Lead Supervisor: Assoc. Prof Salvador Eslava
This PhD studentship aims to harness solar energy for producing valuable chemicals, such as hydrogen and carbon products, from small molecules like carbon dioxide and water. By developing advanced photoelectrochemical (PEC) cells, the project will integrate photovoltaic absorbers, specifically halide perovskites or organic bulk heterojunctions, with innovative catalysts to enable integrated solar devices for highly efficient clean chemical production.

Halide perovskites and organic bulk heterojunctions offer exceptional optoelectronic properties but face challenges with stability in aqueous environments, essential for solar fuel production. The student will explore protective strategies using carbon allotropes and electrochemically stable metal layers to enhance both stability and catalytic efficiency. By aligning device architecture and reaction pathways, the project will mitigate degradation, voltage losses, and improve long-term performance. The project will involve experimental syntheses, wide characterization with advanced techniques such as microscopies and spectroscopies, and tests under simulated sunlight. The student will also learn to integrate and apply their research within the industrial system, understanding the key barriers and opportunities for reaching net zero. This will involve business and professional skills, including secondment to industry partners.
This PhD project will be carried out in specialized labs in Applied Energy Materials at Imperial College South Kensington campus, supervised by Dr Salvador Eslava. The student should have a background in chemistry, chemical engineering, physics, or related.

Lead Supervisor: Dr. Sam Krevor
The importance carbon capture and storage in the mitigation of climate changes arises from the potential capacity for the injection of large volumes of CO₂ into suitable subsurface geologic formations (Krevor et al., 2023). The assessment reports of the Intergovernmental Panel on Climate Change estimate that in the average of scenarios where CO₂ concentration is stabilised at 450 ppm by 2100, storage demand approaches 15 Gt CO₂ per year by 2050 (Zhang. et al., 2024). However, these modelled estimates disregard potential limitations to achieving these rates and volumes of storage from either the geographic availability of subsurface storage reservoirs, or the pressure limitations to allowable rates of injection (Smith et al., 2024).

The PhD project will extend a suite of models developed in our research group to continue to evaluate the potential for geographic and reservoir injectivity constraints to lead to bottlenecks in the development of large scale CO₂ storage globally. Ultimately, we will construct models for plausible development trajectories that may be incorporated into energy systems models of the type used by the IPCC to outline techno-economic pathways for mitigating climate change.

References:
Krevor, S., De Coninck, H., Gasda, S. E., Ghaleigh, N. S., de Gooyert, V., Hajibeygi, H., … & Swennenhuis, F. (2023). Subsurface carbon dioxide and hydrogen storage for a sustainable energy future. Nature Reviews Earth & Environment, 4(2), 102-118

Smith, A., Hampson, G., & Krevor, S. (2024). Global analysis of geological CO₂ storage by pressure-limited injection sites. International Journal of Greenhouse Gas Control, 137, 104220
Zhang, Y., Jackson, C., & Krevor, S. (2024). The feasibility of reaching gigatonne scale CO₂ storage by mid-century. Nature Communications, 15(1), 6913

Lead Supervisor: Prof Paul Fennell
The goal of this project is to overcome the outstanding issues preventing the accurate modelling of injected CO₂ migration and trapping at industrial scale storage projects. Currently modelling of CO₂ storage sites is challenging, with CO₂ observed to move in directions and at speeds that are not predicted through conventional simulation workflows. Advances made in the Subsurface CO2 Storage Research group have identified that the source of these discrepancies are due to small scale heterogeneities in the reservoir systems (Wenck et al., 2025).

Lead Supervisor: Dr. Sam Krevor
In this project we develop and carry out laboratory measurements of the rates of chemical reactions with minerals and under conditions relevant to underground CO₂ storage. The reactions underpinning carbon sequestration by mineralisation using basalt have been observed to be vigorous when occurring in liquid or supercritical CO₂ medium, with a stabilised water film. Industrial demonstration projects in Iceland and the USA have variously injected free CO₂ or CO₂ dissolved in water into subsurface basalt formations to induce mineralisation. However, the dissolution of CO₂ into water incurs major expense and twenty times as much fluid injection as compared with the injection of pure CO₂. Thus, determining whether and in which direction mineralisation reaction kinetics will be affected through the injection of free phase CO₂ as compared with carbonated water will have major implications for the design of industrial carbon mineralisation projects. Our aims are to characterise the kinetics of reactions that take place between basaltic minerals and a water saturated free supercritical or liquid CO₂ phase. These questions are fundamental in nature but the answers may have immediate and profound consequencesfor both engineered carbon storage and the fundamental physics of subsurface multiphase reactive transport.

Lead Supervisors: Dr. Qilei Song, Prof Magda Titirici, Prof Kim Jelfs
This PhD project will focus on the molecular engineering of next-generation ion-conductive polymer membranes for advanced battery technologies, with particular emphasis on sodium and lithium batteries. The research will investigate structure–property relationships in polymer electrolytes and separators, tailoring polymer backbones, side chains, and ionic functionalities to achieve high ionic conductivity, electrochemical stability, and dendrite suppression. The project will combine polymer synthesis, membrane fabrication, and advanced electrochemical and materials characterisation, alongside fundamental transport modelling. The project is associated with a prestigious ERC Consolidator Grant IonPATH, offering a well-resourced and intellectually stimulating research environment. The outcomes will contribute to safer, higher-energy-density, and more sustainable battery systems, while providing the candidate with strong interdisciplinary training at the interface of synthetic chemistry, materials science, electrochemistry, and chemical engineering.

Lead Supervisors: Prof Paul Fennell, Dr Rupert Myers
A fully funded PhD position is available at Imperial College London under the supervision of Professors Paul Fennell and Rupert Myers within the EPSRC Centre for Doctoral Training (CDT) in Green Industrial Futures. The project focuses on developing decarbonised Portland cement by enhancing the fraction of initially reactive phases to achieve comparable or superior performance to conventional cements including LC3 with lower clinker-to-cement ratios. The research will combine experimental work on phase reactivity and hydration with testing of mechanical strength and durability to ensure the new cements can be deployed at industrial scale.  It may also be possible to include thermodynamic modelling of phase development within the project.  The successful candidate will collaborate closely with Professor Karen Scrivener at EPFL and Dr Alex Pisch at CNRS, gaining access to world-class expertise in cement chemistry and materials characterisation. Applicants should have a strong background in chemical engineering, materials science, chemistry, or a similar STEM subject, and a keen interest in sustainable materials and industrial decarbonisation.

Lead Supervisors: Dr Francesca Ceroni, Dr Rodrigo Ledesma Amaro, Dr Cleo Kontoravdi
Mammalian cells are central hosts for biomanufacturing applications ranging from therapeutic protein production to cultivated meat. However, the scalability and sustainability of these systems are constrained by the high costs and environmental impact of cell culturing and scale up. Genetic engineering of mammalian cells impacts their growth leading to lower biomass and product yields while reliance of these hosts on complex media formulations makes the process unsustainable. This project aims to address these challenges by integrating synthetic biology, genome engineering, metabolic modelling and process and life cycle analysis to develop new mammalian cell chassis for sustainable biomanufacturing applications.

Lead Supervisors: Dr Stephen Allen, Dr Melanie Jans-Singh
UK industrial emissions have reduced by nearly 50% since the 1990s, but this has largely been driven by de-industrialisation. At the same time, imports have risen. For example, imports of cement have risen from 10% to 22% in the last 15 years.
As illustrated by recent research^1,2,3, decarbonising industry relies on significant changes, including electrification, CCUS, and increasing circularity.
To grow a market for low carbon products, manufacturers will need to be able to certify low carbon emissions, in growingly complex markets with significant innovation, yet with increasing share of imports.
For a selection of case studies, including from among the Green Industrial Futures CDT partners, this project will explore the environmental footprint of low carbon and circular products, and how these could be certified in the context of international trade. This will support government in developing targets and policies to support decarbonisation of UK industrial sectors, while recognising the global trade flows of industrial materials.
The PhD will involve collaboration with the UK Department of Energy Security and Net Zero.
Research Objectives
Build on the findings of LCA-RSIN, IDRIC and other ongoing research projects
Identify case studies of products which illustrate new UK industry for 2030, i.e. including more waste/reused products, CCUS, electrification with on-site renewables, hydrogen.
Produce life cycle assessments aligned with international standards and highlight key interoperability issues and produce recommendations for alignment.
Explore certification options relevant to international trade, including for Carbon Border Adjustment Mechanisms.
Compare with international products to evaluate the risks of producing in the UK, and important factors for remaining competitive.
Supervisors: Dr Stephen Allen (University of Bath) and Dr Melanie Jans-Singh (Department of Energy Security and Net Zero, UK Government)

Lead Supervisors: Dr Marios Impraimakis, Dr Andrew Hillis
The safe and reliable storage of energy carriers such as hydrogen or ammonia is central to realising a low-carbon energy future. However, the long-term performance of storage vessels remains uncertain due to complex structural degradation processes and evolving material behaviour. For instance, a crack in a hydrogen storage vessel can lead to hydrogen leakage and mixing with air, creating an explosive atmosphere. Past incidents such as the fatal hydrogen tank explosion at the Gangwon Technopark in Gangneung, South Korea in 2019, and large-scale ammonia leaks in the chemical industry, demonstrate that a single storage failure can cause casualties and economic losses. This PhD project, targeted at candidates with a background in mechanical, aerospace, materials, structural, civil, or related engineering disciplines, will develop a low-power digital twin framework for hydrogen storage tanks. The framework will be based on remote sensing-only measurements, and will leverage artificial intelligence and remaining useful life estimation to enable continuous structural health monitoring. It will combine finite element modelling with sustainable AI techniques. By leveraging transfer learning, models trained on related structures and datasets can be efficiently adapted to energy storage systems. These models can dramatically reduce computational cost, energy required for training, and the need for extensive data collection. The digital twin will integrate physics- and mechanics-based simulations with data-driven inference to detect damage, update or infer material properties in real time, and provide probabilistic predictions that explicitly account for uncertainty. Experimental validation at a smaller scale will also be conducted using the multi-axis simulation table, a state-of-the-art shake table facility at the University of Bath. The outcome will enable early warning of potential failures, improved life-cycle management, and safer large-scale deployment of low-carbon energy infrastructure.

Lead Supervisors: Dr Evros Loukaides
Mitigating climate change will necessitate a significant reduction in the production of steel and aluminium, both of which contribute substantially to global emissions. A major issue is the considerable waste generated during metal processing; upwards of 30% of these materials are cut off and don’t make it into final products. This waste often comes from process scrap trimmed after forming operations.
In the sheet metal forming industry, stamping scrap can easily exceed 40% for a typical automotive component. This excess material is frequently included to facilitate forming and simplify die design. Historically, waste reduction hasn’t been a primary focus for die designers, as material costs were relatively low compared to factors like labor and production rates.

Recent research, however, has demonstrated that targeted interventions in die designs can significantly increase material utilization without requiring replacement of existing process infrastructure. The “folding-shearing” process [1], for instance, was introduced to eliminate the need for blank holders in stamping operations and is now being commercialized by DeepForm Ltd, an industrial partner for this project.
The design of stamping dies is a highly technical process, relying primarily on sophisticated simulations and human expertise. It can be time-consuming, and there’s often no guarantee of achieving an optimal design. Current software predominantly focuses on optimising for formability and feasibility, often adhering to conventional die designs.

This project will adopt cutting-edge data-based optimisation approaches to design die geometries that minimize the size of the blank needed to produce a specific part. The scale of this problem necessitates combining dimensional reduction with fast optimisation methods to ensure computational tractability.
DeepForm, a Cambridge-based startup, engages with automotive OEMs and first-tier suppliers. They leverage their patented fold-shear metal pressing processes to reduce costs and CO2 emissions. Originating in academia, DeepForm continues to support research through various avenues. This project will greatly benefit from their extensive knowledge base, internal expertise, and growing exposure to the stamping industry.

Lead Supervisor: Dr Charles Larkin
While meeting net-zero targets and transitioning to a green industrial base have become a major policy objectives for the UK policymaking community, this must still be delivered in the context of the “business as usual” of the State in terms of ongoing fiscal commitments. In this research programme, the student will look at the development of a sustainable climate action fiscal policy strategy that takes into account the need for urgent climate action, a just transition and balancing that against ongoing fiscal demands in health, social transfers, education and security. This work will combine an analysis of Climate and Environmentally Favourable & Unfavourable Revenue & Expenditure actions in the UK with monitoring the implementation of the OECD Paris Collaborative on Green Budgeting and a rights-based approach to a just transition. The objective is to take an all-of-government approach that balances fiscal policy for climate action against well-understood and unavoidable fiscal challenges of demographics and the poorly understood costs of climate change disasters and just transition. The output will be a fiscal policy development and evaluation framework that will assist in the articulation of the appropriate UK fiscal and monetary policy mix to meet stated national objectives for 2030 and 2050.

Lead Supervisors: Dr Shiqi Huang, Dr Matthew Cole
Net Zero is essential for tackling climate change and securing sustainable future. A major challenge lies in developing environmentally friendly and cost-effective CO₂ capture technologies to proactively tackle large-scale industrial carbon emissions. With a global market of 3.5B USD in 2024 and a CAGR of 19%, carbon capture and sequestration technologies have been identified as one of the few truly impactful technologies that are capable of halting and even reversing global warming.
This CDT project at the University of Bath aim to utilise one-pot chemical vapor deposition (CVD) methods to synthesize atomically thin graphene membranes that will form the basis of new generation energy-efficient membranes for CO₂ capture. Our previous studies on this type of membrane have demonstrated its exceptional energy efficiency in CO₂ capture (Huang, et al. Science advances, 2021, 7, eabf0116; Hsu, et al. Nat Energy 2024, 9, 964). With single-atom-thick molecular barriers, these graphene membranes can significantly reduce the energy required for separation whilst simultaneously minimizing the membrane area required. Nevertheless, existing fabrication methods, such as the perforation of nanopores in graphene lattice followed by functionalization, remain complex and are ultimately not feasible for large-scale industrial manufacturing at commercially viable scales. To overcome these limitations, this project will explore the use of an innovative one-pot CVD synthesis method to produce high-performance membranes in a single step, using precisely engineered plasma-enhanced CVD process. This approach aims to simplify the fabrication process and pave the way for future scalable, industrial production of energy- and cost-efficient membranes for carbon capture.

Lead Supervisors: Dr Shuya Zhong, Prof Mi Tian
The UK targets 5GW green hydrogen by 2030, backed by £1bn investment. This requires new approaches to integrated infrastructure planning, as current fragmented methods create systemic inefficiencies.
The green hydrogen supply chain spans production, storage, transport, and application processes, forming a complex network where each process offers multiple technology pathways. Choosing one technology constrains options upstream and downstream, creating interdependent decisions that cannot be effectively managed through traditional sequential planning. Current methods optimise processes separately, with individual stakeholders acting on their own priorities without end-to-end visibility. This shifts costs and emissions between processes, degrading overall performance. For instance, a production facility might choose lower-cost technology that requires expensive downstream modifications, increasing total system costs despite appearing optimal locally. Integrated planning, jointly optimising all processes, is imperative for whole-system efficiency.
Hydrogen infrastructure must balance economic vs. environmental goals. Exploring millions of technology combinations while balancing these trade-offs exceeds traditional simulation-optimisation capabilities, necessitating AI enhancement.
Research Question: How can integrated planning across all hydrogen supply chain processes be achieved through AI-enhanced simulation-optimisation to deliver system-wide economic and environmental sustainability?
The research pursues three objectives:
Developing simulation-optimisation models that capture the full complexity of green hydrogen supply chain networks. These models represent interdependencies and enable simultaneous decision-making across all processes. The models will incorporate strategic decisions including technology selection, capacity sizing, and facility location, accounting for how operational characteristics influence these strategic choices.
Designing machine learning (ML) algorithms learning from simulation-optimisation results, accelerating exploration of vast configuration spaces. ML models will be trained to recognise patterns in solutions and guide optimisation algorithms toward promising regions of the solution space. This hybrid approach maintains solution quality whilst dramatically reducing computation time.
Creating multi-objective optimisation capabilities navigating economic-environmental trade-offs under uncertainty. This involves generating solutions that reveal the relationship between competing objectives, enabling decision-makers to understand implications of different priorities.
 
The methodology develops a decision-making tool combining simulation, optimisation, and ML. Simulation assesses configurations; optimisation identifies best solutions; ML accelerates exploration. Industry case studies validate the tool’s scalability across local to regional scales. This project provides templates for similar energy system challenges, advancing UK net-zero.

Lead Supervisors: Dr Sanjay Nagarajan, Dr Sudhagar Pitchaimuthu (HWU), Dr Nathan Skillen (HWU)
This PhD project aims to develop a scalable and modular technology for generating low-carbon green hydrogen from wastewater by combining cavitation and photo(electro)catalytic systems.
Green hydrogen is a promising low-carbon fuel that can significantly reduce the carbon footprint across various industries and support the transition towards Net Zero targets. Traditionally produced through water electrolysis, this method requires ultra-pure water and critical raw materials, posing limitations due to resource scarcity and a high-water footprint (9 kg of water per kg of hydrogen). As a result, researchers have explored alternative methods such as photocatalysis and photoelectrocatalysis to offer flexibility in the production of low-carbon hydrogen. A key challenge, however, is improving the technology readiness of these process to ensure they are commercially viable.

The project proposes to use wastewater instead of freshwater as a feedstock for green hydrogen production, which supports a circular economy approach. By coupling cavitation—a process involving the formation, growth, and collapse of microbubbles—with photo(electro)catalytic systems, the project aims to create a scalable, modular and efficient process for low-carbon green hydrogen production. Cavitation generates intense localized conditions that can treat wastewater, break down complex compounds into simpler electron donors, improve mass transfer, and help maintain the efficiency of the catalysts. The key characteristics will promote synergy between the technologies thereby enhancing the process efficiency.
The candidate will be based in the Department of Chemical Engineering at the University of Bath, working within the Sustainable Energy Systems Research Centre. They will also conduct experiments at Heriot-Watt University in Dr. Sudhagar Pitchaimuthu’s lab and Dr. Nathan Skillen’s lab.

Building on previous proof-of-concept work, this project aims to address scalability challenges and advance the integration of wastewater treatment with green hydrogen production.

Lead Supervisors: Dr Rick Lupton, Prof Furong Li
Decarbonising the chemicals industry is a major challenge, with GHG emissions arising from feedstocks (e.g. fossil naphtha used to make plastics), direct process emissions from chemical reactions, and energy supply (e.g. combustion of natural gas for heat). These emissions must be eliminated, through “de-fossilisation”, electrification, use of hydrogen, and/or carbon capture, but currently there is huge uncertainty about where, when and to what extent these changes will take place in the UK chemical industry. But what is certain is that different outcomes have major effects on future industrial electricity and gas demand.

The location and timing of electricity supply and demand is a key issue for the low-carbon transition of the transmission and distribution system, but the scenarios used to analyse this transition are highly aggregated: e.g. the NESO “Future Energy Scenarios” show broad pathways with more or less electrification of industry, but without showing uncertainty about what specific industrial processes are involved, nor spatial detail on where they are located. Previous research has investigated spatial patterns of e.g. future electric vehicle charging demand, but this has not been done for the chemical industry.
This project will therefore show how technical changes in the UK chemical industry affect its future energy demand, and hence the energy system. To do this, you will use Industrial Ecology methods such as Material Flow Analysis and Life Cycle Assessment model future chemicals processes operating in the UK and their energy requirements. Uncertainty analysis will enable a full understanding of possible scenarios even with limited data and assumptions. The results of this work are expected to be of direct interest to government and the electricity transmission/distribution grid operators, allowing general technology trends to be understood in terms of the specific processes, production facilities, and regions that would be affected.

Lead Supervisors: Dr Tim Harrell, Dr Fulvio Pinto
The carbon fibre reinforced polymer (CFRP) industry is crucial to many advanced sectors due to its high strength-to-weight ratio and durability. However, the manufacturing process involves significant use of consumables, particularly single-use items like vacuum bags and tacky tape, which contribute to both material waste and increased costs. This project aims to address these challenges by developing new modular tooling technologies using silicone sheets, which will replace traditional consumables in processes such as resin infusion. The research will explore the feasibility of creating reusable, adaptable silicone-based tooling system that allows for precise moulding to drastically reducing single-use consumables. The project will focus on both the performance of these modular systems and the practical integration into existing CFRP production lines. By eliminating disposable components, this innovation has the potential to significantly reduce the environmental impact of CFRP manufacturing. A key part of this study will involve re-evaluating the Life Cycle Assessment (LCA) of CFRP manufacturing, with a focus on how reducing consumables impacts the overall environmental and economic footprint. This work will not only reduce waste and costs in the manufacturing process but also offer an innovative solution to support industries in achieving their sustainability and decarbonisation goals.

Lead Supervisors: Dr Sam Cooper
Various industrial decarbonisation models have been developed over the last decade, but they have limitations that prevent them from addressing several key questions. This PhD will explore these issues and seek to address them. The aim is to move from questions around “Can we decarbonise industry?” to the detail of “How should we?”. There are two key areas:
Modelling outcomes for decisions made by individual stakeholders rather than optimised for society.
Current state of-the-art models calculate optimal pathways for industrial decarbonisation from a societal perspective and it is typically assumed that a suitable carbon price could transfer equivalent signals to individual firms. However, this does not mean that the firms will actually adopt the decarbonisation technologies. They might prefer to wait until technologies are more mature, have limited capacity for change, lack access to finance, or face other barriers. Counterintuitively, the average ensemble outcome is unlikely to be representative of average individual outcomes; the incentives and policies suggested by current models may therefore be ineffective.

Explicit treatment of constraints and scale-up for supply chain and infrastructure.
Industrial decarbonisation is dependent upon the supply of alternative equipment and infrastructure that does not yet exist. It depends upon rapid expansion of the companies that supply the equipment and necessary infrastructure. Existing pathways are very sensitive to assumptions around this expansion.
In both cases, we can generalise that current models provide a coherent perspective on options for what could be done but are far more limited in assessing what will be done.
This PhD lies in the intersection between engineering, computer science, and management. It would suit a candidate who is interested in developing computer-based models with appropriate tools, but also in delivering the insights in a way that they can be used by policy makers.

Lead Supervisors: Dr HaNa Yu, Dr Yang Chen
Recycling carbon-fibre composites is increasingly important due to rising end-of-life waste and the high value of carbon fibres. Unlocking the full potential of reclaimed discontinuous fibres requires precise control of fibre orientation, which strongly influences mechanical, electrical and transport properties.
The HiPerDiF (High-Performance Discontinuous Fibre) process, invented by the primary supervisor (Dr Yu), enables highly aligned fibre architectures from recycled fibre feedstock. It also allows local tailoring of fibre orientation, opening new possibilities such as next-generation gas diffusion layers (GDLs) for proton exchange membrane (PEM) fuel cells.
GDLs play a crucial role in PEM fuel cells, enabling gas transport, water management and electrical conduction. Conventional GDLs made from randomly oriented carbon fibres involve performance trade-offs between porosity, conductivity and water handling. Introducing controlled fibre alignment and designed 3D porosity gradients offers a pathway to improved performance, but is currently beyond commercial manufacturing capabilities.
This PhD project will advance the HiPerDiF method to fabricate customised GDL structures from recycled carbon fibres and explore their structure-property relationships. You will:
1)Develop and optimise fibre-alignment and deposition processes
2)Manufacture GDL preforms with designed porosity and orientation gradients
3)Characterise morphological, transport and electrical properties
4)Use modelling and simulation to guide process optimisation and GDL design
Experimental characterisation will focus on permeability, porosity, wetting behaviour and electrical conductivity. Fuel-cell performance implications will be assessed primarily through modelling, with opportunities for collaboration-based testing where available. Simulation support will be provided by Dr Chen, University of Bath.

Lead Supervisors: Dr HaNa Yu, Dr Yang Chen
This project aims to develop an efficient and sustainable manufacturing process for high-performance composites using reclaimed carbon fibres and a biobased thermoplastic matrix. The core innovation lies in combining aligned short reclaimed carbon fibres with a sustainable polyamide (PA) system to produce structurally strong and environmentally friendly composite materials.
Fibre-reinforced composites offer an excellent strength-to-weight ratio compared to metals, enabling better fuel efficiency and lower emissions. However, recycling composites remains a challenge. While fibre reclamation—particularly for carbon fibres—has advanced, fully sustainable composite manufacturing is still evolving. One promising innovation is the HiPerDiF (High Performance Discontinuous Fibre) process developed by the PI, Dr Yu, which produces dry, highly aligned reclaimed carbon fibre tapes and mats. Initially, epoxy resins were used with these preforms, either as films to create semi-prepregs or via resin infusion, yielding good mechanical properties. However, despite partial recyclability, epoxy-based systems raise sustainability concerns.

This project seeks to replace epoxy with a thermoplastic matrix—specifically biobased polyamide (PA)—to improve sustainability. However, thermoplastic processing poses challenges due to the high viscosity of molten PA, which can disrupt fibre alignment during moulding. To overcome this, the project will use Anionic Polyamide-6 (APA-6), derived from caprolactam monomers, as an infusible thermoplastic. With a viscosity (~0.01 Pa·s) significantly lower than that of epoxy (0.1–1 Pa·s), APA-6 allows faster processing while preserving fibre alignment, making it a strong candidate for sustainable composite manufacturing.
The research will focus on understanding how different matrix infusion techniques affect fibre alignment and, in turn, the mechanical and structural performance of the final composite.

Lead Supervisors: Dr Sanjay Nagarajan, Dr Masih Sadeghi (Potenix Ltd)
The UK generates over 10 million tonnes of food waste annually. While reducing food waste is vital, a significant portion remains unavoidable across the supply chain, contributing to greenhouse gas emissions and climate change. In 2021/22, the value of wasted food in the UK was estimated to be £17 billion, with associated emissions of ~18 million tonnes CO₂ equivalent. With landfilling of food waste now banned, alternative strategies currently rely on anaerobic digestion for biogas production.
This PhD project will explore the development of enzyme-powered food waste to biogas in partnership with Potenix Ltd, a UK-based clean tech startup. Building on Potenix’s proprietary biogenerator and engineering biology platform, the research will focus on improving enzyme stability under diverse waste conditions, enhancing pre-treatment methods to increase substrate accessibility, and optimizing digestion efficiency for higher energy yields in portable anaerobic digestors.

The student will also work with Potenix’s AI-based waste recognition technology, which enables real-time classification and sorting of heterogeneous food waste. Integrating this AI with the engineering biology will support the creation of a responsive, adaptive energy conversion platform capable of handling varied waste profiles efficiently.

Aligned with the themes of sustainable fuels and resource efficiency, this project aims to transform food waste into low-carbon, renewable energy. It supports circular economy principles by valorising waste, reducing emissions from transport and decomposition of food waste while advancing scalable, modular technologies for decentralised energy generation. The collaboration will generate fundamental insights into biobased energy conversion while contributing to the UK’s transition to a more sustainable future.

Lead Supervisor: Prof Lenny Koh
The green industrial futures depend on a sustainable and resilient resource supply chain to achieve the net zero goal. This project aims to develop a new approach to future proof energy and materials feedstock resilience for sustainable aviation fuels supply chain. The dual effects of sustainability and security of energy and critical materials supply will underpin the model development. It will involve a paradigm shift which combines geo-spatial-temporal modelling, prospective life cycle analysis, techno-economic assessment and AI methodologies. It will map and analyse the sustainable aviation fuels supply chain beyond organisation and embeds embodied/hidden layer of direct/indirect dependence and closed-loop/circular relation in the value networks at multi-tier. Primary data will be derived from TERC, SAF-IC, IDRIC and industry partners. Co-developed with partners, future scenario for SAFSC will be examined across 2030, 2040 and 2050 via various technological pathways and fed into industry and policy decision making.

Lead Supervisor: Prof Lenny Koh
Both carbon removal technologies (a.k.a. negative emission technologies) and hydrogen (especially green) will play a key role towards achieving the net zero commitment. A mixed level of maturity of these technologies, supply chain dependency on imported resources, energy content, climate and energy policies variation have led to the dual challenges on scale up viability and global standardisation. This project aims to unpack the integrative systemic and symbiotic effect of carbon dioxide removals (CDR) and hydrogen on operational, economic, social, behavioural and environmental performance. Advanced modelling and comparative assessments will identify optimum systems combination leading to a resource efficient outcome. A new model and framework will be developed for the scale up and standardisation of integrative systems of CDR and hydrogen.

Lead Supervisor: Dr Karen Finney
This project will include experimental work on industrial pilot-scale plants at the Energy Innovation Centre – including the multi-fuel gas turbine and the hydrogen electrolyser. A number of operating parameters can be manipulated to alter the performance of a gas turbine, whether utilising natural gas, hydrogen or blends of these. This project will consider a range of operating parameters, optimising these and various process conditions to enhance the overall performance of the gas turbine employing specific indicators. Used with a complementary CFD modelling approach, the research will define the optimal values for a range of key parameters and explore how these differ for various turndown ratios and fuel blends. This will be applicable to gas turbine power stations, enabling them to devise operating strategies to enhance performance based on fuel availability and load requirements. There may also be applications for industrial gas firing plants, where fuel costs and availability may determine the operating strategy required.