Electrical Conductivity Of Polymer Nanocomposites Research Articles

Assessment of electrical conductivity of polymer nanocomposites containing a deficient interphase around graphene nanosheet

In this study, a poor/imperfect interphase is assumed to express the effective interphase thickness, operative filler concentration, percolation onset and volume share of network in graphene–polymer systems. Additionally, a conventional model is advanced by the mentioned terms for conductivity of samples by the extent of conduction transference between graphene and polymer medium. The model predictions are linked to the experimented data. Likewise, the mentioned terms as well as the conductivity of nanocomposites are expressed at dissimilar ranges of various factors. The novel equations successfully predict the percolation onset and conductivity in the samples containing a poor/imperfect interphase. Thin and long nanosheets with high conduction transportation desirably govern the percolation onset and nanocomposite conductivity, but a bigger tunneling distance causes a lower conductivity.

3D graphene-based scaffold as a platform for highly conductive polymer nanocomposites

Electrical conductivity of polymer nanocomposites (PNCs) is highly dependent on distribution of the conductive material within the polymer matrix, which is often challenging to achieve via conventional methods. This work presents the use of a graphene oxide (GO)-polyacrylamide (PAM) hydrogel that can be dried and reduced into a porous reduced GO-PAM (rGO-PAM) scaffold as a precursor for the fabrication of PNCs. In situ cross-linking of poly(ethylene-glycol) diacrylate (PEGDA) oligomers, polymerization of 2-hydroxyethyl methacrylate (HEMA) monomers, and curing epoxy resins inside the pores of rGO-PAM scaffolds resulted in the successful fabrication of rGO-PAM/xPEGDA, rGO-PAM/poly(HEMA), and rGO-PAM/epoxy nanocomposites, respectively. These PNCs showed similar electrical conductivities (4.24 ± 1.17, 4.36 ± 1.36, and 3.33 ± 0.35 S/mm), which elucidates the robustness of the fabrication approach. This is the first report of the preparation of an isotropic rGO-based porous scaffold used in designing PNCs that can host several types of polymers while maintaining its electrical performance.

Development of a theoretical model for estimating the electrical conductivity of a polymeric system reinforced with silver nanowires applicable for the biosensing of breast cancer cells

A theoretical model is proposed for predicting the electrical conductivity for polymer samples containing silver nanowires (AgNWs). The advanced model reflects the roles of interphase and tunneling regions, waviness and dimension of AgNWs in the percolation inception, network size and conductivity of a sample. Some experimented data from various samples are used to check the validity of this model. The conductivity calculations suggested by this model present the proper accord with the measured data. Furthermore, the effects of different parameters on electrical conductivity are investigated by this model. Thinner and longer nanowires and no waviness produce the large network and low percolation inception, which significantly enhance the nanocomposite's conductivity. In addition, thicker interphase causes a higher conductivity by participating in the creation of networks. The proposed model is useful for estimating the electrical conductivity of polymer nanocomposites in future researches. The developed model can be used to optimize the performance of biosensors detecting the breast cancer cells.

Effects of copper nanoparticles on elastic and thermal properties of conductive polymer nanocomposites

Metallic nanoparticles are extensively used to enhance the thermal and electrical conductivity of polymer nanocomposites. However, despite many experimental studies on such composite materials, their computational analysis has remained unexplored to a large extent. In this study, we investigate the effects of cylindrical and spherical copper nanoparticles on the mechanical and thermal properties of polyethylene matrix using molecular dynamics and finite element methods. To calculate the density of the interphase zone and consequently, its elastic and thermal properties, LAMMPS package is used considering AIREBO, EAM, and Lennard‐Jones potential functions to model the interactions between nanocomposite components. Afterward, three-dimensional finite element modeling is done to obtain Young's modulus and thermal conductivity of the studied nanocomposites for different geometries, orientations, and volume fractions of nano-fillers. The results reveal that reinforcing polymer with copper nanofillers improves its thermal conductivity and elastic modulus, remarkably. Besides, nanoparticles with cylindrical geometry and [1 1 1] axial crystallographic orientation have a greater effect on the enhancement of properties than other orientations or spherical nanoparticles. Furthermore, the orientation of the nanoparticles inside the matrix in the direction of applied force and heat flux has the greatest impact on improving the properties of polymers compared to the other directions.

Advancement of a model for electrical conductivity of polymer nanocomposites reinforced with carbon nanotubes by a known model for thermal conductivity

The models for thermal conductivity of polymer nanocomposites reinforced by carbon nanotubes (CNT) (PCNT) can express the electrical conductivity, because both electrical and thermal conductivities consistently depend on the CNT properties. In this study, a known model for thermal conductivity of PCNT is simplified and developed for electrical conductivity assuming CNT aspect ratio, network fraction, interphase districts, tunneling area between near CNT and CNT wettability by polymer medium. Simple equations express the volume fraction of networked CNT by CNT loading, CNT size and interphase depth. In addition, applicable equations suggest the total conduction of CNT and tunnels. The satisfactory matching among measured records and forecasts in addition to the rational effects of whole factors on the conductivity confirm the advanced model. Lengthy CNT and dense interphase usefully manipulate the conductivity, but short CNT or thin interphase cannot increase the conductivity of insulated medium. Additionally, only the high level of polymer tunneling resistivity prevents the conducting efficiency of CNT in PCNT. Also, wide tunnels and short tunneling distance highly progress the conductivity, but very small tunneling width causes an insulated specimen.

Compression-induced electrical percolation and enhanced mechanical properties of polydimethylsiloxane-based nanocomposites

In this work, a compression-induced percolation threshold was found when the thickness of polydimethylsiloxane (PDMS) nanocomposite samples was reduced via a spatial confining forced network assembly (SCFNA) process from 1.0 mm to 0.1 mm. Such as for PDMS/2 wt% short carbon fiber/4 wt% carbon nanotube (CNT) composite, its conductivity was more than 8 times enhanced to 487 S/m from 59.5 S/m, and the mechanical properties of composites have been improved by more than 15% accordingly. Comparatively, when increased the concentration of CNT or Gr from 1 to 4 wt%, the electrical conductivity of PDMS nanocomposites at 1 mm thickness was barely changed as it generally reached saturation and became independent of filler loading. Compared with the traditional blending method, it indicates that the SCFNA process can further promote the maximum electrical conductivity of polymer nanocomposites when the filler concentration has little effect on the conductivity. Especially under the condition of relatively high filler concentration, the electrical conductivity enhancement effect becomes more significant that is contrary to the classical percolation theory. Moreover, the mechanical properties of the nanocomposites can be slightly improved by the mechanical compression, which makes it more suitable for flexible electronic devices' applications.

Modeling electrical conductivity of polymer nanocomposites with aggregated filler

AbstractA mean‐field model is developed for the electrical conductivity of microcomposites and nanocomposites with polymer matrices. The model accounts for aggregation of filler into clusters (involving both conducting and nonconducting particles) and rearrangement of these clusters with the growth of volume fraction of filler (which leads to a reduction in tunneling resistivity and an increase in the number of bridging contacts between conducting particles). The governing equations involve five material constants with transparent physical meaning: the depolarization factor of clusters, volume fraction of polymer in clusters of filler, effective conductivity of an individual filler particle, and two coefficients characterizing an increase in the effective electrical conductivity of filler driven by the growth of bridging contacts between neighboring particles in clusters. Good agreement is demonstrated between results of simulation and experimental data on the electrical conductivity of epoxy resin reinforced with carbon black and graphite particles, poly(vinyl chloride) reinforced with copper and nickel particles, polypropylene loaded with spherical and spheroidal tin particles, poly(butylene terephthalate) reinforced with graphene nanosheets, and polypropylene loaded with multiwalled carbon nanotubes.

Effects of carbon nanotubes and interphase properties on the interfacial conductivity and electrical conductivity of polymer nanocomposites

AbstractLc is the minimum length of carbon nanotubes (CNTs) required for efficient transfer of filler conductivity to polymer matrix in polymer CNT nanocomposites (PCNTs). In this work, Lc is correlated with the dimensions of the CNTs and the interphase thickness. Subsequently, the interfacial conductivity as well as the effective length and concentration of CNTs are expressed by CNT and interphase properties. Moreover, a simple model for the tunneling conductivity of PCNTs is developed with these effective terms. The impacts of all parameters on Lc, the interfacial conductivity, the fraction of CNTs in the networks and the conductivity of the PCNT are explained and justified. In addition, the predictions of the percolation threshold and conductivity are compared with the experimental results of several samples. The desirable values of interfacial conductivity are achieved by thin, short and super‐conductive CNTs, high waviness and a thick interphase. However, thin and long CNTs, low waviness, a thick interphase, poor tunneling resistivity due to the polymer matrix and a short tunneling distance advantageously affect the conductivity of PCNTs, because they produce large conductive networks. The predictions also show good agreement with the experimental measurements of percolation threshold and conductivity, which confirms the developed equations. © 2020 Society of Chemical Industry

Calculation of tunneling distance in carbon nanotubes nanocomposites: effect of carbon nanotube properties, interphase and networks

In this paper, two developed models for electrical conductivity of polymer nanocomposites are linked to express an equation for tunneling distance between adjacent carbon nanotubes (CNT) by the effective properties of polymer matrix, CNT, interphase and networks. The tunneling distance is calculated for some samples at different CNT concentrations. In addition, the suggested equation is applied to justify the impacts of all parameters on the tunneling distance. The tunneling distance decreases as CNT concentration increases, but its variation reduces at high CNT loading. The suggested equation demonstrates that a thick interphase, thin and short CNT, high filler concentration, poor percolation threshold, low surface energy of polymer and high CNT surface energy produce a short tunneling distance.

Effect of the shear field on the conductive percolated network formation in a nanoparticle filled polymer nanocomposites

ABSTRACT It is very important to improve the electrical conductivity of polymer nanocomposites (PNCs) which can widen their application. In this work, a coarse-grained molecular dynamics simulation is adopted to investigate the effect of the nanoparticle (NP) functionalization on the formation of the conductive network under the quiescent state or under the shear field, which is denoted by the conductive probability. First, the dispersion state of NPs is characterized which varies from the contact aggregation, the relatively uniform dispersion to the local bridging structure via one polymer layer with the increase of the functionalization degree . Corresponding to it, the percolation threshold of PNCs exhibits a continuous decrease with the increase of under the quiescent state which reflects the high conductive probability. However, the percolation threshold shows a weak increase under the shear field. Thus, the decides whether the conductive probability increases or decreases under the shear field. The smallest change in the percolation threshold appears at the moderate which reflects the highest conductive stability. Especially, even though the dispersion state of NPs is different for , the percolation threshold is similar under the shear field. The connection mode among NPs is considered to determine the formation of the conductive network. In summary, this work can help to understand how the NP functionalization affects the electrical conductivity of PNCs.

Determination of electrical percolation threshold of carbon nanotube‐based epoxy nanocomposites and its experimental validation

The electrical conductivity of polymer nanocomposites plays a critical role in applications like aerospace and electromagnetic shielding. Electrical conductivity and other electromagnetic parameters of carbon nanotube (CNT)-based epoxy nanocomposites are being analysed in this work for their suitability in electromagnetic shielding. Unfilled epoxy and CNT epoxy nanocomposite samples with various filler loadings are prepared. Impedance measurements are conducted over the frequency range of 20 Hz–10 MHz. Micromechanics model is used for the theoretical estimation of electrical conductivity of CNT-based epoxy nanocomposites. Interface thickness in the conventional model is modified to avoid the overestimation of electrical conductivity. DC conductivity of nanocomposites determined experimentally is found to obey classical law of percolation theory and percolation threshold is determined to be 0.17 wt% which agrees well with theoretical value of 0.18 wt% obtained using modified micromechanics model. AC conductivity is found to obey universal dynamic response. Dipolar polarisation mechanism is prominent in the tested frequency range whose frequency of occurrence increases with CNT content due to a greater number of interfaces formed between CNT and epoxy. CNT epoxy nanocomposites are found to have negative permeability thus acting as absorbers in electromagnetic shielding.

The effect of carbon nanotube chirality on the electrical conductivity of polymer nanocomposites considering tunneling resistance

In this study, the effect on the conductance of polymer nanocomposites considering quantum tunneling resistance is investigated with respect to the chirality of carbon nanotubes (CNTs) and uncertainties in the geometric parameters of CNTs by using Monte Carlo simulations. The random spatial placement for CNTs was accomplished with a one-dimensional line segment and the periodic boundary conditions were applied to CNTs in the two-dimensional representative volume element. Intersection points between each CNT were calculated to obtain connectivity lists of the connected network path. Both the intrinsic resistance of the CNT and the inter-CNT tunneling resistance were considered in this model. In addition, the in-house code developed was validated by comparison with several experimental datasets from the literature. Unlike past studies, uncertainties in the chiral index of single-wall CNTs concerning armchair and zig–zag structures have been considered here and the electrical conductivity and percolation threshold are predicted.

Unraveling radial dependency effects in fiber thermal drawing

Fiber-based devices with advanced functionalities are emerging as promising solutions for various applications in flexible electronics and bioengineering. Multimaterial thermal drawing, in particular, has attracted strong interest for its ability to generate fibers with complex architectures. Thus far, however, the understanding of its fluid dynamics has only been applied to single material preforms for which higher order effects, such as the radial dependency of the axial velocity, could be neglected. With complex multimaterial preforms, such effects must be taken into account, as they can affect the architecture and the functional properties of the resulting fiber device. Here, we propose a versatile model of the thermal drawing of fibers, which takes into account a radially varying axial velocity. Unlike the commonly used cross section averaged approach, our model is capable of predicting radial variations of functional properties caused by the deformation during drawing. This is demonstrated for two effects observed, namely, by unraveling the deformation of initially straight, transversal lines in the preform and the dependence on the draw ratio and radial position of the in-fiber electrical conductivity of polymer nanocomposites, an important class of materials for emerging fiber devices. This work sets a thus far missing theoretical and practical understanding of multimaterial fiber processing to better engineer advanced fibers and textiles for sensing, health care, robotics, or bioengineering applications.

Increasing the electrical conductivity of polymer nanocomposites under the external field by tuning nanofiller shape

It is very important to improve the electrical conductivity of polymer nanocomposites (PNCs), which can be used under the external field. In this work, by employing a coarse-grained molecular dynamics simulation, the effect of the external fields (tensile field and shear field) on the conductive probability of PNCs has been investigated in details by tuning the nanofiller shape. By carefully analyzing the conductive network, compared with the rod filler and the sphere filler, the Y filler and the X filler can protect the conducive networks well from their breakage perpendicular to the external fields, which thus can significantly enhance the conductive probability under the external fields. In other words, compared with under the quiescent state, the decrease or the increase of the conductive probability under the external fields depends on the nanofiller shape. Meanwhile, it is interesting to find that both the directional conductive probabilities parallel to the shear field and perpendicular to the shear field increase with the shear rate for the Y fillers, which further reflects their high conductive probability under the shear field. The reason can be attributed to two aspects: (1) the gradual aggregation of Y fillers under the external field; (2) the multi-arm structures of Y fillers. In summary, this work presents some further understanding how to improve the electrical conductivity of PNCs under the external fields by tuning the nanofiller shape.

Thermal and electrical properties of the epoxy nanocomposites reinforced with purified carbon nanotubes

The effect of carbon nanotube (CNT) purification treatment on the thermal and electrical properties of epoxy nanocomposites was investigated. The results indicated that purification treatment can significantly affect nanocomposite performance including thermal and electrical properties. Highly-purified single-walled carbon nanotubes (SWCNTs) have the potential to significantly improve the thermal conductivity of polymer nanocomposites, but are unfavorable for electrical properties due to their inability to form an effective percolating network. The thermal conductivity can be improved up to four times higher than the polymer matrix, demonstrating the significance of CNT purification treatment in improving nanocomposite performance. The electrical conductivity of polymer nanocomposites is more sensitive to purification treatment than their thermal conductivity.

Optimizing the electrical conductivity of polymer nanocomposites under the shear field by hybrid fillers: Insights from molecular dynamics simulation

It is very important to simultaneously improve the electrical conductivities of polymer nanocomposites (PNCs) under the quiescent state and under the shear field, which can widen their application. For this purpose, in this work, by adopting a coarse-grained molecular dynamics simulation, the effect of the hybrid nanofillers (rod filler and X filler) on the conductive probability of PNCs under the quiescent state and under the shear field has been investigated in details, which aims to reach their optimization. By analyzing the conductive network, it is found that the conductive probability gradually decreases with the increase of the volume fraction ratio of X filler to all the filler (α) under the quiescent state, which is strongly related to the mean square radius of gyration of nanofiller. Under the shear field, the conductive probability first increases and then saturates with the increase of α. It is interesting to observe the optimization of the conductive probabilities under the quiescent state and under the shear field at α equal to 0.50. As a result, compared with in the quiescent state, the decrease or the increase of the conductive probability depends on α. Last, the conductive network of hybrid fillers is found to be kept very well even at high shear rate. In summary, this work presents that hybrid nanofillers (rod filler and X filler) are actually the suitable candidate to optimize the electrical conductivities of PNCs under the quiescent state and under the shear field.

Percolation analysis of the electrical conductive network in a polymer nanocomposite by nanorod functionalization.

Chemical functionalization of nanofillers is an effective strategy to benefit the formation of the conductive network in the matrix which can enhance the electrical conductivity of polymer nanocomposites (PNCs). In this work, we adopted a coarse-grained molecular dynamics simulation to investigate the effect of the nanorod (NR) functionalization on the conductive probability of PNCs under the quiescent state or under a shear field. It is found that the direct aggregation structure of NRs is gradually broken down with increasing the NR functionalization degree λA, which improves their dispersion state. Moreover, a local bridging structure of NRs sandwiched via one polymer layer is formed at high λA. Corresponding to it, the percolation threshold of PNCs first quickly decreases, then increases and last slightly decreases again with the increase of λA, which exhibits an anti N-type under the quiescent state. Meanwhile, it shows a non-monotonic dependence on the interaction between polymer and the functionalized beads which reaches the lowest value at the moderate interaction. However, the percolation threshold is nearly independent of λA under the shear field. Compared with in the quiescent state, the decrease or the increase of the percolation threshold can be tuned by λA under the shear field. The significant change in the percolation threshold is attributed to the orientation and the dispersion state of NRs under the shear field, which affects the conductive network. Especially, we found that the dispersion state of NRs is different for different λA under the shear field. However, the percolation threshold is similar which indicates that the dispersion state of NRs is not completely correlated to the conductive network. In summary, this work presents some further understanding of how the NR functionalization affects the electrical conductivity of PNCs.

EFFECT OF THE NANOFILLER SHAPE ON THE CONDUCTIVE NETWORK FORMATION OF POLYMER NANOCOMPOSITES VIA A COARSE-GRAINED SIMULATION

ABSTRACT It is very important to improve the electrical conductivity of polymer nanocomposites, which can widen their application. The effect of the nanofiller shape on the relationship between the nanofiller microstructure and the conductive probability of the nanofiller filled polymer nanocomposites (PNCs) has been investigated in detail by employing a coarse-grained molecular dynamics simulation. Four kinds of nanofiller shapes are considered: rod filler, Y filler, X filler, and sphere filler. First, the mean square radius of gyration gradually decreases from rod filler, Y filler, X filler, to sphere filler, which reflects the highest aspect ratio for rod filler. Meanwhile, the dispersion state of the nanofiller is relatively uniform in the matrix. The conductive probability (denoted by the formation probability of the conductive network) is adopted to stand for the conductive property. The results show that the conductive probability gradually decreases from rod filler, Y filler, X filler, to sphere filler, which is attributed to their gradually decreased size. In summary, the nanofiller shape affects the electric conductive property of PNCs.

Tunneling-percolation model of multicomponent nanocomposites

Using a mixture of different types of fillers has been experimentally shown to improve the electrical conductivity of polymer nanocomposites beyond the weighted average due to synergistic effects. In this study, we develop a critical path analysis-based tunneling-percolation model for multicomponent systems of nanocomposites with ellipsoidal fillers. The nature of the interaction between different filler components is controlled by a key modeling parameter capturing the tunneling interactions between fillers. This generalization allows us to examine scenarios where the nature of a given type of filler can be varied continuously from an insulating-type to a conductive-type. The percolation behavior of two-component systems with a combination of prolate, oblate, and spherical fillers is investigated using Monte Carlo simulations for different relative volume fractions and nature of interactions while keeping the total volume fraction fixed. The simulation results are shown to be in semi-quantitative agreement with predictions made by the second-virial-approximation-based theories. Our results suggest that for multicomponent systems with well-dispersed fillers, the synergistic effects are linked directly with the nature of interactions between different filler types. Moreover, addition of prolate fillers to oblate or spherical fillers should generally improve the electrical conductivity of multicomponent nanocomposites.

Effect of Aspect Ratio on Electrical, Rheological and Glass Transition Properties of PC/MWCNT Nanocomposites.

Since the discovery of carbon nanotubes (CNT), significant research works have focused on the application of CNT as conductive filler to polymer nanocomposites which can be used in several fields such as electrostatic dissipation (ESD), electrostatic painting and electromagnetic interference shielding (EMI-shielding). However, the main challenge in the large-scale manufacturing of this technology is the poor electrical conductivity of polymer nanocomposites produced by injection molding process. This study aims to investigate the effect of CNT aspect ratio in improving the electrical conductivity of injection molded nanocomposites. In this work, three types of multiwall carbon nanotubes with different lengths were melt-mixed with polycarbonate in a twin screw extruder followed by injection and compression molding. Results show that nanocomposites with higher CNT aspect ratio exhibit higher electrical conductivity. Longer nanotubes form a stronger conductive network during secondary agglomeration which can withstand the high shear forces during injection molding. Higher melt viscosity and storage modulus were observed in nanocomposites with higher CNT aspect ratio which is attributed to the effective constriction of polymer chains by longer nanotubes. It was also found that Tg of the composites increased with nanotube aspect ratio and the addition of CNT causes degradation which leads to the general Tg depression of polycarbonate.