Nonequilibrium structure of colloidal dumbbells under oscillatory shear

Abstract

We investigate the nonequilibrium behavior of dense, plastic-crystalline suspensions of mildly anisotropic colloidal hard dumbbells under the action of an oscillatory shear field by employing Brownian dynamics computer simulations. In particular, we extend previous investigations, where we uncovered novel nonequilibrium phase transitions, to other aspect ratios and to a larger nonequilibrium parameter space, that is, a wider range of strains and shear frequencies. We compare and discuss selected results in the context of novel scattering and rheological experiments. Both simulations and experiments demonstrate that the previously found transitions from the plastic crystal phase with increasing shear strain also occur at other aspect ratios. We explore the transition behavior in the strain-frequency phase and summarize it in a nonequilibrium phase diagram. Additionally, the experimental rheology results hint at a slowing down of the colloidal dynamics with higher aspect ratio.

Shear-induced deconfinement of hard disks

“…[33]. Dispersions of thermoresponsive colloids [3][4][5][6][7][8][9][10][11][12][13][14][15][16][17][18][19][20] could be excellent model systems for corresponding experimental work that is aimed at systematically studying superadiabatic forces.…”

“…For the important material class of colloidal dispersions, Matthias Ballauff and collaborators have performed sterling work, developing and exploiting ingeniously tailored particles that respond to temperature variation. Despite the quite complex internal core-shell structure of these thermosensitive colloids [3][4][5][6][7][8][9][10][11][12][13][14][15][16][17][18][19][20], the particles interact via an essentially short-ranged steeply repulsive pair potential. Changing the temperature facilitates systematically changing the effective particle size and hence the typical length scale of the interparticle interactions.…”

Unsupervised machine learning for detection of phase transitions in off-lattice systems. I. Foundations

“…[1][2][3][4][5][6] Here, we focus on offlattice models of particle-based systems, i.e., atomic/molecular materials and their colloidal analogs, which are known to exhibit a rich variety of equilibrium phase transitions including condensation, freezing, and compositional demixing, as well as formation of microphases including cluster fluids. 7 Phase transitions that occur due to nonequilibrium driving forces (e.g., oscillatory shear [8][9][10][11][12] , time dependent magnetic/electric fields 13,14 , or particle selfpropulsion [15][16][17] ) are also possible in these materials. Given the diversity of microscopic degrees of freedom and macroscopic outcomes exhibited by such systems, new types of phase transitions and corresponding states of matter can be challenging to detect or predict from first principles.…”

Unsupervised machine learning for detection of phase transitions in off-lattice systems. II. Applications

“…17 We also address active or driven matter, which exhibits phase transitions whose detection and characterization cannot generally be facilitated based on arguments from equilibrium statistical mechanics. [18][19][20][21][22][23][24][25][26][27] We propose several numerical encoding schemes (i.e., feature vector representations) for data describing particle configurations in these systems to detect their phase transitions with PCA. We find that prior knowledge of the phase transition is not required to construct a useful feature vector; consideration of the properties of the model system at hand is sufficient.…”