Destruction and recovery of a nanorod conductive network in polymer nanocomposites via molecular dynamics simulation

Numerical modeling of the effects of the shape and aspect ratio of 3D curved fiber on the percolation threshold and electrical conductivity of conductive polymer composites

For designing conductive polymer composites (CPCs), understanding how the fiber curvature affects the percolation behavior of curved conductive fibers is essential for determining the effective electrical conductivity σeff of the CPCs. In this work, CPCs were considered as a polymer matrix filled with the random packing of overlapped curved spherocylinders. The geometries of the curved spherocylinders were defined, and inter-curved spherocylinder contact-detecting and system-spanning fiber cluster searching algorithms were developed. The finite-size-scaling method was used to explore how the aspect ratio α and bending central angle θ of a curved spherocylinder affect the percolation threshold ϕc of an overlapped curved spherocylinder system in 3D space. The findings suggest that ϕc decreases as α increases and increases initially before declining as θ increases. An empirical approximation formula was proposed to quantify the effect of the curved spherocylinder's morphology, characterized by the dimensionless excluded volume Vdex of the curved spherocylinder, on ϕc. The new rigorous bound for ϕc of the soft-curved spherocylinder system was further proposed. A random resistor network model was constructed, and the reliability of this model was validated by comparing the simulations and published data. Finally, a fitting formula was developed to assess the impacts of the normalized reduced density (η - ηc)/ηc and Vdex on the σeff of CPCs. A distinct linear correlation between σeff and (η - ηc)/ηc was constructed, denoted as σeff ∼ [(η - ηc)/ηc]t(α,θ). An empirical approximation model was proposed to establish the relationship between the fiber shape and conductivity exponent t. Our study may provide a theoretical hint for the design of CPCs.

Synergistic effect in improving the electrical conductivity in polymer nanocomposites by mixing spherical and rod-shaped fillers

In this work, coarse-grained molecular dynamics simulation is adopted to investigate the effect of hybrid fillers [nanospheres (NSs) and nanorods (NRs)] on the conductive probability of polymer nanocomposites (PNCs) in the quiescent state and under the shear field. The percolation threshold gradually rises as the volume fraction ratio (α) of NSs to all the fillers increases in the quiescent state. Compared to the NSs, the greater number of beads in the NRs help them connect to other NRs to form the conductive network. Meanwhile, compared to NSs, more NRs participate in building the conductive network. A transition from the synergistic effect to the antagonistic effect occurs as the NS-NR tunneling distance is reduced. Furthermore, the shear field induces a more direct aggregation structure of NSs, which act as linkers between fillers to protect the conductive network. This result is confirmed by the fact that more NSs occupy the conductive network under the shear field. As a result, the percolation threshold declines with increasing shear rate. Finally, compared to in the quiescent state, the percolation threshold increases at α = 0.0 and remains nearly unchanged for α = 0.25 under the shear field, while it gradually decreases for α≥ 0.5. In total, the results further our understanding of how to realize the synergistic effect between NSs and NRs when forming a conductive network of PNCs.

Molecular dynamics simulation of the electrical conductive network formation of polymer nanocomposites by utilizing diblock copolymer-mediated nanoparticles

It is very important to improve the electrical conductivities of polymer nanocomposites (PNCs) as this can widen their application. In this work, by employing a coarse-grained molecular dynamics simulation, we investigated the effect of the amphiphilic diblock copolymer (BCP)-mediated nanoparticle (NP) on the conductive probability of polymer nanocomposites (PNCs) in the quiescent state and under a shear field. The conductive probability of PNCs first increases and then decreases with increasing content of BCPs while, interestingly, it exhibits an N-type dependence on the A-Block-NP interaction. Furthermore, the conductive probability shows a non-monotonic dependence on the fraction of A block (f_A) in the BCPs, which reaches the maximum value at moderate f_A. Under the shear field, NPs self-assemble to form the sandwich-like structures in the matrix above a critical concentration of BCPs, which leads to the anisotropic conductive probability of PNCs. In addition, the sandwich-like structures of NPs will be broken down at a high shear rate, which reduces the difference of the directional conductive probabilities. Last, the mechanism of the formation of the sandwich-like structures of NPs is discussed. In summary, this work presents a simple method to control the conductive network formation, which can help to design PNCs with high electrical conductivity, and especially anisotropy.

Multiscale Simulation of Branched Nanofillers on Young's Modulus of Polymer Nanocomposites

Nanoscale tailoring the filler morphology in experiment offers new opportunities to modulate the mechanical properties of polymer nanocomposites. Based on the conventical rod and experimentally available tetrapod filler, I compare the nanofiller dispersion and elastic moduli of these two kinds of nanocomposites via molecular dynamics simulation and a lattice spring model. The results show that the tetrapod has better dispersion than the rod, which is facilitate forming the percolation network and thus benefitting the mechanical reinforcement. The elastic modulus of tetrapod filled nanocomposites is much higher than those filled with rod, and the modulus disparity strongly depends on the aspect ratio of fillers and particle-polymer interaction, which agrees well with experimental results. From the stress distribution analysis on single particles, it is concluded that the mechanical disparity between bare rod and tetrapod filled composites is due to the effective stress transfer in the polymer/tetrapod composites.

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

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.

Controlling the electrical conductive network formation of polymer nanocomposites via polymer functionalization

By adopting coarse-grained molecular dynamics simulations, the effect of polymer functionalization on the relationship between the microstructure and the electric percolation probability of nanorod filled polymer nanocomposites has been investigated. At a low chain functionalization degree, the nanorods in the polymer matrix form isolated aggregates with a local order structure. At a moderate chain functionalization degree, the local order structure of the nanorod aggregate is gradually broken up. Meanwhile, excessive functionalization chain beads can connect the isolated aggregates together, which leads to the maximum size of nanorod aggregation. At a high chain functionalization degree, it forms a single nanorod structure in the matrix. As a result, the highest percolation probability of the materials appears at the moderate chain functionalization degree, which is attributed to the formation of the tightly connected nanorod network by analyzing the main cluster. In addition, this optimum chain functionalization degree exists at two chain functionalization modes (random and diblock). Lastly, under the tensile field, even though the contact distance between nanorods nearly remains unchanged, the topological structure of the percolation network is broken down. While under the shear field, the contact distance between nanorods increases and the topological structure of the percolation network is broken down, which leads to a decrease in the percolation probability. In total, the topological structure of the percolation network dominates the percolation probability, which is not a necessary connection with the contact distance between nanorods. In summary, this work presents further understanding of the electric conductive properties of nanorod-filled nanocomposites with functionalized polymers.