Effect of Surface and Salt Properties on the Ion Distribution around Spherical Nanoparticles: Monte Carlo Simulations

Abstract

Nanoparticle surface charge properties represent key parameters to predict their fate, reactivity, and complexation in natural, biological, and industrial dispersions. In this context, we present here an original approach to better understand the surface charge electrostatic properties of spherical nanoparticles (NPs). The ion distribution around one nanoparticle is investigated using Monte Carlo simulations and by adjusting a wide range of parameters including NP properties (surface charge density and site distribution), salt concentration (ionic strength and cation concentration), and salt valency (mono-, di-, and trivalent salt). A canonical Metropolis Monte Carlo method is used to reach equilibrium states and a primitive Coulomb model is applied to describe the electrostatic interactions between explicit discrete sites, counterions, and salt particles. Our results show that the presence of explicit surface charges on the NP and in solution has a strong influence on the local ion distribution and on the effective surface charge of the nanoparticles. The increase of surface charge density reduces the NP effective charge by the formation of a condensation layer around the nanoparticle. However, a limit of condensation is achieved due to steric effects and electrostatic repulsions. The presence of di- and trivalent cations is also found to strongly modify the effective charge and improve condensation state as long as electrostatic repulsion between the cations close to the surface are not so strong. At high trivalent cation concentration, the NP effective charge is greatly reduced and the local environment around the nanoparticle becomes more structured with the formation of a multi layer structure composed by anions and cations.

Destabilization of Titania Nanosheet Suspensions by Inorganic Salts: Hofmeister Series and Schulze-Hardy Rule

“…Similar correlation between the surface charge and the valence of the adsorbing counterions was discovered on the basis of electrophoretic measurements performed with colloidal particles 22,25,30,31 including titania. 46 In addition, Monte Carlo calculations were carried out to describe the distribution of multivalent ions around and on charged colloidal particles 56 and planar surfaces. 57 These results and others 58−60 from model calculations also revealed that the strength of the counterion adsorption increases with the valence leading to a decreased effective charge and to charge reversal of the surfaces.…”

A review of the fate of engineered nanomaterials in municipal solid waste streams

“…Key ENM properties that influence colloidal stability include particle size, size distribution, shape, specific surface area, and surface properties (i.e., grafting density of organic ligands, surface charge, and functionality), where the latter material properties predominantly determines whether an ENM is hydrophilic or -phobic (Part et al 2015). It should also be noted that environmental conditions, such as pH, ionic strength, electrolyte concentration, and NOM content significantly influence ENM mobility and transport in (saturated) porous solid matrices, as experimentally reported by several researchers (Akaighe et al 2011;Clavier et al 2016;Ghosh et al 2008;Radniecki et al 2011;Tejamaya et al 2012;Yang et al 2015;Zhou et al 2013). Significant work in the field of ENMs has been conducted evaluating the influence of these ENM and environmental conditions on ENM stability and thus transport in liquid and solid matrices (e.g., Chen and Elimelech 2006;Hotze et al 2010;Li et al 2008;Louie et al 2016;Petosa et al 2010;Phenrat et al 2010;Saleh et al 2010).…”

Strategies for determining heteroaggregation attachment efficiencies of engineered nanoparticles in aquatic environments

“…18 Therefore, effects of particle (or aggregate) properties, possible surface coatings and medium characteristics on particle interactions need to be considered on a more detailed scale. 42 Challenges in predicting α Despite the theoretical basis described above, calculating α ab initio remains very challenging. The extended DLVO theory can be used as a starting point, but it is very difficult to predict α for environmentally relevant scenarios.…”