Gradient-driven diffusion and pattern formation in crowded mixtures

TL;DR

This study investigates how gradient-driven diffusion influences microstructure and transport in crowded mixtures, revealing complex behaviors like non-monotonic diffusivity and scaling laws through a lattice gas model.

Contribution

It introduces a two-dimensional lattice gas model to analyze the coupling between phase separation, microstructure, and diffusion in crowded solutions, highlighting new mechanisms.

Findings

Tracer diffusivity depends non-monotonically on temperature and crowder density.

Crowder phase separation leads to dynamic obstacle formation affecting transport.

Scaling behavior in low temperature limit matches random resistor network models.

Abstract

Gradient-driven diffusion in crowded, multicomponent mixtures is a topic of high interest because of its role in biological processes such as transport in cell membranes. In partially phase-separated solutions, gradient-driven diffusion affects microstructure, which in turn affects diffusivity; a key question is how this complex coupling controls both transport and pattern formation. To examine these mechanisms, we study a two-dimensional multi-component lattice gas model, where "tracer" molecules diffuse between a source and a sink separated by a solution of sticky "crowder" molecules that cluster to form dynamically evolving obstacles. In the high temperature limit, crowders and tracers are miscible and transport may be predicted analytically. At intermediate temperatures, crowders phase separate into clusters that drift toward the tracer sink. As a result, steady-state tracer diffusivity depends non-monotonically on both temperature and crowder density and we observe a variety of complex microstructures. In the low temperature limit, crowders rapidly aggregate to form obstacles that are kinetically arrested; if crowder density is near the percolation threshold, resulting tracer diffusivity shows scaling behavior with the same scaling exponent as the random resistor network model. Though highly idealized, this simple model reveals fundamental mechanisms governing coupled gradient-driven diffusion, phase separation, and microstructural evolution in crowded solutions.