Dynamics of self-propelled tracer particles inside a polymer network

TL;DR

This study uses computer simulations to explore how self-propelled tracer particles move through polymer networks, revealing how size, stiffness, and activity influence their diffusion and trapping behaviors.

Contribution

It provides new insights into the dynamics of active particles in polymer networks, highlighting the effects of self-propulsion, size, and network stiffness on transport mechanisms.

Findings

Larger sticky particles experience caging and subdiffusion.

Smaller or highly active tracers escape cages and show superdiffusion.

Stiffer networks slow down tracer dynamics and extend superdiffusive behavior.

Abstract

Transport of tracer particles through mesh-like environments such as biological hydrogels and polymer matrices is ubiquitous in nature. These tracers could be passive, such as colloids or active (self-propelled), such as synthetic nanomotors or bacteria. Computer simulations in principle should be extremely useful in exploring the mechanism of active (self-propelled) transport of tracer particles through the mesh-like environments. Therefore, we construct a polymer network on a diamond lattice and use computer simulations to investigate the dynamics of spherical self-propelled particles inside the network. Our main objective is to elucidate the effect of the self-propulsion on the dynamics of the tracer particle as a function of tracer size and stiffness of the polymer network. We compute the time-averaged mean-squared displacement (MSD) and the van-Hove correlations of the tracer. On one hand, in the case of the bigger sticky particle, caging caused by the network particles wins over the escape assisted by the self-propulsion. This results intermediate-time subdiffusion. On the other hand, smaller tracers or tracers with high self-propulsion velocities can easily escape from the cages and show intermediate-time superdiffusion. Stiffer the network, slower the dynamics of the tracer, and the bigger tracers exhibit longer lived intermediate time superdiffusion, as the persistence time scales as $\sim \sigma^3$, where $\sigma$ is the diameter of the tracer. In intermediate time, non-Gaussianity is more pronounced for active tracers. In the long time, the dynamics of the tracer, if passive or weakly active, becomes Gaussian and diffusive, but remains flat for tracers with high self-propulsion, accounting for their seemingly unrestricted motion inside the network.