On the increase of the melting temperature of water confined in one-dimensional nano-cavities.

Abstract

Water confined in nanoscale cavities plays a crucial role in everyday phenomena in geology and biology, as well as technological applications at the water-energy nexus. However, even understanding the basic properties of nano-confined water is extremely challenging for theory, simulations, and experiments. In particular, determining the melting temperature of quasi-one-dimensional ice polymorphs confined in carbon nanotubes has proven to be an exceptionally difficult task, with previous experimental and classical simulation approaches reporting values ranging from ∼180K up to ∼450K at ambient pressure. In this work, we use a machine learning potential that delivers first principles accuracy (trained to the density functional theory approximation revPBE0-D3) to study the phase diagram of water for confinement diameters 9.5 < d < 12.5 Å. We find that several distinct ice polymorphs melt in a surprisingly narrow range between ∼280 and ∼310K, with a melting mechanism that depends on the nanotube diameter. These results shed new light on the melting of ice in one-dimension and have implications for the operating conditions of carbon-based filtration and desalination devices.