Water structuring at stacked graphene interfaces unveiled by machine-learning molecular dynamics

TL;DR

This study uses machine-learning molecular dynamics to clarify how substrate, layer number, and water molecules affect graphene's wettability, revealing that intercalated water influences observed hydrophilic behavior.

Contribution

It introduces a combined first-principles and machine learning approach to elucidate the mechanistic origins of graphene-water interactions at interfaces.

Findings

Intercalated water causes signal cancellation, affecting vSFG spectra.

Thermodynamically favorable intercalated water exists in monolayer graphene on hydrophilic substrates.

Results clarify the role of water in graphene wettability and interface design.

Abstract

The wettability of monolayer and multilayer graphene remains a topic of longstanding debate. Here, we combined first-principles molecular dynamics simulations accelerated with the atomic cluster expansion machine learning interatomic potential to investigate how substrate, graphene layer number, and intercalated water molecules influence graphene's wettability. Simulated vibrational sum-frequency generation (vSFG) spectra revealed that the experimentally observed hydrophilic behavior of monolayer graphene on hydrophilic substrates arose not from wetting transparency, but from signal cancellation induced by intercalated water. Energetic analyses further showed that intercalated water molecules were thermodynamically favorable for monolayer graphene on hydrophilic substrates, but not for multilayer systems, leading to changes in the vSFG response in line with experimental observations. These results offer a mechanistic understanding of graphene-water interactions and have broad implications for the design of graphene-based interfaces and devices.