Modeling of electrical conductivity for graphene-based systems by filler morphology and tunneling length

In this paper, the effective filler fraction, filler aspect ratio, filler conduction, percolation onset and the share of graphene producing the nets are defined considering many important terms such as interphase part, morphology of nanoparticles (stacked and well-dispersed nanosheets) and tunneling effect. In addition, Taherian model for conductivity of graphene-filled system is developed by effective filler fraction, the share of graphene producing the nets, interphase part, morphology of nanoparticles and tunneling effect. Specially, the conductivity is expressed by many parameters associated to graphene, stacks, interphase and tunnels. The effects of several terms on the conductivity are analyzed and many experimented data are used to assess the predictions. The good dispersion of large and thin nanosheets, small number of nanosheets in the stacks, large distance between nanosheets in the stacks, thick interphase and short tunneling length can cause a high conductivity. The positive stimuli of these factors on the extent of conductive nets are conferred. Moreover, the advanced equations can acceptably predict the percolation onset and conductivity. Actually, disregarding of the mentioned parameters cannot present the accurate levels for percolation onset and conductivity. The developed model is applicable to optimize the conductivity of graphene-filled samples in the electronic devices such as biosensors.

Automated summary

This paper defines key factors including interphase part, morphology of nanoparticles (stacked and well-dispersed nanosheets), and tunneling effect in order to determine the effective filler fraction, filler aspect ratio, filler conduction, percolation onset, and the share of graphene producing the nets. The authors also develop the Taherian model for conductivity in graphene-filled systems, taking into account parameters such as effective filler fraction, the share of graphene producing the nets, interphase part, morphology of nanoparticles, and tunneling effect. The model provides accurate predictions for percolation onset and conductivity by considering various factors that affect the conductivity, as demonstrated by experimental data. This model can be applied to optimize the conductivity of graphene-filled samples in electronic devices, including biosensors.