Find the maximum thermal conductivity of graphene reinforced polymer composite: A molecular dynamics approach

Publication typeJournal Article
Publication date2023-09-25
scimago Q1
wos Q1
SJR0.800
CiteScore7.7
Impact factor4.7
ISSN02728397, 15480569
Materials Chemistry
General Chemistry
Ceramics and Composites
Polymers and Plastics
Abstract

The ultrahigh in‐plane thermal conductivity makes the graphene nanoplatelet a promising reinforcement filler for improving the thermal conductivity of polymer materials. Up to now, the highest thermal conductivity enhancement has been achieved by aligning the nanoplatelets along the heat flux direction. In this work, extensive molecular dynamics simulations are carried out to understand the thermal conductivity enhancement capabilities of different architectures of the graphene nanoplatelets within the polyamide‐6 matrix. Surprisingly, we find that the orthogonally arranged graphene nanoplatelets offer even better thermal conductivity enhancement than the simply aligned graphene nanoplatelets. An in‐depth investigation shows that the orthogonal structure can achieve a balance between the global percolation and the alignment of graphene nanoplatelets. Specifically, such an orthogonal structure can take advantage of both thermal percolation and graphene's ultrahigh in‐plane thermal conductivity. Moreover, we have systematically investigated the effects of the size and number density of the nanoplatelets on the thermal conductivity enhancement capability of the orthogonal configuration. Finally, by proposing a validated analytical model, we have identified the pathways to maximize the thermal conductivity of the orthogonally arranged graphene nanoplatelets. The conclusion of this work points out the possible way to develop the graphene‐polymer composite system with exceedingly high thermal conductivity.

Highlights

  • Different graphene configurations are constructed for polymer composite.

  • Chemical reactions at the edge of graphene nanoplatelets are considered.

  • High‐throughput molecular dynamics simulations are conducted to measure thermal conductivity.

  • Competition between graphene alignment and thermal percolation is identified.

  • A theoretical model is established for graphene‐polymer composite.

  • Found 
    Found 

    Top-30

    Journals

    1
    2
    Polymer Composites
    2 publications, 18.18%
    Indian Journal of Physics
    1 publication, 9.09%
    Polymer-Plastics Technology and Materials
    1 publication, 9.09%
    Polymers
    1 publication, 9.09%
    Ceramics International
    1 publication, 9.09%
    Journal of Polymer Research
    1 publication, 9.09%
    Iranian Journal of Science and Technology - Transactions of Mechanical Engineering
    1 publication, 9.09%
    International Journal of Mechanical Sciences
    1 publication, 9.09%
    International Journal of Heat and Mass Transfer
    1 publication, 9.09%
    1
    2

    Publishers

    1
    2
    3
    4
    Elsevier
    4 publications, 36.36%
    Springer Nature
    3 publications, 27.27%
    Wiley
    2 publications, 18.18%
    Taylor & Francis
    1 publication, 9.09%
    MDPI
    1 publication, 9.09%
    1
    2
    3
    4
    • We do not take into account publications without a DOI.
    • Statistics recalculated weekly.

    Are you a researcher?

    Create a profile to get free access to personal recommendations for colleagues and new articles.
    Metrics
    11
    Share
    Cite this
    GOST |
    Cite this
    GOST Copy
    Chen S. et al. Find the maximum thermal conductivity of graphene reinforced polymer composite: A molecular dynamics approach // Polymer Composites. 2023.
    GOST all authors (up to 50) Copy
    Chen S., Xu N., Gorbatikh L., Seveno D. Find the maximum thermal conductivity of graphene reinforced polymer composite: A molecular dynamics approach // Polymer Composites. 2023.
    RIS |
    Cite this
    RIS Copy
    TY - JOUR
    DO - 10.1002/pc.27777
    UR - https://doi.org/10.1002/pc.27777
    TI - Find the maximum thermal conductivity of graphene reinforced polymer composite: A molecular dynamics approach
    T2 - Polymer Composites
    AU - Chen, Shaohua
    AU - Xu, Nuo
    AU - Gorbatikh, Larissa
    AU - Seveno, David
    PY - 2023
    DA - 2023/09/25
    PB - Wiley
    SN - 0272-8397
    SN - 1548-0569
    ER -
    BibTex
    Cite this
    BibTex (up to 50 authors) Copy
    @article{2023_Chen,
    author = {Shaohua Chen and Nuo Xu and Larissa Gorbatikh and David Seveno},
    title = {Find the maximum thermal conductivity of graphene reinforced polymer composite: A molecular dynamics approach},
    journal = {Polymer Composites},
    year = {2023},
    publisher = {Wiley},
    month = {sep},
    url = {https://doi.org/10.1002/pc.27777},
    doi = {10.1002/pc.27777}
    }