The Wetting of H$_2$O by CO$_2$

TL;DR

This study uses advanced simulations with machine-learned potentials to investigate the CO$_2$-H$_2$O interface, revealing detailed interfacial tension behavior and the formation of a CO$_2$ monolayer, with implications for climate and geoscience.

Contribution

It provides the first ab initio-level description of the CO$_2$-H$_2$O interface using machine learning, addressing key ambiguities in interfacial tension and phase behavior.

Findings

Interfacial tension predictions match experimental data at relevant pressures.

Formation of a saturated CO$_2$ film at low pressure with bulk-like properties.

Reduced water structuring near the interface due to CO$_2$ monolayer buildup.

Abstract

Biphasic interfaces are complex but fascinating regimes that display a number of properties distinct from those of the bulk. The CO$_2$-H$_2$O interface, in particular, has been the subject of a number of studies on account of its importance for the carbon life cycle as well as carbon capture and sequestration schemes. Despite this attention, there remain a number of open questions on the nature of the CO$_2$-H$_2$O interface, particularly concerning the interfacial tension and phase behavior of CO$_2$ at the interface. In this paper, we seek to address these ambiguities using ab initio-quality simulations. Harnessing the benefits of machine-learned potentials and enhanced statistical sampling methods, we present an ab initio-level description of the CO$_2$-H$_2$O interface. Interfacial tensions are predicted from 1-500 bar and found to be in close agreement with experiment at the pressures for which experimental data is available. Structural analyses indicate the build-up of an adsorbed, saturated CO$_2$ film forming at low pressure (20 bar) with properties similar to those of the bulk liquid, but preferential perpendicular alignment with respect to the interface. CO$_2$ monolayer build-up coincides with a reduced structuring of water molecules close to the interface. This study highlights the predictive nature of machine-learned potentials for complex macroscopic properties of biphasic interfaces, and the mechanistic insight obtained into carbon dioxide aggregation at the water interface is of high relevance for geoscience, climate research, and materials science.