Arshad, Z., Blacker, A.J., Chamberlain, T.W. et al. (3 more authors) (2024) Droplet microfluidic flow platforms for automated reaction screening and optimisation. Current Opinion in Green and Sustainable Chemistry, 48. 100940. ISSN 2452-2236
Abstract
The demand for efficient and sustainable chemical process development has driven significant advancements in automated droplet flow platforms, which, when coupled with high-throughput experimentation, offer powerful solutions for generating synthetic libraries and optimising reaction parameters. Droplet flow platforms allow for reactions to take place on a microfluidic scale, enabling rapid and sustainable process optimisations. The size of the droplet is varied, with the technique of generating the droplet differing from multiple pumps to advanced robotics. Approaches to integrate multiple analytical tools, phase sensors and parallel reactors have been developed, broadening the capabilities and increasing the throughput of these platforms. Herein, we review recent advancements made within this field, highlighting the type of chemical reactions investigated and the digital technologies which have enabled closed-loop optimisations.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2024 The Authors. This is an open access article under the terms of the Creative Commons Attribution License (CC-BY 4.0), which permits unrestricted use, distribution and reproduction in any medium, provided the original work is properly cited. |
Keywords: | Automation; Continuous Flow; Microfluidics; Optimisation; Robotics |
Dates: |
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Institution: | The University of Leeds |
Academic Units: | The University of Leeds > Faculty of Engineering & Physical Sciences (Leeds) > School of Chemical & Process Engineering (Leeds) |
Funding Information: | Funder Grant number Royal Academy of Engineering RF2122-21-200 |
Depositing User: | Symplectic Publications |
Date Deposited: | 05 Jun 2024 15:17 |
Last Modified: | 22 Jul 2024 13:24 |
Status: | Published |
Publisher: | Elsevier |
Identification Number: | 10.1016/j.cogsc.2024.100940 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:213106 |