Novel computational approach for studying ph effects, excluded volume and ion-ion correlations in electrical double layers around polyelectrolytes and nanoparticles
Highly charged cylindrical and spherical objects (macroions) are probably the simplest structures for modeling nucleic acids, proteins and nanoparticles. Their ubiquitous presence within biophysical systems ensures that Coulomb forces are among the most important interactions that regulate the behavior of these systems. In these systems, ions position themselves in a strongly correlated manner near the surface of a macroion and form electrical double layers (EDLs). These EDLs play an important role in many biophysical and biochemical processes. For instance, the macroion's net charge can change due to the binding of many multivalent ions to its surface. Thus, proper description of EDLs near the surface of a macroion may reveal a counter-intuitive charge inversion behavior, which can generate attraction between like-charged objects. This is relevant for the variety of fields such as self-assembly of DNA and RNA folding, as well as for protein aggregation and neurodegenerative diseases. Certainly, the key factors that contribute to these phenomena cannot be properly understood without an accurate solvation model. With recent advancements in computer technologies, the possibility to use computational tools for fundamental understanding of the role of EDLs around biomolecules and nanoparticles on their physical and chemical properties is becoming more feasible. Establishing the impact of the excluded volume and ion-ion correlations, ionic strength and pH of the electrolyte on the EDL around biomolecules and nanoparticles, and how changes in these properties consequently affect the Zeta potential and surface charge density are still not well understood. Thus, modeling and understanding the role of these properties on EDLs will provide more insights on the stability, adsorption, binding and function of biomolecules and nanoparticles. Existing mean-field theories such as Poisson Boltzmann (PB) often neglect the ion-ion correlations, solvent and ion excluded volume effects, which are important details for proper description of EDL properties. In this thesis, we implement an efficient and accurate classical solvation density functional theory (CDSFT) for EDLs of spherical macroions and cylindrical polyelectrolytes embedded in aqueous electrolytes. This approach extends the capabilities of mean field approximations by taking into account electrostatic ion-ion correlations, size asymmetry and excluded volume effects without compromising the computational cost. We apply the computational tool to study the structural and thermodynamic properties of the ionic atmosphere around B-DNA and spherical nanoparticles. We demonstrate that the presence of solvent molecules at experimental concentration and size values has a significant impact on the layering of ions. This layering directly influences the integrated charge and mean electrostatic potential in the diffuse region of the spherical electrical double layer (SEDL) and have a noticeable impact on the behavior of zeta potential (ZP). Recently, we have extended the aforementioned CSDFT to account for the charge-regulated mechanisms of the macroion surface on the structural and thermodynamic properties of spherical EDLs. In the approach, the CSDFT is combined with a surface complexation model to account for ion correlation and excluded volume effects on the surface titration of spherical macroions. We apply the proposed computational approach to describe the role that the ion size and solvent excluded volume play on the surface titration properties of silica nanoparticles. We analyze the effects of the nanoparticle size, pH and salt concentration of the aqueous solution on the nanoparticle's surface charge and zeta potential. The results reveal that surface charge density and zeta potential significantly depend on excluded volume and ion-ion correlation effects as well as on pH for monovalent ion species at high salt concentrations. Overall, our results are in good agreement with Monte Carlo simulations and available experimental data. We discuss future directions of this work, which includes extension of the solvation model for studying the flexibility properties of rigid peptides and globular proteins, and describes benefits that this research can potentially bring to scientific and non scientific communities.