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Predictions of electrical percolation of graphene‐based nanocomposites by the three‐dimensional Monte Carlo simulation
Author(s) -
Liu Yue,
Wu Chen,
Zhou Huanfeng,
Liu Ping,
Liu Caixia,
Wu Hao,
Zhao Junnan,
Zhang Yugang,
Ma Yuanming,
Huang Ying,
Song Aiguo,
Ge Yunjian
Publication year - 2020
Publication title -
journal of applied polymer science
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.575
H-Index - 166
eISSN - 1097-4628
pISSN - 0021-8995
DOI - 10.1002/app.48999
Subject(s) - percolation (cognitive psychology) , materials science , monte carlo method , volume fraction , nanocomposite , graphene , square (algebra) , percolation threshold , composite material , statistical physics , nanotechnology , physics , mathematics , geometry , electrical resistivity and conductivity , statistics , quantum mechanics , neuroscience , biology
Disk model is usually used to represent graphene nanoplatelets (GNPs) which are considered as frame‐like structure with edges and corners, and it has lack of quantitative accuracy. In order to minimize error caused by morphology, distribution, and interaction between GNPs and matrix, square and folded plate models were constructed to predict the percolation volume fraction ( ϕ c ) of GNPs‐based nanocomposites by calculating connection possibility. Meanwhile, disk model is used for comparison. The results revealed that the ϕ c of square and folded plate models is smaller than that of disk model with consistent parameters, and it is concluded that the ϕ c of GNPs‐based nanocomposites predicted by disk model should be higher than that of experimental. The correctness of mixed model of square and folded plate is also verified by experimental. Due to the agglomeration of GNPs under the actual situation, the result of simulation is slightly smaller than that of experiment.