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Published March 27, 2018 | public
Journal Article

Theory and Simulation of Attractive Nanoparticle Transport in Polymer Melts

Abstract

We theoretically study the diffusion of a single attractive nanoparticle (NP) in unentangled and entangled polymer melts based on combining microscopic "core–shell" and "vehicle" mechanisms in a dynamic bond percolation theory framework. A physical picture is constructed which addresses the role of chain length (N), degree of entanglement, nanoparticle size, and NP–polymer attraction strength. The nanoparticle diffusion constant is predicted to initially decrease with N due to the dominance of the core–shell mechanism, then to cross over to the vehicle diffusion regime with a weaker N dependence, and eventually plateau at large enough N. This behavior corresponds to decoupling of NP diffusivity from the macroscopic melt viscosity, which is reminiscent of repulsive NPs in entangled melts, but here it occurs for a distinct physical reason. Specifically, it reflects a crossover to a transport mechanism whereby nanoparticles adsorb on polymer chains and diffuse using them as "vehicles" over a characteristic desorption time scale. Repetition of random desorption events then leads to Fickian long time NP diffusion. Complementary simulations for a range of chain lengths and low to moderate NP–polymer attraction strengths are also performed. They allow testing of the proposed diffusion mechanisms and qualitatively support the theoretically predicted dynamic crossover behavior. When the desorption time is smaller than or comparable to the onset of entangled polymer dynamics, the NP diffusivity becomes almost chain length independent.

Additional Information

© 2018 American Chemical Society. Received: December 19, 2017. Revised: February 14, 2018. Published: March 8, 2018. This work was supported by the U.S. Department of Energy, Office of Science, Basic Energy Sciences, Materials Science and Engineering Division. Simulations were performed at the Center for Nanophase Materials Sciences, which is a US Department of Energy Office of Science User Facility. This research also used resources of the Oak Ridge Leadership Computing Facility at Oak Ridge National Laboratory, which is supported by the Office of Science of the Department of Energy under Contract DE-AC05-00OR22725. The authors declare no competing financial interest.

Additional details

Created:
August 19, 2023
Modified:
October 18, 2023