Scaling the glassy dynamics of active particles: Tunable fragility and reentrance

TL;DR

This study explores how self-propulsion and persistence time influence the glassy dynamics of dense active particles, revealing tunable fragility, reentrant phase behavior, and a crossover from glassy to jammed states, with implications for biological tissues.

Contribution

It introduces a comprehensive phase diagram for active particles, demonstrating tunable fragility and reentrant behavior, and extends passive glass dynamics analysis to active systems.

Findings

Non-monotonic relaxation time with persistence time.

Transition from sub-Arrhenius to super-Arrhenius dynamics.

Applicability of passive dynamic scaling to active systems.

Abstract

Understanding the influence of activity on dense amorphous assemblies is crucial for biological processes such as wound healing, embryogenesis, or cancer progression. Here, we study the effect of self-propulsion forces of amplitude $f_0$ and persistence time $\tau_p$ in dense assemblies of soft repulsive particles by simulating a model particle system that interpolates between particulate active matter and biological tissues. We identify the fluid and glass phases of the three-dimensional phase diagram obtained by varying $f_0$, $\tau_p$, and the packing fraction $\phi$. The morphology of the phase diagram directly accounts for a non-monotonic evolution of the relaxation time with $\tau_p$, which is a direct consequence of the crossover in the dominant relaxation mechanism, from glassy to jamming. A second major consequence is the evolution of the glassy dynamics from sub-Arrhenius to super-Arrhenius. We show that this tunable glass fragility extends to active systems analogous observations reported for passive particles. This analogy allows us to apply a dynamic scaling analysis proposed for the passive case, in order to account for our results for active systems. Finally, we discuss similarities and differences between our results and recent findings in the context of computational models of biological tissues.