Visualization and Modeling of Polystyrol Colloid Transport in a Silicon Micromodel

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Abstract
A new experimental approach and complementary model analysis are presented for studying colloid transport and fate in porous media. The experimental approach combines high precision etching to create a controlled pore network in a silicon wafer (i.e., micromodel), with epifluorescent microscopy. Two different sizes of latex colloids were used; each was stained with a fluorescent dye. During an experiment, water with colloids was purged through a micromodel at different flow rates. Flow paths and particle velocities were determined and compared with flow paths calculated using a two‐dimensional (2D) lattice Boltzmann (LB) model. For 50% of the colloids evaluated, agreement between measured and calculated flow paths and velocities were excellent. For 20%, flow paths agreed, but calculated velocities were less. This is attributed to the parabolic velocity profile across the micromodel depth, which was not accounted for in the 2D flow model. For 12%, flow paths also agreed, but calculated velocities were less. These colloids were close to grain surfaces, where model errors increase. Also, particle–surface interactions were not accounted for in the model; this may have contributed to the discrepancy. For the remaining 18% of colloids evaluated, neither flow paths nor velocities agreed. The majority of colloids in this last case were observed after breakthrough, when concentrations were high. The discrepancies may be due to particle–particle interactions that were not accounted for in the model. Filtration efficiencies for all colloid sizes at different flow rates were calculated from filtration theory. Attachment rates were obtained from successive images during an experiment. With these, attachment efficiencies were calculated, and these agreed with literature values. The study demonstrates that excellent agreement between experimental and model results for colloid transport at the pore scale can be obtained. The results also demonstrate that when experimental and model results do not agree, mechanistic inferences and system limitations can be evaluated.

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