The design of an efficient crystallisation process for the production of pharmaceuticals, catalysts, fine chemicals and other materials of industrial importance is dependent on a number of factors, including internal properties of the product itself such as molecular structure, preferred crystal shape and size and cohesion energy, and external factors, such as fluid flow and drag. Although all of these factors affect the material's aggregation behaviour, the behaviour of the crystallites upon collision is usually described by a single expression, which does not take into account material-specific properties, or indeed separate intrinsic properties of the crystal from topological considerations, such as the relative geometries of the colliding particles. In addition, no consideration is given either to the colliding particles' shapes or size distribution, or the fact that the forces acting upon them may not just be normal to the point of impact, even though shear forces are likely to be very important when two particles collide. In this project, we propose to carry out a comprehensive study of the collision and aggregation behaviour of three different types of material, varying from a purely inorganic material (calcium carbonate) to a purely organic solid (adipic acid). We will use a combination of computational chemistry methods on the one hand and experimental chemical process engineering techniques on the other, to investigate the shapes of the crystallites in solution, the impact geometries of the colliding particles, the chemical bonding between the collided particles, as well as the shear and tensile forces required to separate the particles again after collision. In addition, we will investigate the precipitation of new material at the point of collision, leading to aggregation of the particles, where the collision point may indeed be a 1-D point, a 2-D line or a 3-D planar area. Once new material has grown at the join, we can calculate its resistance against fracture. The outcome of this project will be an in-depth understanding at the atomic level of the chemical and physical processes occurring upon collision of two nano-crystallites in solution. In addition, the results of the project will enable us to formulate general mathematical descriptions of the aggregation behaviour of a number of representative materials, which will include both intrinsic, material-dependent properties and external factors, including solvent effects (shear forces acting on the particles through water drag) and collision geometries. By combining these two approaches we aim to develop a quantitative kinetic description capable of being used with CFD to predict behaviour in stirred crystallisers.

The fellowship will support the completion of a book, titled 'How Does Speech Timing Work?'. Speakers manipulate speech sound durations for a variety of meaning-related purposes. This book will discuss the kinds of timing patterns people produce when they speak, and will evaluate theories of how speech articulation is controlled to produce these timing patterns. Because speech timing patterns are often termed rhythmic, it will also discuss available definitions of speech rhythm, and will evaluate rhythmicity claims for speech against available evidence. This book will be of interest to anyone interested in how speech production works, including linguists, psycholinguists, motor control specialists, speech technologists, and speech therapists.

The brain is a black-box when it comes to understanding disease. It is full of crucial details that could give untold information on how to treat and manage neurological diseases and disorders, but we lack the tools to effectively read those details. While imaging technologies give us a window to observe certain processes, they are often extremely limited. A good example is magnetic resonance imaging (MRI), which has led to countless breakthroughs in the clinic and is used to diagnose and manage patients every day. It is, however, typically limited to a single channel - essentially, we are looking at the brain in black and white instead of colour. This limitation is particularly true when looking at chemical and biological properties of the brain. There are some techniques that begin to allow imaging of multiple signals (i.e. colours), but they are limited to substances present at very high abundances within the brain. A classic example where this would be relevant is in Alzheimer's disease. There is a well-established relationship between the devastating neurodegeneration observed and brain's natural defence system. This co-occurring condition, neuroinflammation, is linked to the long-term deterioration seen in patients, but we struggle to fully understand how they are connected and how they interplay. Much like the classic chicken and egg conundrum, we are often unsure on which comes first or how that comes to be. If we could simultaneously watch how these different processes work at the same time in a living brain, we would significantly improve our understanding and be able to monitor the effects of treatments and interventions more closely. This scenario is not just limited to Alzheimer's disease, but in almost every neurological disease we can think of. Every neurodegenerative disease, brain tumours, stroke, and even mental health issues would all benefit from an improved understanding of the real-time interplay of various biological systems all working - or, more importantly, failing to work - together. I have developed a technique that greatly expands the range and sensitivity of multi-signal MRI by using carefully designed contrast agents in a process called PARASHIFT MRI. This approach allows much lower levels of compounds to be detected in the living brain with multiple readouts available. We have previously demonstrated its approach in the body, and I now aim to focus on applying the technique to study markers of brain disease in much more detail than we are currently able. This pioneering MRI technique will be supplemented by complementary cutting-edge techniques, such as mass spectrometry imaging, to further understand the brain in unprecedented depth. I will focus on stroke and brain cancer as exemplar model systems in the initial stage of my fellowship as they represent clinically vital examples of both acute and chronic inflammation, respectively. Beyond, the findings from my work will have key applications in neurodegenerative disease and across a broad spectrum of neurological disorders. By combining these new tools for comprehensively detecting, characterising, and monitoring brain disease markers, my approach will reimagine how we look at the diseased brain and retrieve untold levels of information to help us tackle this pressing societal burden.

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.

This project aims to build a lasting collaborative relationship between experts at the University of Manchester (UoM) and the Massachusetts Institute of Technology (MIT) through focused cooperative research and use of active collaboration practices. The aim of the proposed research is to develop novel methods of remote sensing capability modelling and use these in a technology roadmapping framework to identify sustainable future design approaches. As humanity accelerates its efforts to achieve Net Zero, all industries must adapt their practices to continue providing vital products and services, while minimising environmental impact. Remote sensing is a key element of global infrastructure, underpinning our efforts to address the UN Sustainable Development Goals. However, the sustainability of our space missions (the backbone of this capability) is rightly being called into question. Launch of spacecraft has increased by more than 1000% in the past decade, with further increases expected. This will have a significant impact on our terrestrial environment as launch emissions and debris from re-entry damage our atmosphere. Taking inspiration from foundational work at UoM and MIT, this project will develop a model of current orbital remote sensing capabilities as 'shells', using analytical and geometrical approaches. This model would, for the first time, allow the value, and impact, of future systems to be assessed in the framework of the existing orbital regime. Through engagement with stakeholders across the space and drone industries, as well as data users, environmental experts, and members of the public, future priorities will be identified and used to create suitable figures of merit. Integration of the capability model within the Advanced Technology Roadmapping Architecture, alongside the defined priorities, will allow plausible future scenarios to be identified, assessed and compared. This method could be used to provide insights into possible future routes of development, inform technological investment, and guide creation of future legislation. As the UK positions itself to be an international leader in Space Sustainability, this is an ideal opportunity to work with colleagues in the US to develop a unique modelling approach and design framework for sustainable future space missions. While the deterministic nature of satellite orbits makes them ideal for this initial modelling work, it is expected that the proposed technique could be adapted to incorporate aerial systems, such as drones, and even ground based sensors, giving it a wide applicability and paving the way for integrated analysis of remote sensing systems from ground to space. This project will incorporate three core work packages: 1. Develop a lasting partnership between relevant experts at the University of Manchester and the Massachusetts Institute of Technology, via: - Joint 'flipped' active meetings and seminars; - Inter-institutional mentoring and supervision; and - Research collaboration and creation of joint publications. 2. Develop a method of orbital remote sensing modelling, incorporating current capabilities and orbital capacity, as a guide for future sustainable mission design, via: - Inter-institutional research (including a 2-month secondment); - Validation and testing of the developed model using suitable case studies; and - Joint publication of methods and open release of data and code. 3. Use the developed capability model within the Advanced Technology Roadmap Architecture to propose a sustainable remote sensing technology design approach via: - Upstream engagement to identify stakeholder priorities and state-of-the-art; - Use of the capability model to assess plausible scenarios within the Advanced Technology Roadmap Architecture framework; and - Documentation and open release of approach and initial findings.