NanoMan: Self-Optimising Nanoscale Manufacturing Platforms for Achieving Multiscale Precision
Lead Research Organisation:
University of Leeds
Department Name: Chemical and Process Engineering
Abstract
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.
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.
Organisations
Publications
Chen J
(2023)
A deep multi-agent reinforcement learning framework for autonomous aerial navigation to grasping points on loads
in Robotics and Autonomous Systems
Knox S
(2022)
Autonomous polymer synthesis delivered by multi-objective closed-loop optimisation
in Polymer Chemistry
Pittaway P
(2023)
Continuous synthesis of block copolymer nanoparticles via telescoped RAFT solution and dispersion polymerisation in a miniature CSTR cascade
in Reaction Chemistry & Engineering
Wilding CYP
(2023)
Development and Experimental Validation of a Dispersity Model for In Silico RAFT Polymerization.
in Macromolecules
Description | New reactor technologies have been developed which are able to manufacture new polymer based nanoparticles with a broad range of applications. We have integrated online monitoring which can analyse the materials made in real time and pass the information to a computer which can self-optimise the conditions to target a better product and a more efficient process. We have applied this technology to several products and processes. We have also implemented existing machine learning algorithms and developed new algorithms which can take different approaches to optimising the conditions. These are key advances which have the potential to provide tailored optimisation processes which will lead to optimised manufacturing processes. The project has also resulted in four researchers who have developed a unique set of cross-disciplinary skills to work accross the interface of materials science and computer science which will represent new capability the future workforce in science and engineering. The technologies are also being transferred to new classes of products not identified in the proposals, including for lipid nanoparticles, which are key for delivery of future pharmaceutical ingredients. New opportunities have been identified for working with the Centre for Process Innovation (CPI) and other industrial partners to implement the technologies through dissemination at the Symposium held at CPI. In summary, the Precision Manufacturing Project has already achieved some key objectives in developing experimental approaches for optimising nanoparticle formation and rapid optimisation. It has generated individuals with new skillsets and initiated several new collaborative partnerships. |
Exploitation Route | The new reactor platforms will form part of new projects where they are used to optimise a wider range of polymer and particulate product formation processes. This will include working with speciality chemicals manufacturers on impact acceleration projects. New projects with CPI are also being conceived working on lipid nanoparticle products. It is anticipated that the platforms and digital frameworks will be adopted directly by industry and therefore provide extremely important case studies to demonstrate the impact of UKRI research in industry. The individuals trained are among the first generation who will have a multidiscipline focus on both digital skills combined with chemical synthesis. |
Sectors | Chemicals Digital/Communication/Information Technologies (including Software) Manufacturing including Industrial Biotechology Pharmaceuticals and Medical Biotechnology |
URL | https://www.uk-cpi.com/events/digital-precision-lab-self-optimisation-for-nanoparticle-manufacturing-2 |
Description | DigiScale: Digitally driven scale up of chemical processes |
Amount | £970,893 (GBP) |
Funding ID | EP/X024237/1 |
Organisation | Engineering and Physical Sciences Research Council (EPSRC) |
Sector | Public |
Country | United Kingdom |
Start | 09/2023 |
End | 10/2026 |
Description | Institute of Process Research and Development Industry Day |
Form Of Engagement Activity | Participation in an activity, workshop or similar |
Part Of Official Scheme? | No |
Geographic Reach | National |
Primary Audience | Industry/Business |
Results and Impact | The progress and technologies from this award were regularly presented at the IPRD industry club meetings, which comprises a community of over 30 Chemical companies, and equipment suppliers. These meetings allow dissemination of research activity from Leeds and encourage further investment opportunities, ranging from sponsored PhDs to consultancy /translational research. |
Year(s) Of Engagement Activity | 2022,2023 |
URL | https://www.iprd.leeds.ac.uk/working-with-business/industrial-club/ |
Description | Symposium on Digital Precision: Lab Self-Optimisation for Nanoparticle Manufacturing |
Form Of Engagement Activity | Participation in an activity, workshop or similar |
Part Of Official Scheme? | No |
Geographic Reach | National |
Primary Audience | Industry/Business |
Results and Impact | The event comprised a symposium where output from the Nanoman project was disemminated to experts, researchers, and industry professionals to explore the paradigm shift in product development and optimisation facilitated by the technologies. External researchers and industrialists also presented state-of-the-art in the research area and a panel was held to discuss the future of the area. |
Year(s) Of Engagement Activity | 2024 |
URL | https://www.uk-cpi.com/events/digital-precision-lab-self-optimisation-for-nanoparticle-manufacturing... |