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Cited 6 time in webofscience Cited 11 time in scopus
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Uncertainty quantification of percolating electrical conductance for wavy carbon nanotube-filled polymer nanocomposites using Bayesian inference

Authors
Doh, JaehyeokPark, Sang-InYang, QingRaghavan, Nagarajan
Issue Date
Feb-2021
Publisher
PERGAMON-ELSEVIER SCIENCE LTD
Keywords
Polymer nanocomposites (PNC); Carbon nanotube (CNT) waviness; Electrical percolation behavior; Pearson correlation coefficient; Uncertainty quantification (UQ); Bayesian inference
Citation
CARBON, v.172, pp.308 - 323
Indexed
SCIE
SCOPUS
Journal Title
CARBON
Volume
172
Start Page
308
End Page
323
URI
https://scholarworks.bwise.kr/gnu/handle/sw.gnu/4118
DOI
10.1016/j.carbon.2020.09.092
ISSN
0008-6223
Abstract
This research focuses on the uncertainty quantification of electrical percolation behavior in wavy carbon nanotube (CNT)-filled polymer nanocomposites with a three-dimensional representative volume element accounting for both tunneling resistance (quantum carrier tunneling) and stochasticity in CNT waviness. The developed percolation model is validated with existing experimental data, and model parameters for electrical conductance converge to the optimal value with Markov Chain Monte Carlo (MCMC) based on Bayesian inference. The predicted 95% confidence interval of electrical conductance indicates a different trend between two-and three-parameters of the electrical conductance model. The main trend of correlation between the percolation threshold (phi(c)) and a parameter of the phase transition (critical exponent, t) indicates a statistically linear relationship via evaluation of the Pearson correlation coefficient. Moreover, the correlation between intrinsic conductance of CNTs (sigma(o)) and t also strongly affect the magnitude and slope of electrical conductance in uncertainty quantification. This work can contribute to a robust and reliable design of the PNC considering the physical uncertainty satisfying the target electrical performance through controlling phi(c), sigma(o), and t. (C) 2020 Elsevier Ltd. All rights reserved.
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Doh, Jae Hyeok
우주항공대학 (항공우주공학부)
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