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We report the effect of particle surface roughness on creep deformation and subsequent strain recovery in dense colloidal suspensions. The suspensions are composed of hard-spherelike poly(methyl methacrylate) smooth (S) and rough (R) colloids with particle volume fractions ϕS = 0.64 ± 0.01 and ϕR = 0.56 ± 0.01, corresponding to a distance of 3.0% and 3.4% based on their jamming volume fractions (ϕJS=0.66±0.01, ϕJR=0.58±0.01). The suspensions are subject to a range of shear stresses (0.01–0.07 Pa) above and below the yield stress values of the two suspensions (σyS=0.035Pa, σyR=0.02Pa). During creep, suspensions of rough colloids exhibit four to five times higher strain deformation compared to smooth colloids, irrespective of the applied stress. The interlocking of surface asperities in rough colloids is likely to generate a heterogeneous microstructure, favoring dynamic particle activity and percolation of strain heterogeneities, therefore resulting in higher magnitude of strain deformation and an earlier onset of steady flow. Strain recovery after the cessation of stress reveals a nonmonotonic recoverable strain for rough colloids, where the peak recoverable strain is observed near the yield stress, followed by a steep decline with increasing stress. This type of response suggests that frictional constraints between geometrically frustrated interlocking contacts can serve as particle bonds capable of higher elastic recovery but only near the yield stress. Understanding how particle roughness affects macroscopic creep and recovery is useful in designing yield stress fluids for additive manufacturing and product formulations. 

Colloidal gels exhibit solid-like behavior at vanishingly small fractions of solids, owing to ramified space-spanning networks that form due to particle–particle interactions. These networks give the gel its rigidity, and with stronger attractions the elasticity grows as well. The emergence of rigidity can be described through a mean field approach; nonetheless, fundamental understanding of how rigidity varies in gels of different attractions is lacking. Moreover, recovering an accurate gelation phase diagram based on the system’s variables has been an extremely challenging task. Understanding the nature of colloidal clusters, and how rigidity emerges from their connections is key to controlling and designing gels with desirable properties. Here, we employ network analysis tools to interrogate and characterize the colloidal structures. We construct a particle-level network, having all the spatial coordinates of colloids with different attraction levels, and also identify polydisperse rigid fractal clusters using a Gaussian mixture model, to form a coarse-grained cluster network that distinctly shows main physical features of the colloidal gels. A simple mass-spring model then is used to recover quantitatively the elasticity of colloidal gels from these cluster networks. Interrogating the resilience of these gel networks shows that the elasticity of a gel (a dynamic property) is directly correlated to its cluster network’s resilience (a static measure). Finally, we use the resilience investigations to devise [and experimentally validate] a fully resolved phase diagram for colloidal gelation, with a clear solid–liquid phase boundary using a single volume fraction of particles well beyond this phase boundary.