Completed Materials & Manufacturing Chemistry

NanoMan: Self-Optimising Nanoscale Manufacturing Platforms for Achieving Multiscale Precision

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AI plain-English summary

Nanoparticles that power mRNA vaccines, cancer drugs, and ultra-bright TV screens are currently made in tiny batches by hand in research labs, and this project aims to build self-optimising chemical reactors that can mass-produce them with the same atomic-scale precision. The problem is that the exact size of a nanoparticle—thousands of times smaller than a human hair—determines whether a drug dissolves properly, a quantum dot emits the right colour, or a solar panel captures light efficiently. Today, only specialist scientists can achieve that control, and scaling up production reliably has proved nearly impossible. This project tackles that bottleneck by combining advanced chemical reactors with machine learning. The reactors monitor particle formation in real time, and algorithms automatically adjust conditions to hit the target specification. Crucially, the team will then transfer those optimised lab recipes to commercial-scale equipment—a process they call augmented lossless scale-up. If successful, this could transform manufacturing across healthcare, electronics, and energy. Drug delivery systems, medical imaging agents, and high-efficiency solar panels could move from research prototypes to affordable, mass-produced products. The project will demonstrate the approach in both R&D and commercial facilities at partner sites, aiming to unlock materials that are currently confined to the laboratory bench.

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Improving our current lifestyle and ensuring health of a growing population is reliant on the development of more advanced consumer products. Many of these engineered products have advanced functionality delivered by particles with nanometre dimensions, many thousands of times smaller than the width of a human hair. The exact size of these nanoparticles determines the mechanism of action and performance for the specific application. In healthcare, many drugs require encapsulation within polymer nanoparticles for several reasons, including for dissolving insoluble drugs, protecting drugs from unwanted degradation (e.g. mRNA vaccines) and providing efficient delivery (anti-cancer drugs). In electronics, the colour and intensity of light produced can be finely tuned by controlling the size of quantum dot nanoparticles, thus resulting in much higher quality displays, ultra-thin smart coatings (e.g. for wearable technologies), advanced diagnostics, high intensity medical imaging or high efficiency solar panels. The accuracy required to produce these materials is phenomenal and often only achieved reproducibly in dedicated research laboratories by specialist scientists. There has therefore been little progress on scaling up in a cost-effective or sustainable manner. In this project we will build platform technologies, comprising advanced chemical reactors underpinned by computational intelligence, which can scale up production of advanced nanoparticle products without loss in the precise control over structural dimensions which are achieved in research laboratories. We will build laboratory reactors which can be programmed to monitor the nanoparticle formation process in real time and relate conditions to the particle properties. Throughout the manufacturing process the machine learning algorithms will direct the reactors towards achieving the desired specification through 'self-optimisation' of conditions. A critical part of the project is then using the data obtained in the lab experiments to build a relationship between process and product which can be transferred onto equipment which can make the materials on a commercially relevant scale in a process called augmented lossless scale-up. We will take the optimised laboratory nanoparticle formation processes and demonstrate scale in several manufacturing environments, including R&D process laboratories and Commercial manufacturing facilities at our partners sites. Such demonstration will encourage further innovation beyond the lifetime of the project which can work towards realising advanced materials currently confined to research laboratories.

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Researchers

George Panoutsos (Co-Investigator)John Oyekan (Co-Investigator)Nicholas Warren (Principal Investigator)Richard Bourne (Co-Investigator)Thomas Chamberlain (Co-Investigator)

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Original classification

Research Grant

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