITY OF LANCASTER
Country United Kingdom
Call Details Starting Grant (StG), PE4, ERC-2018-STG
Summary This programme aims to convert carbon dioxide into high value hydrocarbon products using carbon neutral electrochemical methods. High value products are materials that may be used as carbon based chemical feedstocks and as synthetic fuels, reducing the ever-present demand on oil and natural gas to fulfil these needs. The project is within the remit of an international ambition to valorise carbon dioxide waste and reduce environmentally harmful greenhouse gas generation, as opposed to stopping at carbon capture and sequestration. This proposal outlines an alternative route to carbon dioxide utilisation (CDU), in which a mediated approach that decouples the electrochemical reduction from the catalytic process is explored. Novel bimetallic catalysts will be synthesised and studied, meditating electron donating solutions will be generated, and a robust and comprehensive analytical arrangement will be implemented to allow total identification and quantification of the wide range of possible products.
Electrocatalytic CO2 reduction is one of the key approaches to CDU, as it has a direct pathway to carbon neutral renewable electricity. Nonetheless it is a field that has shown minimal progress in the past 30 years. A paradigm shift is necessary in the approach to electrochemical CO2 reduction, where conventional heterogeneous interfacial catalysis is limited by mass transport, passivation, and CO2 solubility. This proposal outlines the use of electron donating mediators generated separately to the catalysed chemical reduction of CO2, such that the electrolyte becomes the electrode. This opens a whole new avenue for catalyst research, and here target bimetallic catalysts that suppress side reactions and promote high value product synthesis are described.
Summary
This programme aims to convert carbon dioxide into high value hydrocarbon products using carbon neutral electrochemical methods. High value products are materials that may be used as carbon based chemical feedstocks and as synthetic fuels, reducing the ever-present demand on oil and natural gas to fulfil these needs. The project is within the remit of an international ambition to valorise carbon dioxide waste and reduce environmentally harmful greenhouse gas generation, as opposed to stopping at carbon capture and sequestration. This proposal outlines an alternative route to carbon dioxide utilisation (CDU), in which a mediated approach that decouples the electrochemical reduction from the catalytic process is explored. Novel bimetallic catalysts will be synthesised and studied, meditating electron donating solutions will be generated, and a robust and comprehensive analytical arrangement will be implemented to allow total identification and quantification of the wide range of possible products.
Electrocatalytic CO2 reduction is one of the key approaches to CDU, as it has a direct pathway to carbon neutral renewable electricity. Nonetheless it is a field that has shown minimal progress in the past 30 years. A paradigm shift is necessary in the approach to electrochemical CO2 reduction, where conventional heterogeneous interfacial catalysis is limited by mass transport, passivation, and CO2 solubility. This proposal outlines the use of electron donating mediators generated separately to the catalysed chemical reduction of CO2, such that the electrolyte becomes the electrode. This opens a whole new avenue for catalyst research, and here target bimetallic catalysts that suppress side reactions and promote high value product synthesis are described.
Max ERC Funding
1 499 994 €
Duration
Start date: 2018-10-01, End date: 2024-09-30
