Simulation of Percolation Threshold, Tunneling Distance, and Conductivity for Carbon Nanotube (CNT)-Reinforced Nanocomposites Assuming Effective CNT Concentration

Laser additive manufacturing of shape memory biopolymer bone scaffold: 3D conductive network construction and electrically driven mechanism

INTRODUCTION

The electro-actuated shape memory polymer scaffold has gained increasing attentions on the utilization of minimally invasive surgery for bone defect repair, which requires to construct an efficient conductive network to accomplish electrical-to-thermal conversion from conductive fillers to the entire matrix evenly.

OBJECTIVES

In this study, multiwall carbon nanotube (MWCNT) was convective self-assembled on the ZnO tetrapod (t-ZnO) template, where MWCNT was controlled to disperse uniformly and regulated to contact with each other effectively due to the immersion capillary force during the evaporation loss of the convective self-assembly process, leading to an interwoven layer on the t-ZnO surface.

METHODS

The prepared t-ZnO@MWCNT assembly was embedded in the poly(L-lactic acid)/thermoplastic polyurethane (PLLA/TPU) scaffold fabricated via selective laser sintering to construct a 3D conductive MWCNT network for improving the electro-actuated shape memory properties.

RESULTS

It was observed that the interconnected MWCNT formed a 3D conductive network in the matrix without significant aggregation, which boosted the electrical-to-thermal properties of the scaffold, and the scaffold containing t-ZnO@MWCNT assembly possessed better electro-actuated shape memory properties with shape fixity of 98.0% and shape recovery of 98.8%.

CONCLUSION

The scaffold exhibited improved electro-actuated shape memory properties and mechanical properties and the osteogenic inductivity was promoted with the combined effect of t-ZnO and electrical stimulation.

Computational prediction of electrical percolation threshold in polymer/graphene-based nanocomposites with finite element method

In this research work, a two-dimensional model to predict the electrical percolation threshold (EPT) of the polymer/graphene-based nanocomposites in different concentrations of the randomly dispersed inclusions in various polymer matrices is introduced using the finite element method (FEM). The predicted EPT values were validated by other experimental results for different nanocomposites. Results showed that the electrical conductivity of different nanocomposites is significantly related to the percentage weight of the reinforcing phase in the polymer matrix. Furthermore, the addition of graphene-based nano-fillers in the polymer matrix caused a decrease in the tunneling distance in nanocomposites.

Computational prediction of electrical percolation threshold in polymer/graphene-based nanocomposites with finite element method

In this research work, a two-dimensional model to predict the electrical percolation threshold (EPT) of the polymer/graphene-based nanocomposites in different concentrations of the randomly dispersed inclusions in various polymer matrices is introduced using the finite element method (FEM). The predicted EPT values were validated by other experimental results for different nanocomposites. Results showed that the electrical conductivity of different nanocomposites is significantly related to the percentage weight of the reinforcing phase in the polymer matrix. Furthermore, the addition of graphene-based nano-fillers in the polymer matrix caused a decrease in the tunneling distance in nanocomposites.

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.

Near-Infrared Laser Direct Writing of Conductive Patterns on the Surface of Carbon Nanotube Polymer Nanocomposites

Near-infrared (NIR) laser annealing is used to write conductive patterns on the surface of polypropylene/multi-walled carbon nanotube nanocomposite (PP/MWCNT) plates. Before irradiation, the surface of the nanocomposite is not conductive due to the partial alignment of the MWCNT, which occurs during injection molding. We observe a significant decrease in the surface sheet resistance using NIR laser irradiation, which we explain by a randomization of the orientation of MWCNTs in the PP matrix melt by NIR laser irradiation. After only 5 s of irradiation, the sheet resistance of PP/MWCNTs, annealed with a laser at a power density of 7 W/cm², decreases by more than 4 decades from ∼100 MΩ/sq to ∼1 kΩ/sq. Polarized Raman, TEM, and SEM are used to investigate the changes in the sheet resistance and confirm the physico-chemical processes involved. This allows direct writing of conductive patterns using a NIR laser on the surface of nanocomposite polymer substrates, with the advantages of a fast, easy, and low-energy consumption process.

Using a Novel Approach to Estimate Packing Density and Related Electrical Resistance in Multiwall Carbon Nanotube Networks

In this work, we use contrast image processing to estimate the concentration of multi-wall carbon nanotubes (MWCNT) in a given network. The fractal dimension factor (D) of the CNT network that provides an estimate of its geometrical complexity, is determined and correlated to network resistance. Six fabricated devices with different CNT concentrations exhibit D factors ranging from 1.82 to 1.98. The lower D-factor was associated with the highly complex network with a large number of CNTs in it. The less complex network, having the lower density of CNTs had the highest D factor of approximately 2, which is the characteristic value for a two-dimensional network. The electrical resistance of the thin MWCNT network was found to scale with the areal mass density of MWCNTs by a power law, with a percolation exponent of 1.42 and a percolation threshold of 0.12 μg/cm2. The sheet resistance of the films with a high concentration of MWCNTs was about six orders of magnitude lower than that of less dense networks; an effect attributed to an increase in the number of CNT-CNT contacts, enabling more efficient electron transfer. The dependence of the resistance on the areal density of CNTs in the network and on CNT network complexity was analyzed to validate a two-dimension percolation behavior.