Machine-learning enhanced simulations predict graphene is microscopically hydrophobic and not wetting transparent.

Area of Science:

• Materials Science
• Surface Chemistry
• Computational Physics

Background:

• Graphene-water interactions are crucial for applications like filtration and nano-electronics.
• The intrinsic wettability of graphene and its transparency to substrate wettability is debated.
• Understanding these interactions is key to advancing 2D material interfaces.

Purpose of the Study:

• To determine the intrinsic wettability of pristine graphene.
• To elucidate the microscopic mechanisms behind observed graphene-water interactions.
• To explain thickness-dependent wetting behaviors in graphene.

Main Methods:

• Machine-learning enhanced molecular dynamics simulations.
• Simulation of vibrational sum-frequency generation (VSFG) spectra.
• Thermodynamic analysis of water intercalation.

Main Results:

• Pristine graphene is intrinsically hydrophobic and not wetting transparent.
• Apparent hydrophilic signatures in monolayer graphene are due to water intercalation, not wetting transparency.
• Water intercalation is favorable for monolayers but unfavorable for multilayer graphene.

Conclusions:

• A unified microscopic framework for graphene-water interactions is established.
• The role of confined water at 2D material interfaces is clarified.
• Intrinsic graphene hydrophobicity is demonstrated, resolving longstanding debates.