Dynamic percolation in highly oriented conductive networks formed with different carbon nanofillers

Effects of Filament Extrusion, 3D Printing and Hot-Pressing on Electrical and Tensile Properties of Poly(Lactic) Acid Composites Filled with Carbon Nanotubes and Graphene

: In this study, the effects of three processing stages: filament extrusion, 3D printing (FDM), and hot-pressing are investigated on electrical conductivity and tensile mechanical properties of poly(lactic) acid (PLA) composites filled with 6 wt.% of multiwall carbon nanotubes(MWCNTs), graphene nanoplatelets (GNPs), and combined fillers. The filaments show several decades' higher electrical conductivity and 50-150% higher values of tensile characteristics, compared to the 3D printed and the hot-pressed samples due to the preferential orientation of nanoparticles during filament extrusion. Similar tensile properties and slightly higher electrical conductivity are found for the hot-pressed compared to the 3D printed samples, due to the reduction of interparticle distances, and consequently, the reduced tunneling resistances in the percolated network by hot pressing. Three structural types are observed in nanocomposite filaments depending on the distribution and interactions of fillers, such as segregated network, homogeneous network, and aggregated structure. The type of structural organization of MWCNTs, GNPs, and combined fillers in the matrix polymer is found determinant for the electrical and tensile properties. The crystallinity of the 3D printed samples is higher compared to the filament and hot-pressed samples, but this structural feature has a slight effect on the electrical and tensile properties. The results help in understanding the influence of processing on the properties of the final products based on PLA composites.

The effect of DBP of carbon black on the dynamic self-assembly in a polymer melt

Three types of carbon black with different dibutyl phthalate (DBP) absorption have been used to study the electrical percolation behavior in thermoplastic polyurethane.

Formation of conductive networks with both segregated and double-percolated characteristic in conductive polymer composites with balanced properties

Morphological control of conductive networks involves the construction of segregated or double-percolated conductive networks is often reported to reduce the electrical percolation threshold of conductive polymer composites (CPCs) for better balance among electrical conductivity, mechanical properties, and filler content. Herein, the construction of conductive networks with both segregated and double-percolated characteristics is achieved based on polypropylene (PP)/polyethylene (PE) and multi-wall carbon nanotubes (CNTs). CNTs were firstly dispersed in PE; then PE/CNTs were compounded with PP particles well below the melting temperature of PP. It is observed that the percolation threshold (pc) decreases with increasing PP particle size (size 3.6 mm, pc=0.08 wt %), which agrees with previous theoretical prediction and experiment in much smaller particle size range. To further study this, the amount of CNTs in PE is varied. It is shown that the degree of PE/CNTs coating on PP particles varies with CNTs as well as PE content in these composites, and have significant influence on the final electrical property. Furthermore, a model combines classical percolation theory and model for segregated network has been proposed to analyze the effect of particle size, degree of coating and thickness of coating on the percolation behavior of these CPCs. In such a model the percolation of CNTs in PE phase as well as PENT phase in the segregated structure can be described. Overall, through such method, a much better balance among mechanical property, conductivity, and filler content is achieved in these CPCs comparing with the results in literature.

Towards tunable sensitivity of electrical property to strain for conductive polymer composites based on thermoplastic elastomer

The use of conductive polymer composites (CPCs) as strain sensors has been widely investigated and various resistivity-strain sensitivities are desirable for different applications. In this study, the use of mixed carbon fillers and functionalized carbon nanotubes was demonstrated to be vital for preparing thermoplastic polyurethane (TPU)-based strain sensors with tunable sensitivity. To understand the strain sensing behavior, we carried out scanning electron microscopy (SEM), Raman spectroscopy, wide-angle X-ray diffraction (WAXD), mechanical test, and rheology-electrical measurement. Hybrid fillers of multi-walled carbon nanotubes (MWNTs) and carbon black (CB) could reduce the entanglement in conductive network structure, thus increase the resistivity-strain sensitivity. Furthermore, incorporation of additional functionalized MWNTs in the CPCs could enhance the interfacial interaction between nanofillers and TPU, leading to further increase in sensitivity. Through such a simple method, strain sensors could be efficiently fabricated with large strain-sensing capability (strain as large as 200%) and a wide range of strain sensitivity (gauge factor ranging from 5 to 140238). Finally, the exponential revolution of resistive response to strain was fitted with a model based on tunneling theory by Simmons. It was observed that the change in tunneling distance and the number of conductive pathways could be accelerated significantly by adjusting conductive network structure and interfacial interaction. This study provides a guideline for the preparation of high-performance CPC strain sensors with a large range of resistivity-strain sensitivity.