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We report the ensemble-averaged and pairing-specific network microstructure formed by short-range depletion attractions in bidisperse hard sphere-like colloidal systems. 

A recurring challenge in extracting energy from ambient motion is that devices must maintain high harvesting efficiency and a positive user experience when the interface is undergoing dynamic compression. We show that small amphiphiles can be used to tune friction, haptics, and triboelectric properties by assembling into specific conformations on the surfaces of materials. Molecules that form multiple slip planes under pressure, especially through π-π stacking, produce 80 to 90% lower friction than those that form disordered mesostructures. We propose a scaling framework for their friction reduction properties that accounts for adhesion and contact mechanics. Amphiphile-coated surfaces tend to resist wear and generate distinct tactile perception, with humans preferring more slippery materials. Separately, triboelectric output is enhanced through the use of amphiphiles with high electron affinity. Because device adoption is tied to both friction reduction and electron-withdrawing potential, molecules that self-organize into slippery planes under pressure represent a facile way to advance the development of haptic power harvesters at scale. 

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. 

We report the linear rheology for dense suspensions of sterically stabilized smooth and mesoscopically rough colloids interacting as hard particles. Small amplitude oscillatory measurements reveal that rough colloids at high volume fractions exhibit storage and loss moduli that are orders of magnitude greater than smooth colloids. Frequency-concentration superposition is used to collapse the viscoelasticity data onto a master curve, where shift factors suggest a more elastic microstructure and reduced cage volume for rough particles. A combination of the mode-coupling theory, hydrodynamic modeling, and the activated hopping theory shows that these rough particles with significantly reduced localization lengths tend to become trapped in their glassy cages for extended periods of time. High-frequency data show that rough colloids, but not smooth colloids, display a transition from a free-draining to a fully lubricated state above the crossover volume fraction and, furthermore, exhibit solidlike behavior. Scaling analyses support the idea that lubrication forces between interlocking asperities are enhanced, leading to rotational constraints and stress-bearing structures that significantly elevate the viscoelasticity of dense suspensions. The results provide a framework for how particle surface topology affects the linear rheology in applications such as coatings, cement, consumer products, and shock-absorbing materials. 

We report a procedure to obtain the search distance used to determine particle contact in dense suspensions of smooth and rough colloids. This method works by summing physically relevant length scales in an uncertainty analysis and does not require detailed quantification of the surface roughness. We suspend sterically stabilized, fluorescent poly(methyl methacrylate) colloids in a refractive index-matched solvent, squalene, in order to ensure hard sphere-like behavior. High speed centrifugation is used to pack smooth and rough colloids to their respective jamming points, ϕ J . The jammed suspensions are subsequently diluted with known volumes of solvent to ϕ < ϕ J . Structural parameters obtained from confocal laser scanning micrographs of the diluted colloidal suspensions are extrapolated to ϕ J to determine the mean contact number at jamming, 〈 z 〉 J . Contact below jamming refers to nearest neighbors at a length scale below which the effects of hydrodynamic or geometric friction come into play. Sensitivity analyses show that a deviation of the search distance by 1% of the particle diameter results in 〈 z 〉 changing by up to 10%, with the error in contact number distribution being magnified in dense suspensions ( ϕ > 0.50) due to an increased number of nearest neighbors in the first coordination shell.