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

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.

💡 Research Summary

This paper investigates how edge functionalization of graphene nanoribbons (GNRs) can dramatically improve thermal transport across the graphene–water interface while preserving the material’s intrinsic in‑plane thermal conductivity (κ). The authors first note that conventional surface functionalization (e.g., oxidation of the basal plane to produce graphene oxide, GO) can increase the interfacial thermal conductance (G) by up to an order of magnitude because hydroxyl and epoxy groups introduce stronger hydrogen‑bonding and van der Waals interactions with water. However, the same sp³ defects that enhance G also disrupt the π‑conjugated network, leading to a severe reduction of κ (often >80 %). To overcome this trade‑off, the study proposes functionalizing only the ribbon edges with hydroxyl (–OH) groups, leaving the basal plane pristine.

A deep neural network (DNN) interatomic potential, built within the DeePMD framework, is trained on an extensive ab‑initio molecular dynamics (AIMD) dataset that includes pristine graphene, various edge terminations (OH, COOH, H), and water‑graphene configurations across a broad temperature (200–1000 K) and pressure range. The resulting DNN potential accurately captures both covalent and non‑covalent interactions, enabling large‑scale molecular dynamics (MD) simulations that would be prohibitive with direct first‑principles methods.

Two complementary MD approaches are employed. (1) Equilibrium MD (EMD) combined with the Green‑Kubo formalism yields κ for GNRs of fixed length (≈8 nm) while varying edge functionalization ratios (0–100 %) and ribbon widths (2.3–36.6 nm). (2) Transient non‑equilibrium MD mimics a rapid laser pulse that instantaneously raises the GNR temperature to 900 K; the subsequent temperature decay of the GNR and surrounding water is fitted to a lumped‑capacitance model to extract G. Additionally, a non‑equilibrium heat‑flux distribution analysis partitions the ribbon into ten columns across its width, revealing how functional groups affect the spatial distribution of heat flow.

Key findings include:
• Edge‑functionalized GNRs with only 10 % of edge carbon atoms terminated by OH exhibit an eight‑fold increase in G (from ~20 MW m⁻² K⁻¹ to >150 MW m⁻² K⁻¹). The enhancement is attributed to stronger hydrogen‑bonding and improved wettability at the functionalized edges.
• At the same 10 % functionalization level, κ remains high (≈850–900 W m⁻¹ K⁻¹), a modest reduction compared with pristine graphene (>1000 W m⁻¹ K⁻¹) and a dramatic improvement over surface‑oxidized GO (often <200 W m⁻¹ K⁻¹).
• The dependence of κ on edge functionalization is non‑monotonic. Low functionalization introduces passivation of dangling bonds, which reduces phonon localization and mitigates edge‑induced scattering, slightly raising κ. As the functionalization ratio increases, additional OH groups become scattering centers, decreasing κ. Beyond ~30 % coverage the trend reverses slightly because the edge‑to‑bulk ratio diminishes for wider ribbons.
• Width‑dependent studies show that κ scales roughly linearly with ribbon width for pristine and moderately functionalized ribbons, but the slope is reduced for heavily functionalized edges, indicating that edge effects become less dominant as the bulk cross‑section grows.
• Heat‑flux distribution calculations demonstrate that functionalized edges channel a larger fraction of the total heat flux, confirming that the enhanced interfacial coupling is localized near the edges.

The authors discuss practical routes to achieve selective edge functionalization, such as plasma etching, chemical cutting, or unzipping carbon nanotubes followed by targeted chemistry. They argue that a modest edge functionalization (~10 % OH coverage) offers an optimal balance: G is boosted to values comparable with highly oxidized GO, while κ remains close to that of pristine graphene, making this strategy attractive for applications requiring both efficient lateral heat spreading and rapid heat removal to a liquid coolant. Potential applications include high‑power laser processing of graphene suspensions, thermoelectric devices operating in aqueous environments, and micro‑electronic cooling where graphene serves as a heat spreader interfaced with water‑based heat pipes.

In conclusion, the study provides a comprehensive, atomistically resolved picture of how edge chemistry can be leveraged to decouple interfacial and intrinsic thermal transport properties. By integrating a high‑fidelity DNN potential with both equilibrium and transient MD techniques, the work establishes edge functionalization as a viable design principle for next‑generation graphene‑based thermal management systems, and it opens avenues for exploring other functional groups (e.g., amine, fluorine) and mixed‑functionalization schemes in future research.