Green-energy transition technologies such as carbon storage, geothermal energy extraction, hydrogen storage, and compressed-air energy storage, all rely to some extent on subsurface injection or extraction of fluids. This process of injection and retrieval is well known to industry, as it has been performed all over the world, for decades. Fluid injection processes create mechanical disturbances in the subsurface, leading to local or regional displacements that may result in tremors. In the vast majority of cases, these tremors are imperceptible to humans, and have no effect on engineered structures. Nonetheless, in recent years, low magnitude induced seismic events have had profound consequences on the social acceptance of subsurface technologies, including the halting of natural gas production at the Groningen field in the Netherlands, halting of carbon storage experiments in Spain, halting of geothermal energy projects in Switzerland, and the moratorium on UK onshore gas extraction. In light of the seismic events of increasing severity recently measured during geothermal mining in Cornwall, the need to develop a rigorous fundamental understanding of induced seismicity is clear, significant, and timely, in order to prevent induced seismicity from jeopardising the ability to effectively develop the green energy transition. Most mathematical models that are used to predict and understand tremors rely on past observations of natural tremors and earthquakes. However, fluid-driven displacement in the subsurface is a controlled event, in which the properties of the injected fluids and the conditions of injection can be adjusted and optimised to avoid large events from happening. This project aims to develop a fundamental understanding of how the conditions of subsurface rocks, and the way in which fluid is injected in these rocks, affect the amount of seismicity that may be produced. We will analyse in detail the behaviour of fluid-driven seismic events, and will develop a physically realistic model based on computer simulations, novel laboratory experiments, and comprehensive field observations. Our model will characterise the relationships between specific subsurface properties, the nature of the fluid injection, and the severity of the seismic event. These findings will be linked to hazard analysis, to identify the conditions under which processes such as carbon storage, deep geothermal energy extraction, and compressed-air energy storage, are more or less likely to create tremors. We will also investigate how to best share our scientific findings with regulators and the general public, so as to maximise the impact of this work. This project will lead to an improved understanding of the processes and conditions that underpin the severity of induced seismic events, with a vision of developing strategies that will improve our ability to prevent and control these events. This project will also provide the scientific basis to improve precision and cost-effectiveness of scientific instruments that are used to monitor the subsurface, so that we can identify tremors as they occur, and better interpret what is causing them as we observe them. In the short term, we need to develop these models so that regulators and decision-makers can develop policies based on scientific evidence, using a variety of analysis tools that inter-validate each other, thereby ensuring that their predictions are robust. This is an important step in supporting the ability of developing a resilient, diversified, sustainable, and environmentally responsible energy security strategy for the UK. In the long term, by creating confidence in the understanding of these subsurface events, and demonstrating evidence of our ability to control them, we will lead the UK into an era where humans understand why certain seismic events have occurred, allowing them to potentially develop mechanisms to forecast their occurrence, and reduce their severity.

Catalysis is the process of speeding up a chemical reaction by action of a catalyst, a substance that triggers this acceleration without itself being used up. This ability to efficiently convert one substance into another is hugely important to the economy and society; it serves both to add value to simple chemical building blocks by increasing complexity (for example, converting gas and oil fractions into products ranging from fuels and solvents to materials and pharmaceutical products) and to alleviate harmful waste streams (for example, catalytic convertors in car exhausts). It is estimated that catalysts are involved in the manufacture of over 80% of the materials around us and account for over 20% of UK GDP. But this does not mean that catalysis is a mature technology. There are still fundamental unanswered scientific questions and a growing need for new catalyst technologies, especially related to achieving clean growth for industry. The catalysts used today have been honed over decades to work with specific, fossil fuel-derived feedstocks. As we move to a low carbon, more sustainable, net-zero future, we need catalysts that will convert biomass, waste and carbon dioxide into valuable products. The current generation of catalysts cannot achieve this. This project will develop these new catalysts, providing a key technology to achieve net zero carbon ambitions. Achieving this objective requires fundamental scientific advances. It also requires these advanced to be translated into real technologies to deliver their impact and bring value to the business partners. Inspired by nature, breaking down the traditional silos of catalysis research, and embracing emerging areas such as electrification, we will bring together a wide range of catalysis expertise, computation, materials science and advanced analysis to uncover new science and contribute towards achieving net zero - perhaps the most pressing objective for us all.

Battery electrified power is predicted to become the dominant mode of propulsion in future light duty transport. For sustainable heavy duty applications challenges remain around practical range, payload and total cost. Currently there is no economically viable single solution. For commercial marine vessels the problem is compounded by long service lives, with bulk carriers, tankers and container ships the main contributors to greenhouse gases. Ammonia (NH3) has excellent potential to play a significant role as a sustainable future fuel in both retrofitted and advanced engines. However, significant uncertainties remain around safe and effective end use, with these unknowns spanning across fundamental understanding, effective application and acceptance. This multi-disciplinary programme seeks to overcome the key related technical, economic and social unknowns through flexible, multidisciplinary research set around disruptive NH3 engine concepts capable of high thermal efficiency and ultra low NOx. The goal is to accelerate understanding, technologies and ultimately policies which are appropriately scaled and "right first time".

Batteries and electrocatalytic devices (i.e electrolysers, fuel cells) have multiple components spanning different length scales. The materials design space in these research fields is too large to be explored empirically. While experimental work can be directed by computational modelling to make this challenge more tenable, this is time consuming, and the number of tests/syntheses is still be too large on the experimental scale. DIGIBAT will combine computational tools (e.g. atomistic and molecular modelling, process modelling, computer-aided design, machine learning algorithms, data science) and automated HT synthesis, characterisation and testing from atoms to devices to accelerate the discovery and optimisation of new batteries and electrofuels. Specifically, DIGIBAT will comprise three HT stations: Platform A dedicated to materials synthesis and characterisation, Platform B dedicated to HT electrodes manufacturing all the way to device manufacturing and Platform C dedicated to HT electrochemical testing for both batteries and electrocatalysts. DIGIBAT will be paired with materials characterisation also applied in HT, including in operando characterisation. By executing data-rich experiments, DIGIBAT will increase the pace of innovation, while enhancing reproducibility by eliminating human errors. The research enabled by ATLAS will target challenges related to: (1) the discovery and optimisation of new battery chemistries, (2) synthesising, optimising, and testing recycled battery materials; (3) Discovering precious metal free electrocatalysts for green H2 production and fuel cells; (4) Efficient N2 to ammonia and CO2 reduction to fuels and chemicals for electrocatalysts discovery

The materials design space is too large to be explored empirically. While experimental work can be directed by computational modeling to make this challenge more tenable, the number of tests/syntheses may still be too large on an experimental time-scale. The goal of this project is to combine computational tools (e.g. molecular modelling, process modelling, computer-aided design) and automated HT synthesis and screening platforms to drive and accelerate the discovery and optimisation of new materials. Specifically, ATLAS (Automated high-Throughput pLatform Suite) will comprise three robotic stations dedicated to the synthesis (two platforms) and screening (one platform) of materials. It will be located at Imperial College South Kensington Campus and be paired with materials characterisation equipment able to handle many samples owing to dedicated auto-sampling stations. By executing data-rich experiments, ATLAS will increase the pace of innovation, while enhancing reproducibility. The research enabled by ATLAS will initially target challenges related to the discovery and optimisation of new medicines, sustainable polymers and clean energy materials.