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The Circular Economy (CE) is a revolutionary alternative to a traditional linear, make-use-dispose economy. It is based on the central principle of maintaining continuous flows of resources at their highest value for the longest period and then recovering, cascading and regenerating products and materials at the end of each life cycle. Metals are ideal flows for a circular economy. With careful stewardship and good technology, metals mined from the Earth can be reused indefinitely. Technology metals (techmetals) are an essential, distinct, subset of specialist metals. Although they are used in much smaller quantities than industrial metals such as iron and aluminium, each techmetal has its own specific and special properties that give it essential functions in devices ranging from smart phones, batteries, wind turbines and solar cells to electric vehicles. Techmetals are thus essential enablers of a future circular, low carbon economy and demand for many is increasing rapidly. E.g., to meet the UK's 2050 ambition for offshore wind turbines will require 10 years' worth of global neodymium production. To replace all UK-based vehicles with electric vehicles would require 200% of cobalt and 75% of lithium currently produced globally each year. The UK is 100% reliant on imports of techmetals including from countries that represent geopolitical risks. Some techmetals are therefore called Critical Raw Materials (high economic importance and high risk of supply disruption). Only four of the 27 raw materials considered critical by the EU have an end-of-life recycling input rate higher than 10%. Our UKRI TechMet CE Centre brings together for the first time world-leading researchers to maximise opportunities around the provision of techmetals from primary and secondary sources, and lead materials stewardship, creating a National Techmetals Circular Economy Roadmap to accelerate us towards a circular economy. This will help the UK meet its Industrial Strategy Clean Growth agenda and its ambitious UK 2050 climate change targets with secure and environmentally-acceptable supplies of techmetals. There are many challenges to a future techmetal circular economy. With growing demand, new mining is needed and we must keep the environmental footprint of this primary production as low as possible. Materials stewardship of techmetals is difficult because their fate is often difficult to track. Most arrive in the UK 'hidden' in complex products from which they are difficult to recover. Collection is inefficient, consumers may not feel incentivised to recycle, and policy and legislative initiatives such as Extended Producer Responsibility focus on large volume metals rather than small quantity techmetals. There is a lack of end-to-end visibility and connection between different parts of techmetal value chains. The TechMet consortium brings together the Universities of Exeter, Birmingham, Leicester, Manchester and the British Geological Survey who are already working on how to improve the raw materials cycle, manufacture goods to be re-used and recycled, recycle complex goods such as batteries and use and re-use equipment for as long as possible before it needs recycling. One of our first tasks is to track the current flows of techmetals through the UK economy, which although fundamental, is poorly known. The Centre will conduct new interdisciplinary research on interventions to improve each stage in the cycle and join up the value chain - raw materials can be newly mined and recycled, and manufacturing technology can be linked directly to re-use and recycling. The environmental footprint of our techmetals will be evaluated. Business, regulatory and social experts will recommend how the UK can best put all these stages together to make a new techmetals circular economy and produce a strategy for its implementation.

The emerging era of exascale computing that will be ushered in by the forthcoming generation of supercomputers will provide both opportunities and challenges. The raw compute power of such high performance computing (HPC) hardware has the potential to revolutionize many areas of science and industry. However, novel computing algorithms and software must be developed to ensure the potential of novel HPC architectures is realized. Computational imaging, where the goal is to recover images of interest from raw data acquired by some observational instrument, is one of the most widely encountered class of problem in science and industry, with myriad applications across astronomy, medicine, planetary and climate science, computer graphics and virtual reality, geophysics, molecular biology, and beyond. The rise of exascale computing, coupled with recent advances in instrumentation, is leading to novel and often huge datasets that, in principle, could be imaged for the first time in an interpretable manner at high fidelity. However, to unlock interpretable, high-fidelity imaging of big-data novel methodological approaches, algorithms and software implementations are required -- we will develop precisely these components as part of the Learned EXascale Computational Imaging (LEXCI) project. Firstly, whereas traditional computational imaging algorithms are based on relatively simple hand-crafted prior models of images, in LEXCI we will learn appropriate image priors and physical instrument simulation models from data, leading to much more accurate representations. Our hybrid techniques will be guided by model-based approaches to ensure effectiveness, efficiency, generalizability and uncertainty quantification. Secondly, we will develop novel algorithmic structures that support highly parallelized and distributed implementations, for deployment across a wide range of modern HPC architectures. Thirdly, we will implement these algorithms in professional research software. The structure of our algorithms will not only allow computations to be distributed across multi-node architectures, but memory and storage requirements also. We will develop a tiered parallelization approach targeting both large-scale distributed-memory parallelization, for distributing work across processors and co-processors, and light-weight data parallelism through vectorization or light-weight threads, for distributing work on processors and co-processors. Our tiered parallelization approach will ensure the software can be used across the full range of modern HPC systems. Combined, these developments will provide a future computing paradigm to help usher in the era of exascale computational imaging. The resulting computational imaging framework will have widespread application and will be applied to a number of diverse problems as part of the project, including radio interferometric imaging, magnetic resonance imaging, seismic imaging, computer graphics, and beyond. The resulting software will be deployed on the latest HPC computing resources to evaluate their performance and to feed back to the community the computing lessons learned and techniques developed, so as to support the general advance of exascale computing.

The DART project aims to pioneer a ground-breaking capability to enhance the performance and energy efficiency of reconfigurable hardware accelerators for next-generation computing systems. This capability will be achieved by a novel foundation for a transformation engine based on heterogeneous graphs for design optimisation and diagnosis. While hardware designers are familiar with transformations by Boolean algebra, the proposed research promotes a design-by-transformation style by providing, for the first time, tools which facilitate experimentation with design transformations and their regulation by meta-programming. These tools will cover design space exploration based on machine learning, and end-to-end tool chains mapping designs captured in multiple source languages to heterogeneous reconfigurable devices targeting cloud computing, Internet-of-Things and supercomputing. The proposed approach will be evaluated through a variety of benchmarks involving hardware acceleration, and through codifying strategies for automating the search of neural architectures for hardware implementation with both high accuracy and high efficiency.

Ultra-short and ultra-intense laser pulses provide an impressive camera into the world of electron motion. Attoseconds and sub-femtoseconds are the natural time scale of multi-electron dynamics during the ionization and break-up of atoms and molecules. The overall aim of the proposed work is to investigate attosecond phenomena, pathways of correlated electron dynamics and effects due to the magnetic field of light in three and four-electron ionization in atoms and molecules triggered by intense near-infrared and mid-infrared laser pulses. Correlated electron dynamics is of fundamental interest to attosecond technology. For instance, an electron extracted from an atom or molecule carries information for probing the spatio-temporal properties of an ionic system with angstrom resolution and attosecond precision paving the way for holography with photoelectrons. Moreover, studies of effects due to the magnetic field of light in correlated multi-electron processes are crucial for understanding a variety of chemical and biological processes, such as the response of driven chiral molecules. Chiral molecules are not superimposable to their mirror image and are of particular interest, since they are abundant in nature. The proposed research will explore highly challenging ultra-fast phenomena involving three and four-electron dynamics and effects due to the magnetic field of light in driven atoms and during the break-up of driven two and three-center molecules. We will investigate the physical mechanisms that underly these phenomena and devise schemes to probe and control them. Exploring these ultra-fast phenomena constitutes a scientific frontier due to the fast advances in attosecond technology. These fundamental processes are largely unexplored since most theoretical studies are developed in a framework that does not account for the magnetic field of light. Moreover, correlated three and four-electron escape is currently beyond the reach of quantum mechanical techniques. Hence, new theoretical tools are urgently needed to address the challenges facing attoscience. In response to this quest, we will develop novel, efficient and cutting-edge semi-classical methods that are much faster than quantum-mechanical ones, allow for significant insights into the physical mechanisms, compliment experimental results and predict novel ultra-fast phenomena. These semi-classical techniques are appropriate for ionization processes through long-range Coulomb forces. Using these techniques, we will address some of the most fundamental problems facing attoscience. Our objectives are: 1) Identify and time-resolve novel pathways of correlated three-electron dynamics in atoms driven by near-infrared and mid-infrared laser pulses. 2) Explore effects due to the magnetic field of light in correlated two and three-electron escape during ionization in atoms as well as in two and three-center molecules driven by near-infrared and mid-infrared laser pulses that are either linearly or elliptically polarized or by vector beams, i.e. "twisted" laser fields, an intriguing form of light that twists like a helical corkscrew. 3) Control correlated multi-electron ionization and the formation of highly exited Rydberg states in four-active-electron three-center molecules by employing two-color laser fields or vector beams.

The quest for improved energy storage is currently one of the most important scientific challenges. The UK is investing heavily in energy storage and renewable energy technologies and is committed to reducing its CO2 emissions by replacing the majority of its electricity generating capacity over the next few decades. Building better batteries is key to the use of electricity in a low-carbon future and for the exploitation of current and next-generation technologies. Current Li-ion batteries based on liquid electrolytes cannot meet the requirements of future applications. The creation of safer, cheaper, recyclable and higher energy density batteries is therefore essential for the electrification of transport and grid-scale storage of energy from renewable resources. This EPSRC New Investigator Award will develop transformative methods that will deliver solutions to these societally and industrially critical problems. Solid-state Li-ion batteries are a rapidly emerging technology with the potential to revolutionise energy storage. This technology utilises solid electrolytes instead of the flammable liquid electrolytes found in current Li-ion batteries. The solid-state architecture has the potential to significantly increase both the safety and energy density of next-generation batteries. Their performance is, however, currently limited by a number of underlying challenges, including the presence of highly resistive interfaces and difficulties in controlling the microstructures of the solid electrolytes that these batteries are built around. These challenges greatly hinder Li-ion transport and are therefore highly detrimental to the operation of the battery. To address these pertinent issues, the team will develop and apply state-of-the-art computational and experimental techniques to provide a fundamental understanding of ion transport at the microscale of solid electrolytes for solid-state batteries. Such an understanding will allow for the design of solid electrolyte microstructures that promote Li-ion transport instead of restricting it. The insights obtained for solid-state batteries in this project will also have direct implications for other battery and energy technologies where the microstructure and solid-solid interfaces again play crucial roles in determining their performance.

The ability to accurately measure the power and frequency (or wavelength) distribution of an optical signal is crucial to a vast range of applications, for spectroscopy in medicine, ensuring the safety of food or pharmaceuticals to remote sensing of gasses and fundamental science, e.g. characterising short laser pulses or finding the atmospheres of extrasolar planets. Currently, this is achieved using Optical Spectrum analyzers or optical monochromators, which have a key limitation. To achieve high-resolution they need a large optical path length and therefore large footprint (optical path length on the order of 0.5-1 m is common). Thus these devices are bulky and expensive. While not an issue for lab-based low-volume applications, this excludes their use - and thus the use of high-resolution spectroscopy - in large volume, or footprint and weight-sensitive applications, e.g. integration into lab-on-a-chip devices, mobile phones and low mass satellites (e.g. cube-sat). These applications can only be served by integrated on-chip spectrometers. Here the use of speckle spectrometers, using the random scattering of light to achieve a high wavelength resolution in an ultra-small footprint would be highly promising if it were not for the case that typical the multiple scattering needed to create the speckle results in most of the light being scattered out of the device before it can be detected. However, over the last decade, several groups (including myself) have shown that the statistical distribution of scattering sites can be used to control the amount and direction (e.g. within the plane of the device vs out-of-plane) of light scattering. In this project we merge these advances with speckle spectrometers, i.e. using controlled disorder to efficiently generate a speckle pattern, while virtually eliminating out-of-plane scattering and optical losses. Building on this advance we will demonstrate a high resolution, low footprint on-chip spectrometer that outperforms the state of the art by orders of magnitude (in device footprint) without sacrificing the device resolution. We will also demonstrate that these devices are suitable for future large scale manufacturing, using pre-existing CMOS facilities, are suitable for gas spectroscopy and laser pulse spectrum analysis and compatible with future integration with optical detectors for a direct electronic readout. This would present a game-changing advance in the field of integrated spectrometers and lay the foundation for future commercialization of integrated speckle spectrometers.

There is an extremely high demand for laboratory-based blood tests from community settings in the UK and analysis suggests an important role in the future for remote blood monitoring that would enable patients and health professionals to carry out their own tests remotely, greatly benefiting patients and speeding up decision making. The COVID-19 pandemic has further highlighted the need for remote and connected blood testing that is beyond the online virtual clinics in the NHS outpatient setting. In current blood testing services for community healthcare, it is challenging to obtain and process blood samples outside of the clinical setting without training and lab facilities, and patients are required to attend a GP surgery or hospital for tests with travel burden and infection risk. Many blood analyses are done in batches that take a long time to build up, meaning the speed of blood sample analysis of routine tests and time taken for diagnosis are further challenges. Despite recent innovations in point of care, current blood analysis tools in practice are mainly mechanical or labour-intensive that require extensive filtering and manual tweaking and not suitable for regular at-home monitoring and longitudinal analytics. There is no personalised real-time approach available to inform disease complexity and conditions over time, which are critical for early detection of acute diseases and the management of chronic conditions. In England, around 95% of clinical pathways rely on patients having access to efficient, timely and cost-effective pathology services and there are 500 million biochemistry and 130 million haematology tests are carried out per year. This means inefficient and infrequent blood testing leads to late diagnosis, incomplete knowledge of disease progression and potential complications in a wide range of populations. Taking those challenges into account and current digital transformation in healthcare, this is a timely opportunity to bring researchers, clinicians and industrialist together to address the challenges of blood monitoring and analytics. The proposed Network+ will build an interdisciplinary community that will explore future blood testing solutions to achieve remote, inclusive, rapid, affordable and personalised blood monitoring, and address the above challenges in community health and care. To achieve the Network+ vision, research of technologies will be conducted from collaborations among information and communication technology (ICT), data and analytical science, clinical science, applied optics, biochemistry, engineering and social sciences in the Network+. The network will address three key technical challenges in blood testing: Remote monitoring, ICT, Personalised data and AI in a range of examplar clinical areas including cancer, autoimmune diseases, sickle cell disease, preoperative care, pathology services and general primary care.

The proposed research aims to develop an innovative mitigation device to protect the next-generation onshore and offshore wind farms from dynamic loading caused by extreme natural events. In 2020, 20% of the UK's electricity was obtained from wind using both onshore and offshore windfarms. In order to increase this percentage and help the UK address its climate change target, new wind farms, with taller and larger wind turbines, and situated in more extreme locations are planned. Projections of growth also indicate the expansion into emerging markets and construction of new wind farms in developing countries. Therefore, these next-generation wind turbines will have to cope with harsher climate conditions induced by stronger storms and taller sea waves, and extreme events such as earthquakes and tsunamis. Several simplifying assumptions used for the design of previous generations of wind turbines can no longer be applied and new critical factors and uncertainties linked to power-generation efficiency and structural safety will emerge, affecting their resilience and life-cycle. The particular area of focus of this research is the traditional transition piece of a wind turbine, which is a structural element that connects the tower with its foundation and will have to tolerate extreme stresses induced by dynamic loading during extreme natural events. The aim is to replace the traditional connector with a novel mechanical joint of hourglass shape, termed an Hourglass Lattice Structure (HLS). This innovation will combine the unique features of two proven technologies extremely effective in seismic engineering, namely the "reduced beam section" approach and the "rocking foundation" design. In particular, the proposed HLS device, because of its hourglass shape, will facilitate the rocking behaviour in order to create a highly dissipating "fuse" which will protect the wind tower and foundation. Performance of the novel proposed device on the structural life-cycle risk will be assessed through analytical, numerical, and experimental investigation by using, as a measure of efficiency, the levelized cost of energy (LCOE), namely the cost per unit of energy based on amortized capital cost over the project life. In addition, experimental testing of offshore small-scale wind turbines will be carried out by means of an innovative test rig, the first-ever underwater shake-table hosted in a hydraulic flume that will be deployed, calibrated, and used to simulate multi-hazard scenarios such as those recently discovered and dubbed "stormquakes". The successful outcome of this timely project will allow next-generation wind turbines to be more resilient and cost effective so that wind energy can develop as a competitive renewable energy resource with less need for government subsidy. The inclusion of industrial partners in all stages of the project ensures that the technical developments will be included in commercial devices for a medium-long term impact.