Self-driving laboratory platform for many-objective self-optimisation of polymer nanoparticle synthesis with cloud-integrated machine learning and orthogonal online analytics

Knox, S.T., Wu, K.E., Islam, N. et al. (8 more authors) (2025) Self-driving laboratory platform for many-objective self-optimisation of polymer nanoparticle synthesis with cloud-integrated machine learning and orthogonal online analytics. Polymer Chemistry. ISSN 1759-9954

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Item Type: Article
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© The Royal Society of Chemistry 2025. This article is licensed under a Creative Commons Attribution 3.0 Unported Licence.

Dates:
  • Published (online): 11 February 2025
  • Accepted: 8 February 2025
Institution: The University of Leeds
Academic Units: The University of Leeds > Faculty of Engineering & Physical Sciences (Leeds) > School of Chemical & Process Engineering (Leeds)
The University of Leeds > Faculty of Engineering & Physical Sciences (Leeds) > School of Chemistry (Leeds)
Depositing User: Symplectic Publications
Date Deposited: 05 Mar 2025 09:56
Last Modified: 05 Mar 2025 09:56
Status: Published online
Publisher: Royal Society of Chemistry (RSC)
Identification Number: 10.1039/d5py00123d
Open Archives Initiative ID (OAI ID):

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