Effect of deposition, detachment and aggregation processes on nanoparticle transport in porous media using Monte Carlo simulations

Abstract

A novel off-lattice three-dimensional coarse-grained Monte Carlo model is developed to study engineered nanoparticle (ENP) behavior in porous media. Based on individual particle tracking and on the assumption that different physicochemical processes may occur with different probabilities, our model is used to independently evaluate the influence of homoaggregation, attachment and detachment processes on ENP transport and retention inside porous media made of colloidal collectors. The possibility of straining, i.e. trapping of ENPs or aggregates that are too large to pass pore necks, is also included in the model. The overall probability of ENP retention as a function of the above mentioned processes is quantified using functional tests in the form of a α_global(t_ref) retention parameter. High α_global(t_ref) values were obtained for moderate probabilities of homoaggregation between ENPs (α_ENP–ENP) and very small probabilities of attachment between ENPs and collectors (α_att), thus indicating the important role of homoaggregation and attachment in ENP retention. Moreover, attaching ENPs and large aggregates was found to cause pore neck enclosure and thus largely contributed to the straining of unbound ENPs. An analysis of depth distribution of retained ENPs revealed that, depending on the dominating conditions, the number of ENPs was decreasing monotonously or exponentially with depth. The introduction of the ENP detachment probability (α_det) from collectors resulted in an increased ENP occurrence at the porous media matrix outlet. It was also found that different sets of α_det and α_att values, reflecting different ENPs and collector physicochemical properties and inter-particle forces, lead to identical α_global(t_ref) values. This constitutes an important outcome indicating that α_global(t_ref) values determined from functional tests are not mechanistic but operationally defined parameters and thus cannot be deemed predictive beyond these tests.