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1,173 Projects

  • 2012-2021
  • UK Research and Innovation
  • UKRI|EPSRC
  • 2016

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  • Funder: UKRI Project Code: EP/K031805/1
    Funder Contribution: 221,071 GBP

    During the last twenty years mathematics and physics have significantly influenced each other and became highly entangled. Mathematical physics was always producing a wide variety of new concepts and problems that became important subjects of the pure mathematical research. The growth of gauge, gravity and string theories have made the relation between these subjects closer than ever before. An important driving force was the discovery of quantum groups and of the gauge/gravity dualities. Here the leading role was played by the the so-called AdS/CFT duality and the underlying integrable structure of it. A far-reaching concept is the effect of boundaries and the corresponding boundary conditions. They are unavoidable in almost all models of mathematical physics and are of the fundamental importance. The introduction of boundaries into the theory of quantum groups leads to a whole new class of the so-called reflection algebras. Such algebras were shown to appear in numerous mathematical models and are at the core of the integrable structure of them. Furthermore, these algebras were also shown to play a prominent role in the AdS/CFT. However a coherent framework for describing such algebras is not known, and many properties of the reflection algebras are still an open question. The goal of this research is to develop new algebraic methods and intradisciplinary connections between the axiomatic theory of algebras and the theory of quantum groups inspired by the integrable structure of the AdS/CFT, in particular by shedding more light on the effects of boundaries and different boundary configurations. The research is driven by applying algebraic objects such as the quantum affine and Yangian algebras to find elegant, exact solutions describing the models that arise from and are inspired by the gauge/gravity dualities.

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  • Funder: UKRI Project Code: EP/J020915/1
    Funder Contribution: 583,832 GBP

    Argumentation provides a powerful mechanism for dealing with incomplete, possibly inconsistent information and for the resolution of conflicts and differences of opinion amongst different parties. Further, it is useful for justifying outcomes. Thus, argumentation can support several aspects of decision-making, either by individual entities performing critical thinking (needing to evaluate pros and cons of conflicting decisions) or by multiple entities dialectically engaged to come to mutually agreeable decisions (needing to assess the validity of information the entities become aware of and resolve conflicts), especially when decisions need to be transparently justified (e.g. in medicine). Because of its potential to support decision-making when transparently justifying decisions is essential, the use of argumentation has been considered in a number of settings, including medicine, law, e-procurement, e-business and design rationale in engineering. Potential users of existing argumentation-based decision-making methods are empowered by transparent methods, afforded by argumentation, but lack either means of formal evaluation sanctioning decisions as (individually or collectively) rational or a computational framework for supporting automation. The combination of these three features (transparency, rationality and computational tools for automation) is essential for argumentation-based decision-making to have a fruitful impact on applications. Indeed, for example, a medical practitioner would not find a "black-box" recommended decision useful, but he/she would also not trust a fully transparent, dialectically justified decision unless he/she were sure that this is the best one (rational). In addition, the plethora of information doctors need to take into account nowadays to make decisions requires automated support. TRaDAr aims at providing methods and prototype systems for various kinds of argumentation-based (individual and collaborative) decision-making that generate automatically transparent, rational decisions, while developing case studies in smart electricity and e-health to inform and validate methods and systems. In this context, TRaDAr's technical objectives are: (O1) to provide novel argumentation-based formulations of decision problems for individual and collaborative decision-making; (O2) to study formal properties of the formulations at (O1), sanctioning the rationality of decisions; (O3) to provide real-world case studies in smart electricity and e-health for (individual and collaborative) decision-making, using the formulations at (O1) and demonstrating the importance of the properties at (O2) as well as the transparent nature of argumentation-based decision-making; (O4) to define provably correct algorithms for the formulations at (O1), supporting rational and transparent (individual and collaborative) decision-making; (O5) to implement prototype systems incorporating the computational methods at (O4), and use these systems to demonstrate the methodology at (O1-O2) for the case studies at (O3). The project intends to develop novel techniques within an existing framework of computational argumentation, termed assumption-based argumentation, towards the achievements of these objectives, and adapting notions and techniques from classical (quantitative) decision theory and mechanism design in economics. The envisaged TRaDAr's methodology and systems will contribute to a sustainable society supported by the digital economy, and in particular they will support people in making informed choices. The project will focus on demonstrating the proposed techniques in specific case studies (smart electricity and e-health for breast cancer) in two chosen application areas (digital economy and e-health), but its outcomes could be far-reaching into other case studies (e.g. in other areas of medicine) as well as other sectors (e.g. in engineering, for supporting decisions on design choices).

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  • Funder: UKRI Project Code: EP/J012521/1
    Funder Contribution: 669,334 GBP

    Human-Autonomous Systems (HAS) are collections of human and autonomous agencies of great importance to, defence, disaster and emergency response, transport, and energy services (especially in hostile/inhospitable environments). However, current reality is that HAS do not provide the right information at the right time to the right agent (human or autonomous); cause information overload; and produce rigid, inflexible and ineffective rule bound behaviours. The current state-of the art in Human-Autonomous Systems is that they often involve disparate, incompatible, and 'stove-piped' communication and information structures with conflicting technologies. This has resulted in failures, ineffectiveness and inefficiency, costing resources and even lives. Improving the collective capability of human-autonomous systems requires agile and flexible behaviour in the face of complex and rapidly changing situations. Developing the collective capability of HAS requires and leads to improving; i) the levels of local and global awareness and utility of information and knowledge, ii) the quality and trustworthiness of decision-making and consideration of alternatives, iii) the ability to increase the level of "command by intent" through the development of lightweight but richer reporting and monitoring mechanisms; and iv) the ability to globally exploit and learn from local initiatives. Underlying all of these lies the importance of the, representation, interactive manipulation and communication of information and knowledge. This 36 month research project will achieve improvements in HAS performance through novel breakthroughs in important areas of Collective Capability for Human-Autonomous Systems (HASCC0. Those breakthroughs will enable improved levels of shared awareness, collective decision-making, agile, responsive command, and collective learning. To achieve this we will develop protocols and technologies for information and knowledge abstraction and representation, argumentation, rationale, command and reporting structures. Our approach is to develop protocols and technologies to support the interactions and knowledge manipulations needed to enhance HAS collective awareness and decision-making and capable of representing and interacting with; - the (rich but lightweight) Argumentation, Rationale, Command and Reporting Structures, - which influence local and global and include strategic, tactical and operational decision-making. Enabling HAS collectives to be agile and responsive. Our investigations comprise two cycles corresponding to different application domain scenarios. Each application domain will present different information and decision-making requirements, and will require different strategic, tactical and operational deployments of HAS. In this way we will seek to assess the generality and wider applicability of our research findings. In the first cycle, we will focus on the situation awareness and decision-making required of HAS for "Multiple Vehicle Cooperative Autonomy". In the second, we will expand our research to investigate HAS for "investigation and repair of defective infrastructure". In each cycle, we will undertake scenario development, modelling, prototyping, evaluation and revision. At the end of each cycle we will produce versions of Protoypes, Models and Principles of HASCC. The research will directly contribute to several EPSRC strategic priority themes by providing science and technology that strengthens critical national infrastructure in: Global Uncertainties - Collective Capability to underpin agile, coherent and integrated HAS, in Defence and Disaster Emergency Response Services Digital Economy - the development of novel Collective Capability Technologies to advance Autonomous Systems, Energy - Collective Capability to underpin HAS enabling safe and reliable energy provision. Transport - Collective Capability for HAS to provide reliable, safe and efficient Transport Services.

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  • Funder: UKRI Project Code: EP/K033166/1
    Funder Contribution: 587,661 GBP

    Future deployments of wireless sensor network (WSN) infrastructures for environmental, industrial or event monitoring are expected to be equipped with energy harvesters (e.g. piezoelectric, thermal or photovoltaic) in order to substantially increase their autonomy and lifetime. However, it is also widely recognized that the existing gap between the sensors' energy availability and the sensors' energy consumption requirements is not likely to close in the near future due to limitations in current energy harvesting (EH) technology, together with the surge in demand for more data-intensive applications. Hence, perpetually operating WSNs are currently impossible to realize for data-intensive applications, as significant (and costly) human intervention is required to replace batteries. With the continuous improvement of energy efficiency representing a major drive in WSN research, the major objective of this research project is to develop transformative sensing mechanisms, which can be used in conjunction with current or upcoming EH capabilities, in order to enable the deployment of energy neutral or nearly energy neutral WSNs with practical network lifetime and data gathering rates up to two orders of magnitude higher than the current state-of-the-art. The theoretical foundations of the proposed research are the emerging paradigms of compressive sensing (CS) and distributed compressive sensing (DCS) as well as energy- and information-optimal data acquisition and transmission protocols. These elements offer the means to tightly couple the energy consumption process to the random nature of the energy harvesting process in a WSN in order to achieve the breakthroughs in network lifetime and data gathering rates. The proposed project brings together a team of theoreticians and experimentalists working in areas of the EPSRC ICT portfolio that have been identified for expansion. This team is well placed to be able to develop, implement and evaluate the novel WSN technology. The consortium also comprises a number of established and early stage companies that clearly view the project as one that will impact their medium and long term product developments and also strengthen their strategic links with world class academic institutions. We anticipate that a successful demonstration of the novel WSN technology will generate significant interest in the machine-to-machine (M2M) and Internet of Things (IoT) industries both in the UK and abroad.

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  • Funder: UKRI Project Code: EP/L003309/1
    Funder Contribution: 973,522 GBP

    The overall aim of the proposed research is to enable the development and operation of new, agile, more cost-effective and sustainable chemical manufacturing processes. The future of sustainable chemicals manufacturing is in flexible, modular and intensive processes. New automated reaction tools and hardware are becoming ubiquitous but optimisation of how they are used and the methods of dealing with the larger amounts of experimental data available are still largely manual processes, and generally only carried out for long duration production runs. A crucial missing component is a fast automated closed-loop methodology for development and running of optimised chemicals manufacturing processes. This proposal will close this gap by developing an automated system for experimentation that brings together automated hardware for reaction execution, methods for reaction composition data acquisition and analysis, the intelligent selection of future experiments, and the development of process models in real-time. The multi-disciplinary challenge of this topic requires research in a variety of fields, including chemistry, statistics, engineering, chemometrics and computer science. Each of the individual research questions are novel and substantial challenges in their own right; their fusion will allow the automatic optimisation of reaction chemistry for a variety of applications and on a variety of different scales. Such a system would become a key tool in both academic and industrial chemistry, making feasible the routine manufacture of even small amounts of material via optimised processes, and increasing the efficiency of processes on all scales. Hence, it has the potential to enable new ways of working towards sustainable and green chemistry.

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  • Funder: UKRI Project Code: EP/K006835/1
    Funder Contribution: 354,296 GBP

    The global market for lithium-ion batteries is expected to increase from an estimated $8bn in 2008 to $30bn by 2017, according to independent market analyst Takeshita. Lithium-air or lithium-oxygen batteries are an important technology for future energy storage because they have theoretical energy densities that are almost an order of magnitude greater than the state-of-the-art Li-ion battery. The energy storage needs of society in the long-term are likely to demand batteries for both stationary power storage to collect unwanted energy generated from wind farms and batteries to power electric vehicles. The success of these technologies underpins the UK's need to move to a lower carbon and greener economy which is less reliant on carbon dioxide generating fossil fuels. The development of lithium-oxygen batteries is being hampered by lack of understanding of the complexity of products formed on the air-cathode during reduction and oxidation. Spectroscopy is critical for identification of products and the understanding of the chemistry at the interface of electrodes. Moreover advanced in situ spectroelectrochemical techniques help us to comprehend these complex interfaces whilst under full electrochemical control. A particularly sensitive technique, surface-enhanced infrared absorption spectroscopy (SEIRAS) has not been applied to these systems. Furthermore development of in situ far-IR spectroscopy would enable us to identify lithium-oxygen compounds at these low frequencies. The goal of this proposal is therefore to further the progress of lithium-oxygen technology by fully understanding the reduction and oxidation pathways taking place within the battery and to comprehend the role of electrocatalytic surfaces.

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  • Funder: UKRI Project Code: EP/M008797/1
    Funder Contribution: 97,027 GBP

    The aim of the proposal is the development of a high throughput, broadband method to access real-time information about the fine details of physical and biological objects. This will be achieved by transforming evanescent electromagnetic waves, the information carriers for small scale features, to free-space propagating waves that can be collected easily by standard optical techniques. The suggested metamaterial platform will impact most fields where fine-scale diagnostics are important (materials science, condensed matter physics, biology) and will also allow the recording and retrieval of encrypted information for which specially designed metamaterials will act as a unique decryption key.

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  • Funder: UKRI Project Code: EP/J015296/1
    Funder Contribution: 250,481 GBP

    We aim to achieve a breakthrough in the performance of "dilute nitride" semiconductor materials to enable the development of novel light sources and photodetectors which can operate in the mid-infrared spectral range. The 3-5 um wavelength range is technologically important because it is used for applications including; remote gas sensing, range-finding and night vision, bio-medical imaging for diagnosis in healthcare and sensitive detection in optical spectroscopy. However, the development of instrumentation is limited by the availability of efficient, affordable light sources and photodetectors, which is directly determined by the semiconductor materials which are currently available. By introducing small amounts (~ 1%) of N into InAs(Sb) we have shown that it is possible to access the mid-infrared using a new (dilute nitride) semiconductor and we are now seeking to engineer its band structure in order to significantly enhance the material's optical properties and increase quantum efficiency for light detection and emission. To enable the development of new photodetectors we will exploit the sensitivity of the conduction band to the resonant interaction of the N-level with the extended states of the host InAsSb crystal lattice to tailor the photoresponse and create a near ideal situation for electron acceleration and avalanche multiplication, resulting in a much larger detectable signal. To minimise the unwanted processes causing excessive noise and dark current, which compete with the avalanche multiplication and light detection in the detector, we shall arrange for the avalanche multiplication to be initiated by only one carrier type (electrons in our case). Many applications rely on the detection of very weak signals consisting of only a few photons. Conventional photodiodes have a limited sensitivity, especially if high speed detection is needed. In applications which are "photon starved", avalanche photodiodes (APDs) can provide an effective solution. However, at present effective avalanche multiplication in the mid-infrared spectral range can only be obtained by using exotic CdHgTe (CMT) semiconductor alloys. The resulting detectors require cooling, thus making CMT-based APDs prohibitively expensive for all except military applications. Simpler fabrication, low noise, low operating voltage, inexpensive manufacturing and room temperature operation, together with monopolar electron ionisation are all significant advantages of APDs based on the dilute nitride materials compared to existing technologies. Similarly, we shall enable the development of more efficient mid-infrared light sources. By adjusting the N content within InAsN(Sb) quantum wells and carefully tailoring the residual strain and carrier confinement, we shall be able to defeat competing non-radiative recombination processes whilst simultaneously enhancing the light generation efficiency. These novel quantum wells would then form the basis of the active region from where the light is generated, either within an LED or a diode laser. Currently mid-infrared LED efficiency is low at room temperature, and with the improvements which we shall deliver; we envisage that devices with significantly higher dc output power will be developed following our lead. Mid-infrared diode lasers incorporating our strained dilute nitride quantum wells are also expected to exhibit a reduced threshold current and could offer an affordable alternative to existing technology, especially in the 3-4 um spectral range. We will produce prototype photodetectors and LEDs and use these to demonstrate the above-mentioned avalanche behaviour and quantum efficiency improvements respectively. We shall validate our dilute nitride materials and structures in close collaboration with our collaborators at NPL, SELEX, CST and INSTRO to evaluate performance for use in practical applications and help ensure uptake of our technology.

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  • Funder: UKRI Project Code: EP/L505791/1
    Funder Contribution: 20,166 GBP

    The project will build a demonstration information and knowledge management system (IKMS) to facilitate innovation with new and replacement chemical materials from renewable biomass in formulated products. The IKMS will enable functional ingredients from simple transformations of feedstocks to be identified more quickly and recommend the best feedstocks for a particular function. If successful, it will repair a disconnection in the supply chain for exploitation of bio-based and renewable materials as functional ingredients in formulated products, creating significant business benefit to the commercial partners and, following dissemination and further development, to the UK bio-based materials sector and formulated products businesses as a whole. The demonstrator will focus on a search for bio-surfactant innovations, and will be innovative in itself by both integrating several IT tools for the first time in a radical approach to formulated product design and by being the first of its kind to be applied across a chemical using industry supply chain. The ambition of the system is that it will collate and manage existing data with new data recovered from the experimental measurements and use this to update the models applied by the search tools. An automated data-driven modelling tool will be developed and integrated into the system for this purpose. As data is added and as models are improved, the performance of the selection algorithms will improve along with the chances that the selected ingredient and formulation candidates will meet downstream commercialisation criteria. It is important to note that modelling methods used here are quite different but complementary to those to be developed under application 33587-239245, which are physics-based rather than data-driven, and will provide powerful capability for fast selection of novel chemistries against a subset of filter criteria and provide mechanistic insights to sharpen these filters for better precision and better experimental assay design. To achieve its objectives, the project will extend the 101508 information model and add a repository to store formulation information (composition and assembly) and property data (experimental and computed) to complement the feedstock and transformation repositories. The information model and repository will need to be chemically intelligent, use readily extensible RDF and triple store technologies, and incorporate semantic search capabilities to facilitate integration. Modelling tools will be adapted and implemented using modern machine learning methods to find the mathematical relationships between ingredient structure and properties, and between formulation composition and assembly with application performance. The models will be built on data created during the project and added to the 101508 model repository. The 101508 tools for enumerating ingredient options (from feedstocks and chemical transformation processes) will be extended to enumerating formulations (from ingredients and assembly processes). The enumeration tools will be coupled to a global many-objective search tool using diversity or chemical structure/formulation composition/assembly - property models for efficient exploration of the combinatorial ingredient/formulation space. We will also develop tools to help maintain and grow the IKMS with minimal overhead to future projects. These include semantic search and semi-automated extraction of appropriate data from literature and other available resources, and for ontological integration and semi-autonomous building of ontologies where these do not exist. In order to demonstrate how this system will work in practice, novel bio-surfactants identified in 101508 will be made and their properties measured, a selected sub-set formulated and evaluated and the data and derived models used to drive another cycle of bio-surfactant selection and formulation optimisation.

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  • Funder: UKRI Project Code: EP/J018058/1
    Funder Contribution: 1,372,980 GBP

    We propose to create in the UK a novel research capability providing Angstrom Analysis for dynamic in-situ reaction studies under controlled conditions of temperature and continuous gas atmosphere rather than the usual high vacuum. The new design provides the world's first full function aberration corrected environmental scanning transmission electron microscope (AC ESTEM). In association with partners in the vibrant UK chemical and energy industries we will generate fundamental application science underpinning nanoparticle based solid state heterogeneous catalysis used in gas-solid reactions. We will modify an existing AC TEM/STEM instrument to complement and extend with gas reaction studies the National AC STEM Facility's superior image and energy resolutions in high vacuum. It will be used in York programmes and collaborative projects with other groups through the AC STEM. It builds on the PIs' established reputations for global leadership in ETEM, with most of the worldwide activity to date - all overseas - based on >10 high resolution ETEMs and many of them AC (on the TEM image side only), using core technology from the authors' earlier developments. Preliminary 'proof-of-principle' has been demonstrated on the remotely controlled double aberration corrected JEOL 2200FS TEM/STEM at York; combining sub-Angstrom (<0.1nm) resolution, unrestricted HAADF Z-contrast STEM imaging, wide angle electron diffraction and EDX (+ EELS) chemical analysis not available on ETEMs. The double aberration correction collects, in a single and often directly interpretable TEM image, a full range of spatial frequencies at close to zero defocus to minimise image delocalisation at internal interfaces such as grain boundaries, external surfaces, defects and other key discontinuities. This is especially important for dynamic in-situ studies with continuously changing data making impractical older through-focal series reconstruction methods. AC also transforms the sensitivity of STEM analysis. The work will use analytical methods established with 'frozen' and process extracted samples, and apply them to the study of continuous processes at new levels of sensitivity and relevance. Access to key intermediate states and phases may be critical to understand and control process mechanisms; but they may be metastable with respect to conditions, including temperature or chemical environment, and therefore not accessible through ex-situ or pulse studies. A very practical example, in which there is leading UK industry interest and support, is the nano-structure and related property stability of supported metal nanoparticle heterogeneous catalysts. Through synthesis, activation, operation, deactivation, reactivation and recovery mechanisms, understanding at a fundamental level is critical for managing on a rational basis industrial practice for sustained activity and selectivity; and where necessary recovering these key attributes when lost. The project direction is closely aligned with the domain science needs of real world academic and industrial applications, and there are early adoption prospects for underpinning key technologies; including to extend useful process life cycles. For example, this is critical for the wider commercial viability of fuel cells. The proposal has the support of leading UK companies in the vibrant and internationally competitive chemical industry sector, and of academic collaborators. At the same time, the new learnings in basic domain science are also directed towards opening up new applications of pressing societal value in the environment. Fundamental physical science research with strategic and tactical industrial applications leads to differentiated intellectual products with an initiative unique in the UK and fully competitive globally. The project will extend and apply core nanoparticle catalysis science and technology, and train a new cohort of students, postdocs, senior staff and visitors.

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  • Funder: UKRI Project Code: EP/K031805/1
    Funder Contribution: 221,071 GBP

    During the last twenty years mathematics and physics have significantly influenced each other and became highly entangled. Mathematical physics was always producing a wide variety of new concepts and problems that became important subjects of the pure mathematical research. The growth of gauge, gravity and string theories have made the relation between these subjects closer than ever before. An important driving force was the discovery of quantum groups and of the gauge/gravity dualities. Here the leading role was played by the the so-called AdS/CFT duality and the underlying integrable structure of it. A far-reaching concept is the effect of boundaries and the corresponding boundary conditions. They are unavoidable in almost all models of mathematical physics and are of the fundamental importance. The introduction of boundaries into the theory of quantum groups leads to a whole new class of the so-called reflection algebras. Such algebras were shown to appear in numerous mathematical models and are at the core of the integrable structure of them. Furthermore, these algebras were also shown to play a prominent role in the AdS/CFT. However a coherent framework for describing such algebras is not known, and many properties of the reflection algebras are still an open question. The goal of this research is to develop new algebraic methods and intradisciplinary connections between the axiomatic theory of algebras and the theory of quantum groups inspired by the integrable structure of the AdS/CFT, in particular by shedding more light on the effects of boundaries and different boundary configurations. The research is driven by applying algebraic objects such as the quantum affine and Yangian algebras to find elegant, exact solutions describing the models that arise from and are inspired by the gauge/gravity dualities.

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  • Funder: UKRI Project Code: EP/J020915/1
    Funder Contribution: 583,832 GBP

    Argumentation provides a powerful mechanism for dealing with incomplete, possibly inconsistent information and for the resolution of conflicts and differences of opinion amongst different parties. Further, it is useful for justifying outcomes. Thus, argumentation can support several aspects of decision-making, either by individual entities performing critical thinking (needing to evaluate pros and cons of conflicting decisions) or by multiple entities dialectically engaged to come to mutually agreeable decisions (needing to assess the validity of information the entities become aware of and resolve conflicts), especially when decisions need to be transparently justified (e.g. in medicine). Because of its potential to support decision-making when transparently justifying decisions is essential, the use of argumentation has been considered in a number of settings, including medicine, law, e-procurement, e-business and design rationale in engineering. Potential users of existing argumentation-based decision-making methods are empowered by transparent methods, afforded by argumentation, but lack either means of formal evaluation sanctioning decisions as (individually or collectively) rational or a computational framework for supporting automation. The combination of these three features (transparency, rationality and computational tools for automation) is essential for argumentation-based decision-making to have a fruitful impact on applications. Indeed, for example, a medical practitioner would not find a "black-box" recommended decision useful, but he/she would also not trust a fully transparent, dialectically justified decision unless he/she were sure that this is the best one (rational). In addition, the plethora of information doctors need to take into account nowadays to make decisions requires automated support. TRaDAr aims at providing methods and prototype systems for various kinds of argumentation-based (individual and collaborative) decision-making that generate automatically transparent, rational decisions, while developing case studies in smart electricity and e-health to inform and validate methods and systems. In this context, TRaDAr's technical objectives are: (O1) to provide novel argumentation-based formulations of decision problems for individual and collaborative decision-making; (O2) to study formal properties of the formulations at (O1), sanctioning the rationality of decisions; (O3) to provide real-world case studies in smart electricity and e-health for (individual and collaborative) decision-making, using the formulations at (O1) and demonstrating the importance of the properties at (O2) as well as the transparent nature of argumentation-based decision-making; (O4) to define provably correct algorithms for the formulations at (O1), supporting rational and transparent (individual and collaborative) decision-making; (O5) to implement prototype systems incorporating the computational methods at (O4), and use these systems to demonstrate the methodology at (O1-O2) for the case studies at (O3). The project intends to develop novel techniques within an existing framework of computational argumentation, termed assumption-based argumentation, towards the achievements of these objectives, and adapting notions and techniques from classical (quantitative) decision theory and mechanism design in economics. The envisaged TRaDAr's methodology and systems will contribute to a sustainable society supported by the digital economy, and in particular they will support people in making informed choices. The project will focus on demonstrating the proposed techniques in specific case studies (smart electricity and e-health for breast cancer) in two chosen application areas (digital economy and e-health), but its outcomes could be far-reaching into other case studies (e.g. in other areas of medicine) as well as other sectors (e.g. in engineering, for supporting decisions on design choices).

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  • Funder: UKRI Project Code: EP/J012521/1
    Funder Contribution: 669,334 GBP

    Human-Autonomous Systems (HAS) are collections of human and autonomous agencies of great importance to, defence, disaster and emergency response, transport, and energy services (especially in hostile/inhospitable environments). However, current reality is that HAS do not provide the right information at the right time to the right agent (human or autonomous); cause information overload; and produce rigid, inflexible and ineffective rule bound behaviours. The current state-of the art in Human-Autonomous Systems is that they often involve disparate, incompatible, and 'stove-piped' communication and information structures with conflicting technologies. This has resulted in failures, ineffectiveness and inefficiency, costing resources and even lives. Improving the collective capability of human-autonomous systems requires agile and flexible behaviour in the face of complex and rapidly changing situations. Developing the collective capability of HAS requires and leads to improving; i) the levels of local and global awareness and utility of information and knowledge, ii) the quality and trustworthiness of decision-making and consideration of alternatives, iii) the ability to increase the level of "command by intent" through the development of lightweight but richer reporting and monitoring mechanisms; and iv) the ability to globally exploit and learn from local initiatives. Underlying all of these lies the importance of the, representation, interactive manipulation and communication of information and knowledge. This 36 month research project will achieve improvements in HAS performance through novel breakthroughs in important areas of Collective Capability for Human-Autonomous Systems (HASCC0. Those breakthroughs will enable improved levels of shared awareness, collective decision-making, agile, responsive command, and collective learning. To achieve this we will develop protocols and technologies for information and knowledge abstraction and representation, argumentation, rationale, command and reporting structures. Our approach is to develop protocols and technologies to support the interactions and knowledge manipulations needed to enhance HAS collective awareness and decision-making and capable of representing and interacting with; - the (rich but lightweight) Argumentation, Rationale, Command and Reporting Structures, - which influence local and global and include strategic, tactical and operational decision-making. Enabling HAS collectives to be agile and responsive. Our investigations comprise two cycles corresponding to different application domain scenarios. Each application domain will present different information and decision-making requirements, and will require different strategic, tactical and operational deployments of HAS. In this way we will seek to assess the generality and wider applicability of our research findings. In the first cycle, we will focus on the situation awareness and decision-making required of HAS for "Multiple Vehicle Cooperative Autonomy". In the second, we will expand our research to investigate HAS for "investigation and repair of defective infrastructure". In each cycle, we will undertake scenario development, modelling, prototyping, evaluation and revision. At the end of each cycle we will produce versions of Protoypes, Models and Principles of HASCC. The research will directly contribute to several EPSRC strategic priority themes by providing science and technology that strengthens critical national infrastructure in: Global Uncertainties - Collective Capability to underpin agile, coherent and integrated HAS, in Defence and Disaster Emergency Response Services Digital Economy - the development of novel Collective Capability Technologies to advance Autonomous Systems, Energy - Collective Capability to underpin HAS enabling safe and reliable energy provision. Transport - Collective Capability for HAS to provide reliable, safe and efficient Transport Services.

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  • Funder: UKRI Project Code: EP/K033166/1
    Funder Contribution: 587,661 GBP

    Future deployments of wireless sensor network (WSN) infrastructures for environmental, industrial or event monitoring are expected to be equipped with energy harvesters (e.g. piezoelectric, thermal or photovoltaic) in order to substantially increase their autonomy and lifetime. However, it is also widely recognized that the existing gap between the sensors' energy availability and the sensors' energy consumption requirements is not likely to close in the near future due to limitations in current energy harvesting (EH) technology, together with the surge in demand for more data-intensive applications. Hence, perpetually operating WSNs are currently impossible to realize for data-intensive applications, as significant (and costly) human intervention is required to replace batteries. With the continuous improvement of energy efficiency representing a major drive in WSN research, the major objective of this research project is to develop transformative sensing mechanisms, which can be used in conjunction with current or upcoming EH capabilities, in order to enable the deployment of energy neutral or nearly energy neutral WSNs with practical network lifetime and data gathering rates up to two orders of magnitude higher than the current state-of-the-art. The theoretical foundations of the proposed research are the emerging paradigms of compressive sensing (CS) and distributed compressive sensing (DCS) as well as energy- and information-optimal data acquisition and transmission protocols. These elements offer the means to tightly couple the energy consumption process to the random nature of the energy harvesting process in a WSN in order to achieve the breakthroughs in network lifetime and data gathering rates. The proposed project brings together a team of theoreticians and experimentalists working in areas of the EPSRC ICT portfolio that have been identified for expansion. This team is well placed to be able to develop, implement and evaluate the novel WSN technology. The consortium also comprises a number of established and early stage companies that clearly view the project as one that will impact their medium and long term product developments and also strengthen their strategic links with world class academic institutions. We anticipate that a successful demonstration of the novel WSN technology will generate significant interest in the machine-to-machine (M2M) and Internet of Things (IoT) industries both in the UK and abroad.

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  • Funder: UKRI Project Code: EP/L003309/1
    Funder Contribution: 973,522 GBP

    The overall aim of the proposed research is to enable the development and operation of new, agile, more cost-effective and sustainable chemical manufacturing processes. The future of sustainable chemicals manufacturing is in flexible, modular and intensive processes. New automated reaction tools and hardware are becoming ubiquitous but optimisation of how they are used and the methods of dealing with the larger amounts of experimental data available are still largely manual processes, and generally only carried out for long duration production runs. A crucial missing component is a fast automated closed-loop methodology for development and running of optimised chemicals manufacturing processes. This proposal will close this gap by developing an automated system for experimentation that brings together automated hardware for reaction execution, methods for reaction composition data acquisition and analysis, the intelligent selection of future experiments, and the development of process models in real-time. The multi-disciplinary challenge of this topic requires research in a variety of fields, including chemistry, statistics, engineering, chemometrics and computer science. Each of the individual research questions are novel and substantial challenges in their own right; their fusion will allow the automatic optimisation of reaction chemistry for a variety of applications and on a variety of different scales. Such a system would become a key tool in both academic and industrial chemistry, making feasible the routine manufacture of even small amounts of material via optimised processes, and increasing the efficiency of processes on all scales. Hence, it has the potential to enable new ways of working towards sustainable and green chemistry.

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  • Funder: UKRI Project Code: EP/K006835/1
    Funder Contribution: 354,296 GBP

    The global market for lithium-ion batteries is expected to increase from an estimated $8bn in 2008 to $30bn by 2017, according to independent market analyst Takeshita. Lithium-air or lithium-oxygen batteries are an important technology for future energy storage because they have theoretical energy densities that are almost an order of magnitude greater than the state-of-the-art Li-ion battery. The energy storage needs of society in the long-term are likely to demand batteries for both stationary power storage to collect unwanted energy generated from wind farms and batteries to power electric vehicles. The success of these technologies underpins the UK's need to move to a lower carbon and greener economy which is less reliant on carbon dioxide generating fossil fuels. The development of lithium-oxygen batteries is being hampered by lack of understanding of the complexity of products formed on the air-cathode during reduction and oxidation. Spectroscopy is critical for identification of products and the understanding of the chemistry at the interface of electrodes. Moreover advanced in situ spectroelectrochemical techniques help us to comprehend these complex interfaces whilst under full electrochemical control. A particularly sensitive technique, surface-enhanced infrared absorption spectroscopy (SEIRAS) has not been applied to these systems. Furthermore development of in situ far-IR spectroscopy would enable us to identify lithium-oxygen compounds at these low frequencies. The goal of this proposal is therefore to further the progress of lithium-oxygen technology by fully understanding the reduction and oxidation pathways taking place within the battery and to comprehend the role of electrocatalytic surfaces.

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  • Funder: UKRI Project Code: EP/M008797/1
    Funder Contribution: 97,027 GBP

    The aim of the proposal is the development of a high throughput, broadband method to access real-time information about the fine details of physical and biological objects. This will be achieved by transforming evanescent electromagnetic waves, the information carriers for small scale features, to free-space propagating waves that can be collected easily by standard optical techniques. The suggested metamaterial platform will impact most fields where fine-scale diagnostics are important (materials science, condensed matter physics, biology) and will also allow the recording and retrieval of encrypted information for which specially designed metamaterials will act as a unique decryption key.

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  • Funder: UKRI Project Code: EP/J015296/1
    Funder Contribution: 250,481 GBP

    We aim to achieve a breakthrough in the performance of "dilute nitride" semiconductor materials to enable the development of novel light sources and photodetectors which can operate in the mid-infrared spectral range. The 3-5 um wavelength range is technologically important because it is used for applications including; remote gas sensing, range-finding and night vision, bio-medical imaging for diagnosis in healthcare and sensitive detection in optical spectroscopy. However, the development of instrumentation is limited by the availability of efficient, affordable light sources and photodetectors, which is directly determined by the semiconductor materials which are currently available. By introducing small amounts (~ 1%) of N into InAs(Sb) we have shown that it is possible to access the mid-infrared using a new (dilute nitride) semiconductor and we are now seeking to engineer its band structure in order to significantly enhance the material's optical properties and increase quantum efficiency for light detection and emission. To enable the development of new photodetectors we will exploit the sensitivity of the conduction band to the resonant interaction of the N-level with the extended states of the host InAsSb crystal lattice to tailor the photoresponse and create a near ideal situation for electron acceleration and avalanche multiplication, resulting in a much larger detectable signal. To minimise the unwanted processes causing excessive noise and dark current, which compete with the avalanche multiplication and light detection in the detector, we shall arrange for the avalanche multiplication to be initiated by only one carrier type (electrons in our case). Many applications rely on the detection of very weak signals consisting of only a few photons. Conventional photodiodes have a limited sensitivity, especially if high speed detection is needed. In applications which are "photon starved", avalanche photodiodes (APDs) can provide an effective solution. However, at present effective avalanche multiplication in the mid-infrared spectral range can only be obtained by using exotic CdHgTe (CMT) semiconductor alloys. The resulting detectors require cooling, thus making CMT-based APDs prohibitively expensive for all except military applications. Simpler fabrication, low noise, low operating voltage, inexpensive manufacturing and room temperature operation, together with monopolar electron ionisation are all significant advantages of APDs based on the dilute nitride materials compared to existing technologies. Similarly, we shall enable the development of more efficient mid-infrared light sources. By adjusting the N content within InAsN(Sb) quantum wells and carefully tailoring the residual strain and carrier confinement, we shall be able to defeat competing non-radiative recombination processes whilst simultaneously enhancing the light generation efficiency. These novel quantum wells would then form the basis of the active region from where the light is generated, either within an LED or a diode laser. Currently mid-infrared LED efficiency is low at room temperature, and with the improvements which we shall deliver; we envisage that devices with significantly higher dc output power will be developed following our lead. Mid-infrared diode lasers incorporating our strained dilute nitride quantum wells are also expected to exhibit a reduced threshold current and could offer an affordable alternative to existing technology, especially in the 3-4 um spectral range. We will produce prototype photodetectors and LEDs and use these to demonstrate the above-mentioned avalanche behaviour and quantum efficiency improvements respectively. We shall validate our dilute nitride materials and structures in close collaboration with our collaborators at NPL, SELEX, CST and INSTRO to evaluate performance for use in practical applications and help ensure uptake of our technology.

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  • Funder: UKRI Project Code: EP/L505791/1
    Funder Contribution: 20,166 GBP

    The project will build a demonstration information and knowledge management system (IKMS) to facilitate innovation with new and replacement chemical materials from renewable biomass in formulated products. The IKMS will enable functional ingredients from simple transformations of feedstocks to be identified more quickly and recommend the best feedstocks for a particular function. If successful, it will repair a disconnection in the supply chain for exploitation of bio-based and renewable materials as functional ingredients in formulated products, creating significant business benefit to the commercial partners and, following dissemination and further development, to the UK bio-based materials sector and formulated products businesses as a whole. The demonstrator will focus on a search for bio-surfactant innovations, and will be innovative in itself by both integrating several IT tools for the first time in a radical approach to formulated product design and by being the first of its kind to be applied across a chemical using industry supply chain. The ambition of the system is that it will collate and manage existing data with new data recovered from the experimental measurements and use this to update the models applied by the search tools. An automated data-driven modelling tool will be developed and integrated into the system for this purpose. As data is added and as models are improved, the performance of the selection algorithms will improve along with the chances that the selected ingredient and formulation candidates will meet downstream commercialisation criteria. It is important to note that modelling methods used here are quite different but complementary to those to be developed under application 33587-239245, which are physics-based rather than data-driven, and will provide powerful capability for fast selection of novel chemistries against a subset of filter criteria and provide mechanistic insights to sharpen these filters for better precision and better experimental assay design. To achieve its objectives, the project will extend the 101508 information model and add a repository to store formulation information (composition and assembly) and property data (experimental and computed) to complement the feedstock and transformation repositories. The information model and repository will need to be chemically intelligent, use readily extensible RDF and triple store technologies, and incorporate semantic search capabilities to facilitate integration. Modelling tools will be adapted and implemented using modern machine learning methods to find the mathematical relationships between ingredient structure and properties, and between formulation composition and assembly with application performance. The models will be built on data created during the project and added to the 101508 model repository. The 101508 tools for enumerating ingredient options (from feedstocks and chemical transformation processes) will be extended to enumerating formulations (from ingredients and assembly processes). The enumeration tools will be coupled to a global many-objective search tool using diversity or chemical structure/formulation composition/assembly - property models for efficient exploration of the combinatorial ingredient/formulation space. We will also develop tools to help maintain and grow the IKMS with minimal overhead to future projects. These include semantic search and semi-automated extraction of appropriate data from literature and other available resources, and for ontological integration and semi-autonomous building of ontologies where these do not exist. In order to demonstrate how this system will work in practice, novel bio-surfactants identified in 101508 will be made and their properties measured, a selected sub-set formulated and evaluated and the data and derived models used to drive another cycle of bio-surfactant selection and formulation optimisation.

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  • Funder: UKRI Project Code: EP/J018058/1
    Funder Contribution: 1,372,980 GBP

    We propose to create in the UK a novel research capability providing Angstrom Analysis for dynamic in-situ reaction studies under controlled conditions of temperature and continuous gas atmosphere rather than the usual high vacuum. The new design provides the world's first full function aberration corrected environmental scanning transmission electron microscope (AC ESTEM). In association with partners in the vibrant UK chemical and energy industries we will generate fundamental application science underpinning nanoparticle based solid state heterogeneous catalysis used in gas-solid reactions. We will modify an existing AC TEM/STEM instrument to complement and extend with gas reaction studies the National AC STEM Facility's superior image and energy resolutions in high vacuum. It will be used in York programmes and collaborative projects with other groups through the AC STEM. It builds on the PIs' established reputations for global leadership in ETEM, with most of the worldwide activity to date - all overseas - based on >10 high resolution ETEMs and many of them AC (on the TEM image side only), using core technology from the authors' earlier developments. Preliminary 'proof-of-principle' has been demonstrated on the remotely controlled double aberration corrected JEOL 2200FS TEM/STEM at York; combining sub-Angstrom (<0.1nm) resolution, unrestricted HAADF Z-contrast STEM imaging, wide angle electron diffraction and EDX (+ EELS) chemical analysis not available on ETEMs. The double aberration correction collects, in a single and often directly interpretable TEM image, a full range of spatial frequencies at close to zero defocus to minimise image delocalisation at internal interfaces such as grain boundaries, external surfaces, defects and other key discontinuities. This is especially important for dynamic in-situ studies with continuously changing data making impractical older through-focal series reconstruction methods. AC also transforms the sensitivity of STEM analysis. The work will use analytical methods established with 'frozen' and process extracted samples, and apply them to the study of continuous processes at new levels of sensitivity and relevance. Access to key intermediate states and phases may be critical to understand and control process mechanisms; but they may be metastable with respect to conditions, including temperature or chemical environment, and therefore not accessible through ex-situ or pulse studies. A very practical example, in which there is leading UK industry interest and support, is the nano-structure and related property stability of supported metal nanoparticle heterogeneous catalysts. Through synthesis, activation, operation, deactivation, reactivation and recovery mechanisms, understanding at a fundamental level is critical for managing on a rational basis industrial practice for sustained activity and selectivity; and where necessary recovering these key attributes when lost. The project direction is closely aligned with the domain science needs of real world academic and industrial applications, and there are early adoption prospects for underpinning key technologies; including to extend useful process life cycles. For example, this is critical for the wider commercial viability of fuel cells. The proposal has the support of leading UK companies in the vibrant and internationally competitive chemical industry sector, and of academic collaborators. At the same time, the new learnings in basic domain science are also directed towards opening up new applications of pressing societal value in the environment. Fundamental physical science research with strategic and tactical industrial applications leads to differentiated intellectual products with an initiative unique in the UK and fully competitive globally. The project will extend and apply core nanoparticle catalysis science and technology, and train a new cohort of students, postdocs, senior staff and visitors.

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