Taking off! We kick off our new Sprint programme with 18 world class projects

With the drive towards Net Zero, there is a need for high-performance composite materials that reduce reliance on conventional petroleum-derived polymers. This new Brunel-AMRC collaboration will investigate sustainable thermoplastic matrices for continuous fibre-reinforced thermoplastic tapes (FRTTs), an important intermediate material for advanced composite manufacturing. Brunel will lead polymer selection, formulation and characterisation, while AMRC will use its EPSRC-funded FRTT capability to optimise continuous-fibre impregnation and tape processing. The project will benchmark the resulting tapes against established thermoplastic systems, demonstrating a practical route from sustainable polymer development to scalable composite manufacturing and future industrial translation.

Carbon fibre composites are critical to Net Zero technologies, where lightweight, highstiffness structures are increasingly required. However, compressive performance remains a major design constraint. Recent research has built new mechanistic understanding of how fibre microstructure and interfacial behaviour combines with matrix performance to control compression failure. This project will translate a university-developed characterisation methodology into an industry-relevant material selection tool. Working with NCC and its industrial network, commercially relevant fibre–matrix systems will be assessed against compression critical applications, such as wind turbine blade spar caps, enabling faster material screening, reduced empirical testing and more efficient composite structures.

Future in-space servicing, assembly and manufacturing (ISAM) missions will require robotic systems to operate with limited supervision in uncertain, dynamic and safety-critical environments. This project will develop a structured methodology for translating high-level ISAM mission requirements into executable goals, tasks, behaviours, decision points and recovery strategies. It will investigate how multi-objective decision-making under uncertainty can support task planning, execution monitoring, replanning and safe recovery. The methodology will be evaluated using ISAM use cases in simulation and relevant robotic test environments.

Co-location of offshore renewable energy with storage and/or low-carbon hydrogen production via electrolysis offers a promising solution to curtailment periods, clustering infrastructure, grid connection and flexibility. This project will bring together the Offshore Renewable Energy Catapult’s hydrogen expertise in PEM (Proton Exchange Membrane) electrolyser simulation and the Researcher in Residence from the University of Hull with process simulation expertise. This collaboration will investigate the PEM electrolyser degradation under fluctuating loads caused by intermittent renewable energy and provide opportunities for process optimisation, which could potentially reduce the cost of hydrogen at large scale.

To support the UK’s net Zero ambitions and advanced manufacturing sector, lightweight, highperformance alloys, e.g. Scalmalloy, are essential. However, the vaporisation of critical alloying elements during additive manufacturing (AM) remains poorly understood, causing costly material losses and degraded performance. VAPOUR will establish the relationships between AM processing conditions, elemental vaporisation, alloy chemistry, microstructure, and mechanical properties. Through the RiR programme, AMRC will provide processing parameters and test coupons, while UCL will deploy proprietary in situ vapour-monitoring technology to quantify elemental losses and develop vapour-aware processing strategies that improve process robustness, accelerate industrial adoption, and strengthen the UK’s competitiveness in advanced manufacturing.

ACCEL-CO₂ will translate Swansea University’s patent-pending pressure-balanced CO 2 electrolysis technology from TRL 3 to a metre-scale demonstrator for low-carbon chemical manufacturing. Swansea will design, build and validate the system, producing scale-up evidence. As host Catapult, CPI will use its partnerships and convening role to identify UK capabilities and demonstration partners and produce an industrial development plan. INEOS will review results and advise on requirements, scale-up, adoption barriers and deployment. The resulting demonstrator, evidence and plan will strengthen UK capability and resilience in sustainable electrochemical manufacturing, support Net Zero, and prepare for ICURe Explore, where market and commercial potential will be tested.

Electricity networks in Great Britain are facing a data revolution, but barriers prevent the adoption of proven forecasting capabilities, resulting in duplicated effort at consumers’ expense. OpenNetCast will create a collaborative open-source platform to develop and implement forecasting solutions across GB. It will remove barriers to forecast improvement, e.g., data access, forecast assessment, and knowledge sharing, by connecting open weather data and distribution network operator (DNO) data portals with proven forecast models, established evaluation protocols, and user interfaces. This platform will foster industry-academia collaboration, build workforce capability, in alignment with Ofgem’s vision for open energy data and standardised forecasting infrastructure.

The project is aimed to upskill suppliers in England and Wales and strengthen their resilience utilising a new toolkit and specially designed training materials. The target supply market for building resilience is high-potential SMEs in the technology and digital sector. The enhanced skills will lead to accelerated growth opportunities through public contracts. The collaborative project draws on expertise in the procurement of innovation from the Connected Places Catapult (IPEC) Manchester, and sustainability and supply chain management expertise from the Centre of Public Value Procurement, Cardiff. The upskilling of SMEs takes place through Masterclass stye workshops in Cardiff and Manchester.

This proposal investigates how to improve the resilience of CAR T cell therapy manufacturing and supply chains, with a focus on autologous, allogeneic and in vivo lentivirus platforms. It combines techno-economic modelling, supply chain optimisation, and life cycle assessment to identify bottlenecks, cost drivers, environmental hotspots, and vulnerabilities under different demand scenarios. By integrating manufacturing, distribution, and sustainability perspectives, the project will evaluate how alternative process and network designs affect robustness, scalability, affordability, and environmental performance. The outcome will be a quantitative evidence base to guide innovation and support the development of more resilient, cost-effective, and sustainable CAR T cell therapy delivery systems.

This project applies Northumbria University’s depolymerisable EAD (epoxy-aminedioxazaborocane) polymer technology to adhesives that can be debonded at end of life. Unlike conventional epoxy adhesives, EAD adhesives resist common solvents in service but dissolve rapidly on contact with a chemo-selective, low cost, non-hazardous depolymerisation solution. Debondable adhesion is important across a wide range of sectors, from electronics and batteries, where critical metals must be recovered, to automotive, aerospace, construction and wind turbine blades. Supported by CPI, the project will evaluate the potential of EAD in this application, optimise promising formulations and generate data for patent application and commercial discussions.

Artificial intelligence advances in bioprocessing routinely stall in academia from a scarcity of real manufacturing data. This project applies a reinforcement learning (RL) framework developed at UCL to the Cell and Gene Therapy Catapult’s industrial Raman datasets, spanning viral vector, cell therapy and stem cell manufacturing. We will build universal models capturing the spectral features common to all cell culture modalities, then use reinforcement learning to retune them for an unseen process, scale or instrument. The model will be validated and embedded in the Catapult’s own analysis, moving advanced AI from academic research into industrial use.

ViTSDED will develop a vision-integrated thermomechanical simulation approach for LaserDirected Energy Deposition (LDED) of thin-walled structures relevant to high-value defence applications, including aircraft wing ribs and structural webs. In-situ HDR vision will capture melt-pool and evolving layer geometry for integration into Finite Element Analysis (FEA) simulations of thermal behaviour, residual stress and distortion. Building on Ulster University’s vision and FEA research, the approach will be validated through LDED manufacture and residual-stress measurement at NMIS. Simulation-guided process parameter selection will reduce physical trial-and-error, limiting material waste and failed builds while improving the resource efficiency of LDED manufacture.

This project will investigate how flexible low-carbon heating in residential buildings can help address the peak heat gap during cold-weather events. Detailed modelling will quantify domestic heating demand and evaluate the performance of different thermal energy storage configurations, including their capacity, duration, charging and discharging behaviour, and ability to shift peak demand. The study will also explore district heating networks with larger central thermal stores as a potential solution for longer-duration flexibility across groups of homes. The resulting quantified scenarios will be translated into parameters suitable for ESME/ESME-Flex, enabling assessment of the wider whole-energy-system implications of alternative flexible heating approaches.

Cardiovascular disease is a leading killer worldwide, yet RNA therapeutics rarely reach the heart because lipid nanoparticles (LNPs) accumulate in the liver. The biodistribution is dictated in part by the protein corona -serum proteins adsorbing onto LNPs in circulationwhich governs targeting and clearance but remains unpredictable. How do surface peptides reshape it? Using hydrophilic or hydrophobic non-targeting peptides, anchored via non-PEG helper lipid at controlled densities, I will map how peptide chemistry and density determine corona composition using LC/MS proteomics, complemented by microscopy. At MDC, this will establish corona modulation as a predictive design principle for LNP biodistribution.

The University of Northampton (UON) has developed the ISOBand, an innovative isometric exercise device designed to improve cardiovascular health by helping to reduce blood pressure through short, accessible exercise sessions that can be easily integrated into daily routines. This project will undertake a real-world feasibility and effectiveness study involving professional transport drivers, a workforce at increased risk of hypertension, to evaluate adoption, usability, compliance, and blood pressure outcomes. The study will generate evidence on real-world implementation, user engagement, and effectiveness, support product optimisation, and demonstrate the potential of the ISOBand as a practical occupational health intervention for professional drivers, strengthening commercial and investment readiness.

The UK’s £2.8–3.2 billion annual offshore wind operation and maintenance (O&M) sector offers a major addressable market for transformative human-augmented robotic technologies. This project systematically maps offshore O&M field activities and critical technical challenges to determine core design and operational requirements for humanaugmented maintenance robotics in harsh marine environments. By establishing a strategic roadmap tailored to UK offshore renewable energy needs, the study lays the foundation for next-generation, AI-driven human-augmented robotic solutions. Outcomes will guide future technology development, revolutionizing offshore O&M through improved operating efficiency, sustainability, and worker safety while strengthening the UK as a researchleading, innovative nation.

Wire harnesses, the branched cable bundles that power every vehicle and aircraft, remain largely assembled by hand, making production labour-intensive and difficult to automate. As a result, manufacturing has concentrated in low-cost labour markets, leaving UK manufacturers dependent on extended global supply chains for a critical component. This project will develop and demonstrate a braiding machine capable of producing branched harness structures in a single automated process. The technology has the potential to strengthen UK supply chain resilience, reduce reliance on overseas production, and represent a significant step towards the industrialisation of automated wire harness manufacturing in the UK.

This project targets weld-crack detection in gas pipelines. We will develop machine learning methods to automatically detect weld cracking from in-pipe images, captured at Digital Catapult (DC) using pipeline sections supplied by its industry partner. Predictions from multiple viewpoints of each weld will be fused to boost detection confidence. We will also capture low-light images and develop an enhancement method, re-evaluating the model under conditions representative of a buried pipeline. Finally, we will compare the vision-based results with DC’s quantum magnetometer sensing and explore fusing the two modalities to reduce false alarms and support targeted maintenance, informing future opportunities.