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Developments in the last century, and especially in the last 50 years, have advanced understanding of suspension rheology greatly. Here, a limited review of suspension work over this period is presented, emphasizing advances over the last three decades in understanding of the particle pressure and strong shear thickening, which were motivated by crucial experimental observations, computational advances, and a critical review, all from the 1980s. This review serves as a preview to some outstanding challenges in suspension mechanics. This article considers primarily dispersions of spherical particles, which serve not only as a model material for understanding the rheology of more complex fluids of practical relevance, but also as a basic system for the study of nonequilibrium statistical physics. 

A colloidal motor driven by surface tension forces is theoretically designed by encapsulating an active Janus particle in a liquid drop which is immiscible in the suspending medium. The Janus particle produces an asymmetric flux of a solute species which induces surface tension gradients along the liquid–liquid interface between the drop and the surrounding fluid. The resulting Marangoni forces at the interface propel the compound drop/Janus particle system. The propulsion speeds of the motor are evaluated for a range of relative sizes and positions of the drop and the particle and across a range of transport properties of the drop and the suspending medium. It is demonstrated that the proposed design can produce higher propulsion velocities than the traditional Janus-particle-based colloidal motors propelled by neutral diffusiophoresis. 

Discrete-particle simulations of bidisperse shear thickening suspensions are reported. The work considers two packing parameters, the large-to-small particle radius ratio ranging from [Formula: see text] (nearly monodisperse) to [Formula: see text], and the large particle fraction of the total solid loading with values [Formula: see text], 0.5, and 0.85. Particle-scale simulations are performed over a broad range of shear stresses using a simulation model for spherical particles accounting for short-range lubrication forces, frictional interaction, and repulsion between particles. The variation of rheological properties and the maximum packing fraction [Formula: see text] with shear stress [Formula: see text] are reported. At a fixed volume fraction [Formula: see text], bidispersity decreases the suspension relative viscosity [Formula: see text], where [Formula: see text] is the suspension viscosity and [Formula: see text] is the suspending fluid viscosity, over the entire range of shear stresses studied. However, under low shear stress conditions, the suspension exhibits an unusual rheological behavior: the minimum viscosity does not occur as expected at [Formula: see text], but instead decreases with further increase of [Formula: see text] to [Formula: see text]. The second normal stress difference [Formula: see text] acts similarly. This behavior is caused by particles ordering into a layered structure, as is also reflected by the zero slope with respect to time of the mean-square displacement in the velocity gradient direction. The relative viscosity [Formula: see text] of bidisperse rate-dependent suspensions can be predicted by a power law linking it to [Formula: see text], [Formula: see text] in both low and high shear stress regimes. The agreement between the power law and experimental data from literature demonstrates that the model captures well the effect of particle size distribution, showing that viscosity roughly collapses onto a single master curve when plotted against the reduced volume fraction [Formula: see text]. 

Dense suspensions of particles in viscous liquid often demonstrate the striking phenomenon of abrupt shear thickening, where their viscosity increases strongly with increase of the imposed stress or shear rate. In this work, discrete-particle simulations accounting for short-range hydrodynamic, repulsive, and contact forces are performed to simulate flow of shear thickening bidisperse suspensions, with the packing parameters of large-to-small particle radius ratio δ = 3 and large particle fraction ζ = 0.15, 0.50, and 0.85. The simulations are carried out for volume fractions 0.54 ≤ ϕ ≤ 0.60 and a wide range of shear stresses. The repulsive forces, of magnitude F R , model the effects of surface charge and electric double-layer overlap, and result in shear thinning at small stress, with shear thickening beginning at stresses σ ∼ F R a −2 . A crossover scaling analysis used to describe systems with more than one thermodynamic critical point has recently been shown to successfully describe the experimentally-observed shear thickening behavior in suspensions. The scaling theory is tested here on simulated shear thickening data of the bidisperse mixtures, and also on nearly monodisperse suspensions with δ = 1.4 and ζ = 0.50. Presenting the viscosity in terms of a universal crossover scaling function between the frictionless and frictional maximum packing fractions collapses the viscosity for most of the suspensions studied. Two scaling regimes having different exponents are observed. The scaling analysis shows that the second normal stress difference N 2 and the particle pressure Π also collapse on their respective curves, with the latter featuring a different exponent from the viscosity and normal stress difference. The influence of the fraction of frictional contacts, one of the parameters of the scaling analysis, and its dependence on the packing parameters are also presented. 

Cancer cell lines serve as modelin vitrosystems for investigating therapeutic interventions. Recent advances in high‐throughput genomic profiling have enabled the systematic comparison between cell lines and patient tumor samples. The highly interconnected nature of biological data, however, presents a challenge when mapping patient tumors to cell lines. Standard clustering methods can be particularly susceptible to the high level of noise present in these datasets and only output clusters at one unknown scale of the data. In light of these challenges, we present NetCellMatch, a robust framework for network‐based matching of cell lines to patient tumors. NetCellMatch first constructs a global network across all cell line‐patient samples using their genomic similarity. Then, a multi‐scale community detection algorithm integrates information across topologically meaningful (clustering) scales to obtain Network‐Based Matching Scores (NBMS). NBMS are measures ofcluster robustnesswhich map patient tumors to cell lines. We use NBMS to determine representative “avatar” cell lines for subgroups of patients. We apply NetCellMatch to reverse‐phase protein array data obtained from The Cancer Genome Atlas for patients and the MD Anderson Cell Line Project for cell lines. Along with avatar cell line identification, we evaluate connectivity patterns for breast, lung, and colon cancer and explore the proteomic profiles of avatars and their corresponding top matching patients. Our results demonstrate our framework's ability to identify both patient‐cell line matches and potential proteomic drivers of similarity. Our methods are general and can be easily adapted to other'omic datasets.