Tun H.M.Keawmaungkom S.Srimongkol S.Kunnam P.Chayasombat B.Muthitamongkol P.Wiroonpochit P.Tapracharoen K.Phadungbut P.Srisawadi S.Chinkanjanarot S.Mahidol University2025-11-202025-11-202025-01-01Journal of Applied Polymer Science (2025)00218995https://repository.li.mahidol.ac.th/handle/123456789/113136Conductive natural rubber composites (CNRs) filled with carbon black (CB) are widely explored for flexible sensing applications due to their tunable electrical properties. This study introduces a simulation framework integrating Monte Carlo methods with two representative volume element (RVE) models—uniform (Uni-RVE) and overlap face-centered cubic (OFCC-RVE)—to investigate the electrical conductivity and percolation behavior in CB-filled CNRs. An onion-like progressive tunneling effect is introduced to represent the distance-dependent conductivity more realistically. The OFCC-RVE model, incorporating particle agglomeration and variable tunneling distances, demonstrates strong agreement with experimental data. A power-law decay function is applied to capture conductance attenuation at different interparticle distances. The model achieves a mean absolute percentage error of 5.2% when compared with experimental measurements, highlighting its predictive accuracy and efficiency. This approach provides a computationally efficient and physically grounded framework for evaluating and optimizing the electrical performance of conductive rubber composites.Materials ScienceChemistryArticleSCOPUS10.1002/app.581262-s2.0-10502122452510974628