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Enhanced Graphene-Water Thermal Transport via Edge Functionalization without Compromising In-Plane Thermal Conductivity

John Crosby, Haoran Cui, Mehrab Lotfpour, Yan Wang, Lei Cao

Abstract

Interfacial thermal transport between graphene and water plays a critical role in a wide range of thermal and energy applications. Although chemical functionalization can significantly enhance graphene-water interfacial thermal conductance, it often degrades graphene's intrinsic in-plane phonon transport. In this work, we perform a systematic deep neural network molecular dynamics study comparing edge-functionalized graphene nanoribbons with surface-functionalized graphene in aqueous environments. We demonstrate that functionalizing only 10% of the ribbon edges with hydroxyl groups increases the graphene-water interfacial thermal conductance by more than eightfold, primarily due to strengthened interfacial interactions and improved wettability at the edges. In contrast to basal-plane oxidation, edge functionalization largely preserves in-plane thermal conductivity. Importantly, hydroxyl edge groups exert competing effects on phonon transport: they introduce additional boundary scattering that suppresses heat conduction, while simultaneously passivating dangling bonds at bare edges, thereby reducing phonon localization and edge-induced scattering. This competition leads to a non-monotonic dependence of in-plane thermal conductivity on edge functionalization ratio. These results establish edge functionalization as an effective strategy for enhancing graphene-water interfacial thermal transport without sacrificing intrinsic phonon transport properties.

Enhanced Graphene-Water Thermal Transport via Edge Functionalization without Compromising In-Plane Thermal Conductivity

Abstract

Interfacial thermal transport between graphene and water plays a critical role in a wide range of thermal and energy applications. Although chemical functionalization can significantly enhance graphene-water interfacial thermal conductance, it often degrades graphene's intrinsic in-plane phonon transport. In this work, we perform a systematic deep neural network molecular dynamics study comparing edge-functionalized graphene nanoribbons with surface-functionalized graphene in aqueous environments. We demonstrate that functionalizing only 10% of the ribbon edges with hydroxyl groups increases the graphene-water interfacial thermal conductance by more than eightfold, primarily due to strengthened interfacial interactions and improved wettability at the edges. In contrast to basal-plane oxidation, edge functionalization largely preserves in-plane thermal conductivity. Importantly, hydroxyl edge groups exert competing effects on phonon transport: they introduce additional boundary scattering that suppresses heat conduction, while simultaneously passivating dangling bonds at bare edges, thereby reducing phonon localization and edge-induced scattering. This competition leads to a non-monotonic dependence of in-plane thermal conductivity on edge functionalization ratio. These results establish edge functionalization as an effective strategy for enhancing graphene-water interfacial thermal transport without sacrificing intrinsic phonon transport properties.
Paper Structure (11 sections, 7 equations, 6 figures, 1 table)

This paper contains 11 sections, 7 equations, 6 figures, 1 table.

Table of Contents

  1. Introduction
  2. Methodology
  3. Development of Deep Neural Network Interatomic Potential
  4. Equilibrium molecular dynamics
  5. Transient molecular dynamics and lumped capacitance model
  6. Cross-sectional heat flux distribution
  7. Results and Discussions
  8. The effect of edge functionalization on thermal transport in GNRO
  9. Structure of GNRO-water interface
  10. Transient heating of GNROs in water
  11. Conclusion

Figures (6)

  • Figure 1: Time dependent (a) temperature and (b) temperature difference decay curves. $G$ is extracted from the linear fit shown in panel (b). (c) A representative structure for transient MD simulations used in this study. The box is periodic in the $x$ direction and padded with enough water in the $y$ direction to prevent interactions across the periodic boundary. Grey atoms represent carbon atoms, blue atoms represent hydroxyl functional groups, while red and white atoms represent oxygen and hydrogen of water, respectively.
  • Figure 2: Diagram of cross-sectional heat flux distribution simulation configuration.
  • Figure 3: $\kappa$ dependence on (a) OH edge functionalization ratio of GNRO of varying widths, (b) on OH surface functionalization ratio for GO flake, (c) on nanoribbon width for both pristine and 50% edge-functionalized nanoribbon.
  • Figure 4: Cross-sectional heat flux distribution, $\alpha$, for (a) a fixed GNRO width (2.32 nm) with varying edge functionalization ratios, and (b) a fixed edge functionalization ratio (50%) with varying GNRO widths. The CV values reported in the legend are defined as the ratio of the standard deviation of $\alpha$ to its mean value, providing a quantitative measure of the dispersion of $\alpha$.
  • Figure 5: Atomic configurations of water molecules interacting with (a) the bare GNR basal plane, (b) the functionalized basal plane, (c) the bare GNR edge, and (d) the functionalized GNR edge.
  • ...and 1 more figures