Designing bioinspired green nanosilicas using statistical and machine learning approaches

Dewulf, L., Chiacchia, M., Yeardley, A. orcid.org/0000-0001-7996-0589 et al. (3 more authors) (2021) Designing bioinspired green nanosilicas using statistical and machine learning approaches. Molecular Systems Design && Engineering. ISSN 2058-9689

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Authors/Creators:
Copyright, Publisher and Additional Information: © The Royal Society of Chemistry and IChemE 2021. This article is licensed under a Creative Commons Attribution 3.0 Unported Licence. (http://creativecommons.org/licenses/by/3.0/)
Dates:
  • Accepted: 22 February 2021
  • Published (online): 4 March 2021
Institution: The University of Sheffield
Academic Units: The University of Sheffield > Faculty of Engineering (Sheffield) > Department of Chemical and Biological Engineering (Sheffield)
Funding Information:
FunderGrant number
ENGINEERING AND PHYSICAL SCIENCE RESEARCH COUNCILEP/P006892/1
ENGINEERING AND PHYSICAL SCIENCE RESEARCH COUNCILEP/R025983/1
ROYAL ACADEMY OF ENGINEERING (THE)IF\192046
Depositing User: Symplectic Sheffield
Date Deposited: 09 Mar 2021 14:46
Last Modified: 09 Mar 2021 14:46
Status: Published online
Publisher: Royal Society of Chemistry
Refereed: Yes
Identification Number: https://doi.org/10.1039/D0ME00167H

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