Exploring the performance limits of MOF/polymer MMMs for O2/N2 separation using computational screening

Construction of metal-organic framework/cellulose nanofibers-based hybrid membranes and their ion transport property for efficient osmotic energy conversion

The development of advanced nanofluidic membranes with better ion selectivity, efficient energy conversion and high output power density remains challenging. Herein, we prepared nanofluidic hybrid membranes based on TEMPO oxidized cellulose nanofibers (T-CNF) and manganese-based metal organic framework (MOF) using a simple in situ synthesis method. Incorporated T-CNF endows the MOF/T-CNF hybrid membrane with a high cation selectivity up to 0.93. Nanoporous MOF in three-dimensional interconnected nanochannels provides massive ion transport pathways. High transmembrane ion flux and low ion permeation energy barrier are correlated with a superior energy conversion efficiency (36 %) in MOF/T-CNF hybrid membrane. When operating under 50-fold salinity gradient by mixing simulated seawater and river water, the MOF/T-CNF hybrid membrane achieves a maximum power density value of 1.87 W m-2. About 5-fold increase in output power density was achieved compared to pure T-CNF membrane. The integration of natural nanofibers with high charge density and nanoporous MOF materials is demonstrated an effective and novel strategy for the enhancement of output power density of nanofluidic membranes, showing the great potential of MOF/T-CNF hybrid membranes as efficient nanofluidic osmotic energy generators.

Preparation of PDMS and PDMS-UiO-66 oxygen-rich membranes and modules for membrane-aerated biofilm reactors

A membrane-aerated biofilm reactor (MABR) combines membrane technology with biofilm processes and has unique advantages in the treatment of organic wastewater and volatile wastewater. The common membranes for MABR systems usually have relatively uneven pore structures and low bubble point pressure, resulting in unsatisfactory O2 utilization and wastewater treatment efficiency. In this work, polydimethylsiloxane (PDMS) and UiO-66 (a Zr-based metal organic framework) were coated on the surface of a commercial polypropylene (PP) hollow fiber membrane to prepare oxygen-rich MABR membranes and modules, which showed an attractive O2 utilization rate and wastewater treatment efficiency. The bubble points of the PDMS and PDMS-UiO-66 membranes were significantly higher than those of the PP membranes, and the PDMS-UiO-66 membranes had better oxygen enrichment capacity and biological affinity. The optimal PDMS-UiO-66 membrane modules had an O2 permeance of 31.65 GPU (1 GPU = 3.35 × 10-10 mol m-2 s-1 Pa-1), with O2/N2 selectivity of 2.21. The membrane hanging effect and processing capacity for domestic sewage were greatly improved. This study may provide insights and guidelines to fabricate porous mixed matrix membranes and modules in the industry for MABR. The developed products are expected to be applied in the actual separation process.

Machine Learning Models for Predicting Molecular Diffusion in Metal-Organic Frameworks Accounting for the Impact of Framework Flexibility

Molecular diffusion in MOFs plays an important role in determining whether equilibrium can be reached in adsorption-based chemical separations and is a key driving force in membrane-based separations. Molecular dynamics (MD) simulations have shown that in some cases inclusion of framework flexibility in MOF changes predicted molecular diffusivities by orders of magnitude relative to more efficient MD simulations using rigid structures. Despite this, all previous efforts to predict molecular diffusion in MOFs in a high-throughput way have relied on MD data from rigid structures. We use a diverse data set of MD simulations in flexible and rigid MOFs to develop a classification model that reliably predicts whether framework flexibility has a strong impact on molecular diffusion in a given MOF/molecule pair. We then combine this approach with previous high-throughput MD simulations to develop a reliable model that efficiently predicts molecular diffusivities in cases in which framework flexibility can be neglected. The use of this approach is illustrated by making predictions of molecular diffusivities in ∼70,000 MOF/molecule pairs for molecules relevant to gas separations.

Combining Machine Learning and Molecular Simulations to Unlock Gas Separation Potentials of MOF Membranes and MOF/Polymer MMMs

Due to the enormous increase in the number of metal-organic frameworks (MOFs), combining molecular simulations with machine learning (ML) would be a very useful approach for the accurate and rapid assessment of the separation performances of thousands of materials. In this work, we combined these two powerful approaches, molecular simulations and ML, to evaluate MOF membranes and MOF/polymer mixed matrix membranes (MMMs) for six different gas separations: He/H₂, He/N₂, He/CH₄, H₂/N₂, H₂/CH₄, and N₂/CH₄. Single-component gas uptakes and diffusivities were computed by grand canonical Monte Carlo (GCMC) and molecular dynamics (MD) simulations, respectively, and these simulation results were used to assess gas permeabilities and selectivities of MOF membranes. Physical, chemical, and energetic features of MOFs were used as descriptors, and eight different ML models were developed to predict gas adsorption and diffusion properties of MOFs. Gas permeabilities and membrane selectivities of 5249 MOFs and 31,494 MOF/polymer MMMs were predicted using these ML models. To examine the transferability of the ML models, we also focused on computer-generated, hypothetical MOFs (hMOFs) and predicted the gas permeability and selectivity of 1000 hMOF/polymer MMMs. The ML models that we developed accurately predict the uptake and diffusion properties of He, H₂, N₂, and CH₄ gases in MOFs and will significantly accelerate the assessment of separation performances of MOF membranes and MOF/polymer MMMs. These models will also be useful to direct the extensive experimental efforts and computationally demanding molecular simulations to the fabrication and analysis of membrane materials offering high performance for a target gas separation.

High-Throughput Screening of COF Membranes and COF/Polymer MMMs for Helium Separation and Hydrogen Purification

Hundreds of covalent organic frameworks (COFs) have been synthesized, and thousands of them have been computationally designed. However, it is impractical to experimentally test each material as a membrane for gas separations. In this work, we focused on the membrane-based gas separation performances of experimentally synthesized COFs and hypothetical COFs (hypoCOFs). Gas permeabilities of COFs were computed by combining the results of grand canonical Monte Carlo (GCMC) and molecular dynamics (MD) simulations, and many COF membranes were found to overcome the upper bound of polymeric membranes for He/H₂, N₂/CH₄, H₂/N₂, He/CH₄, H₂/CH₄, and He/N₂ separations. We then examined the structure-permeability relations of the COF membranes that are above the upper bound for each of the six gas separations, and based on these relations, we proposed an efficient approach for the selection of the best hypoCOFs from a very large database. Molecular simulations showed that 120 hypoCOFs that we identified to be promising based on these structure-performance relations exceed the upper bound for He/CH₄, He/N₂, H₂/CH₄, and H₂/N₂ separations. Both real and hypothetical COFs were then studied as fillers in 25 different polymers, leading to a total of 29 020 COF/polymer and hypoCOF/polymer mixed matrix membranes (MMMs), representing the largest number of COF-based MMMs investigated to date. Permeabilities and selectivities of COF/polymer MMMs were computed for six different gas separations, and results revealed that 18 of the 25 polymers can be carried above the upper bound when COFs were used as fillers. The comprehensive analysis of COFs provided in this work will fully unlock the potential of COF membranes and COF/polymer MMMs for helium separation and hydrogen purification.

Prediction of O2/N2 Selectivity in Metal-Organic Frameworks via High-Throughput Computational Screening and Machine Learning

Machine learning (ML), which is becoming an increasingly popular tool in various scientific fields, also shows the potential to aid in the screening of materials for diverse applications. In this study, the computation-ready experimental (CoRE) metal-organic framework (MOF) data set for which the O₂ and N₂ uptakes, self-diffusivities, and Henry's constants were calculated was used to fit the ML models. The obtained models were subsequently employed to predict such properties for a hypothetical MOF (hMOF) data set and to identify structures having a high O₂/N₂ selectivity at room temperature. The performance of the model on known entries indicated that it would serve as a useful tool for the prediction of MOF characteristics with r² correlations between the true and predicted values typically falling between 0.7 and 0.8. The use of different descriptor groups (geometric, atom type, and chemical) was studied; the inclusion of all descriptor groups yielded the best overall results. Only a small number of entries surpassed the performance of those in the CoRE MOF set; however, the use of ML was able to present the structure-property relationship and to identity the top performing hMOFs for O₂/N₂ separation based on the adsorption and diffusion selectivity.

Recent advances in simulating gas permeation through MOF membranes

In the last two decades, metal organic frameworks (MOFs) have gained increasing attention in membrane-based gas separations due to their tunable structural properties. Computational methods play a critical role in providing molecular-level information about the membrane properties and identifying the most promising MOF membranes for various gas separations. In this review, we discuss the current state-of-the-art in molecular modeling methods to simulate gas permeation through MOF membranes and review the recent advancements. We finally address current opportunities and challenges of simulating gas permeation through MOF membranes to guide the development of high-performance MOF membranes in the future.

Purification of Propylene and Ethylene by a Robust Metal-Organic Framework Mediated by Host-Guest Interactions

Industrial purification of propylene and ethylene requires cryogenic distillation and selective hydrogenation over palladium catalysts to remove propane, ethane and/or trace amounts of acetylene. Here, we report the excellent separation of equimolar mixtures of propylene/propane and ethylene/ethane, and of a 1/100 mixture of acetylene/ethylene by a highly robust microporous material, MFM-520, under dynamic conditions. In situ synchrotron single crystal X-ray diffraction, inelastic neutron scattering and analysis of adsorption thermodynamic parameters reveal that a series of synergistic host-guest interactions involving hydrogen bonding and π⋅⋅⋅π stacking interactions underpin the cooperative binding of alkenes within the pore. Notably, the optimal pore geometry of the material enables selective accommodation of acetylene. The practical potential of this porous material has been demonstrated by fabricating mixed-matrix membranes comprising MFM-520, Matrimid and PIM-1, and these exhibit not only a high permeability for propylene (≈1984 Barrer), but also a separation factor of 7.8 for an equimolar mixture of propylene/propane at 298 K.

Eu3+ functionalized robust membranes based on the post-synthetic copolymerization of a metal-organic framework and ethyl methacrylate

Metal-organic frameworks (MOFs) are recognized as a class of promising crystalline materials. However, their subsequent processing and shaping still remain a challenge, and one emerging strategy is to hybridize MOFs with flexible polymers. Herein, by utilizing a simple and cost-effective post-synthetic polymerization method, under mild conditions, MOF particles with olefin bonds are covalently linked to polymer chains. Moreover, photoactive europium ions are also introduced into this system during the polymerization process. Importantly, the resulting MOF-based membrane (MOF1-Eu3+@PEMA) is uniform, showing great structural and fluorescence stability against strict conditions (aqueous solutions with pH 0.98-13.11). Besides, given its good luminescence properties, the membrane is employed for the identification of common volatile organic compounds and a selective response to toluene was achieved. This work accelerates the practical applications of MOF-based membranes and enriches the methods for MOF modification.

Eu-MOF and its mixed-matrix membranes as a fluorescent sensor for quantitative ratiometric pH and folic acid detection, and visible fingerprint identifying

The integration of 1 and polymer matrices leads to the fabrication of 1@polymer MMMs, which can be used in the detection of pH and folic acid. Powder samples of 1 also show potential for application in fingerprint identification.

The Evolution in Electrochemical Performance of Honeycomb-Like Ni(OH)2 Derived from MOF Template with Morphology as a High-Performance Electrode Material for Supercapacitors

Ni(OH)2 derived from an MOF template was synthesized as an electrode material for supercapacitors. The electrochemical performance of the electrode was adjusted by effectively regulating the morphology of Ni(OH)2. The evolution of electrochemical performance of the electrode with morphology of Ni(OH)2 was highlighted in detail, based on which honeycomb-like Ni(OH)2 was successfully synthesized, and endowed the electrode with outstanding electrochemical performance. For the three-electrode testing system, honeycomb-like Ni(OH)2 exhibited a very high specific capacitance (1865 F·g-1 at 1 A·g-1, 1550 F·g-1 at 5 mV·s-1). Moreover, it also presented an excellent rate capability and cycling stability, due to 59.46 % of the initial value (1 A·g-1) being retained at 10 A·g-1, and 172% of initial value (first circle at 50 mV·s-1) being retained after 20,000 cycles. With respect to the assembled hybrid supercapacitor, honeycomb-like Ni(OH)2 also displayed superior electrochemical performance, with a high energy density (83.9 Wh·kg-1 at a power density of 374.8 W·kg-1). The outstanding electrochemical performance of Ni(OH)2 should be attributed to its unique honeycomb-like structure, with a very high specific surface area, which greatly accelerates the transformation and diffusion of active ions.