Systematic studies on gas permeation of pure SF₆ and N₂ as well as their mixture in poly(4-methyl-1-pentene) (PMP) at different temperatures and pressures, using commercially available thin PMP films, are reported in this article. The effective separation of SF₆ from binary mixtures with N₂ is critical for the proposed replacement of pure SF₆, used as an insulating gas in high power industry, by the mixtures of these two gases. This replacement is driven by the fact that SF₆ is the most potent greenhouse gas, with a global warming potential of 22,200 times that of CO_2. The experiments with a 1:1 mixture of N₂ and SF₆ revealed the permselectivity of PMP as high as 476 with the corresponding N₂ permeability coefficient of 7.6 Barrer. These properties, which are much better than those of other glassy polymers considered for this separation, were not affected by a long-term exposure to SF₆, which indicates the excellent resistance of PMP to plasticization by this gas. Using a single stage membrane system utilizing the PMP membrane would allow separating the above gas mixture into a 99% pure SF₆ product with the corresponding recovery rate of SF₆ greater than 99%.

In the packaging industry, the barrier property of packaging materials is of paramount importance. The enhancement of barrier properties of materials can be achieved by adding impermeable nanoparticles into thin polymeric films, known as mixed-matrix membranes (MMMs). Three-dimensional numerical simulations were performed to study the barrier property of these MMMs and to estimate the effective membrane gas permeability. Results show that horizontally-aligned thin cuboid nanoparticles offer far superior barrier properties than spherical nanoparticles for an identical solid volume fraction. Maxwell’s model predicts very well the relative permeability of spherical and cubic nanoparticles over a wide range of the solid volume fraction. However, Maxwell’s model shows an increasingly poor prediction of the relative permeability of MMM as the aspect ratio of cuboid nanoparticles tends to zero or infinity. An artificial neural network (ANN) model was developed successfully to predict the relative permeability of MMMs as a function of the relative thickness and the relative projected area of the embedded nanoparticles. However, since an ANN model does not provide an explicit form of the relation of the relative permeability with the physical characteristics of the MMM, a new model based on multivariable regression analysis is introduced to represent the relative permeability in a MMM with impermeable cuboid nanoparticles. The new model possesses a simple explicit form and can predict, very well, the relative permeability over an extensive range of the solid volume fraction and aspect ratio, compared with many existing models.