Dataset Information

Detecting stable adsorbates of (1S)-camphor on Cu(111) with Bayesian optimization.

ABSTRACT: Identifying the atomic structure of organic-inorganic interfaces is challenging with current research tools. Interpreting the structure of complex molecular adsorbates from microscopy images can be difficult, and using atomistic simulations to find the most stable structures is limited to partial exploration of the potential energy surface due to the high-dimensional phase space. In this study, we present the recently developed Bayesian Optimization Structure Search (BOSS) method as an efficient solution for identifying the structure of non-planar adsorbates. We apply BOSS with density-functional theory simulations to detect the stable adsorbate structures of (1S)-camphor on the Cu(111) surface. We identify the optimal structure among eight unique types of stable adsorbates, in which camphor chemisorbs via oxygen (global minimum) or physisorbs via hydrocarbons to the Cu(111) surface. This study demonstrates that new cross-disciplinary tools, such as BOSS, facilitate the description of complex surface structures and their properties, and ultimately allow us to tune the functionality of advanced materials.

SUBMITTER: Jarvi J

PROVIDER: S-EPMC7590619 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature

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Detecting stable adsorbates of (1<i>S</i>)-camphor on Cu(111) with Bayesian optimization.

Järvi Jari J Rinke Patrick P Todorović Milica M

Beilstein journal of nanotechnology 20201019

Identifying the atomic structure of organic-inorganic interfaces is challenging with current research tools. Interpreting the structure of complex molecular adsorbates from microscopy images can be difficult, and using atomistic simulations to find the most stable structures is limited to partial exploration of the potential energy surface due to the high-dimensional phase space. In this study, we present the recently developed Bayesian Optimization Structure Search (BOSS) method as an efficient ...[more]

PMID: 33134002

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Project description:Copper-based catalysts gain activity through the presence of poorly coordinated Cu atoms and incomplete oxidation at the surface. The catalytic mechanisms can in principle be observed by controlled dosing of reactants to single-crystal substrates. However, the interconnected influences of surface defects, partial oxidation, and adsorbate coverage present a large matrix of conditions that have not been fully explored in the literature. We recently characterized oxygen and carbon monoxide coadsorption on Cu(111), a nominally defect-free surface, and now extend our study to the stepped surface Cu(211). Temperature-programmed desorption of CO adsorbed to bare metal surfaces confirms that two sites dominate desorption from a saturated layer: atop terrace atoms of local (111) character and atop step edge atoms with CO bound more strongly to the latter. At low coverage, discrete CO resonances in reflection adsorption infrared spectra can be assigned to these sites: 2077 cm-1 for extended (111) terraces, 2093 cm-1 for step sites, and additional kink-adsorbed molecules at 2110 cm-1. With increasing coverage, in contrast to Cu(111), the infrared spectral features on Cu(211) evolve and shift as a consequence of dipole-dipole coupling between differentially occupied types of sites. Auger electron spectroscopy shows that exposure to background O2 oxidizes the (211) surface at a rate nearly 1 order of magnitude greater than (111); we argue that the resulting surface is stoichiometric Cu2O, as previously found for Cu(111). This oxide binds CO less strongly than the bare metal and the underlying crystal cut continues to influence the adsorption sites available to CO. On oxidized (111) terraces, broad absorption peaks at 2115-2120 cm-1; on oxidized Cu(211), CO adsorbed to step sites appears as a resolved secondary peak at 2144 cm-1. This suite of spectroscopic signatures, obtained under carefully controlled conditions, will help to determine the origin and fate of adsorbed species in future studies of reaction mechanisms on copper.

Dataset Information

Detecting stable adsorbates of (1S)-camphor on Cu(111) with Bayesian optimization.

Publications

Detecting stable adsorbates of (1<i>S</i>)-camphor on Cu(111) with Bayesian optimization.

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