(150f) Datamining Multicomponent Active Matter | AIChE

(150f) Datamining Multicomponent Active Matter


Klotsa, D. - Presenter, University of North Carolina at Chapel Hill
Systems which convert energy into motion on the local scale exhibit a variety of emergent, steady-state behaviors. From a materials standpoint, this raises the possibility of a single ‘active’ material which has the ability to perform many, diverse, functions. However, leveraging non-equilibrium matter in new technologies necessitates an understanding of what, if any, quantity equilibrates at steady-state. We probe two fundamental sources of heterogeneity in multicomponent mixtures: distinct swim-speeds and distinct softness. For mixtures of fast and slow particles, we discover two regimes of behavior one with segregated domains of fast and slow species and one homogeneous. We show that a weighted average of constituent particle activities, which we term the net activity, defines a binodal for the motility-induced phase separation transition in active binary mixtures with distinct speeds and examine the critical point. Varying the softness of the active particles, we present an analytical approach which provides the density of the dense phase as well as the pressure. We also find that the surface tension, computed from equilibrium quantities, has a linear relationship with activity and collapses data from a variety of particle softnesses. We thus demonstrate how introducing heterogeneity to active matter leads to new emergent behaviors as well as a deeper theoretical understanding.