A Double-Spring Model for Nanoparticle Diffusion in a Polymer Network

TL;DR

This paper introduces a double-spring model combining junction deviation and loop stretching effects to predict nanoparticle diffusivity in polymer networks, validated by simulations and relevant for biomedical engineering.

Contribution

A novel double-spring model is proposed to accurately describe nanoparticle diffusion considering both junction deviation and loop stretching effects.

Findings

The deformation due to junction deviation significantly affects potential barriers.

The double-spring model aligns well with numerical simulations.

The model enhances understanding of nanoparticle dynamics in biological environments.

Abstract

The transport of nanoparticles (NPs) in polymer networks, as a typical simplified model describing various structures in living systems, is profoundly important in biomedical engineering and nanotechnology. Predicting the effective diffusivity of NP confined in an ordered network has been an intriguing focus in this frontier field. In the present study, the diffusion of NPs in an unentangled polymer network for different NP radii and network stiffness is numerically investigated by single particle dissipative particle dynamics (DPD). It is found that, the deformation due to the junction deviation contributes significantly to the the potential barrier $U$ for the NP to overcome during hopping, and it is dominated over the strain energy induced by loop stretching for larger NPs and lower network rigidity. Analyses based on the theory of continuum mechanics reveal that the relation between this deformed energy and the junction deviation can be described by a non-linear spring. Taking into account both effects of the loop stretching and junction deviation, a double-spring model is proposed to characterize the diffusivity of the NPs in the ordered network. The theoretical prediction is in good agreement with our numerical simulations, and qualitatively consistent with the investigations available. This model is helpful to improve our understanding on the dynamic behavior of nanoparticle in complex biological environment, and provide theoretical guidance in designing biomedical applications.