Modeling the selective partitioning of cations into negatively charged nanopores in water
Lu Yang, Shekhar Garde
Abstract
Partitioning and transport of water and small solutes into and through nanopores is important to a variety of chemical and biological processes and applications. Here we study the partitioning of positive ions of increasing size into the pore interior using extensive molecular dynamics simulations. Despite the simplicity of the simulation system, the results provide new and useful insights on both the pore hydration and ion partitioning. For low negative charge densities on the pore, the driving force for partitioning of positive ions into the pore is weak, and no partitioning is observed. Increasing the pore charge gradually leads to partitioning of positive ions into the pore. Interestingly, over a range of intermediate negative charge densities, nanopores display both thermodynamic as well as kinetic selectivity toward partitioning of the larger K+ and Cs+ ions into their interior over the smaller Na+ ions. The variation of thermodynamic driving force and the average partitioning time with the pore charge density together suggest the presence of free energy barriers in the partitioning process. We discuss the role of ion hydration in the bulk and in the pore interior as well as of the pore hydration in determining the barrier heights for ion partitioning and the observed thermodynamic and kinetic selectivity.
Create a lesson
Related papers
Molecular Geometry Understanding Has Unintendedly Emerged in Frontier Large Language Models
Gregorii A. Semakin, Timofey V. Losev, Ilya V. Prolomov et al.
Truncated automatic sparse differentiation for machine learning interatomic potentials
Marcel F. Langer, Adrian Hill, Michele Ceriotti
Collective Ion Dynamics from Finite-Volume Fluctuations in Model Explicit-Solvent Electrolytes
Jeongmin Kim
FOSY: Segmental Backbone Assignment in Intrinsically Disordered Proteins
Dmitry M. Lesovoy, Tatiana Agback, Panagiota S. Georgoulia et al.
Efficient tensorized evaluation of permutation invariant polynomials for representing potential energy surfaces
Junhong Li, Kaisheng Song, Hua Guo et al.
DFT GGA based datasets for H2O potential energy surfaces, permanent moment and polarizability tensors
Anoop Ajaya Kumar Nair, Elvar Örn Jónsson