Atomic-resolution imaging of gold species at organic liquid-solid interfaces.

Abstract

The structure and dynamics of adsorbed atoms (adatoms) at solid-liquid interfaces determine the performance of advanced catalysts, electrochemical devices, molecular separation technologies, and metal extraction from waste streams. However, in situ investigations of atomically dispersed metals in various chemical environments have been prevented by insufficient imaging resolution and solvent incompatibility. In this study, we combined a specimen design that provides atomic resolution in liquid-phase electron microscopy with deep learning-enabled analysis to explore the interactions between gold adatoms, graphite support, and the solvent collectively. We tracked the locations of >106 graphite-supported gold adatoms, dimers, and larger clusters in five solvents. Although their initial atomic dispersion was determined by the solvent polarity, fast drying kinetics at low temperature was required for optimizing catalytic performance.

Metadata

Item Type:	Article
Authors/Creators:	Sullivan-Allsop, S. https://orcid.org/0000-0002-8697-6922 Clark, N. https://orcid.org/0000-0003-3351-5628 Wang, W. https://orcid.org/0000-0003-1045-7170 Cai, R. https://orcid.org/0000-0002-2148-0563 Thornley, W. https://orcid.org/0000-0003-2556-6302 Hopkinson, D.G. https://orcid.org/0000-0003-4259-7450 McHugh, J.G. https://orcid.org/0000-0001-8509-4883 Davies, B. Pattisson, S. https://orcid.org/0000-0002-6520-2194 Dummer, N.F. Zhang, R. https://orcid.org/0000-0002-6120-2752 Lindley, M. https://orcid.org/0000-0002-9116-3862 Tainton, G. https://orcid.org/0000-0002-7072-4640 Harrison, J. https://orcid.org/0009-0001-7736-5206 De Latour, H. Parker, J. https://orcid.org/0000-0002-7713-8150 Swindell, J. https://orcid.org/0000-0002-3776-6689 Castanon, E.G. https://orcid.org/0000-0003-4316-4796 Carl, A. https://orcid.org/0000-0003-3601-1636 Lewis, D.J. https://orcid.org/0000-0001-5950-1350 Martsinovich, N. https://orcid.org/0000-0001-9226-8175 Allen, C.S. https://orcid.org/0000-0002-6353-6000 Danaie, M. https://orcid.org/0000-0002-9325-7571 Logsdail, A.J. https://orcid.org/0000-0002-2277-415X Fal'ko, V. https://orcid.org/0000-0003-0828-0310 Hutchings, G.J. https://orcid.org/0000-0001-8885-1560 Summerfield, A. https://orcid.org/0000-0001-6091-3969 Gorbachev, R. https://orcid.org/0000-0003-3604-5617 Haigh, S.J. https://orcid.org/0000-0001-5509-6706
Copyright, Publisher and Additional Information:	© 2026 The Authors. Except as otherwise noted, this author-accepted version of a journal article published in Science is made available via the University of Sheffield Research Publications and Copyright Policy under the terms of the Creative Commons Attribution 4.0 International License (CC-BY 4.0), which permits unrestricted use, distribution and reproduction in any medium, provided the original work is properly cited. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/
Keywords:	Physical Sciences; Engineering; Chemical Sciences; Physical Chemistry; Machine Learning and Artificial Intelligence
Dates:	Submitted: 30 January 2025 Accepted: 2 February 2026 Published (online): 2 April 2026 Published: 2 April 2026
Institution:	The University of Sheffield
Academic Units:	The University of Sheffield > Faculty of Science (Sheffield) > School of Mathematical and Physical Sciences
Date Deposited:	09 Apr 2026 13:15
Last Modified:	09 Apr 2026 13:15
Status:	Published
Publisher:	American Association for the Advancement of Science (AAAS)
Refereed:	Yes
Identification Number:	10.1126/science.adw2469
Related URLs:	PubMed URL arXiv URL
Open Archives Initiative ID (OAI ID):	oai:eprints.whiterose.ac.uk:239890

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Atomic-resolution imaging of gold species at organic liquid-solid interfaces.

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