Electrical conductivity of geopolymer-graphite composites: Percolation, mesostructure and analytical modeling

Abstract

The incorporation of carbon fillers into geopolymers enhances conductivity, forming intricate networks for integrated energy conversion in construction materials. This study employs a novel computational modeling approach to investigate the electrical conductivity of geopolymer-graphite composites. Utilizing a particle-based Monte Carlo method, percolation thresholds in virtual composite mesostructures are simulated, and Bruggeman's effective medium model is applied to predict conductivity. The research explores how mesostructural characteristics, including pore and particle size distribution, influence percolation and conductivity in multi-phase mixtures. The findings indicate that graphite polydispersity increases the percolation threshold from 10 vol% to 24.4 vol%, while the introduction of two additional (segregated) phases, metakaolin and pores, decreases the percolation threshold to 11.9 vol%. The concept of effective volume fraction is introduced to assess the influence of metakaolin and porosity on the percolation threshold. This enabled to establish a linear relationship between the electrical conductivity and effective volume fraction of graphite with a slope of 2 S/m/vol%. In a three-phase mixture, the percolation threshold is 12.18 vol%, with a 2.35% increase due to capillary water drying, enhancing graphite particle aggregation in the dried pores. Finally, Bruggeman's analytical effective medium model is used to simulate conductivity variation with increasing graphite content, aligning well with conductivity measurements. These findings provide valuable insights into percolation behavior and offer a relatively simple quantitative modeling approach to understand the factors influencing the effective conductivity of geopolymer-graphite composites.