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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. 

 

Spatial heterogeneity in the local strength of attraction with a porous medium influences particle transport under quiescent and flow conditions. 

Rheology-informed neural networks (RhINNs) have recently been popularized as data-driven platforms for solving rheologically relevant differential equations. While RhINNs can be employed to solve different constitutive equations of interest in a forward or inverse manner, their ability to do so strictly depends on the type of data and the choice of models embedded within their structure. Here, the applicability of RhINNs in general, and the interplay between the choice of models, parameters of the neural network itself, and the type of data at hand are studied. To do so, a RhINN is informed by a series of thixotropic elasto-visco-plastic (TEVP) constitutive models, and its ability to accurately recover model parameters from stress growth and oscillatory shear flow protocols is investigated. We observed that by simplifying the constitutive model, RhINN convergence is improved in terms of parameter recovery accuracy and computation speed while over-simplifying the model is detrimental to accuracy. Moreover, several hyperparameters, e.g., the learning rate, activation function, initial conditions for the fitting parameters, and error heuristics, should be at the top of the checklist when aiming to improve parameter recovery using RhINNs. Finally, the given data form plays a pivotal role, and no convergence is observed when one set of experiments is used as the given data for either of the flow protocols. The range of parameters is also a limiting factor when employing RhINNs for parameter recovery, and ad hoc modifications to the constitutive model can be trivial remedies to guarantee convergence when recovering fitting parameters with large values. 

Abstract Developing constitutive models that can describe a complex fluid’s response to an applied stimulus has been one of the critical pursuits of rheologists. The complexity of the models typically goes hand-in-hand with that of the observed behaviors and can quickly become prohibitive depending on the choice of materials and/or flow protocols. Therefore, reducing the number of fitting parameters by seeking compact representations of those constitutive models can obviate extra experimentation to confine the parameter space. To this end, fractional derivatives in which the differential response of matter accepts non-integer orders have shown promise. Here, we develop neural networks that are informed by a series of different fractional constitutive models. These fractional rheology-informed neural networks (RhINNs) are then used to recover the relevant parameters (fractional derivative orders) of three fractional viscoelastic constitutive models, i.e., fractional Maxwell, Kelvin-Voigt, and Zener models. We find that for all three studied models, RhINNs recover the observed behavior accurately, although in some cases, the fractional derivative order is recovered with significant deviations from what is known as ground truth. This suggests that extra fractional elements are redundant when the material response is relatively simple. Therefore, choosing a fractional constitutive model for a given material response is contingent upon the response complexity, as fractional elements embody a wide range of transient material behaviors. 

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. 

Abstract We investigate the effect of steric and hydrodynamic interactions (HI) on quiescent diffusion and flow‐driven transport of finite‐sized nanoparticles through periodic 2D (two‐dimensional) and 3D (three‐dimensional) nanopost arrays using Stokesian dynamics simulations. We find that steric and HI hinder particle diffusivity under quiescent conditions and enhance longitudinal dispersion under flow. Moreover, the presence of HI leads to a power‐law increase in the longitudinal dispersion coefficient with Pe due to spatial variations in the fluid velocity. Lastly, our simulations reveal that longitudinal particle dispersion coefficients behave similarly in 2D and 3D when HI are included.