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Dataset . 2022
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ZENODO
Dataset . 2022
License: CC BY
Data sources: Datacite
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ZENODO
Dataset . 2022
License: CC BY
Data sources: ZENODO
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Supporting data set for: Simulations of the Electrochemical Oxidation of Shape-Selected Nanoparticle Catalysts

Authors: Björn Kirchhoff; Christoph Jung; Hannes Jónsson; Donato Fantauzzi; Timo Jacob;

Supporting data set for: Simulations of the Electrochemical Oxidation of Shape-Selected Nanoparticle Catalysts

Abstract

This dataset contains input and output files for simulations of the oxidation of a set of shape-selected, 3 nm platinum nanoparticles associated with the manuscript found at https://arxiv.org/abs/2201.07605. The simulations are performed using a grand-canonical Monte-Carlo algorithm[1,2] in combination with the ReaxFF reactive force field method as implemented in the Amsterdam Density Functional (ADF) software package version 2017.106 by Software for Chemistry and Materials (SCM). The Pt/O ReaxFF force field parameterized by Fantauzzi et al. was used for the simulations.[3] Simulations were performed at oxygen chemical potential conditions corresponding to 200-1000 K at ultra-high vacuum (UHV, pO2 = 10-10 mbar) and 400-1200 K at near-ambient pressure (NAP, pO2 = 1 mbar) conditions. The following nanoparticle shapes were used as input structures for the simulations: (111)-indexed octahedron, (100)-indexed cube, (110)-indexed dodecahedron, (111)- and (100)-indexed cuboctahedron, mixed-indexed sphere, and (730)-indexed tetrahexahedron. The folder structure is as follows: Particle shape -> pressure condition -> temperature condition -> simulation input and output files The simulation input and output files are of the following filetypes: control: Input parameters for the ReaxFF software. control_MC: Input parameters for the GCMC subroutine that interacts with the ReaxFF software. geo: Atomic input coordinates in BGF file format. geo_MCXXXXXX: Atomic output coordinates in BGF file format and ReaxFF total energy result for GCMC step XXXXXX. Simulations were performed for a total of 25,000 iterations. Only accepted GCMC steps result in the creation of a geo_XXXXXX output file. Therefore, the index XXXXXX is not continuous since output files are not written at every iteration. Other ReaxFF-specific output has been filtered in order to declutter the dataset. [1] T. P. Senftle, R. J. Meyer, M. J. Janik, A. C. T. van Duin, J. Chem. Phys. 2013, 139, 044109. [2] T. P. Senftle, M. J. Janik, A. C. T. van Duin, J. Phys. Chem. C 2014, 118, 4967–4981. [3] D. Fantauzzi, J. Bandlow, L. Sabo, J. E. Mueller, A. C. T. van Duin, T. Jacob, Phys. Chem. Chem. Phys. 2014, 16, 23118–23133.

This work was supported by Deutsche Forschungsgemeinschaft (DFG) through the collaborative research center SFB-1316 as well as the priority program SPP-2080. The state of Baden-Württemberg is acknowledged through bwHCP and DFT through grant no INST 37/935-1 FUGG. The Volkswagen Group Wolfsburg is acknowledged for partial funding, as well as the Icelandic Research Fund. BK acknowledges the University of Iceland Research Fund for funding through a PhD fellowship.

Keywords

oxidation, monte carlo, nanoparticles, platinum, fuel cells, material modeling

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This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
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popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
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influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
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