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, specifically reducing intrinsic energy demand in polymer production through process redesign, integration, and intensification. The project is conducted in collaboration with Victrex , a global leader in high
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of future high-performance electrification technologies with broad industrial impact. Aim The project will investigate application requirements for future defence and aerospace propulsion systems before
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and predictive design framework for thin-film composite mixed-matrix membranes (TFC-MMMs). The primary objective is to design TFC-MMMs capable of efficient high-pressure gas separation for carbon
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to create lightweight, high-performance systems with tailored mechanical and thermal behaviour. To date, however, most design frameworks rely on periodic unit cells, limiting the achievable performance
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the large-scale deployment of hydrogen. Central to the performance of these systems is the ion-conducting membrane, which governs ionic transport, efficiency, durability, and operating conditions. Currently
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note, this opportunity is only available to UK Home students. An annual stipend for up to three years - confirmed amount to be inserted. Access to specialist research facilities, computing infrastructure
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. The latter reactions of particular interest since they can be used to perform annulations, and hence the rapid construction of multicyclic molecular scaffolds. We have established that such quinoid
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PhD Studentship: Advancing solid-state battery electrolyte manufacturing with terahertz spectroscopy
-generation solid-state batteries due to their improved thermal stability, reduced flammability, enhanced durability, and potential for higher energy density. However, manufacturing high-performance
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intensification, dynamic operation, and resource recovery. The project combines modelling, pilot-scale experimentation, and techno-economic assessment to establish MECs as viable unit operations within next
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functions and linear multiply–accumulate operations directly in hardware. 3. To quantitatively characterise magnonic device performance within neuromorphic computing architectures, evaluating metrics such as