Triggering Gaussian-to-Exponential Transition of Displacement Distribution in Polymer Nanocomposites via Adsorption-Induced Trapping

Ming Hu, Hongbo Chen, Hongru Wang, Stanislav Burov, Eli Barkai, Dapeng Wang

Research output: Contribution to journalArticlepeer-review

3 Scopus citations

Abstract

In many disordered systems, the diffusion of classical particles is described by a displacement distribution P(x, t) that displays exponential tails instead of Gaussian statistics expected for Brownian motion. However, the experimental demonstration of control of this behavior by increasing the disorder strength has remained challenging. In this work, we explore the Gaussian-to-exponential transition by using diffusion of poly(ethylene glycol) (PEG) in attractive nanoparticle-polymer mixtures and controlling the volume fraction of the nanoparticles. In this work, we find “knobs”, namely nanoparticle concentration and interaction, which enable the change in the shape of P(x,t) in a well-defined way. The Gaussian-to-exponential transition is consistent with a modified large deviation approach for a continuous time random walk and also with Monte Carlo simulations involving a microscopic model of polymer trapping via reversible adsorption to the nanoparticle surface. Our work bears significance in unraveling the fundamental physics behind the exponential decay of the displacement distribution at the tails, which is commonly observed in soft materials and nanomaterials.

Original languageEnglish
Pages (from-to)21708-21718
Number of pages11
JournalACS Nano
Volume17
Issue number21
DOIs
StatePublished - 14 Nov 2023

Bibliographical note

Publisher Copyright:
© 2023 American Chemical Society.

Keywords

  • continuous time random walk
  • exponential decay
  • polymer diffusion
  • polymer nanocomposites
  • single-molecule tracking

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