Abstract
Heat transfer between graphene and water is pivotal for various applications, including solar-thermal vapor generation and the advanced manufacturing of graphene-based hierarchical structures in solution. In this study, we employ a deep-neural network potential derived from ab initio molecular dynamics to conduct extensive simulations of single-layer graphene-water systems with different levels of oxidation (carbon/oxygen ratio) of the graphene layer. Remarkably, our findings reveal a one-order-of-magnitude enhancement in heat transfer upon oxidizing graphene with hydroxyl or epoxide groups at the graphene surface, underscoring the significant tunability of heat transfer within this system. Given the same oxidation ratio, more dispersed locations of functional groups on graphene surface leads to faster heat dissipation to water.
Original language | English (US) |
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Article number | 119910 |
Journal | Carbon |
Volume | 234 |
DOIs | |
State | Published - Mar 5 2025 |
Keywords
- Deep neural network potential
- Graphene oxide
- Graphene oxide-water interface
- Interfacial thermal conductance
- Laser pulse
- Molecular dynamics
ASJC Scopus subject areas
- General Chemistry
- General Materials Science