The carbon nano-peapod is a representative structure with interlayer van der Waals (vdW) interactions, in which encapsulated fullerene molecules play a critical role in modulating the transport properties of the carbon nanotubes (CNTs). In particular, their influence on the thermal transport characteristics has been the focal point of considerable attention. In this study, we trained an accurate machine learning potential for fullerene-encapsulated CNTs based on the efficient NEP model to investigate their thermal properties. Using equilibrium molecular dynamics simulation along with the spectral decomposition method for thermal conductivity, we find that the thermal conductivity of fullerene-encapsulated CNTs is roughly lower than that of empty CNTs, aligning with experimental observations for CNT bundles with fullerene encapsulation [Kodama et al., Nat. Mater. 16, 892 (2017)]. The research suggests that weak vdW interactions between both the fullerene and CNTs, as well as between fullerene molecules themselves, hinder phonon propagation. The encapsulated fullerene contributes to an increase in phonon scattering within the CNTs, ultimately leading to a reduction in thermal conductivity. We utilized machine learning potential to investigate the structure of fullerene-encapsulated CNTs and their heat transport property. This approach provides valuable insights for performance research of complex systems featuring interlayer vdW interactions.
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28 December 2023
Research Article|
December 22 2023
Reduction of thermal conductivity in carbon nanotubes by fullerene encapsulation from machine-learning molecular dynamics simulations
Special Collection:
Machine Learning for Thermal Transport
Yimu Lu;
Yimu Lu
a)
(Data curation, Formal analysis, Investigation, Methodology, Software, Validation, Visualization, Writing – original draft)
1
Dalian University of Technology and Belarusian State University Joint Institute, Dalian University of Technology
, Dalian 116024, China
Search for other works by this author on:
Yongbo Shi;
Yongbo Shi
(Conceptualization, Data curation, Formal analysis, Software)
2
College of Physical Science and Technology, Bohai University
, Jinzhou 121013, China
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Junyuan Wang
;
Junyuan Wang
(Investigation, Resources, Software, Visualization)
2
College of Physical Science and Technology, Bohai University
, Jinzhou 121013, China
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Haikuan Dong
;
Haikuan Dong
b)
(Conceptualization, Funding acquisition, Methodology, Resources, Software, Writing – review & editing)
2
College of Physical Science and Technology, Bohai University
, Jinzhou 121013, China
b)Author to whom correspondence should be addressed: [email protected]
Search for other works by this author on:
Yimu Lu
1,a)
Yongbo Shi
2
Junyuan Wang
2
Haikuan Dong
2,b)
Jie Yu
3,c)
1
Dalian University of Technology and Belarusian State University Joint Institute, Dalian University of Technology
, Dalian 116024, China
2
College of Physical Science and Technology, Bohai University
, Jinzhou 121013, China
3
School of Physics, Dalian University of Technology
, Dalian 116024, China
b)Author to whom correspondence should be addressed: [email protected]
a)
Electronic mail: [email protected]
c)
Electronic mail: [email protected]
J. Appl. Phys. 134, 244901 (2023)
Article history
Received:
September 12 2023
Accepted:
December 02 2023
Citation
Yimu Lu, Yongbo Shi, Junyuan Wang, Haikuan Dong, Jie Yu; Reduction of thermal conductivity in carbon nanotubes by fullerene encapsulation from machine-learning molecular dynamics simulations. J. Appl. Phys. 28 December 2023; 134 (24): 244901. https://doi.org/10.1063/5.0176338
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