Graphene at Liquid Copper Catalysts: Atomic‐Scale Agreement of Experimental and First‐Principles Adsorption Height

TitleGraphene at Liquid Copper Catalysts: Atomic‐Scale Agreement of Experimental and First‐Principles Adsorption Height
Publication TypeJournal Article
Year of Publication2022
AuthorsGao H, Belova V, La Porta F, Cingolani JSantiago, Andersen M, Saedi M, Konovalov OV, Jankowski M, Heenen HH, Groot IMN, Renaud G, Reuter K
JournalAdvanced Science
Pagination2204684
Date PublishedSep-11-2022
ISSN2198-3844
Keywordsgraphene, liquid metal catalyst, machine learning, X-ray reflectivity
Abstract

Liquid metal catalysts have recently attracted attention for synthesizing high-quality 2D materials facilitated via the catalysts’ perfectly smooth surface. However, the microscopic catalytic processes occurring at the surface are still largely unclear because liquid metals escape the accessibility of traditional experimental and computational surface science approaches. Hence, numerous controversies are found regarding different applications, with graphene (Gr) growth on liquid copper (Cu) as a prominent prototype. In this work, novel in situ and in silico techniques are employed to achieve an atomic-level characterization of the graphene adsorption height above liquid Cu, reaching quantitative agreement within 0.1 Å between experiment and theory. The results are obtained via in situ synchrotron X-ray reflectivity (XRR) measurements over wide-range q-vectors and large-scale molecular dynamics simulations based on efficient machine-learning (ML) potentials trained to first-principles density functional theory (DFT) data. The computational insight is demonstrated to be robust against inherent DFT errors and reveals the nature of graphene binding to be highly comparable at liquid Cu and solid Cu(111). Transporting the predictive first-principles quality via ML potentials to the scales required for liquid metal catalysis thus provides a powerful approach to reach microscopic understanding, analogous to the established computational approaches for catalysis at solid surfaces.

URLhttps://onlinelibrary.wiley.com/doi/10.1002/advs.202204684https://onlinelibrary.wiley.com/doi/pdf/10.1002/advs.202204684https://onlinelibrary.wiley.com/doi/pdf/10.1002/advs.202204684https://onlinelibrary.wiley.com/doi/full-xml/10.1002/advs.202204684
DOI10.1002/advs.202204684
Short TitleAdvanced Science