Nanoindentation of polycrystalline Pd hollow nanoparticles: Grain size role
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2020Metadata
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Valencia, Felipe
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Nanoindentation of polycrystalline Pd hollow nanoparticles: Grain size role
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Abstract
Polycrystalline hollow nanoparticles present a unique combination of strength and flexibility. However, the exact role displayed by their grain structure in mechanical properties has not been yet fully understood. Here, by means of molecular dynamics simulations, the role of grain boundary structure during the nanoindentation of metallic hollow nanoparticles with a polycrystalline shell was investigated. Our simulations were performed for a range of grain sizes and shell thicknesses, including the large strain regime. Our results show that hNP mechanical properties can be controlled by tuning the grain size of the polycrystalline shell, following an inverse Hall-Perch type dependence with the grain size. Deformation involves dislocation activity, twin hardening, grain boundary sliding, coalescence, and rotation. For single crystal shells at large strain there is hardenning following the closure of the internal cavity. For nanocrystalline shells at large strains a constant flow stress regime is observed even for deformations as high as 80%, thanks to grain boundary activity. Surprisingly, some particular grain size not only leads to an improvement in strength, but also a flow stress higher than the observed in their single-crystalline counterparts. Our work, suggest that grain boundary structure can be employed to improve and tailor desired mechanical properties in hollow nanostructures.
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Comisión Nacional de Investigación Cientifica y Tecnológica (CONICYT) CONICYT FONDECYT 1160639 1190662
United States Department of Defense Air Force Office of Scientific Research (AFOSR) FA9550-16-1-0122
Comisión Nacional de Investigación Cientifica y Tecnológica (CONICYT) CONICYT PIA/BASAL AFB180001
Comisión Nacional de Investigación Cientifica y Tecnológica (CONICYT) CONICYT FONDECYT 11190484
ANPCyT PICT-2014-0696
SeCTyP-UN Cuyo M003
NLHPC ECM-02
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URI: https://repositorio.uchile.cl/handle/2250/175527
DOI: 10.1016/j.commatsci.2020.109642
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Computational Materials Science 179 (2020) 109642
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