A new approach combining molecular fingerprints and machine learning to estimate relative ionization efficiency in electrospray ionization

Hamilton, Jacqui orcid.org/0000-0003-0975-4311 (2020) A new approach combining molecular fingerprints and machine learning to estimate relative ionization efficiency in electrospray ionization. ACS Omega. pp. 9510-9516. ISSN 2470-1343

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Item Type: Article
Authors/Creators:
Copyright, Publisher and Additional Information:

© 2020 American Chemical Society
Dates:
  • Published (online): 14 April 2020
  • Accepted: 8 April 2020
Institution: The University of York
Academic Units: The University of York > Faculty of Sciences (York) > Chemistry (York)
Funding Information:
Funder
Grant number
NATURAL ENVIRONMENT RESEARCH COUNCIL
NE/S010467/1
Depositing User: Pure (York)
Date Deposited: 15 Apr 2020 11:30
Last Modified: 18 Dec 2024 00:16
Published Version: https://doi.org/10.1021/acsomega.0c00732
Status: Published online
Refereed: Yes
Identification Number: 10.1021/acsomega.0c00732
Open Archives Initiative ID (OAI ID):

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