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Binding Modes of Carboxylic Acids on Cobalt Nanoparticles - dataset

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posted on 2024-09-18, 10:41 authored by Barbara FarkasBarbara Farkas, Umberto TerranovaUmberto Terranova, NH de Leeuw

In order to study binding modes of carboxylic acid on cobalt nanoparticles, optimal system size has to be assesed through structural, energetical, and electronical properties. Density functional theory (DFT) theoretical simulation datasets are available in the .xlsx format (can be viewed either by MS Office or Libre Office) comprising 8 data sheets, with first 5 sheets corresponding to the structural properties of cobalt clusters of varying sizes (N=6, 13, 19, 30, 57, 76, 153, 323) with and without carboxylic acids as adsorbates as well as of cobalt clusters in vacuum. Geometry of each system contains a lattice constant which is given as a scaling factor for the accompanying matrix with lattice vectors, followed by species and number of atoms and their direct coordinates (x,y,z). One additional sheet contains geometries and Bader charges (in e-) for adsorption of valeric and oleic acid on 57-atom cobalt cluster. Wavenlengths with accompanying IR intensites which were used to construct IR spectra are listed in the following sheet for valeric acid in vacuum and valeric acid adsorbed on 57-atom cluster in three different binding modes (monodentate, bridging bidentate, and chelate). Metadynamics free energy landscape both as a table and as a matrix of free energy surface spanned over collective variables (coordination numbers of two carboxylic oxygen atoms) is presented in the last sheet. All units have been given alongside the name of the physical property. 

Data has been generated through the density functional theory as implemented in the VASP code, and therefore all information is in the form as provided by the software. Metadynamics data was obtained through cp2k code and format of the last sheet corresponds to cp2k output.

Research results based upon these data are published at http://doi.org/10.1039/c9cp04485j


Funding

Computational Nano-materials and Catalysis (2017-10-01 - 2020-09-30); Farkas, Barbara. Funder: Engineering and Physical Sciences Research Council

History

Language(s) in dataset

  • English-Great Britain (EN-GB)

Data-collection start date

2018-08-01

Data-collection end date

2019-04-27

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    School of Chemistry

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