The profound influence of repulsive colloid-surface interactions on colloid transport under environmental conditions (so-called unfavorable conditions) has rendered quantitative prediction of colloid transport in porous media under these conditions unattainable to date. A major question is whether surfaces can generally be interpreted/categorized in terms of a bulk repulsive surface that includes attractive ?heterodomains? of given size and spatial frequency (surface coverage). Specifically, it is unknown whether characterizing surfaces in this way allows prediction of colloid transport in porous media under unfavorable conditions. This discrete heterogeneity model predicts (and experiments indicate) that a fraction of colloids in the fluid surrounding a porous media grain will spend long residence times in the near-surface fluid due to weak attractive interactions, which raises another fundamental question: how do the long residence times of colloids in the near-surface fluid under unfavorable conditions influence transport behavior at the Darcy scale? What scale-up strategies are needed to recognize the influence of long near-surface residence times? We posit that these long near-surface residence times yield incomplete mixing of colloids across the fluid domain, necessitating memory function/multirate or correlated random walk approaches to upscaling. Our recently-developed mechanistic model with heterodomains captures colloid transport and retention behavior at the pore scale under unfavorable conditions. This model will serve as the platform from which to scale up predictions to the Darcy scale under unfavorable conditions. The model results will be compared to experimental results for colloid transport in an impinging jet and in porous media for a range of colloid sizes, solution ionic strengths, and fluid velocities. We anticipate producing a mechanistic model that will predict the extent and mode of colloid retention under unfavorable conditions.
This project will represent unfavorable surfaces in numerical models of colloid transport in order to provide mechanistic prediction of colloid retention during transport in porous media. The results of this mechanistic model will be used to explore the influence of colloid-surface interaction on strategies to upscale transport prediction from the pore to the Darcy scale. This project will greatly enhance our ability to predict colloid transport under environmental conditions, in contexts such as pathogen transport in the environment, and targeted delivery of bacteria with novel metabolic properties.
