Cross-Scale Simulation-Driven Design of rGO-PFAN/Epoxy Coatings: Synergistic Physical Barrier-Chemical Repulsion for Superior Moisture Resistance

In harsh humid environments, conventional epoxy coatings suffer from inadequate corrosion resistance due to microporous defects formed during curing, while the trial-and-error optimization of fillers faces challenges such as unclear mechanisms and low design efficiency. This study proposes a novel "computation-driven material design" paradigm, elucidating the synergistic moisture-resistant mechanisms of fillers through cross-scale simulations. Molecular dynamics (MD) simulations show that adding 3 wt% graphene oxide (GO) reduces the free volume of epoxy by 15% and decreases the water diffusion coefficient by 10%. Density functional theory (DFT) calculations identify a high adsorption energy barrier (21.831 eV) generated by fluorine groups in polyfluoroaniline (PFAN) through electron cloud redistribution, effectively suppressing water penetration. Monte Carlo (MC) simulations further bridge microscopic energy barriers with macroscopic penetration flux. Guided by these insights, reduced graphene oxide-polyfluoroaniline (rGO-PFAN) composite fillers are synthesized via covalent grafting, experimentally demonstrating synergistic barrier-hydrophobic effects. Epoxy coatings containing 1.5 wt% rGO-PFAN retain an impedance modulus of 4.44×10 11 Ω·cm 2 after 90-day immersion, while 2 wt% filler reduces long-term water absorption by 73.63%. Salt spray tests confirm superior corrosion suppression at defect regions. Mechanical property tests show that the coating exhibits significantly reduced wear loss, enhanced adhesion strength, and perfect adhesion after thermal cycling. This work pioneers the multiscale correlation from electron-cloud interactions to macroscopic anticorrosion performance, establishing a theoretical framework for the rational design of intelligent coatings in extreme environments.