Advanced percolation models incorporating excluded volume effects in segregated composites via nano-interconnection and micro-void structure optimization

Segregated conductive polymer composites, in which three-dimensional (3D) conductive networks are formed through the selective localization of conductive fillers, have attracted considerable attention due to their remarkable electrical and thermal conductivities even at low filler contents. Nevertheless, theoretical models that account for their uniquely segregated structures remain limited. Here, we propose advanced segregated electrical and thermal percolation models by considering a hierarchical nano-micro network architecture. The key structural features of this segregated architecture, such as micro-voids and excluded volume, were quantitatively analyzed using 3D non-destructive micro-computed tomography. These structural parameters were incorporated into a conventional percolation model to more accurately represent the segregated network features and to overcome limitations of existing approaches. Furthermore, to mitigate the inevitable degradation in mechanical properties caused by micro-voids formed during the localization of conductive networks at particle interfaces, we developed a facile fabrication method involving the introduction of a terpolymer with a lower melting point than that of the base matrix. This approach significantly reduced micro-void formation, enabling the incorporation of up to 4.93 vol% additional filler into GNP-based segregated composites (G-SC) and up to 12.15 vol% into h-BN-based segregated composites (B-SC). As a result, the G-SC exhibited enhancements of up to 124.07% in electrical conductivity and 68.11% in thermal conductivity, while the B-SC achieved up to a 53.54% improvement in thermal conductivity. These findings demonstrate that nano-micro network optimization through terpolymer incorporation enables the realization of significantly improved conductive performance, validating the effectiveness of the newly proposed segregated percolation models.