Yang G, Schubert DW, Qu M, Nilsson F (2018)
Publication Language: English
Publication Status: Published
Publication Type: Journal article, Original article
Publication year: 2018
Publisher: Wiley-VCH Verlag
Book Volume: 27
Article Number: 1700105
Journal Issue: 4
The electrical conductivity of polymeric fiber composites is generally strongly dependent on the constituent conductivities, the fiber filler fraction, the fiber aspect ratio, and on the orientation of the fibers. Even though electrically conductive polymer composites are emerging materials of high scientific and commercial interest, accurate mathematical models for describing such materials are rare. A very promising mathematical model for predicting the electrical conductivity below the electrical percolation threshold, for both isotropic and anisotropic composites, is however recently published by Schubert. The shortcomings of that study are that the model includes so far only one predicted parameter and that it is not sufficiently validated. In the current study, finite element modeling is used to successfully validate the model of Schubert for isotropic fiber composites and to accurately determine the predicted parameter. These theoretical predictions are finally compared with experimental conductivity data for isotropic carbon fiber/poly(methyl methacrylate) (PMMA) composites with fiber filler fractions in the range 0-12 vol% and fiber aspect ratios from 5 to 30. The model forecasts, without any adjustable parameters, are satisfactory close to the experimental data.
APA:
Yang, G., Schubert, D.W., Qu, M., & Nilsson, F. (2018). Novel Theoretical Self-Consistent Mean-Field Approach to Describe the Conductivity of Carbon Fiber–Filled Thermoplastics: PART II. Validation by Computer Simulation. Macromolecular Theory and Simulations, 27(4). https://doi.org/10.1002/mats.201700105
MLA:
Yang, Guanda, et al. "Novel Theoretical Self-Consistent Mean-Field Approach to Describe the Conductivity of Carbon Fiber–Filled Thermoplastics: PART II. Validation by Computer Simulation." Macromolecular Theory and Simulations 27.4 (2018).
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