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The majority of materials used for membrane-based separation of gas mixtures are non-renewable and non-biodegradable, and the assessment of alternative bio-based polymers requires expensive and time-consuming experimental campaigns. This effort can be reduced by adopting suitable modelling approaches. In this series of works, we propose various modelling approaches to assess the CO2/CH4 separation performance of eight different copolymers of 3-hydroxybutyrate and 3-hydroxyvalerate (PHBV) using a limited amount of experimental data for model calibration. In part 1, we adopted a fully atomistic approach based on Molecular Dynamics (MD), while, in this work, we propose a multiscale methodology where a molecular description of the polymers is bridged to a macroscopic prediction of its gas sorption behaviour. PHBV structures were simulated using MD to obtain pressure–volume–temperature data, which were used to parametrise the Sanchez–Lacombe Equation of State. This, in turn, allows for the evaluation of the CO2 and CH4 solubility in the copolymers at various pressures and compositions with little computational effort, enabling the estimate of the sorption-based selectivity. The gas separation performance obtained with this multiscale technique was compared to results obtained with a fully atomistic model and experimental data. The solubility–selectivity for the CO2/CH4 mixture is in reasonable agreement between the two models and the experimental data. The multiscale method presented is a time-efficient alternative to fully atomistic methods and detailed experimental campaigns and can accelerate the introduction of renewable materials in different applications. [ABSTRACT FROM AUTHOR]

The Robeson bound is a theoretical limit that applies to kinetics-driven membrane separations of gas mixtures. However, this bound does not apply to sorption-driven membrane processes such as CO2/N2 separation, which lacks a theoretical explanation. As a result, we are uncertain about the factors that control the limiting behavior of sorption-driven separations. To address this issue, we employed a simple lattice model and dynamic mean field theory to examine the transport properties of disordered model structures, isolating sorption effects from purely kinetic effects. Our findings indicate that transport effects play a crucial role in sorption-driven processes, and perm-selectivity is consistently lower than sorption selectivity, which is an unattainable limit. We used basic geometric fragments of the structure to explain how transport effects emerge and manifest themselves in sorption-driven processes. [ABSTRACT FROM AUTHOR]

In the portfolio of technologies available for net zero-enabling solutions, such as carbon capture and low-carbon production of hydrogen, membrane-based gas separation is a sustainable alternative to energy-intensive processes, such as solvent-based absorption or cryogenic distillation. Detailed knowledge of membrane materials performance in wide operative ranges is a necessary prerequisite for the design of efficient membrane processes. With the increasing popularization of data-driven methods in natural sciences and engineering, the investigation of their potential to support materials and process design for gas separation with membranes has received increasing attention, as it can help compact the lab-to-market cycle. In this work we review several machine learning (ML) strategies for the estimation of the gas separation performance of polymer membranes. New hybrid modelling strategies, in which ML complements physics-based models and simulation methods, are also discussed. Such strategies can enable the fast screening of large databases of existing materials for a specific separation, as well as assist in de-novo materials design. We conclude by highlighting the challenges and future directions envisioned for the ML-assisted design and optimization of membrane materials and processes for traditional, as well as new, membrane separations. [ABSTRACT FROM AUTHOR]

The design of miniaturized hemodialysis devices, such as wearable artificial kidneys, requires regeneration of the dialysate stream to remove uremic toxins from water. Adsorption has the potential to capture such molecules, but conventional adsorbents have low urea/water selectivity. In this work, we performed a comprehensive computational study of 560 porous crystalline adsorbents comprising mainly covalent organic frameworks (COFs), as well as some siliceous zeolites, metal organic frameworks (MOFs) and graphitic materials. An initial screening using Widom insertion method assessed the excess chemical potential at infinite dilution for water and urea at 310 K, providing information on the strength and selectivity of urea adsorption. From such analysis it was observed that urea adsorption and urea/water selectivity increased strongly with fluorine content in COFs, while other compositional or structural parameters did not correlate with material performance. Two COFs, namely COF-F6 and Tf-DHzDPr were explored further through Molecular Dynamics simulations. The results agree with those of the Widom method and allow to identify the urea binding sites, the contribution of electrostatic and van der Waals interactions, and the position of preferential urea–urea and urea–framework interactions. This study paves the way for a well-informed experimental campaign and accelerates the development of novel sorbents for urea removal, ultimately advancing on the path to achieve wearable artificial kidneys. [ABSTRACT FROM AUTHOR]