A theoretical model of gas diffusivity in graphene nanochannels

Gas diffusion in graphene nanochannels is pivotal for applications such as gas sensing and membrane separation, where nanoscale confinement introduces unique transport phenomena. Unlike bulk-phases, diffusion in graphene nanochannels is significantly influenced by adsorption, which modifies density distributions and alters diffusivity behavior. In this study, molecular dynamics simulations are combined with a theoretical framework to comprehensively investigate gas diffusion under varying pressures and channel heights. A modified Chapman-Enskog model, derived from atomistic Lennard-Jones potential parameters, is proposed to account for the effects of confinement. Simulation results reveal that gas diffusivity decreases with increasing gas-phase pressure and decreasing channel height due to enhanced density in the nanochannels. Interestingly, for ultra-narrow channels (h ≲ 0.7 nm), the diffusivity correction factor exhibits non-monotonic behavior, initially decreasing but subsequently increasing due to overlapping repulsive potential fields. The proposed model integrates adsorption effects through density predictions based on the Boltzmann distribution and effectively predicts gas diffusivities with relative errors of less than 13%, even under strong confinement. These findings highlight the critical interplay between adsorption and confinement in shaping gas transport within graphene nanochannels. The theoretical model provides a predictive tool for designing graphene-based gas separation and sensing devices, offering fundamental insights for optimizing their performance.