Publication Type Journal Article
Title Surface charge density model for predicting the permittivity of liquid mixtures and composites materials
Authors T. P. Iglesias JC Reis
Groups MTFT
Journal JOURNAL OF APPLIED PHYSICS
Year 2012
Month March
Volume 111
Number 6
Pages
Abstract The case cube inside cube for the recent predictive equivalent capacitance model (ECM) is resolved into three different analytic equations expressing the relative permittivity of a composite in terms of constituent relative permittivities and inclusion volume fraction, and they are averaged analytically (ECM-average). Although ECM represents an advance, it requires a specific calculation for each inclusion shape. Sharing the same assumptions and basic physics with ECM an alternative numerical model, named surface charge density model (SCDM), is developed. Using this model it is shown that ECM is an approximation in any of the three solutions mentioned above. Since the approach cube inside cube leads to isotropic systems where the volume fraction of the inclusion can be varied from zero to one, an attempt is made to apply SCDM and ECM to binary liquid mixtures. Literature values for relative permittivities of some organic-organic liquid systems are used to test values predicted by SCDM and ECM, as well as by four classic predictive mixing equations. It is concluded that ECM-average and SCDM can be applied to binary liquid mixtures with dissimilar molar volumes, when the component of bigger molar volume is considered as inclusion, and that ECM-average is generally an acceptable approximation to the numerical SCDM. Present results suggest that the SCDM performs better when bigger molar volume is associated with higher permittivity. Finally, using an example in 2D for an anisotropic inclusion it is shown that the assumption of non-reflecting boundary potential, which has been used by different authors, is satisfied only for highly symmetric inclusion distributions. (C) 2012 American Institute of Physics. [http://dx.doi.org/10.1063/1.3693024]
DOI http://dx.doi.org/10.1063/1.3693024
ISBN
Publisher
Book Title
ISSN 0021-8979
EISSN
Conference Name
Bibtex ID ISI:000302221700093
Observations
Back to Publications List