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in catalytically relevant systems. The insights obtained will guide the design of new catalysts that demonstrate truly orthogonal selectivity compared to conventional alternatives. The project will
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About the project: Machine learning accelerated Inverse Design of Graphene Nanoribbons for Green Energy Supervisor: Dr Sara Sangtarash, University of Warwick Thermoelectric materials convert heat
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There exists an inherent variability in how lithium-ion batteries fail – an event commonly referred to as “thermal runway”. This uncertainty drives additional cost and complexity into the design and
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stacking error and removes the options for easy disassembly for repair, replace or recycle. In this project modification of the cell end cap design is to be investigated through FE analysis, prototype build
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to examine how donor structure and solvent environment govern chalcogen bonding interactions. More applied projects involve designing, synthesising, and evaluating new chalcogen bond donor architectures
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science with the overarching ambition to meticulously design novel chemical building blocks and finetune their reactivity to design materials with advanced functionality and improved sustainability
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physics-based and data-driven methods to support the design and scale-up of these systems. This approach will reduce the need for costly experiments, improve scale-up predictions, and provide confidence
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, as even small delays can affect robot responsiveness and create safety risks. This issue is exacerbated by the fact that the modern Internet infrastructure was originally designed to provide best
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to unlock new functionalities. In the latter phases of the project, the refined technique will be applied to characterise both commercial and custom-designed membranes, generating fundamental insights
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the THz labs at Warwick and by our collaborators at the Institute of Saint-Louis (ISL). By suggesting design modifications to the molecular structures, your work will improve the next generation of