Current-voltage characteristics of nanoplatelet-based conductive nanocomposites

Abstract

In this study, a numerical modeling approach was used to investigate the current-voltage behavior of conductive nanoplatelet-based nanocomposites. A three-dimensional continuum Monte Carlo model was employed to randomly disperse the nanoplatelets in a cubic representative volume element. A nonlinear finite element-based model was developed to evaluate the electrical behavior of the nanocomposite for different levels of the applied electric field. Also, the effect of filler loading on nonlinear conductivity behavior of nanocomposites was investigated. The validity of the developed model was verified through qualitative comparison of the simulation results with results obtained from experimental works.

Mechanochemical Reinforcement of Graphene Sheets into Alkyd Resin Matrix for the Development of Electrically Conductive Paints

“…[43,44] Further, the studies on the graphene based polymer nanocomposites also evidences the presence of nonlinear I-V characteristics which might due to the (i) tunneling of electrons in the interconnected graphene sheets inside the insulating polymer matrix, and (ii) insulating polymeric network which as electron trapping centers and the nature of percolation pathways. [45,46] The obtained non-linearity can be related to the Zener tunneling of electrons via the interconnected graphene sheets in the paint matrix as a similar phenomenon observed by L. He et al [47] In the current scenario, the weight content of graphene is about 25% to the overall weight content of the paint with a high pigment to binder ratio of about 2.4%. Due to the ball milling process, the graphene and alkyd binders efficiently mixed together which result in the formation of effective conductive pathways for the electrons to 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 flow throughout the paint coatings via the interconnected graphene sheets.…”

“…The origin of these nonlinear I–V behavior is already seen in graphene sheets which can be arised due to several factors including (i) deformation of at the sheets, (ii) external doping, (iii) defects, (iv) residual oxygen groups (in case of chemically derived graphene) which can act as electron trapping centers and (v) the carrier concentration, etc ,. Further, the studies on the graphene based polymer nanocomposites also evidences the presence of nonlinear I–V characteristics which might due to the (i) tunneling of electrons in the interconnected graphene sheets inside the insulating polymer matrix, and (ii) insulating polymeric network which as electron trapping centers and the nature of percolation pathways ,. The obtained non‐linearity can be related to the Zener tunneling of electrons via the interconnected graphene sheets in the paint matrix as a similar phenomenon observed by L. He et al .…”

Shape Recovery Behavior of Carbon Nanopaper Shape Memory Polymer Composite

“…Several numerical studies have been published to quantify the effect of different parameters such as the aspect ratio of fillers [60,61], volume fraction of fillers, alignment of CNTs [59], etc. Wang et al [60] state that the average junction gap variation can be used as a quantitative parameter to describe the resistive behavior of a CNT network upon strain.…”

“…If d is large enough, electrons cannot jump between CNTs and conductivity decreases drastically. The critical value of d is called cut-off distanceFigure 2.3[61]. Following work from Mertiny et al[61] improved model for volume fractions larger than percolation threshold.…”

Modelling and Design of MEMS Piezoresistive Out-of-Plane Shear and Normal Stress Sensors

“…For example, Hwang et al [5] proposed a polymer-based tactile sensor, which consists of a micromachined polydimethylsiloxane (PDMS) structure and a flexible printed circuit board (FPCB). It is worth mentioning that polymer-based sensors are dependent on the operating voltage [6,7,8,9], which is different from silicon-based sensing solution. A silicon-based shear stress sensor based on diaphragm sensor structure was developed by Wang and Beebe [10,11].…”