Improving Thermal Transport at Carbon Hybrid Interfaces by Covalent Bonds

Sun, Shuming; Samani, Majid Kabiri; Fu, Ying; Xu, Tao; Ye, Lilei; Satwara, Maulik; Jeppson, Kjell; Nilsson, Torbjörn; Sun, Litao; Liu, Johan

doi:10.1002/admi.201800318

Cited by 22 publications

(19 citation statements)

References 44 publications

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“…Although several papers have reported the successful synthesis of the pillared graphene, their properties have rarely been studied experimentally, primarily due to the preparation and measurement difficulties. To date, only one study has investigated the thermal transport in CNT pillared graphene, which however only measured the thermal resistance (9 × 10 −10 m 2 K W −1 ) at the CNT–graphene junction and demonstrated the high heat dissipation capability of the structure. In comparison, several papers have investigated the thermal conductivity (either in‐plane or cross‐plane) of the pillared graphene through molecular dynamics (MD) simulations, from which the major factors influencing their thermal transport properties have been identified, such as the junction configurations, and the CNT length and density (or inter‐CNT distance) …”

Section: D Nanostructures For High Thermal Conductivitymentioning

confidence: 99%

Thermal Transport in 3D Nanostructures

Zhan

Nie

Chen

et al. 2019

Adv Funct Materials

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This work summarizes the recent progress on the thermal transport properties of 3D nanostructures, with an emphasis on experimental results. Depending on the applications, different 3D nanostructures can be prepared or designed to either achieve a low thermal conductivity for thermal insulation or thermoelectric devices or a high thermal conductivity for thermal interface materials used in the continuing miniaturization of electronics. A broad range of 3D nanostructures are discussed, ranging from colloidal crystals/assemblies, array structures, holey structures, hierarchical structures, to 3D nanostructured fillers for metal matrix composites and polymer composites. Different factors that impact the thermal conductivity of these 3D structures are compared and analyzed. This work provides an overall understanding of the thermal transport properties of various 3D nanostructures, which will shed light on the thermal management at nanoscale.

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Section: D Nanostructures For High Thermal Conductivitymentioning

confidence: 99%

Thermal Transport in 3D Nanostructures

Zhan

Nie

Chen

et al. 2019

Adv Funct Materials

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“…Thermal conductivity is among the most important intrinsic properties of materials playing a key role in energy device engineering and thermal management [1][2][3][4]. For the majority of advanced applications, as those in electronics and energy storage/conversion systems, components with higher thermal conductivities are highly desirable to enhance the heat dissipation rates and suppress the overheating issues.…”

Section: Introductionmentioning

confidence: 99%

Efficient machine-learning based interatomic potentialsfor exploring thermal conductivity in two-dimensional materials

et al. 2020

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It is well-known that the calculation of thermal conductivity using classical molecular dynamics (MD) simulations strongly depends on the choice of the appropriate interatomic potentials. As proven for the case of graphene, while most of the available interatomic potentials estimate the structural and elastic constants with high accuracy, when employed to predict the lattice thermal conductivity they however lead to a variation of predictions by one order of magnitude. Here we present our results on using machine-learning interatomic potentials (MLIPs) passively fitted to computationally inexpensive ab-initio molecular dynamics trajectories without any tuning or optimizing of hyperparameters. These first-attempt potentials could reproduce the phononic properties of different two-dimensional (2D) materials obtained using density functional theory (DFT) simulations. To illustrate the efficiency of the trained MLIPs, we consider polyaniline C 3 N nanosheets. C 3 N monolayer was selected because the classical MD and different first-principles results contradict each other, resulting in a scientific dilemma. It is shown that the predicted thermal conductivity of 418 ± 20 W mK −1 for C 3 N monolayer by the non-equilibrium MD simulations on the basis of a first-attempt MLIP evidences an improved accuracy when compared with the commonly employed MD models. Moreover, MLIP-based prediction can be considered as a solution to the debated reports in the literature. This study highlights that passively fitted MLIPs can be effectively employed as versatile and efficient tools to obtain accurate estimations of thermal conductivities of complex materials using classical MD simulations. In response to remarkable growth of 2D materials family, the devised modeling methodology could play a fundamental role to predict the thermal conductivity.

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“…Another challenge is to combine 1D carbon nanotubes with 2D graphene in a controlled manner [187]. Theoretical work suggests that covalent bonds between these two carbon materials might have enhanced heat dissipation properties.…”

Section: Discussionmentioning

confidence: 99%