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

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.

24 pages, 7 figures

References in corpus (11)

• Electric Field Effect in Atomically Thin Carbon Films
• The electronic properties of graphene
• Canonical sampling through velocity-rescaling
• On-the-fly machine learning force field generation: Application to melting points
• Phase transitions of hybrid perovskites simulated by machine-learning force fields trained on-the-fly with Bayesian inference
• Surface energy engineering of graphene
• First-principles studies of water adsorption on graphene: The role of the substrate
• On the mechanism of hydrophilicity of graphene
• Two-Dimensional Water Diffusion at a Graphene-Silica Interface
• Inverse Temperature Dependence of Nuclear Quantum Effects in DNA Base Pairs
• Machine Learning Accelerated Computational Surface-Specific Vibrational Spectroscopy Reveals Oxidation Level of Graphene in Contact with Water