What Factors Determine the Retention Behavior of Engineered Nanomaterials in Saturated Porous Media?

Abstract

A fundamental problem associated with the vertical transport of engineered nanomaterials (ENMs) in saturated porous media is the occurrence of nonexponential, for example, nonmonotonic or linearly increasing, retention profiles. To investigate this problem, we compiled an extensive database of ENMs transport experiments in saturated porous media. Using this database we trained a decision tree that shows the order of importance, and range of influence, of the physicochemical factors that control the retention profile shape. Our results help identify domains where current particle-transport models can be used, but also highlight, for the first time, large domains where nonexponential retention profiles dominate and new approaches are needed to understand ENM transport. Importantly, highly advective flow and high ENM influent mass can mask the influence of other physicochemical factors on the retention profile shape; notably, this occurs in 50% of the experiments investigated. Where the relationship between physicochemical factors and retention profile shape can be investigated in detail, our results agree with, and provide validation for, the current understanding of how these factors influence ENM transport.

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“…Alternatively, physically based mechanistic models like two-region, two-domain, and dual-permeability models can explicitly account for preferential flow and local/bulk transport in porous media at different scales (e.g., pore, representative elementary volume, and field scales) . To better correlate CMNHs’ retention mechanisms with their newly emerging attributes (e.g., morphology, dimensionality, and MLC), porous media properties (e.g., physical and hydrodynamic properties), and environmental conditions (e.g., water content, water chemistry, and pore-water velocity), machine learning technique (i.e., decision tree) can be advantageous for identifying factors influencing CMNHs transport and retention in porous media. , This may facilitate the development of quantitative relationships between CMNHs mobility in porous media and their physicochemical properties using X-ray CT techniques, mathematical modeling, and machine learning techniques.…”

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“…High ISs decreased the ζ-potential (less negative) and reduced the viscosity (smaller difference in viscosity among CMC populations) 33 3 and S6), and straining was responsible for the hyperexponential RPs (described above). Very recently, Goldberg et al 67 employed a novel decision tree method to quantitatively identify the influence of physicochemical conditions on the retention of NMs in water-saturated porous media and highlighted that high advective flows and influent concentrations masked the impact of other physicochemical conditions, yielding exponential RPs. This well explains the observed exponential RPs of NHs under different particle concentrations (Figure S6d).…”

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“…Increasing the water flow was found to enhance the transport of NPs in a saturated sand-packed column , mainly by influencing the mass transfer rate of the particles from the bulk phase to the solid phase, and by decreasing the fraction of the solid surface area favorable to NP deposition . In our experimental conditions where advection dominates ( N Pe ≫ 1), this factor contributes to the low η 0 values. ,, Application of eq yielded the attachment efficiency α d for each transport experiment displayed in Figure . The values were very similar for AuNP-50 nm and AuNP-100 nm (7% larger for AuNP-100 nm in average) and decreased regularly from 0.8 to 0.9 for the first injection, that is, close to the favorable condition where all the particles approaching the collector would be deposited (α d = 1), to reach the value of 0.4 for the last injections.…”

“…42 In our experimental conditions where advection dominates (N Pe ≫ 1), this factor contributes to the low η 0 values. 7,44,45 Application of eq 2 yielded the attachment efficiency α d for each transport experiment displayed in Figure 2. The values were very similar for AuNP-50 nm and AuNP-100 nm (7% larger for AuNP-100 nm in average) and decreased regularly from 0.8 to 0.9 for the first injection, that is, close to the favorable condition where all the particles approaching the collector would be deposited (α d = 1), to reach the value of 0.4 for the last injections.…”