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Details of Grant 

EPSRC Reference: EP/V055089/1
Title: NanoMan: Self-Optimising Nanoscale Manufacturing Platforms for Achieving Multiscale Precision
Principal Investigator: Warren, Dr N J
Other Investigators:
Panoutsos, Professor G Oyekan, Dr JOJ Chamberlain, Dr TW
Bourne, Professor RA
Researcher Co-Investigators:
Dr S T Knox
Project Partners:
AstraZeneca DeepMatter Samsung R&D Institute UK
Department: Chemical and Process Engineering
Organisation: University of Leeds
Scheme: Standard Research
Starts: 13 January 2022 Ends: 12 January 2025 Value (£): 1,432,279
EPSRC Research Topic Classifications:
Design of Process systems Intelligent & Expert Systems
Manufacturing Machine & Plant
EPSRC Industrial Sector Classifications:
Manufacturing Chemicals
Pharmaceuticals and Biotechnology
Related Grants:
Panel History:
Panel DatePanel NameOutcome
27 Apr 2021 Precision Manufacturing - Full Proposals Announced
Summary on Grant Application Form
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.

Key Findings
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Potential use in non-academic contexts
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Organisation Website: http://www.leeds.ac.uk