Development of a Model for Electrical Conductivity of Polymer/Graphene Nanocomposites Assuming Interphase and Tunneling Regions in Conductive Networks

Graphene-Doped Hydrogels Promoting Ionic Conductivity in Gel-Valve-Regulated Lead Acid Batteries

In this study, the impact of graphene-doped poly(vinyl alcohol) hydrogels on gel-valve-regulated lead acid batteries was examined. The gel formulations were made by adding various amounts of graphene into the gel system comprising poly(vinyl alcohol) and sulfuric acid. Gel formulations were subjected to an ionic conductivity study and Fourier transform infrared spectroscopy (FTIR) to understand ionic mobility and material interaction, respectively. Cyclic voltammetry (CV), electrochemical impedance spectroscopy (EIS), and potentiodynamic polarization (PDP) were utilized to find the optimized amount of graphene in gel formulations. Galvanostatic charge-discharge (GCD) techniques were employed on a battery comprising an optimized gel electrolyte. The battery exhibited a discharge capacity of 12.82 mAh at a current density of 15 mA cm-2. After 500 prolonged cycles, the battery displayed a discharge capacity of 87% at 25 mA cm-2 current density, indicating that graphene-doped hydrogels can be a promising gel electrolyte for lead acid batteries.

Effect of contact number among graphene nanosheets on the conductivities of tunnels and polymer composites

Simple equations are expressed for tunnel conductivity, tunnel resistance and conductivity of a graphene-filled composite by the number of contacts and interphase part. More specially, the active filler amount is suggested by interphase depth, which changes the contact number. The conductivity of nanocomposite is presented by filler content, filler dimensions, tunneling length and interphase depth. The innovative model is surveyed by the experimented conductivity of real examples. Too, the impacts of numerous issues on the tunnel resistance, tunnel conductivity and conductivity of nanocomposite are discussed to validate the novel equations. The estimates agree with the experimented data and the impacts of several terms on the tunnel resistance, tunnel conductivity and conductivity of system are sensible. Thin and big nanosheets positively affect the nanocomposite's conductivity, but thick nanosheets improve the tunnel conductivity. High conductivity is found at short tunnels, while the nanocomposite's conductivity directly depends on the tunneling length. The dissimilar effects of these features on the tunneling properties and conductivity are described.

Effective Conductivity of Carbon-Nanotube-Filled Systems by Interfacial Conductivity to Optimize Breast Cancer Cell Sensors

Interfacial conductivity and “L_c”, i.e., the least carbon-nanotube (CNT) length required for the operative transfer of CNT conductivity to the insulated medium, were used to establish the most effective CNT concentration and portion of CNTs needed for a network structure in polymer CNT nanocomposites (PCNT). The mentioned parameters and tunneling effect define the effective conductivity of PCNT. The impact of the parameters on the beginning of percolation, the net concentration, and the effective conductivity of PCNT was investigated and the outputs were explained. Moreover, the calculations of the beginning of percolation and the conductivity demonstrate that the experimental results and the developed equations are in acceptable agreement. A small “L_c” and high interfacial conductivity affect the beginning of percolation, the fraction of networked CNTs, and the effective conductivity. Additionally, a low tunneling resistivity, a wide contact diameter, and small tunnels produce a highly effective conductivity. The developed model can be used to optimize breast cancer cell sensors.

Effect of contact resistance on the electrical conductivity of polymer graphene nanocomposites to optimize the biosensors detecting breast cancer cells

This study focuses on the contact regions among neighboring nanoparticles in polymer graphene nanocomposites by the extension of nanosheets. The resistance of graphene and the contact zones represent the total resistance of the prolonged nanosheets. Furthermore, the graphene size, interphase depth, and tunneling distance express the effective volume portion of graphene, while the onset of percolation affects the fraction of percolated nanosheets. Finally, a model is developed to investigate the conductivity of the samples using the graphene size, interphase depth, and tunneling size. In addition to the roles played by certain factors in conductivity, the experimental conductivity data for several samples confirm the conductivity predictions. Generally, the polymer sheet in tunnels determines the total resistance of the extended nanosheets because graphene ordinarily exhibits negligible resistance. In addition, a large tunnel positively accelerates the onset of percolation, but increases the tunneling resistance and attenuates the conductivity of the nanocomposite. Further, a thicker interphase and lower percolation threshold promote the conductivity of the system. The developed model can be applied to optimize the biosensors detecting the breast cancer cells.

Multiscale modelling of strain-resistance behaviour for graphene rubber composites under large deformation

The electrical conductivity of graphene rubber nanocomposites under large deformation is studied based on multiscale modeling with effective medium theory and molecular dynamics simulations. The effects of graphene filling volume fractions, graphene dispersion patterns and interfacial interaction strengths of graphene/rubber are investigated on the electrical properties of the composites. The results show that the strain-resistance sensitivity of the composites is determined by graphene volume fractions and the relationship between the average spacing of graphene sheets and the strain, which might take a nonlinear form for the system with large initial spacing ratios of graphene. Detailed analysis of graphene clusters and system energy reveals that too high or too low interfacial interaction might decrease the strain-resistance sensitivity by preventing the decomposition of large graphene clusters while moderate interfacial interaction can help maintain the structure of graphene conductive networks with high strain-resistance sensitivity.

Partial Discharge in Nanofluid Insulation Material with Conductive and Semiconductive Nanoparticles

This study provides a thorough investigation of partial discharge (PD) activities in nanofluid insulation material consisting of different types of nanoparticles, which are conductive and semiconductive when subjected to high voltage stress is presented. Nanofluids have become a topic of interest because they can be an alternative to liquid insulation in electrical apparatus due to their promising dielectric strength and cooling ability. However, during in-service operation, PDs can occur between conductors in the insulation system. Therefore, this study presents the behavior of PDs within nanofluid dielectric materials consisting of conductive and semiconductive nanoparticles. The results show that there is an improvement in the PD resistance and a reduction in the tan delta of nanofluids at power frequency after the incorporation of conductive or semiconductive nanoparticles in the nanofluid oil. However, the most suitable concentration of conductive and semiconductive nanoparticles in the base fluid was found to be, respectively, 0.01 g/L and 1.0 g/L at PD inception and PD steady-state conditions. The clustering of nanoparticles in a nanofluid suspension due to PD activities is also discussed in this study.

Effects of Size and Aggregation/Agglomeration of Nanoparticles on the Interfacial/Interphase Properties and Tensile Strength of Polymer Nanocomposites

In this study, several simple equations are suggested to investigate the effects of size and density on the number, surface area, stiffening efficiency, and specific surface area of nanoparticles in polymer nanocomposites. In addition, the roles of nanoparticle size and interphase thickness in the interfacial/interphase properties and tensile strength of nanocomposites are explained by various equations. The aggregates/agglomerates of nanoparticles are also assumed as large particles in nanocomposites, and their influences on the nanoparticle characteristics, interface/interphase properties, and tensile strength are discussed. The small size advantageously affects the number, surface area, stiffening efficiency, and specific surface area of nanoparticles. Only 2 g of isolated and well-dispersed nanoparticles with radius of 10 nm (R = 10 nm) and density of 2 g/cm³ produce the significant interfacial area of 250 m² with polymer matrix. Moreover, only a thick interphase cannot produce high interfacial/interphase parameters and significant mechanical properties in nanocomposites because the filler size and aggregates/agglomerates also control these terms. It is found that a thick interphase (t = 25 nm) surrounding the big nanoparticles (R = 50 nm) only improves the B interphase parameter to about 4, while B = 13 is obtained by the smallest nanoparticles and the thickest interphase.

A simple model for constant storage modulus of poly (lactic acid)/poly (ethylene oxide)/carbon nanotubes nanocomposites at low frequencies assuming the properties of interphase regions and networks

The networks of nanoparticles in nanocomposites cause solid-like behavior demonstrating a constant storage modulus at low frequencies. This study examines the storage modulus of poly (lactic acid)/poly (ethylene oxide)/carbon nanotubes (CNT) nanocomposites. The experimental data of the storage modulus in the plateau regions are obtained by a frequency sweep test. In addition, a simple model is developed to predict the constant storage modulus assuming the properties of the interphase regions and the CNT networks. The model calculations are compared with the experimental results, and the parametric analyses are applied to validate the predictability of the developed model. The calculations properly agree with the experimental data at all polymer and CNT concentrations. Moreover, all parameters acceptably modulate the constant storage modulus. The percentage of the networked CNT, the modulus of networks, and the thickness and modulus of the interphase regions directly govern the storage modulus of nanocomposites. The outputs reveal the important roles of the interphase properties in the storage modulus.

A multistep methodology for calculation of the tensile modulus in polymer/carbon nanotube nanocomposites above the percolation threshold based on the modified rule of mixtures

A multistep model is proposed for calculating the tensile modulus values of polymer/carbon nanotube (CNT) nanocomposites (PCNTs) based on the modified rule of mixtures, assuming a percolated network of nanoparticles. In the first step, the network of nanoparticles is considered as a new phase with a novel volume fraction and Young's modulus. Then, the volume fraction of the filler network in the PCNTs is correlated to the density of the network. Also, the percolation of the nanoparticles is related to the aspect ratio of the nanoparticles. Finally, a new model is proposed based on the modified rule of mixtures (the Riley model) of the properties of the filler network. The predictions of the proposed model are compared with experimental results and the roles of the nanoparticles and network properties in the modulus values of nanocomposites are determined. The proposed model presents acceptable predictions when compared with the experimental data. Moreover, the density and modulus of the filler network, as well as the aspect ratio and diameter of the nanoparticles was found to directly affect the moduli of the nanocomposites.

A multistep model is proposed for calculating the tensile modulus values of polymer/carbon nanotube (CNT) nanocomposites (PCNTs) based on the modified rule of mixtures, assuming a percolated network of nanoparticles.

Predicting the electrical conductivity in polymer carbon nanotube nanocomposites based on the volume fractions and resistances of the nanoparticle, interphase, and tunneling regions in conductive networks

Some limited models have been suggested to determine the conductivity of polymer carbon nanotube (CNT) nanocomposites (PCNTs). However, earlier models (e.g., the Kovacs model) cannot properly consider the roles of the interphase regions or tunneling properties on the percolation threshold and subsequent conductivity of PCNTs. In this paper, the Kovacs model is further developed by assuming that the CNT, interphase, and tunneling regions are separate phases. Also, some simple equations are provided to calculate the percolation threshold as well as the volume fractions and resistances of the CNT, interphase, and tunneling regions in conductive networks. The experimental conductivity results for several samples are compared with the predictions of the developed model. In addition, the calculations of the developed model at different parameter levels are explained and justified. The conductivity calculations show good agreement with the experimental data. Moreover, the developed model reasonably explains the roles of the different parameters on the conductivity. For example, long, thin, and straight CNTs efficiently improve the conductivity because they form large networks in the nanocomposites. Additionally, a thick interphase enlarges the conductive networks, resulting in a desirable conductivity. The conductivity of PCNTs only depends on the tunneling resistance; this is the case because the poor resistance/significant conductivity of the CNT and interphase regions do not influence the conductivity. The developed equations can replace conventional approaches for predicting the conductivity of nanocomposites.

Some limited models have been suggested to determine the conductivity of polymer carbon nanotube (CNT) nanocomposites (PCNTs).

Estimation of the tensile modulus of polymer carbon nanotube nanocomposites containing filler networks and interphase regions by development of the Kolarik model

The Kolarik model for the tensile modulus of co-continuous blends is developed for polymer/carbon nanotube (CNT) nanocomposites assuming continuous CNT networks and the reinforcing and percolating efficiencies of the interphase.

A two-step model for the tunneling conductivity of polymer carbon nanotube nanocomposites assuming the conduction of interphase regions

This work develops a two-step model for the conductivity of polymer carbon nanotube (CNT) nanocomposites (PCNT) assuming the properties of tunneling and interphase regions.