Optimizing conductive thermoset composites for bipolar plates using statistical mixture design

A key challenge in developing composite bipolar plates (BPs) lies in optimizing the composition and synergy of filler and matrix materials. Random filler ratios often lead to suboptimal properties, limiting performance. In this study, we address this gap by employing an I-optimal mixture design, incorporating ANOVA analysis, to systematically optimize 21 formulations. This approach enabled the development of a low-cost, high-performance thermoset composite using novolac type phenol formaldehyde resin (NPFR) reinforced with expanded graphite (EG), carbon black (CB), graphite (G), and short carbon fibers (sCF). Model analysis (R2 = 0.97) revealed synergistic EG/sCF interactions enhancing conductivity, while CB/G disrupted networks. The combination of NPFR and sCF improved flexural strength, while higher G content may have reduced structural cohesion, despite the low R2 value (0.54). Increasing NPFR and CB decreased water absorption (WA) by reducing hydrophilicity. Antagonistic EG–G, EG–sCF, and G–sCF interactions further suppressed WA, as validated by quadratic modeling (R2 = 0.99). The optimum composite, fabricated via compression molding, exhibited in-plane conductivity of 69.1 S/cm, flexural strength of 29.6 MPa, and water absorption of 1.2%. The results reveal inherent trade-offs between electrical conductivity, mechanical integrity, and durability under high conductive filler loading. Moreover, this study provides a fundamental basis for understanding filler interactions, enabling the rational design of composite bipolar plates for PEM fuel cell applications.