Open Access Nano Commentary

Size-dependent mechanical behavior of nanoscale polymer particles through coarse-grained molecular dynamics simulation

Junhua Zhao1, Shijo Nagao1, Gregory M Odegard2, Zhiliang Zhang1, Helge Kristiansen3 and Jianying He1*

Author Affiliations

1 NTNU Nanomechancial Lab, Department of Structural Engineering, Norwegian University of Science and Technology (NTNU), 7491 Trondheim, Norway

2 Department of Mechanical Engineering - Engineering Mechanics, Michigan Technological University, 1400 Townsend Drive, Houghton, MI 49931 USA

3 Conpart AS, 2013 Kjeller, Norway

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Nanoscale Research Letters 2013, 8:541  doi:10.1186/1556-276X-8-541

Published: 21 December 2013

Abstract

Anisotropic conductive adhesives (ACAs) are promising materials used for producing ultra-thin liquid-crystal displays. Because the mechanical response of polymer particles can have a significant impact in the performance of ACAs, understanding of this apparent size effect is of fundamental importance in the electronics industry. The objective of this research is to use a coarse-grained molecular dynamics model to verify and gain physical insight into the observed size dependence effect in polymer particles. In agreement with experimental studies, the results of this study clearly indicate that there is a strong size effect in spherical polymer particles with diameters approaching the nanometer length scale. The results of the simulations also clearly indicate that the source for the increases in modulus is the increase in relative surface energy for decreasing particle sizes. Finally, the actual contact conditions at the surface of the polymer nanoparticles are shown to be similar to those predicted using Hertz and perfectly plastic contact theory. As ACA thicknesses are reduced in response to reductions in polymer particle size, it is expected that the overall compressive stiffness of the ACA will increase, thus influencing the manufacturing process.

Keywords:
Size effect; Polyethylene particles; Coarse-grained molecular dynamics simulations