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Details of Grant 

EPSRC Reference: EP/V025759/1
Title: Dial-a-particle: model-driven self-optimised manufacturing platform of nanoparticles
Principal Investigator: Torrente Murciano, Dr L
Other Investigators:
Researcher Co-Investigators:
Dr B Pinho
Project Partners:
Corning SAS Johnson Matthey Ocean Optics
Department: Chemical Engineering and Biotechnology
Organisation: University of Cambridge
Scheme: Standard Research
Starts: 01 March 2021 Ends: 29 February 2024 Value (£): 727,398
EPSRC Research Topic Classifications:
Manufacturing Machine & Plant Particle Technology
Reactor Engineering
EPSRC Industrial Sector Classifications:
Chemicals Manufacturing
Related Grants:
Panel History:
Panel DatePanel NameOutcome
08 Dec 2020 Engineering Prioritisation Panel Meeting 8 and 9 December 2020 Announced
Summary on Grant Application Form
Their surface asymmetry, high surface-to-volume ratios and confinement quantum effects of nanoparticles result in unprecedented properties for applications in healthcare, diagnosis, energy storage, electronics, sensors, catalysis, etc. However, the full impact of these nanomaterials to overcome some of the most pressing global challenges, is hindered by the lack of a manufacturing technology capable of their production in a continuous and reproducible manner in large scale. A plethora of nanoparticle syntheses has been developed over the last decades, aiming for the control of the size, shape and composition of nanoparticles as property-determining parameters. Conventionally, nanoparticles are synthesised in poorly characterised batch reactors. Flow systems enable the continuous synthesis, but they are currently limited to rapid processes (ms to a few minutes) due to their inherent instability issues. This project will deliver a novel model-driven self-optimised manufacturing technology for on-demand size- and composition-customised nanoparticles. The dial-a-particle platform will integrate, for the first time, real-time characterisation and hydrodynamic understanding to enable the development of mathematical predictive algorithms. They will be the pillar for the autonomous identification of the most interesting manufacturing route. The distinguishing novelty features of this approach are i. On-demand synthesis with a wide range size (2-100 nm) and composition (core-shell, hollow, multicomponent), ii. Self-control to mitigate instability sources associated to multi-stage continuous processes (extending the current state-of-the-art from seconds to minutes/hours) and iii. Universality, thanks to the mechanistic knowledge underpinning the mathematical models.
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Organisation Website: http://www.cam.ac.uk