Independent experimental measurements of diffusion, sorption, and permeability support the solution-diffusion model of membrane transport

The solution-diffusion (SD) model remains the cornerstone framework for interpreting mass transport in nonporous polymeric membranes, yet its applicability has recently been scrutinized. In this study, we highlight that independent measurement of the SD parameters can be reliably utilized to make reasonable flux predictions across many different types of transport modalities; in this article, we highlight three: hydraulic permeation and organic solvent reverse osmosis (OSRO), pervaporation, and vapor permeation. First, independent measurements of sorption (equilibrium uptake under varying penetrant molecule fugacities) and diffusion (from pulsed field gradient nuclear magnetic resonance) were used to parameterize the SD model. Afterwards, the SD predictions of membrane transport were compared with independent experimental permeation data. Our findings demonstrate that, when independently measured sorption and diffusion coefficients are used to calculate permeation rates, the resulting values align closely with those obtained through direct permeation experiments across all three processes for different organic molecules. Our analysis underscores that the SD model provides a physically consistent and quantitatively accurate description of transport phenomena in dense membranes. This work reinforces the broad utility of the SD model and provides a framework for its validation in current and emerging membrane technologies. • Molecular diffusion in polymers can be measured using equilibrium techniques. • Experimental equilibrium relationships between solvents and polymer membranes can be measured. • These two independent experiments can be used to parameterize the solution-diffusion (SD) model. • The SD model is then validated via comparison to RO, vapor permeation, and pervaporation experiments for organic solvents and water. • A full sorption isotherm is essential for making precise SD predictions.