Chemical biology is the scientific discipline that harnesses the ability of small molecules to perturb biological processes. It is used to improve our understanding of those biological processes, to identify the genes that control them and to discover novel compounds that can be used to improve human health or increase crop productivity. Whilst chemical biology is widely exploited in other fields, and despite its proven power as a gene discovery tool in plants, it has been slow to gain acceptance amongst plant biologists. A primary reason for this is that the methods previously available to screen small molecules for their effects on the plant phenotype are laborious and limited in the number of traits they can monitor. At Lancaster University a novel technology has recently been developed that for the first time allows Arabidopsis seedlings to be grown under conditions suitable for studying the effects of small molecules on the development of both roots and shoots. However, it is still a laborious process to screen more than a few hundred molecules using the current version of this technology, and there are some intrinsic problems that preclude reliable quantitative analysis of root architecture. In this 15 month multidisciplinary project, a team of biologists, engineers and computer scientists will address these problems to develop the 'Microphenotron', a robotic version of the phenotyping system that will automate the process of image capture and analysis. The development of the Microphenotron will greatly expand the accessibility and utility of chemical biology approaches to the wider plant biology community, leading to a greater understanding of plant gene function. It will also provide a new tool for the development of synthetic and natural molecules for improved agricultural sustainability, with resulting benefits for farmers, the environment and society.

We propose to set up the basis for an AI-based digital tool for adaptation/mitigation to the impacts of climate change and pollution on respiratory health in an urban setting. This will enable users to explore interactions between exposure to pollutants, changing weather patterns and their effect on respiratory health, accounting for the complex interactions between environment and health. The project has two coupled aspects: 1. AI model to create a digital twin to establish this interaction using asthmatic and healthy subjects as group test case. This will incorporate big data from health cohorts as well as other studies linking exposure to respiratory outcomes and cell response to pollution, as well as air quality and weather data. 2. Building on this exposure-response model, develop AI-based personalised models using deep learning techniques to include individual circumstances (e.g., age, sex, lifestyle, medical history), combined with air pollution exposure to give a prediction of individual respiratory health. Up to 90% of the world's population breathe air with high levels of both indoor and outdoor pollution which takes ~7 million lives each year worldwide. In the UK, it is rated as one of the most serious threats to public health with only cancer, obesity and heart disease eclipsing it. The health risks associated with fine and ultrafine particulate matter (PM2.5 and PM0.1) include development and exacerbating respiratory diseases such as chronic obstructive lung diseases including asthma, respiratory infections and lung cancer. While measures are being taken to curb pollution levels, it is essential for individuals to reduce their personal exposure and abate the ill-health effects of pollution. One way of doing this would be to predict who are those individuals who would be at most risk of developing health ill-effects in the long-term. There is virtually no information of this kind of risk assessment at an individualised level and the most available information at the moment is that those at risk are children, the elderly and those already suffering from chronic lung and cardiovascular disease. The integrated AI modelling will also represent various intervention scenarios (e.g. avoiding certain more polluted travel routes for at-risk people such as asthmatics) to assess reduced exposure and corresponding changes in health outcomes. These biologic parameters of exposure will be integrated with the respiratory responses to pollution in individuals using a combination of cardio-respiratory, physical activity and personal fine particles exposure data from satellite to personal monitors e.g. smart watches. We will also integrate cellular, biochemical and biomarker personal data with the other parameters. We will numerically model the pollution and air flows at the neighbourhood scale and apply an approach centred on the impact of pollution on health to all aspects of modelling, sensor placement and management of the environment as well as the individuals. Thus, any mitigation strategies can be designed to minimize the impact of pollution on health. We develop two unique AI capabilities (1) a new AI method for solving differential equations that we call AI4HFM that can determine the dispersion of pollution through the air and (2) a unique generative method to predict health impacts from pollution levels as well as a level of uncertainty associated with this. This will be combined with reinforcement learning to tailor the AI model for an individual based on information obtained from that individual. Thus the approach may be used to guide healthy activity, prevention, diagnosis and management of respiratory diseases. It will also empower individuals so they can make informed decisions that will influence their health.

Material degradation is a primary concern to every material scientist and engineer, not only does degradation lead to failure, but results in the need for repair - a very costly endeavour. In this perspective, it is of interest to develop self-healing materials that will make maintenance redundant. As opposed to inorganic semiconductors, organic semiconducting materials are soft, which makes them ideal to be used in flexible and stretchable electronic devices, which can be directly applied to the human skin. Wearable electronics, however, are particularly prone to mechanical damage and fatigue, which is why it is paramount to develop more robust materials, like self-healable semiconductors. This fellowship will, for the first time, make it possible to synthesise intrinsic self-healing organic semiconductors and incorporate them into fully flexible, stretchable and wearable electronic devices, respectively bionic skin, to measure biological metabolites associated with diabetes (glucose), fatigue (lactate) and stress (cortisol). The electric charges will be transported via the conjugated polymer backbone, while additional supramolecular functionalities (i.e. non-binding interactions) will be incorporated into the chemical structure to ensure self-healing via the formation of dynamic bonds. The study of the new self-healing polymers will then be extended to other dynamically bonding functional groups to evaluate which chemistry is best suited for organic semiconductors. Subsequent steps will focus on the self-healing dynamics and rates, and the incorporation of the new materials into flexible electronic prototype devices. The realisation of healable organic semiconductors, for the first time, will allow the fabrication of lightweight, -wearable sensors directly applied to the human skin. This will make it possible to continuously monitor medically relevant body functions and present a significant step forward in the development of affordable biological sensors and continuous patient monitoring, ultimately enhancing medical diagnostics and opening-up new treatment possibilities.

This proposal seeks to develop a set of modelling protocols to design, characterize and invent macromolecular materials for molecular capture, separation and detection. The approach combines multi-scale modelling of the structural, dynamic, electronic and optical properties of the target materials with an evolutionary algorithm (EA) approach to the selection of material designs with optimised functionality. Microscopic modelling will provide the relationship between chemical and physical structure and the fitness parameters to be optimized during the EA, while multi-scale modelling and comparison with experiment allow evaluation of the proposed structures. As examples of technologically relevant material systems, we will first study membranes for molecular separations, including small molecule separation and desalination. The methods will then be adapted to other applications, specifically porous polymer materials for photocatalysis and optical sensing, and conjugated polymer based ion sensors. An ancillary aim is to evaluate the EA approach as a tool for materials discovery.

The Internet of Things (IoT) revolution and UK's strategy to reach net zero carbon emissions by 2050 requires establishing efficient energy scavenging technologies that can be utilised to power small electronic devices for sensing, processing and communicating data. The development of such technologies is essential for supporting modern societal needs in ubiquitous computing and AI. At the same time however, it becomes of vital importance that such technologies are built with environmentally friendly (green) approaches, taking into account the entire life cycle of the product - from raw materials and manufacturing to end-of-life. It is thus important to minimise as much as possible the use of toxic materials and chemicals, as well as develop procedures without the need to utilise equipment that consume huge amounts of energy. A key example is the Si photovoltaics industry that employs toxic chemicals in their production that are not easy to be recycled. It has been estimated that by 2050, over 60 million tons of waste will be generated from silicon solar panels alone. The aim of this fellowship is to develop novel self-powered electronic technologies, without the need to be operated by batteries; all developed with green materials and low-energy manufacturing techniques. Along these lines, I will use organic semiconductors (OSCs) that allow developing high-performance photovoltaic cells without resourcing to toxic materials. When compared to alternative conventional materials used in PVs my approach will allow for easy processing, low-cost manufacturing and attaining high performance. This will entail appropriate device engineering and material's processing strategies for prototyping high performing OPVs on rigid and flexible substrates. In parallel, I will develop low power consuming electronic components such as, sensors and supercapacitors, from green solvents and materials, in order to couple them with OPVs. Operation of such electronics will be mainly attained via light illumination, for outdoor and indoor conditions that will be exploited in a variety of practical applications. The overarching vision of this fellowship is to establish a new pathway in the IoT industry, enabling the use of such technologies in hard-to-reach areas, wearables and disposable biosensing platforms.