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Colloidal gelation phase diagram has been traditionally characterized using three key factors: particle volume fraction, strength of attraction, and range of attraction. While there's a rich body of literature on the role of attraction strength and particle volume fraction, majority of studies have been limited to short range interactions. Using Brownian dynamics simulations, we explored the effect that the range of attractions has on the microstructure and dynamics of both weakly and strongly attractive colloidal systems. Although gelation occurs significantly faster at high attraction strength, by an order of magnitude compared to low strength, we did not observe any clear trend in gelation-rate with respect to a change in the range of interaction. However, as the attraction range increases in both systems, the final structure undergoes a transition from a single connected network to a fluid of dense clusters. This results in a new gelation phase boundary for long range attractive colloids.
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Colloidal gels typically exhibit mechanical properties akin to a viscoelastic solid, influenced by their underlying particulate network. Hence, the structural and morphological characteristics of the colloidal network have a significant effect on the rigidity of the gel. In this study, we show how seemingly small variations in the particle-level interactions throughout the system result in larger scale structural heterogeneities. While the microscale particle level descriptors of the colloidal network remain largely unaffected by heterogeneous interactions, larger scale properties of a colloidal gel change appreciably. The overall cluster-level mesostructure of a colloidal gel is found to be sensitive to the small variations in the interaction potential at the particle level.
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Porous media used in many practical applications contain natural spatial variations in composition and surface charge that lead to heterogeneous physicochemical attractions between the media and transported particles. We performed Stokesian dynamics (SD) simulations to examine the effects of heterogeneous attractions on quiescent diffusion and hydrodynamic dispersion of particles within geometrically ordered arrays of nanoposts. We find that transport under quiescent conditions occurs by two mechanisms, diffusion through the void space and intermittent hopping between the attractive wells of different nanoposts. As the attraction heterogeneity increases, the latter mechanism becomes dominant, resulting in an increase in the particle trajectory tortuosity, deviations from Gaussian behavior in the particle displacement distributions, and a decrease in the long-time particle diffusivity. Similarly, under flow conditions corresponding to low Péclet number (Pe), increased attraction heterogeneity leads to transient localization near the nanoposts, resulting in a broadening of the particle distribution and enhanced longitudinal dispersion in the direction of flow. At high Pe where advection strongly dominates, however, the longitudinal dispersion coefficient is insensitive to attraction heterogeneity and exhibits Taylor-Aris dispersion behavior. Our findings provide insight into how heterogeneous interactions may influence particle transport in complex 3-D porous media.
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Yielding of the particulate network in colloidal gels under applied deformation is accompanied by various microstructural changes, including rearrangement, bond rupture, anisotropy, and reformation of secondary structures. While much work has been done to understand the physical underpinnings of yielding in colloidal gels, its topological origins remain poorly understood. Here, employing a series of tools from network science, we characterize the bonds using their orientation and network centrality. We find that bonds with higher centralities in the network are ruptured the most at all applied deformation rates. This suggests that a network analysis of the particulate structure can be used to predict the failure points in colloidal gels a priori.
Asunto(s)
Geles , Geles/químicaRESUMEN
One of the defining characteristics of soft glassy materials is their ability to exhibit a yield stress, which can result in an overall elasto-visco-plastic mechanics. To design soft materials with specific properties, it is essential to gain a comprehensive understanding of the topological and structural failure points that occur during yielding. However, predicting these failure points, which lead to yielding, is challenging due to the dynamic nature of structure development and its cooccurrence with other complicated processes, such as local rearrangements and anisotropy. In this study, we employ a series of tools from network science to investigate colloidal gels as a model for soft glassy materials during yielding. Our findings reveal that edge betweenness centrality can be utilized as a universal predictor for yielding across various state variables, including the volume fraction of solids, the strength, and the range of attraction between colloids.
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We investigate the effects of physicochemical attractions on the transport of finite-sized particles in three-dimensional ordered nanopost arrays using Stokesian dynamics simulations. We find that weak particle-nanopost attractions negligibly affect diffusion due to the dominance of Brownian fluctuations. Strong attractions, however, significantly hinder particle diffusion due to localization of particles around the nanoposts. Conversely, under flow, attractions significantly enhance longitudinal dispersion at low to moderate Péclet number (Pe). At high Pe, by contrast, advection becomes dominant and attractions weakly enhance dispersion. Moreover, attractions frustrate directional locking at moderate flow rates, and shift the onset of this behavior to higher Pe.
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Control over dispersion of nanoparticles in polymer solutions through porous media is important for subsurface applications such as soil remediation and enhanced oil recovery. Dispersion is affected by the spatial heterogeneity of porous media, the non-Newtonian behavior of polymer solutions, and the Brownian motion of nanoparticles. Here, we use the Euler-Lagrangian method to simulate the flow of nanoparticles and inelastic non-Newtonian fluids (described by Meter model) in a range of porous media samples and injection rates. In one case, we use a fine mesh of more than 3 million mesh points to model nanoparticles transport in a sandstone sample. The results show that the velocity distribution of nanoparticles in the porous medium is non-Gaussian, which leads to the non-Fickian behavior of nanoparticles dispersion. Due to pore-space confinement, the long-time mean-square displacement of nanoparticles depends nonlinearly on time. Additionally, the gradient of shear stress in the pore space of the porous medium dictates the transport behavior of nanoparticles in the porous medium. Furthermore, the Brownian motion of nanoparticles increases the dispersion of nanoparticles along the longitudinal and transverse direction.
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We investigate the effects of array geometry and flow orientation on transport of finite-sized particles in ordered arrays using Stokesian dynamics simulations. We find that quiescent diffusion is independent of array geometry over the range of volume fraction of the nanoposts examined. Longitudinal dispersion under flow depends on the direction of incident flow relative to the array lattice vectors. Taylor-Aris behavior is recovered for flow along the lattice directions, whereas a nonmonotonic dependence of the dispersion coefficient on the Péclet number is obtained for flow orientations slightly perturbed from certain lattice vectors, owing to a competition between directional locking and spatial velocity variations.