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

TL;DR

This study uses a machine learning potential to accurately determine the melting temperatures of quasi-one-dimensional ice in carbon nanotubes, revealing a narrow melting range and diameter-dependent mechanisms, with implications for filtration technologies.

Contribution

First principles accuracy machine learning simulations reveal the phase diagram and melting behavior of nano-confined water in one-dimensional carbon nanotubes.

Findings

Melting temperatures range between 280 K and 310 K.

Multiple ice polymorphs melt within a narrow temperature window.

Melting mechanisms vary with nanotube diameter.

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 simulations approaches report values ranging from $\sim 180 \text{ K}$ up to $\sim 450 \text{ K}$ at ambient pressure. In this work, we use a machine learning potential that delivers first principles accuracy to study the phase diagram of water for confinement diameters $ 9.5 < d < 12.5 \text{ \AA}$. We find that several distinct ice polymorphs melt in a surprisingly narrow range between $\sim 280 \text{ K}$ and $\sim 310 \text{ K}$, 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.