Experimentation, modeling, and sensitivity analysis on the mobility of aluminum oxide nanoparticles in saturated sand: Effects of ionic strength, flow rate, and nanoparticle concentration
Nanomaterials have been widely studied and used in the last decade for industrial and commercial applications. Assessing the risks of these materials in the environment requires an understanding of their mobility, reactivity, eco-toxicity and persistency. In this study, the effect of ionic strength (IS), flow rate, and nanoparticle concentration on the transport and deposition of aluminum oxide nanoparticles (Al2O3 NPs) in saturated sand was investigated. Two types of Al2O3 NPs were used: colloidal dispersion, 82 nm suspended (S_82nm) and powdered particles in solution, ~ 244 nm suspended (P_244nm). Mobility of Al2O3 NPs was influenced by IS, the highest mobility was observed in DI water for both the nanoparticles (77% and 99% for P_244nm and S_82nm, respectively) and decreased with increasing ionic strength. Decreased mobility of the NPs was due to aggregation as the IS increased. Varying flow conditions did not have a significant effect on the mobility of S_82nm nanoparticles. However, increased and faster elution was observed when the influent concentration was increased from 50 mg/L to 400 mg/L. The influent and effluent nanoparticle sizes were also measured using dynamic light scattering. For most conditions, the size was observed to be below 100 nm and there was no significant change to the influent and effluent particle sizes for S_82 nm nanoparticles. Significant elution was observed although conditions were electrostatically favorable, which was attributed to the small, stable size (~82 nm) of the particles and blocking. DLVO theory was also applied to the data to better understand the mechanisms of mobility. In general, P_244nm nanoparticles showed higher attachment and removal efficiency than S_82nm nanoparticles. In most cases, the maximum breakthrough concentration was less than 4% of the influent. The mechanism for removal for these nanoparticles was most likely due to aggregation and straining. In addition, the experimental data was fitted with a transport model in order to further explain the transport mechanisms and apply uncertainty quantification to identify parameters that are important to the mobility of these particles. The two kinetic sites model with two attachment sites (fast and slow attachment) and a blocking function fitted the experimental breakthrough curves well for S_82nm nanoparticles. The simulation was performed a number of times using this model to generate 95% confidence bounds over the modeled breakthrough curves. The parameters for slow attachment coefficient (for 150mg/L influent concentration) and maximum solid phase concentration of retained nanoparticles (for 50 mg/L influent concentration) showed the most effects on the variability of effluent concentrations. Results from sensitivity analysis also showed that for particle size less than ~1µm, the efficiency due to diffusion showed greater influence on overall single collector contact efficiency. All these results will contribute to a better understanding of the mechanisms of mobility for nanoparticles less than 100 nm in the environment.