The goal of this Korea-UK research initiative is to address Research theme 1 (Innovative concepts from Electrodes to stack) of the EPSRC-KETEP Call for Collaborative Research with Korea on Fuel Cell Technologies. The proposal also covers some aspects of Research theme 2 (Predictive control for performance and degradation mitigation). Hence, this research is associated with improving the lifetime and performance of polymer electrolyte fuel cells. Within this project we will develop new corrosion resistant catalyst supports and catalyse those supports utilising a new catalysis technique. We will also examine the development of porous bipolar plates and see how we can integrate those bipolar plates and catalysts within a fuel cell. We will trial the materials in test stacks and look at the performance and longevity of these new materials. Parallel to this work, we will use state of the art x-ray tomography and other imaging techniques to assess the performance of the materials under real operating conditions. Information from these tests will allow us to develop a methodological framework to simulate the performance of the fuel cells. This framework will then be used to build more efficient control strategies for our higher performance fuel cell systems. We will also build a strong and long-lasting collaborative framework between Korea and the UK for both academic research and commercial trade. The project will benefit from the complementary strengths of the Korean and UK PEFC programmes, and represents a significant international activity in fuel cell research that includes a focus on the challenging issues of cost reduction and performance enhancement. The project will have particularly high impact and added value due to a strong personnel exchange programme with researchers spending several months in each other's labs; highly relevant industrial collaboration; and links with the H2FC Supergen. We have strong support from industrial companies in both the UK and Korea who will support our goals of developing new catalysts for fuel cells (Amalyst - UK, and RTX Corporation - Korea), new corrosion resistant porous bipolar plates (NPL-UK; Hyundai Hysco and Hankook tire (Korea)), and fuel cell and system integrators (Arcola Energy and Intelligent Energy (UK)).

Sensors permeate our society, measurement underpins quantitative action and standardized accurate measurements are a foundation of all commerce. The ability to measure parameters and sense phenomena with increasing precision has always led to dramatic advances in science and in technology - for example X-ray imaging, magnetic resonance imaging (MRI), interferometry and the scanning-tunneling microscope. Our rapidly growing understanding of how to engineer and control quantum systems vastly expands the limits of measurement and of sensing, opening up opportunities in radically alternative methods to the current state of the art in sensing. Through the developments proposed in this Fellowship, I aim to deliver sensors enhanced by the harnessing of unique quantum mechanical phenomena and principles inspired by insights into quantum physics to develop a series of prototypes with end-users. I plan to provide alternative approaches to the state of the art, to potentially reduce overall cost and dramatically increase capability, to reach new limits of precision measurement and to develop this technology for commercialization. Light is an excellent probe for sensing and measurement. Unique wavelength dependent absorption, and reemission of photons by atoms enable the properties of matter to be measured and the identification of constituent components. Interferometers provide ultra-sensitive measurement of optical path length changes on the nanometer-scale, translating to physical changes in distance, material expansion or sample density for example. However, for any canonical optical sensor, quantum mechanics predicts a fundamental limit of how much noise in such experiment can be suppressed - this is the so-called shot noise and is routinely observed as a noise floor when using a laser, the canonical "clean" source of radiation. By harnessing the quantum properties of light, it is possible reach precision beyond shot noise, enabling a new paradigm of precision sensors to be realized. Such quantum-enhanced sensors can use less light in the optical probe to gain the same level of precision in a conventional optical sensor. This enables, for example: the reduction of detrimental absorption in biological samples that can alter sample properties or damage it; the resolution of weak signals in trace gas detection; reduction of photon pressure in interferometry that can alter the measurement outcome; increase in precision when a limit of optical laser input is reached. Quantum-enhanced techniques are being used by the Laser Interferometer Gravitational Wave Observatory (LIGO) scientific collaboration to reach sub-shot noise precision interferometry of gravitational wave detection in kilometer-scale Michelson interferometers (GEO600). However, there is otherwise a distinct lack of practical devices that prove the potential of quantum-enhanced sensing as a disruptive technology for healthcare, precision manufacture, national security and commerce. For quantum-enhanced sensors to become small-scale, portable and therefore practical for an increased range of applications outside of the specialized quantum optics laboratory, it is clear that there is an urgent need to engineer an integrated optics platform, tailored to the needs of quantum-enhanced sensing. Requirements include robustness, miniaturization inherent phase stability and greater efficiency. Lithographic fabrication of much of the platform offers repeatable and affordable manufacture. My Fellowship proposal aims to bring together revolutionary quantum-enhanced sensing capabilities and photonic chip scale architectures. This will enable capabilities beyond the limits of classical physics for: absorbance spectroscopy, lab-on-chip interferometry and process tomography (revealing an unknown quantum process with fewer measurements and fewer probe photons).

The industrial adoption of graphene requires large area, high quality material. In order to produce the necessary material on substrates of choice, we wish to use our patented photo-thermal chemical vapour deposition (PT-CVD) system. PT-CVD uses a high intensity optical source to efficiently couple energy to grow high quality graphene on specially engineered catalyst substrates. In relation to this Strategic Equipment bid the principal equipment requested is a photo-thermal chemical vapour deposition (PT-CVD) system from Thermco-Tetreon Technology. The system comprises an in-situ catalyst deposition system (to avoid oxidation and contamination) and a dedicated growth chamber capable of growth on 100 mm sized substrates, a bespoke rapid optical growth stabilisation array, in-situ Raman monitoring, residual gas analysis system and an AFM with Kelvin probe. The photo-thermal energy for growth is delivered from the rapid-growth stabilisation array consisting of high power optical sources, which can provide temperatures in excess of 1000C at the reaction front, whilst the substrate remains below 450C, compatible with CMOS integration. For both process control and further development the PT-CVD system will have an ancillary atomic force microscope, capable of scanning over large areas at high frame rates and high resolution to provide fast, accurate metrology of the graphene product. Also included is an in-situ Raman mapping system and residual gas analysis which will provide unique insight into the science of catalytic graphene growth as well as for quality control. The principal PT-CVD graphene growth system, its rapid growth catalyst stabilisation unit, the ancillary quality control and in-situ growth monitoring equipment should be considered as a single unit system in order for us to develop this strategic material growth process for the establishment of electronic grade material for the UK and beyond.

We propose a Centre for Doctoral Training in Integrative Sensing and Measurement that addresses the unmet UK need for specialist training in innovative sensing and measurement systems identified by EPSRC priorities the TSB and EPOSS . The proposed CDT will benefit from the strategic, targeted investment of >£20M by the partners in enhancing sensing and measurement research capability and by alignment with the complementary, industry-focused Innovation Centre in Sensor and Imaging Systems (CENSIS). This investment provides both the breadth and depth required to provide high quality cohort-based training in sensing across the sciences, medicine and engineering and into the myriad of sensing applications, whilst ensuring PhD supervision by well-resourced internationally leading academics with a passion for sensor science and technology. The synergistic partnership of GU and UoE with their active sensors-related research collaborations with over 160 companies provides a unique research excellence and capability to provide a dynamic and innovative research programme in sensing and measurement to fuel the development pipeline from initial concept to industrial exploitation.

Inverse problems deal with the reconstruction of some quantity of interest from indirectly measured data. A typical example is medical imaging, where there is no direct access to the quantity of interest (the inside of the patient's body) and imaging techniques, such X-Ray imaging and magnetic resonance imaging (MRI), are used. The classical approach to inverse problems uses models that describe the physics of the measurement. For example, in X-Ray imaging this model would describe how X-Rays pass through the body. In the era of big data, however, it becomes increasingly popular not to model the physics but to use vast amounts of data instead that relate known images with corresponding measurements. The theory of such data driven methods, however, is not well developed yet. It is not well understood, under which conditions on the training data such methods are stable with respect to small changes in the measurement and how well they adapt to images that are different from the training images. It is important to understand this, since otherwise the reconstruction algorithm can miss important features of the image if they weren't present in the training set, such as tumours at previously unseen locations. In this project I will extend the state-of-the-art model based theory to this data driven setting. I will study under which conditions can data driven methods achieve regularisation, i.e. when can they stably solve an otherwise unstable problem. This will make it easier to analyse stability of data driven reconstruction methods and help developing novel, stable data driven inversion methods with mathematical guarantees. I will also collaborate with the National Physical Laboratory and the Department of Chemical Engineering and Biotechnology in Cambridge on applications of my methods in imaging to reduce the time needed to acquire an image and make the reconstructions more reliable.