Prediction of Nanoparticle and Colloid Attachment on Unfavorable Mineral Surfaces Using Representative Discrete...

Despite several decades of research there currently exists no mechanistic theory to predict colloid attachment in porous media under environmental conditions where colloid-collector repulsion exists (unfavorable conditions for attachment). It has long been inferred that nano- to microscale surface heterogeneity (herein called discrete heterogeneity) drives colloid attachment under unfavorable conditions. Incorporating discrete heterogeneity into colloid-collector interaction calculations in particle trajectory simulations predicts colloid attachment under unfavorable conditions. As yet, discrete heterogeneity cannot be independently measured by spectroscopic or other approaches in ways directly relevant to colloid-surface interaction. This, combined with the fact that a given discrete heterogeneity representation will interact differently with differently sized colloids as well as different ionic strengths for a given sized colloid, suggests a strategy to back out representative discrete heterogeneity by a comparison of simulations to experiments performed across a range of colloid size, solution IS, and fluid velocity. This has recently been performed for interaction of carboxylate-modified polystyrene latex (CML) microsphere attachment to soda lime glass at pH 6.7 with NaCl electrolyte. However, extension to other surfaces, pH values, and electrolytes is needed. For this reason, the attachment of CML (0.25, 1.1, and 2.0 μm diameters) from aqueous suspension onto a variety of unfavorable mineral surfaces (soda lime glass, muscovite, and albite) was examined for pH values of 6.7 and 8.0), fluid velocities (1.71 × 10(-3) and 5.94 × 10(-3) m s(-1)), IS (6.0 and 20 mM), and electrolytes (NaCl, CaSO4, and multivalent mixtures). The resulting representative heterogeneities (heterodomain size and surface coverage, where heterodomain refers to nano- to microscale attractive domains) yielded colloid attachment predictions that were compared to predictions from existing applicable semiempirical expressions in order to examine the strengths and weaknesses of the discrete heterogeneity approach and opportunities for improvement.