Ultralow thermal conductance of the van der Waals interface between organic nanoribbons

Xiong, Yucheng; Yu, Xiaoxiang; Huang, Yu-Wen; Yang, Juekuan; Li, L.; Yang, Nuo; Xu, Dahai

doi:10.1016/j.mtphys.2019.100139

Cited by 28 publications

(16 citation statements)

References 63 publications

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“…Thus, there is a strong impetus to search for high thermal conductivity (HTC) substrate materials, which can be integrated with hot-spot units for e cient heat dissipation. Therein the emerging interfacial thermal resistance (ITR) at the interface of heterostructure [11][12][13][14][15][16] plays a crucial role, which may hamper the heat transfer as shown in Fig. 1(a).…”

Section: Introductionmentioning

confidence: 99%

Deep-potential enabled multiscale simulation of gallium nitride devices on boron arsenide cooling substrates

Huang

Zhang

et al. 2022

Preprint

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High-efficient heat dissipation plays critical role for high-power-density electronics. Experimental synthesis of ultrahigh thermal conductivity boron arsenide (BAs, 1300 W m−1K−1) cooling substrates into the wide-bandgap semiconductor of gallium nitride (GaN) devices has been realized [Nature Electronics 4, 416-423 (2021)]. However, the lack of systematic analysis on the heat transfer across the BAs-GaN interface hampers the practical applications. In this study, by constructing the accurate and high-efficient machine learning interatomic potentials, we performed multiscale simulations of the BAs-GaN heterostructures. Ultrahigh interfacial thermal conductance (ITC) of 265 MW m−2K−1 is achieved, which lies in the well-matched lattice vibrations of BAs and GaN. Moreover, the competition between grain size and boundary resistance was revealed with size increasing from 1 nm to 100 µm. Such deep-potential equipped multiscale simulations not only promote the practical applications of BAs cooling substrates in electronics, but also offer new approach for designing advanced thermal management systems.

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Section: Introductionmentioning

confidence: 99%

Deep-potential enabled multiscale simulation of gallium nitride devices on boron arsenide cooling substrates

Huang

Zhang

et al. 2022

Preprint

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“…Recently, with the rapid development of artificial intelligence (AI) technology based on machine learning, more and more researchers use AI technology to construct interatomic potentials for two-dimensional (2D) materials and further complex compounds [20][21][22][23][24][25][26][27][28][29][30] , which display high accuracy compared to the classical empirical potentials. Currently, lots of AI technology based methods have been employed to generate interatomic potential, such as Gaussian approximation potentials 31 (GAP), Spectral neighbor analysis potential 32,33 (SNAP), Artificial neural networks 34 (ANN), and Moment tensor potentials 35,36 (MTP).…”

Section: Introductionmentioning

confidence: 99%

Accessing negative Poisson’s ratio of graphene by machine learning interatomic potentials

Wu¹,

Qin²,

Zhang³

2022

Nanotechnology

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The negative Poisson’s ratio (NPR) is a novel property of materials, which enhances the mechanical feature and creates a wide range of application prospects in lots of fields, such as aerospace, electronics, medicine, etc. Fundamental understanding on the mechanism underlying NPR plays an important role in designing advanced mechanical functional materials. However, with different methods used, the origin of NPR is found different and conflicting with each other, for instance, in the representative graphene. In this study, based on machine learning technique, we constructed a moment tensor potential (MTP) for molecular dynamics (MD) simulations of graphene. By analyzing the evolution of key geometries, the increase of bond angle is found to be responsible for the NPR of graphene instead of bond length. The results on the origin of NPR are well consistent with the start-of-art first-principles, which amend the results from MD simulations using classic empirical potentials. Our study facilitates the understanding on the origin of NPR of graphene and paves the way to improve the accuracy of MD simulations being comparable to first-principle calculations. Our study would also promote the applications of machine learning interatomic potentials in multiscale simulations of functional materials.

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“…The modulation of thermal conductivity is desirable for heat dissipation, thermal insulation, and thermoelectrics. − To engineer the thermal properties of 2D materials, a variety of strategies have been proposed, such as wrinkles, twists, isotope contents, and so on. − Different strategies have their advances, so one can choose different strategies for specific applications. For example, wrinkles enable wide-range manipulation of thermal conductivity because of the large deformation, while atomic-plane rotations can control the thermal conductivity and keep the structure flat .…”

Section: Introductionmentioning

confidence: 99%

Nanoscale Topological Morphology Transition and Controllable Thermal Conductivity of Wrinkled Hexagonal Boron Nitride: Implications for Thermal Manipulation and Management

Qiu

Wang

et al. 2021

ACS Appl. Nano Mater.

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Understanding the nanoscale morphology and transition process of two-dimensional (2D) materials would promote the design of emerging 2D devices with controllable properties. Wrinkles induced by mechanical strains have attracted extensive interest as a promising approach to the predictable modulation of structures. Here, a reciprocal-space characterization is implemented to capture the nanoscale topological morphology of free-standing monolayer hexagonal boron nitride (h-BN). Various morphology types and transition processes under different strains are observed. For biaxial compressive strain, the topological morphology of wrinkled h-BN is found to be the same as that of pristine h-BN. For uniaxial compressive strain, different from commonly supposed one-step transition, a three-step morphology transition process within a very narrow strain range is uncovered. The topological morphology under shear strain and angular pinching strain is investigated as well. In addition, the manipulation of thermal conductivity of h-BN by wrinkles is demonstrated to exemplify wrinkle-based thermal manipulation and thermal management in electronic devices.

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Ultralow thermal conductance of the van der Waals interface between organic nanoribbons

Cited by 28 publications

References 63 publications

Deep-potential enabled multiscale simulation of gallium nitride devices on boron arsenide cooling substrates

Deep-potential enabled multiscale simulation of gallium nitride devices on boron arsenide cooling substrates

Accessing negative Poisson’s ratio of graphene by machine learning interatomic potentials

Nanoscale Topological Morphology Transition and Controllable Thermal Conductivity of Wrinkled Hexagonal Boron Nitride: Implications for Thermal Manipulation and Management

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