Predictions of the electrical conductivity of composites of polymers and carbon nanotubes by an artificial neural network

Abstract

Industrial applications of conductive polymer composites with carbon nanotubes require precise tailoring of their electrical properties. While existing theoretical methods to predict the bulk conductivity require fitting to experiments and often employ power-laws valid only in the vicinity of the percolation threshold, the accuracy of numerical methods is accompanied with substantial computational efforts. In this paper we use recently developed physically-based finite element analyses to successfully train an artificial neural network to make predictions of the bulk conductivity of CNT-polymer composites at negligible computational cost. Main Conductive composites of carbon nanotubes (CNT) and polymers enable a wide range of applications [1, 2] which require precise tailoring of their electrical properties. Existing theoretical methods [3, 4] to predict the bulk conductivity of such composites require fitting to experiments and often employ power-law approximations which are accurate only close to the

Influence of particle aspect ratio and ability to aggregate on electrical conductivity of fiber-forming polymer composites

“…With the further increase in the concentration of the conducting filler, the volume of the endless cluster increases as a result of absorption of the final clusters, especially the largest ones. Thus, the average size of the final clusters decreases (Kinloch et al 2018;Matos et al 2019;Moskalyuk et al 2012).…”

“…With increasing concentration of conductive particles, the average cluster size also increases. At ϑ f = ϑ f *, most of the clusters at first isolated from each other are combined into an infinite cluster, penetrating the entire system with the formation of a conduction channel (Kinloch et al 2018;Matos et al 2019;Moskalyuk et al 2012). With the further increase in the concentration of the conducting filler, the volume of the endless cluster increases as a result of absorption of the final clusters, especially the largest ones.…”

“…The answer (cut) is determined in such a way that the grid breaks into two parts. During the node identification, nodes are blocked (network nodes are destroyed) (Matos et al 2019). Percolation on a square grid is just one of possible patterns.…”

“…Random lattice processes are also considered (Kovacs et al 2007). The analysis of the topology of an infinite cluster showed that it contributes to infinite clustering and permittivity, but not to conductivity (Kovacs et al 2007;Matos et al 2019). Such chains were called "dead ends".…”

Carbon nanotube localization at interface in cocontinuous blends of polyethylene and polycarbonate

“…Carbon nanotubes (CNTs) are one of the best‐known conductive nanofillers . The interfacial tension Γ with a polymer can be estimated by the Girifalco–Good equation from the surface free energy γ . …”