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Department of Chemical Engineering and Biotechnology

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Dr Bruno Pinho and Dr Laura Torrente-Murciano, from our Catalysis and Process Integration Group, have developed a novel technology for the automated synthesis of metal nanoparticles with a wide-range of tuneable sizes.

The research, published in Advanced Energy Materialspresents their 'dial-a-particle' method, achieved by combining a novel, real-time, integrated particle sizing method with a “plug-n-play” modular platform of reactors in series, a distributed feed and in-situ multipoint analysis.

Until now, multistep continuous material manufacturing was limited by the instability and lack of repeatability caused by many uncontrollable parameters, such as fouling and stock ageing, leading to off-target inaccuracies of approximately 15%. This work overcomes such issues, showcasing an automation approach, where real-time monitoring leads to precise and on-target control over the whole production timeline. Their work enables the synthesis of metal nanoparticles with tuneable sizes ranging from four to 100 nanometres. 

Bringing automation to 'slow' syntheses

Over the last few years, the merits of automation have been successfully implemented in flow chemistry, especially for fast (less than a few minutes) organic transformations and active pharmaceutical ingredients. However, they have been applied much less in the field of nanomaterials synthesis, due to the slow (from tens of minutes to hours) and often multistage synthesis required for materials with 'large' dimensions (tens of nm) and complex structures, such as core vs shell composition or multicomponent alloys. This work overcomes these issues, redefining automation for slow, multi-step, complex material synthesis and facilitating the deployment of nano-research into real applications in a wide range of fields, such as catalysis, energy, and healthcare.

The new technology is built on a mechanistic understanding of the synthesis of the nanoparticles, especially the manipulation of the nucleation and growth stages. The team's central innovation is the careful control of early growth to optimise the final size, essential for the precision manufacturing of nanomaterials in flow.

Real-time analysis for reproducible manufacturing

Where previous approaches have used a single reactor, their manufacturing technology consists of a number of microreactors in series, used in a 'plug-n-play' manner, with a distributed feed across the different reactors to keep the concentrations of the reagents within a certain limit to avoid parallel phenomena (for example, Ostwald ripening). They have also developed a new spectroscopy approach for in situ analysis, coupled with a Mie-theory-based algorithm to provide real-time information on the size and shape of the nanoparticles. Such real-time feedback control allows them to analyse, without human intervention, for the first time, the interference of 'uncontrollable' parameters, such as fouling of the reactors and stock ageing, two of the main limitations in the field.

The combination of both of these approaches enables the real-time adaptation of the system to deal with, these, up to now, uncontrollable variations, leading to a stable and reproducible manufacturing synthesis.

This technology presents a completely different approach to machine learning methodologies, which are restricted to trained networks with rich data sets, and therefore seen as impracticable for systems like this, due to the lack of repeatability due to uncontrollable, continuously changing conditions, as well as the need of long residence times (ML normally deals with minute scale while this technology can perfectly cope with tens of minutes of residence times). The system can reach the target size in just minutes, while continuously self-adapting itself to any changing conditions. Up to now, no automation system has been successfully implemented to non-repeatable processes for nanomaterials with long residence times (from few minutes to hours) which are required to achieve a wide range of particle sizes.

 

Read the full paper

Pinho, B.Torrente-Murciano, L.Dial-A-Particle: Precise Manufacturing of Plasmonic Nanoparticles Based on Early Growth Information—Redefining Automation for Slow Material SynthesisAdv. Energy Mater. 2021, 2100918.

https://doi.org/10.1002/aenm.202100918

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