On the physical origins of reduced ionic conductivity in nanoconfined electrolytes

Ion transport through nanoscale pores is at the heart of numerous energy storage and separation technologies. Despite significant efforts to uncover the complex interplay of ion-ion, ion-water, and ion-pore interactions that give rise to these transport processes, the atomistic mechanisms of ion motion in confined electrolytes remain poorly understood. In this work, we use machine learning-based molecular dynamics simulations to characterize ion transport with first principles-level accuracy in aqueous NaCl confined to graphene slit pores. We find that ionic conductivity decreases as the degree of confinement increases, a trend governed by changes in both ion self-diffusion and dynamic ion-ion correlations. We show that the self-diffusion coefficients of our confined ions are strongly influenced by the overall electrolyte density, which changes non-monotonically with slit height based on the layering of water molecules within the pore. We further observe a shift in the ions' diffusion mechanism towards more vehicular motion as the degree of confinement increases. Non-ideal (beyond Nernst-Einstein) contributions to transport become more prominent under confinement due to increased cation-anion correlations arising from long-lived contact ion pairs. By building a mechanistic understanding of confined electrolyte transport, this work provides insights that could guide the design of nanoporous materials optimized for efficient and selective ion transport.