To date, most research into the impact of microplastics in the environment has focussed on marine (coastal and ocean) environments. However, there is growing acceptance that microplastics are also pervasive within freshwater (river and lake) systems. The limited number of studies from rivers around the world have all found microplastics to be present within samples of river bed sediments or the water column. This is of concern as the ecotoxicological impact of microplastics will likely have a negative impact on a range of freshwater species with an additional public health concern if pollutants associated with microplastics then enter the human food chain. A fundamental issue regarding the science of microplastics in freshwaters is a lack of data with which to generate physically based models. This thus makes it very hard to establish what are 'normal' levels of microplastics within our rivers and hence whether such levels represent an acceptable level of risk to ecosystems or society more generally, or where clean-up or remediation strategies should be targeted. To make meaningful progress, this issue requires international consensus to be agreed quickly so that ongoing and future research efforts can be properly synthesised to provide meaningful evidence-based policy. The purpose of this proposal is to meet this challenge by assembling a new network of internationally leading freshwater microplastics experts. This network will undertake a focused programme of data collection. By pooling this data and using it to generate new numerical models at a series of workshops the network will be able to reach more robust conclusions as to the overall freshwater plastic flux to the oceans. This will address the significant stumbling block the discipline currently faces and thus allow further development of more physically based models. Such a significant deliverable can only be achieved by the sort of networking opportunity that is facilitated by the global partnerships seedcorn fund.

Flooding is the deadliest and most costly natural hazard on the planet, affecting societies across the globe. Nearly one billion people are exposed to the risk of flooding in their lifetimes and around 300 million are impacted by floods in any given year. The impacts on individuals and societies are extreme: each year there are over 6,000 fatalities and economic losses exceed US$60 billion. These problems will become much worse in the future. There is now clear consensus that climate change will, in many parts of the globe, cause substantial increases in the frequency of occurrence of extreme rainfall events, which in turn will generate increases in peak flood flows and therefore flood vast areas of land. Meanwhile, societal exposure to this hazard is compounded still further as a result of population growth and encroachment of people and key infrastructure onto floodplains. Faced with this pressing challenge, reliable tools are required to predict how flood hazard and exposure will change in the future. Existing state-of-the-art Global Flood Models (GFMs) are used to simulate the probability of flooding across the Earth, but unfortunately they are highly constrained by two fundamental limitations. First, current GFMs represent the topography and roughness of river channels and floodplains in highly simplified ways, and their relatively low resolution inadequately represents the natural connectivity between channels and floodplains. This restricts severely their ability to predict flood inundation extent and frequency, how it varies in space, and how it depends on flood magnitude. The second limitation is that current GFMs treat rivers and their floodplains essentially as 'static pipes' that remain unchanged over time. In reality, river channels evolve through processes of erosion and sedimentation, driven by the impacts of diverse environmental changes (e.g., climate and land use change, dam construction), and leading to changes in channel flow conveyance capacity and floodplain connectivity. Until GFMs are able to account for these changes they will remain fundamentally unsuitable for predicting the evolution of future flood hazard, understanding its underlying causes, or quantifying associated uncertainties. To address these issues we will develop an entirely new generation of Global Flood Models by: (i) using Big Data sets and novel methods to enhance substantially their representation of channel and floodplain morphology and roughness, thereby making GFMs more morphologically aware; (ii) including new approaches to representing the evolution of channel morphology and channel-floodplain connectivity; and (iii) combining these developments with tools for projecting changes in catchment flow and sediment supply regimes over the 21st century. These advances will enable us to deliver new understanding on how the feedbacks between climate, hydrology, and channel morphodynamics drive changes in flood conveyance and future flooding. Moreover, we will also connect our next generation GFM with innovative population models that are based on the integration of satellite, survey, cell phone and census data. We will apply the coupled model system under a range of future climate, environmental and societal change scenarios, enabling us to fully interrogate and assess the extent to which people are exposed, and dynamically respond, to evolving flood hazard and risk. Overall, the project will deliver a fundamental change in the quantification, mapping and prediction of the interactions between channel-floodplain morphology and connectivity, and flood hazard across the world's river basins. We will share models and data on open source platforms. Project outcomes will be embedded with scientists, global numerical modelling groups, policy-makers, humanitarian agencies, river basin stakeholders, communities prone to regular or extreme flooding, the general public and school children.

Periodic water shortages in many regions throughout the world are increasing because of population growth, urbanization, economic development, and climate change. The need to provide a safe drinking water supply from increasingly complex sources polluted by multiple contaminants has motivated the development of novel membrane technologies. Pressure-driven nanofiltration (NF) and reverse osmosis (RO) membrane processes are increasingly used for drinking water treatment because they are capable of removing all pathogens and most organic and inorganic contaminants in a single treatment step. However, more widespread adoption of these technologies has been limited because of inadequate resistance of state-of-the-art NF and RO membranes to (bio)fouling, compaction, and chemical oxidation coupled with a relatively narrow range of solute selectivity. This project will overcome the current NF and RO membrane challenges by using pioneering interfacial polymerization (IP) methods to fabricate active layers of two-dimensional covalent organic frameworks (COFs) interfaced with compatible support media. 2D COFs are crystalline, permanently porous, and layered macromolecules with structure, chemical composition, and porosity set through the rational design of their monomers. COFs will provide separating layers comprising uniform pores with tailored size, shape, and variable chemical functionality in contrast to the amorphous and empirically optimized polyamide active layers present in the state-of-the-art NF/RO membranes. The project contributes fundamental knowledge towards a new class of membranes to affordably solve many of the global water challenges through the design, synthesis, and characterization of a new library of COF-based membrane active layers that will be formed directly on novel support layers and tailored to meet specific performance targets. The novel COF-based membranes have the potential of significantly decreasing the operating costs of membrane based water treatment systems and increasing broader implementation of these technologies.

The proposal builds on an existing collaboration which has focussed on achieving a multi-scale understanding of the material-structure response to thermoacoustic excitation at up to 750K and 800 Hz using detailed experiments and simulations, in plates and beams of conventionally-manufactured metals, ranging from aluminium to Hastelloy X. Results have shown, at a microscale, a tendency for deformation to concentrate in the larger grains of oligocrystal within the material microstructure at locations disparate from where macroscale homogeneous analysis predicts (Carroll et al., Int. J. Fatigue, 57: 140-150, 2013), demonstrating that non-uniformity in the microstructure can lead to significant and service critical errors in predicting failure. Further laboratory-scale experiments, using maps of surface deformation measured during broadband thermoacoustic excitation, have confirmed the presence of mode jumping and shifting when non-uniform heating generates thermal buckling (Lopez-Alba et al, J. Sound & Vibration 439:241-250, 2019). With this in mind, the research team scaled these tests to component scale, establishing quantitative validation procedures for coupled models of thermoacoustic excitation of simple components (Berke et al, Exptl. Mech., 56(2):231-243, 2016). In doing so, the team developed two unique pieces of experimental apparatus: in Illinois, for localised heating and modal excitation of coupons; and in Liverpool, to deliver spatially distributed heating at 21kW while simultaneously applying random broadband excitation to small components. Both rigs have real-time, full-field temperature and displacement measurement capability. Lambros and Patterson have correspondingly complementary expertise in multi-scale mechanics of materials under extreme loading (Lambros) and in measurement, simulation and validation of structural responses (Patterson). It is proposed to exploit these findings, facilities and expertise to understand the potential for additive manufacturing in the production of components subject to extreme thermomechanical excitation in demanding environments. It is likely that this type of structure will be produced in small quantities rendering it appropriate to consider additive manufacturing; however, the extreme conditions of temperature and mechanical loading make it a challenging application for any material. Successful design, manufacture and service deployment of such components requires an understanding of the multi-scale material-structure response to loading and its evolution with a component's progression from its virgin state through shake-down towards initiation of detectable non-critical damage. These responses are understood at a fundamental level for subtractively-manufactured metals; however, there is very limited fundamental understanding of these material-structural interactions for additively-manufactured metals, at either room temperature (Attar et al, IJ Mach. Tools & Manu., 133: 85-102, 2018, Foehring et al, Mat. Sci. Eng. A, 724: 536-546, 2018) or elevated temperatures (Roberts et al, Progress. Add. Manu., 1-8, 2018). It is hypothesized, because of the unique microstructure containing the previously studied larger grains of oligocrystal, the complex thermomechanical history of their manufacture and the presence of significant residual stresses, that the response of additively-manufactured metals under extreme thermoacoustic loading will be significantly different from their subtractively-manufactured counterparts, especially in defect-driven processes such as failure. This proposal extends the research of Lambros and Patterson by adding the additive manufacturing expertise and facilities provided by Sutcliffe (R&D Director at Renishaw AMPD, RAe Silver Medallist 2018 with over 20 years researching metal additive manufacturing) who has unparalleled access to the latest additive manufacturing technology.

The aim of this proposal is to establish a standard digital code for the synthesis of molecules. Like Spotify, which allows the distribution of music in an mp3 (or similar) digital format, the development of a chemical code for synthesis will allow users to share their code as a result of the digitisation 'Chemify' process. The code will be demonstrated both manually and on basic robotic systems available in our laboratory (GU) and with our international collaborators based in the USA (MB), Canada (AAG), Germany (PS), and Poland (BG) who are experts in modular organic scaffold synthesis (MB), computational chemistry and statistics for experimental design (AAG), robotic carbohydrate synthesis (PS), and networks and rules of chemical synthesis (BG). In the long term, the ability to automate the synthesis of molecules will lower the cost of manufacture by enabling the automatic and unbiased exploration of chemical space giving a digital code. Such codes are needed if chemists are to develop systems that ensure reproducibility, and the ability to explore new reactions and statistics driven design of experiments to target unknown molecules. Recently we took a key step to encoding a multi-step synthesis into a digital blueprint,1 but the vision to go from code to molecules represents a gigantic problem. In this proposal, we will aim to develop a chemical ontology for synthetic chemistry that will lead to the first version of a programming language for chemical synthesis. We will then demonstrate the code can be used to synthesise important molecules, already robotically synthesised by us, and examples from our collaborators in the USA, Germany, Canada and Poland on the same universal 'chemputer' synthesise robot.