Elite athletes walk a fine line between performance success and failure. Although regarded by the public as examples of ultimate fitness, in reality they often exhibit vital signs bordering on clinical pathology. Their physiological parameters challenge our notions of what we consider clinically normal, for, as individuals, athletes represent a unique model of human stress adaptation and often, sadly, mal-adaptation. Understanding this human variance may assist ultimately in understanding aspects of well being in the population at large, in the work place and during healthy exercise, as well as when undergoing lifestyle changes to overcome disease, age-related changes and chronic stress.To maximise the potential of GB athletes and support the quest for gold at future World Championships, Summer and Winter Olympic and Paralympic Games, the UK's sports governing bodies and the UK sports governing bodies and research councils have identified the opportunity for engineering and physical science disciplines to support and interact with the sports community during training. Not only will this secure competitive advantage for UK athletes, it will also, of more general application, contribute understanding of the biology of athletic performance to gain insights which will improve the health and wellbeing of the population at large.The vision of ESPRIT is to position UK at the forefront of pervasive sensing in elite sports and promote its wider application in public life-long health, wellbeing and healthcare, whilst also addressing the EPSRC's key criteria for UK science and engineering research. The proposed programme represents a unique synergy of leading UK research in body sensor networks (BSN), biosensor design, sports performance monitoring and equipment design. The provision of ubiquitous and pervasive monitoring of physical, physiological, and biochemical parameters in any environment and without activity restriction and behaviour modification is the primary motivation of BSN research. This has become a reality with the recent advances in sensor design, MEMS integration, and ultra-low power micro-processor and wireless technologies. Since its inception, BSN has advanced very rapidly internationally. The proposing team has already contributed to a range of novel, low cost, miniaturised wireless devices and prototypes for sports and healthcare.

Degradation of lithium battery cells is a complex process occurring over multiple temporal and spatial domains. Improved understanding of cell health is a prerequisite for expanded use of Li-ion battery technology in many challenging applications. Early detection of changes in critical parameters would enable performance assessment and degradation forecasting, as well as providing a route to predict the most likely eventual failure modes. Parameter detection requires the ability to measure a diverse set of static and dynamic properties that elucidate the state of a battery system. To enable efficient and safe battery operation, diagnostic schemes need to be fast, accurate, and reliable, work in near real-time, and detect potential faults as early as possible; to enable widespread practical adoption, parameter detection must be achieved with minimal added cost. In tandem, the need to run accurate in-service battery models is critical, and would enable model-based control. Second only to safety monitoring of voltage and temperature, state-of-charge (SOC) estimation is the most important function of a battery management system (BMS). Better BMS SOC could help maximize battery performance and lifetime, but is often accurate to only +/- 10% - and simple methods to improve this accuracy do not currently exist. Models capable of predicting Li-ion performance under modest conditions are highly advanced. But significant progress is still needed to couple operational models suitable for the diagnosis and prognosis of degradation and failure with models of degradation mechanisms. Generally faults and the resulting degradation manifest as capacity or power fade and often state-of-the-art techniques such as X-ray CT, open circuit voltage measurements, and thermal measurements are used to characterise the degradation. This proposal brings together a world-class team to address the critical issue of degradation and health estimation for leading lithium-ion-battery chemistries. We place particular focus on Translational Diagnostics, which we define as diagnostic methods that translate across length scales, across different domains, and across academic research into industry practice. Key outputs from our work will be a suite of new and validated diagnostic tools integrated with battery models for both leading and emerging lithium-ion and sodium- ion battery chemistries. We aim to ensure that these diagnostic tools are capable of cost-effective deployment on both small and large battery systems, and able to run in real time with sufficient accuracy and reliability, such that safer, more durable and lower cost electrochemical energy storage systems can be achieved

Abstracts are not currently available in GtR for all funded research. This is normally because the abstract was not required at the time of proposal submission, but may be because it included sensitive information such as personal details.

To support the development of challenging, difficult to manufacture products, increased reliance is placed on techniques to allow accurate dimensional measurement of parts and components. New measurement systems are needed that provide data quickly with higher levels of accuracy and precision than is currently possible. Currently high accuracy measurements are made using dedicated expensive instrumentation in temperature controlled labs. The wide range of measurement challenges mean there is no single instrument available to suit all needs. In fact, the range of lab based instrument systems required to meet the measurement needs of industry continues to grow. It includes techniques ranging from contact measurements made using a mechanical probe, to non-contact measurements which use light, lasers, or X-ray based measurement methods. The main drawback of these systems is that they are usually slow to set-up, and significant time is required to take measurements. This means that although they are very accurate they are less useful for the control and improvement of challenging manufacturing processes, where data must be collected and analysed quickly. Improved measurement systems are required which provide higher speed measurements, at lower cost, without compromising accuracy. Currently two approaches address this need. One approach uses on machine sensors to provide high-speed measurements, while the other approach is to position instruments closer to the manufacturing environment to reduce the time required to transfer work to the measurement lab. Both approaches have obvious benefits as they provide faster data; however, they are also less accurate as a result of the unwanted disturbances experienced on the factory floor. These limitations result in a trade-off: the user can either have high accuracy, or high speed measurement, but not both at once. The research undertaken within this Fellowship will develop a new way of collecting and effectively processing critical measurement data. Instead of a reliance on high accuracy instruments, this approach will provide a new way of thinking with respect to how measurement systems are designed and implemented. The goal will be to allow different types of lower accuracy data to be combined in a beneficial way. For example, computer simulations of a machine, product, and process will be combined with sensors that monitor environmental conditions. In addition sensors used to take high speed measurements of parts during the manufacturing process itself will be used. Through a collaborative process these data will be combined to provide fast high quality data. To verify and further improve the system a small quantity of accurate feedback data from high accuracy instruments in temperature controlled labs will be used. In effect the approach will be to combine slow accurate data, with fast less reliable data, to produce enhanced accuracy fast measurements. For example, if a batch of high precision components must be produced, the components must also have their geometry verified and corrected if required. On machine sensors may be used to verify geometry, but accuracy is limited due to environmental effects such as temperature and humidity. To compensate for these errors a collaborative measurement system will initially make measurements using both on-machine sensors as well as off-machine lab instruments. It will blend these data sets in addition to data from on-machine environmental monitoring sensors, and computer simulations to correct for errors and therefor enhance the accuracy of the measurements. The system will automatically adapt to changing environmental conditions by triggering the need for more lab-based data which will allow an improved error correction to be made. In this way the system will adapt and optimise the measurement process to suit the current manufacturing conditions.

The aim of this project is to develop reliable and quantifiable measurements of the surface chemistry of printed glycan arrays. The printing process leads to a multi-component surface either through diluents, contaminants or additives in the printing solution or through incomplete surface coverage after printing owing to differences in surface energies and wettability. Secondary ion mass spectrometry (SIMS) is a powerful technique to image the surface chemistry at high spatial resolution (200 nm is possible) however the mass spectrum is highly complex making it very difficult to separate out the chemical contributions. We propose to use the spatial chemical heterogeneity with discriminant multivariate analytical techniques to separate out the chemical components as well as develop novel approaches with G-SIMS (developed at NPL) to identify the separate chemical components. Once chemical components have been separated the challenge is then todeduce structural information about the molecule from the mass spectrum and consequently identify the molecule. Recently, we have developed a novel informatics approach based on the SMILES molecular structure format to develop a database, G-DB1, of fragmentation ions for mass spectrometry. This has proved to be very effective for the identification of molecules in other surface analytical areas. This project will develop the approach for glycans using an informatics approach to search through relevant publicly available databases of chemical structures such as KEGG and PubChem and the FunctionalGlycomicsGateway. Nanoparticles may be used for both diagnostic and delivery mechanisms for glycan binding proteins. For example, gold nanoparticles functionalised with lectin exhibit specific interactions with glycans which may be measured using a variety of transduction methodologies including electrochemical. Also, gold nanoparticles functionalised with glycans may bind to viruses such as HIV providing a steric barrier to cell entry. For correct correlation with measured activity it is important to know what is at the nanoparticle surface. This project will develop methods to chemically characterise the surface of gold nanoparticles using C60 SIMS. Whilst SIMS is powerful and has high resolution it necessitates the use of vacuum which slows the rate of analysis and sample throughput. Over the last few years the field of ambient surface mass spectrometry has advanced rapidly especially for desorption electrospray ionisation (DESI) and plasma assisted desorption ionisation (PADI). In this project we will investigate the effectiveness of these techniques for the identification of glycans at surfaces and bound proteins (DESI). The two ionisations sources will be coupled to an Orbitrap mass spectrometer providing a mass resolution of > 100,000 and a mass accuracy of better than 3 ppm which will significantly help structural elucidation.