Metal–organic frameworks (MOFs) represent the largest known class of porous crystalline materials ever synthesized. Their narrow pore windows and nearly unlimited structural and chemical features have made these materials of significant interest for membrane-based gas separations. In this comprehensive review, we discuss opportunities and challenges related to the formation of pure MOF films and mixed-matrix membranes (MMMs). Common and emerging separation applications are identified, and membrane transport theory for MOFs is described and contextualized relative to the governing principles that describe transport in polymers. Additionally, cross-cutting research opportunities using advanced metrologies and computational techniques are reviewed. To quantify membrane performance, we introduce a simple membrane performance score that has been tabulated for all of the literature data compiled in this review. These data are reported on upper bound plots, revealing classes of MOF materials that consistently demonstrate promising separation performance. Recommendations are provided with the intent of identifying the most promising materials and directions for the field in terms of fundamental science and eventual deployment of MOF materials for commercial membrane-based gas separations.

Mixed-matrix membranes (MMMs) formed by dispersing metal–organic framework (MOF) particles in polymers have attracted significant attention because these composite systems can potentially surpass the separation performance of pure polymers alone. However, performance improvements are often unrealized because of poor interfacial compatibility between the MOF and the polymer, which results in interfacial defects. From a practical perspective, strategies are needed to address these defects so that MMMs can be deployed in real-world separation processes. From a fundamental perspective, strategies are needed to reliably form defect-free MMMs so that transport models can be applied to estimate pure MOF property sets, thereby enabling the development of robust structure–property relationships. To address these interfacial challenges, we have developed a method to surface-functionalize a UiO-66-NH 2 MOF with a nanoscopic shell of covalently tethered 4,4′-(hexafluoroisopropylidene)diphthalic anhydride–Durene oligomers. When combined with a high-molecular-weight polymer of identical chemical structure to that of the imide-functional MOF surface, defect-free MMMs with uniform particle dispersions can be formed. With this technique, both permeabilities and selectivities of select gases in the MMMs were improved at loadings ranging from 5 to 40 wt %. At a 40 wt % loading, CO 2 permeability and CO 2 /CH 4 selectivity were enhanced by 48 and 15%, respectively. Additionally, pure MOF permeabilities for H 2 , N 2 , O 2 , CH 4 , and CO 2 were predicted by the Maxwell model.

In practice, the usefulness of metal–organic frameworks (MOFs) for many gas storage applications depends on their ability to rapidly dissipate the heat generated during the exothermic adsorption process. MOFs can be precisely designed to have a wide variety of architectures which can allow tuning their thermal conductivity. In this work, we use molecular dynamics simulations to investigate the effect of interpenetration on the thermal conductivity of MOFs. We find that the addition of a parallel thermal transport pathway yields a thermal conductivity nearly the sum of the two constituent frameworks. This relationship holds for a variety of interpenetrating MOFs with different atomic masses and a wide range of interframework interaction parameters. We show that both the strength and range of interactions between constituent frameworks play a significant role in framework mobility as well as framework coupling which can result in deviation from this relationship. We propose a simple model to predict thermal conductivity of the interpenetrated framework based on thermal conductivities of individual frameworks and an interframework interaction parameter which can account for this deviation.

No abstract

To reliably deploy lithium-ion batteries, a fundamental understanding of cycling and aging behavior is critical. Battery aging, however, consists of complex and highly coupled phenomena, making it challenging to develop a holistic interpretation. In this work, we generate a diverse battery cycling dataset with a broad range of degradation trajectories, consisting of 363 high energy density commercial Li(Ni,Co,Al)O$_2$/Graphite + SiO$_x$ cylindrical 21700 cells cycled under 218 unique cycling protocols. We consolidate aging via 16 mechanistic state-of-health (SOH) metrics, including cell-level performance metrics, electrode-specific capacities/state-of-charges (SOCs), and aging trajectory descriptors. Through the use of interpretable machine learning and explainable features, we deconvolute the underlying factors that contribute to battery degradation. This generalizable data-driven framework reveals the complex interplay between cycling conditions, degradation modes, and SOH, representing a holistic approach towards understanding battery aging.