Data-driven design of metal-organic frameworks for wet flue gas CO2 capture

From Fundamentals to Synthesis: Covalent Organic Frameworks as Promising Materials for CO2 Adsorption

Covalent organic frameworks (COFs) are highly crystalline polymers that possess exceptional porosity and surface area, making them a subject of significant research interest. COF materials are synthesized by chemically linking organic molecules in a repetitive arrangement, creating a highly effective porous crystalline structure that adsorbs and retains gases. They are highly effective in removing impurities, such as CO2, because of their desirable characteristics, such as durability, high reactivity, stable porosity, and increased surface area. This study offers a background overview, encompassing a concise discussion of the current issue of excessive carbon emissions, and a synopsis of the materials most frequently used for CO2 collection. This review provides a detailed overview of COF materials, particularly emphasizing their synthesis methods and applications in carbon capture. It presents the latest research findings on COFs synthesized using various covalent bond formation techniques. Moreover, it discusses emerging trends and future prospects in this particular field.

Architecting Metal-Organic Frameworks at Molecular Level toward Direct Air Capture

Escalating carbon dioxide (CO2) emissions have intensified the greenhouse effect, posing a significant long-term threat to environmental sustainability. Direct air capture (DAC) has emerged as a promising approach to achieving a net-zero carbon future, which offers several practical advantages, such as independence from specific CO2 emission sources, economic feasibility, flexible deployment, and minimal risk of CO2 leakage. The design and optimization of DAC sorbents are crucial for accelerating industrial adoption. Metal-organic frameworks (MOFs), with high structural order and tunable pore sizes, present an ideal solution for achieving strong guest-host interactions under trace CO2 conditions. This perspective highlights recent advancements in using MOFs for DAC, examines the molecular-level effects of water vapor on trace CO2 capture, reviews data-driven computational screening methods to develop a molecularly programmable MOF platform for identifying optimal DAC sorbents, and discusses scale-up and cost of MOFs for DAC.

Functional flexible adsorbents and their potential utility

Physisorbents are poised to address global challenges such as CO2 capture, mitigation of water scarcity and energy-efficient commodity gas storage and separation. Rigid physisorbents, i.e. those adsorbents that retain their structures upon gas or vapour exposure, are well studied in this context. Conversely, cooperatively flexible physisorbents undergo long-range structural transformations stimulated by guest exposure. Discovered serendipitously, flexible adsorbents have generally been regarded as scientific curiosities, which has contributed to misconceptions about their potential utility. Recently, increased scientific interest and insight into the properties of flexible adsorbents has afforded materials whose performance suggests that flexible adsorbents can compete with rigid adsorbents for both storage and separation applications. With respect to gas storage, adsorbents that undergo guest-induced phase transformations between low and high porosity phases in the right pressure range can offer improved working capacity and heat management, as exemplified by studies on adsorbed natural gas storage. For gas and vapour separations, the very nature of flexible adsorbents means that they can undergo induced fit mechanisms of guest binding, i.e. the adsorbent can adapt to a specific adsorbate. Such flexible adsorbents have set several new benchmarks for certain hydrocarbon separations in terms of selectivity and separation performance. This Feature Article reviews progress made by us and others towards the crystal engineering (design and control) of flexible adsorbents and addresses several of the myths that have emerged since their initial discovery, particularly with respect to those performance parameters of relevance to natural gas storage, water harvesting and hydrocarbon gas/vapour separation.

Attention-Based Interpretable Multiscale Graph Neural Network for MOFs

Metal-organic frameworks (MOFs) hold great potential in gas separation and storage. Graph neural networks (GNNs) have proven effective in exploring structure-property relationships and discovering new MOF structures. Unlike molecular graphs, crystal graphs must consider the periodicity and patterns. MOFs' specific features at different scales, such as covalent bonds, functional groups, and global structures, influenced by interatomic interactions, exert varying degrees of impact on gas adsorption or selectivity. Moreover, redundant interatomic interactions hinder training accuracy, leading to overfitting. This research introduces a construction method for multiscale crystal graphs, which considers specific features at different scales by decomposing the crystal graph into multiple subgraphs based on interatomic interactions within varying distance ranges. Additionally, it takes into account the global structure of the crystal by encoding the periodic patterns of the unit cells. We propose MSAIGNN, a multiscale atomic interaction graph neural network with self-attention-based graph pooling mechanism, which incorporates three-body bond angle information, accounts for structural features at different scales, and minimizes interference from redundant interactions. Compared with traditional methods, MSAIGNN demonstrates higher prediction accuracy in assessing single-component adsorption, gas separation, and structural features. Visualization of attention scores confirms effective learning of structural features at different scales, highlighting MSAIGNN's interpretability. Overall, MSAIGNN offers a novel, efficient, multilayered, and interpretable approach for property prediction of complex porous crystal structures like MOFs using deep learning.

Unraveling metal effects on CO2 uptake in pyrene-based metal-organic frameworks

Pyrene-based metal-organic frameworks (MOFs) have tremendous potential for various applications. With infinite structural possibilities, the MOF community often relies on simulations to identify the most promising candidates for given applications. Among thousands of reported structures, many exhibit limited reproducibility - in either synthesis, performance, or both - owing to the sensitivity of synthetic conditions. Geometric distortions that may arise in the functional groups of pyrene-based ligands during synthesis and/or activation cannot easily be predicted. This sometimes leads to discrepancies between in silico and experimental results. Here, we investigate a series of pyrene-based MOFs for carbon capture. These structures share the same ligand (1,3,6,8-tetrakis(p-benzoic acid)pyrene (TBAPy)) but have different metals (M-TBAPy, M = Al, Ga, In, and Sc). The ligands stack parallel in their orthorhombic crystal structure, creating a promising binding site for CO2. As predicted, the metal is shown to affect the pyrene stacking distance and, therefore, the CO2 uptake. Here, we investigate the metal's intrinsic effects on the MOFs' crystal structure. Crystallographic analysis shows the emergence of additional phases, which thus impacts the overall adsorption characteristics of the MOFs. Considering these additional phases improves the prediction of adsorption isotherms, enhancing our understanding of pyrene-based MOFs for carbon capture.

A Microporous Hydrogen-Bonded Organic Framework with Open Pyrene Sites Isolated by Hydrogen-Bonded Helical Chains for Efficient Separation of Xenon and Krypton

Achieving efficient xenon/krypton (Xe/Kr) separation in emerging hydrogen-bonded organic frameworks (HOFs) is highly challenging because of the lack of gas-binding sites on their pore surfaces. Herein, we report the first microporous HOF (HOF-FJU-168) based on hydrogen-bonded helical chains, which prevent self-aggregation of the pyrene core, thereby preserving open pyrene sites on the pore surfaces. Its activated form, HOF-FJU-168a is capable of separating Xe/Kr under ambient conditions while achieving an excellent balance between adsorption capacity and selectivity. At 296 K and 1 bar, the Xe adsorption capacity of HOF-FJU-168a reached 78.31 cm3/g, with an Xe/Kr IAST selectivity of 22.0; both values surpass those of currently known top-performing HOFs. Breakthrough experiments confirmed its superior separation performance with a separation factor of 8.6 and a yield of high-purity Kr (>99.5 %) of 184 mL/g. Furthermore HOF-FJU-168 exhibits excellent thermal and chemical stability, as well as renewability. Single-crystal X-ray diffraction and molecular modeling revealed that the unique electrostatic surface potential around the open pyrene sites creates a micro-electric field, exerting a stronger polarizing effect on Xe than on Kr, thereby enhancing host-Xe interactions.

Mixed-Matrix Membrane-Based Piezoelectric CO2 Sensor with Self-Humidity Compensation

Monitoring the CO2 concentration is crucial for assessing respiratory illnesses in humans and safeguarding the environment. The ongoing difficulty lies in achieving highly sensitive detection while also eliminating the interference caused by humidity. There is an unmet need for portable sensors with both high sensitivity and good moisture resistance to monitor CO2 in real time. In this study, a novel sensor capable of capturing the piezoelectric signals induced by CO2 gas is developed. A quartz crystal microbalance (QCM) coated with a mixed- matrix membrane of metal-organic framework (MOF)/polyether block amide (Pebax) is designed as a transducer to detect CO2 at room temperature. The change in the concentration of CO2 can be detected by the frequency shift of the QCM sensor. The sensor shows an ultrahigh sensitivity of 371.8 Hz to 1000 ppm of CO2 because of the abundant polar group and nitrogen Lewis basic groups. Furthermore, the implementation of a self-humidity compensation algorithm significantly enhances the accuracy and reliability of CO2 concentration monitoring by effectively addressing the issue of humidity interference. Our research underscores the immense potential of MOF/Pebax QCM sensors with self-humidity compensation ability in the field of CO2 gas monitoring.

Superhydrophobic and Self-Healing Porous Organic Macrocycle Crystals for Methane Purification under Humid Conditions

Purifying methane from natural gas using adsorbents not only requires the adsorbents to possess excellent separation performance but also to overcome additional daunting challenges such as humidity interference and durability requirements for sustainable use. Herein, porous organic crystals of a new macrocycle (CaC9) with superhydrophobic and self-healing features are prepared and employed for the purification of methane (>99.99% purity) from ternary methane/ethane/propane mixtures under 97% relative humidity. The high selectivity for methane and water-resistance are attributed to the unique chemical structure of CaC9, possessing an intrinsic 4.2 Å pore along with a pore environment modified with saturated alkyl chains. Besides, CaC9 crystals exhibit a self-healing capacity to realize in situ reconstruction of porosity within 15 min. The transformation of CaC9 crystals from a nonporous state to a porous state can be easily achieved upon treatment with n-hexane vapor, thereby presenting a novel solution to enhance the sustainable separation processes of porous materials. This work introduces a novel molecular-level porous adsorbent for natural gas separation, providing a valuable impetus for designing novel adsorbents with unexpected functions.

Rational Design of Metal-Organic Frameworks for Pancreatic Cancer Therapy: from Machine Learning Screening to In Vivo Efficacy

Despite improvements in cancer survival rates, metastatic and surgery-resistant cancers, such as pancreatic cancer, remain challenging, with poor prognoses and limited treatment options. Enhancing drug bioavailability in tumors, while minimizing off-target effects, is crucial. Metal-organic frameworks (MOFs) have emerged as promising drug delivery vehicles owing to their high loading capacity, biocompatibility, and functional tunability. However, the vast chemical diversity of MOFs complicates the rational design of biocompatible materials. This study employed machine learning and molecular simulations to identify MOFs suitable for encapsulating gemcitabine, paclitaxel, and SN-38, and identified PCN-222 as an optimal candidate. Following drug loading, MOF formulations are improved for colloidal stability and biocompatibility. In vitro studies on pancreatic cancer cell lines have shown high biocompatibility, cellular internalization, and delayed drug release. Long-term stability tests demonstrated a consistent performance over 12 months. In vivo studies in pancreatic tumor-bearing mice revealed that paclitaxel-loaded PCN-222, particularly with a hydrogel for local administration, significantly reduced metastatic spread and tumor growth compared to the free drug. These findings underscore the potential of PCN-222 as an effective drug delivery system for the treatment of hard-to-treat cancers.

Machine Learning Descriptors for CO2 Capture Materials

The influence of machine learning (ML) on scientific domains continues to grow, and the number of publications at the intersection of ML, CO2 capture, and material science is growing rapidly. Approaches for building ML models vary in both objectives and the methods through which materials are represented (i.e., featurised). Featurisation based on descriptors, being a crucial step in building ML models, is the focus of this review. Metal organic frameworks, ionic liquids, and other materials are discussed in this paper with a focus on the descriptors used in the representation of CO2-capturing materials. It is shown that operating conditions must be included in ML models in which multiple temperatures and/or pressures are used. Material descriptors can be used to differentiate the CO2 capture candidates through descriptors falling under the broad categories of charge and orbital, thermodynamic, structural, and chemical composition-based descriptors. Depending on the application, dataset, and ML model used, these descriptors carry varying degrees of importance in the predictions made. Design strategies can then be derived based on a selection of important features. Overall, this review predicts that ML will play an even greater role in future innovations in CO2 capture.

MOSAEC-DB: a comprehensive database of experimental metal-organic frameworks with verified chemical accuracy suitable for molecular simulations

Ongoing developments in computational databases seek to improve the accessibility and breadth of high-throughput screening and materials discovery efforts. Their reliance on experimental crystal structures necessitates significant processing prior to computation in order to resolve any crystallographic disorder or partial occupancies and remove any residual solvent molecules in the case of activated porous materials. Contemporary investigations revealed that deficiencies in the experimental characterization and computational preprocessing methods generated considerable occurrence of structural errors in metal-organic framework (MOF) databases. The MOSAEC MOF database (MOSAEC-DB) tackles these structural reliability concerns through utilization of innovative preprocessing and error analysis protocols applying the concepts of oxidation state and formal charge to exclude erroneous crystal structures. Comprising more than 124k crystal structures, this work maintains the largest and most accurate dataset of experimental MOFs ready for immediate deployment in molecular simulations. The databases' comparative diversity is demonstrated through its enhanced coverage of the periodic table, expansive quantity of structures, and balance of chemical properties relative to existing MOF databases. Chemical and geometric descriptors, as well as DFT electrostatic potential-fitted charges, are included to facilitate subsequent atomistic simulation and machine-learning (ML) studies. Curated subsets-sampled according to their chemical properties and structural uniqueness-are also provided to further enable ML studies in recognition of the strict demand for duplicate structure elimination and dataset diversity in such applications.

Leveraging Cross-Diversity Machine Learning to Unveil Metal-Organic Frameworks with Open Copper Sites for Biogas Upgrading

Biogas, primarily composed of methane (CH4) and carbon dioxide (CO2), is considered an alternative renewable energy resource. Efficient CO2/CH4 separation is essential for biogas upgrading to increase energy density, and in this context, metal-organic frameworks (MOFs) have demonstrated significant potential. Here, we integrate multiscale modeling with cross-diversity machine learning (ML) to unveil MOFs with open copper sites (OCS-MOFs) that exhibit exceptional CO2/CH4 separation performance. Our focus on diversity-adaptable ML guarantees that ML models trained in one chemical space are rigorously transferable to unseen MOFs from distinct chemical spaces, assuring their robustness in real-world applications. By leveraging a meticulously curated data set of 27592 OCS-MOFs, we develop ML models with high predictive accuracy, capable of identifying top-performing OCS-MOFs across diverse chemical environments. This work not only elucidates the reticular chemistry that governs optimal CO2/CH4 separation performance in OCS-MOFs but also establishes a new benchmark for scalable and resilient digital MOF discovery, with cross-diversity accuracy as the key determinant of model transferability.

A data driven machine learning approach for predicting and optimizing sulfur compound adsorption on metal organic frameworks

This study employed some machine learning (ML) techniques with Python programming to forecast the adsorption capacity of MOF adsorbents for thiophenic compounds namely benzothiophene (BT), dibenzothiophene (DBT), and 4,6-dimethyl dibenzothiophene (4,6-DMDBT). Five ML models were developed with the help of a dataset containing 676 rows to correlate the adsorbent features, adsorption conditions, and adsorbate characteristics to the MOF sample's sulfur adsorption capability. Among the ML approaches, MLP model achieved the best performance with a low mean squared error (MSE) of 0.0032 on the test set and 0.0021 on the training set and mean relative error (MRE) of 15.26% on the test set. Also, Random Forest model yielded a higher test MSE of 0.0045 and MRE of 17.83%. Feature importance analysis was performed by utilizing MLP model and shapely additive plan (SHAP) method, and the findings revealed that "initial concentration of sulfur" (SHAP value 0.51) and "contact time" (SHAP value 0.37) were the crucial factors influenced desulfurization process efficiency. Additionally, a comparative analysis of the features utilizing the MLP network classified the factors into three primary categories: process conditions, adsorbent characteristics, and adsorbate characteristics. Consequently, the process condition was identified as the most significant group compared to others. Finally, the desulfurization process optimization indicated the maximum DBT adsorption of 161.6 mg/g for Zr-based MOF could be achieved when the features including BET, TPV, pore size, oil/adsorbent ration, and temperature were tuned around 756 m2/g, 0.955 cm3/g, 5.96 nm, 449.85 g/g, 20.1 °C, respectively.

Toward a Generalizable Machine-Learned Potential for Metal-Organic Frameworks

Machine-learned potentials (MLPs) have transformed the field of molecular simulations by scaling "quantum-accurate" potentials to linear time complexity. While they provide more accurate reproduction of physical properties as compared to empirical force fields, it is still computationally costly to generate their training data sets from ab initio calculations. Despite the emergence of foundational or general MLPs for organic molecules and dense materials, it is unexplored if one general MLP can be effectively developed for a wide variety of nanoporous metal-organic frameworks (MOFs) with different chemical moieties and geometric properties. Herein, by leveraging upon data-efficient equivariant MLPs, we demonstrate the possibility of developing a general MLP for nearly 3000 Zn-based MOFs. After curating a training data set comprising augmented MOF structures generated from density functional theory optimization, we validate the reliability of the general MLP in predicting accurate forces and energies when evaluated on a test set with chemically distinct MOF structures. Despite incurring slightly higher errors on structures containing rare chemical moieties, the general MLP can reliably reproduce physical (e.g., vibrational, thermodynamic, and mechanical) properties for a large sample of Zn-based MOFs. Crucially, by developing one MLP for many MOFs, the computational cost of high-throughput screening is potentially reduced by a few orders of magnitude. This enables us to predict quantum-accurate properties for notable Zn-MOFs that were previously uninvestigated via expensive theoretical calculations. To facilitate computational discovery among other families of complex chemical structures, we provide our data set and codes in the public Zenodo repository.

Incorporation of Ligand Charge and Metal Oxidation State Considerations into the Computational Solvent Removal and Activation of Experimental Crystal Structures Preceding Molecular Simulation

Efficient computational screenings are integral to materials discovery in highly sought-after gas adsorption and storage applications, such as CO2 capture. Preprocessing techniques have been developed to render experimental crystal structures suitable for molecular simulations by mimicking experimental activation protocols, particularly residual solvent removal. Current accounts examining these preprocessed materials databases indicate the presence of assorted structural errors introduced by solvent removal and preprocessing, including improper elimination of charge-balancing ions and ligands. Here, we address the need for a reliable experimental crystal structure preprocessing protocol by introducing a novel solvent removal method, which we call SAMOSA, that is informed by systematic ligand charge and metal oxidation state calculations. A robust set of solvent removal criteria is outlined, which identifies solvent molecules and counterions without predefined molecule lists or significant reliance on experimental chemical information. Validation results against popular metal-organic framework (MOF) databases suggest that this method observes significant performance improvements regarding the retention of charged ligands and recognition of charged frameworks. SAMOSA enhances structure fidelity with respect to the original material as-synthesized, thereby representing a powerful tool in computational materials database curation and preprocessing for molecular simulation. The source code is accessible at https://github.com/uowoolab/SAMOSA.

Synthetic Aspects and Characterization Needs in MOF Chemistry - from Discovery to Applications

Even if MOFs are recently developed for large-scale applications, the road to applications of MOFs is long and rocky. This requires to overcome challenges associated with phase discovery, synthesis optimization, basic and advanced characterization, and computational studies. Lab-scale results need to be transferred to large-scale processes, which is often not trivial, and life-cycle analyses and techno-economic analyses need to be performed to realistically assess their potential for industrial relevance. Based on the experience in the field of stable, functional MOFs combining advanced synthesis, characterization, and modeling, this mini-review gives recommendations especially for non-specialists, for example, from chemical engineers to medical doctors, to accelerate and facilitate knowledge transfer which will ultimately lead to the application of MOFs. The recommendations will include the reporting of synthesis and characterization data as well as standardization and detailed information required for the application of data mining and machine learning techniques, which are increasingly used to accelerate the discovery of new materials and data analysis. Once a suitable MOF is identified and its key properties determined, translational studies shall finally be carried out in collaboration with end-users to validate performance under real conditions and allow understanding of the processes involved.

Development of the design and synthesis of metal-organic frameworks (MOFs) - from large scale attempts, functional oriented modifications, to artificial intelligence (AI) predictions

Owing to the exceptional porous properties of metal-organic frameworks (MOFs), there has recently been a surge of interest, evidenced by a plethora of research into their design, synthesis, properties, and applications. This expanding research landscape has driven significant advancements in the precise regulation of MOF design and synthesis. Initially dominated by large-scale synthesis approaches, this field has evolved towards more targeted functional modifications. Recently, the integration of computational science, particularly through artificial intelligence predictions, has ushered in a new era of innovation, enabling more precise and efficient MOF design and synthesis methodologies. The objective of this review is to provide readers with an extensive overview of the development process of MOF design and synthesis, and to present visions for future developments.

Multi-modal conditional diffusion model using signed distance functions for metal-organic frameworks generation

The design of porous materials with user-desired properties has been a great interest for the last few decades. However, the flexibility of target properties has been highly limited, and targeting multiple properties of diverse modalities simultaneously has been scarcely explored. Furthermore, although deep generative models have opened a new paradigm in materials generation, their incorporation into porous materials such as metal-organic frameworks (MOFs) has not been satisfactory due to their structural complexity. In this work, we introduce MOFFUSION, a latent diffusion model that addresses the aforementioned challenges. Signed distance functions (SDFs) are employed for the input representation of MOFs, marking their first usage in representing porous materials for generative models. Using the suitability of SDFs in describing complicated pore structures, MOFFUSION exhibits exceptional generation performance, and demonstrates its versatile capability of conditional generation with handling diverse modalities of data, including numeric, categorical, text data, and their combinations.

Mechanochemical "Cage-on-MOF" Strategy for Enhancing Gas Adsorption and Separation through Aperture Matching

Post-modification of porous materials with molecular modulators has emerged as a well-established strategy for improving gas adsorption and separation. However, a notable challenge lies in maintaining porosity and the limited applicability of the current method. In this study, we employed the mechanochemical "Cage-on-MOF" strategy, utilizing porous coordination cages (PCCs) with intrinsic pores and apertures as surface modulators to improve the gas adsorption and separation properties of the parent MOFs. We demonstrated the fast and facile preparation of 28 distinct MOF@PCC composites by combining 7 MOFs with 4 PCCs with varying aperture sizes and exposed functional groups through a mechanochemical reaction in 5 mins. Only the combinations of PCCs and MOFs with closely matched aperture sizes exhibited enhanced gas adsorption and separation performance. Specifically, MOF-808@PCC-4 exhibited a significantly increased C2H2 uptake (+64 %) and a longer CO2/C2H2 separation retention time (+40 %). MIL-101@PCC-4 achieved a substantial C2H2 adsorption capacity of 6.11 mmol/g. This work not only highlights the broad applicability of the mechanochemical "Cage-on-MOF" strategy for the functionalization of a wide range of MOFs but also establishes potential design principles for the development of hybrid porous materials with enhanced gas adsorption and separation capabilities, along with promising applications in catalysis and intracellular delivery.

Process-based screening of porous materials for vacuum swing adsorption based on 1D classical density functional theory and PC-SAFT

Adsorption-based processes are showing substantial potential for carbon capture. Due to the vast space of potential solid adsorbents and their influence on the process performance, the choice of the material is not trivial but requires systematic approaches. In particular, the material choice should be based on the performance of the resulting process. In this work, we present a method for the process-based screening of porous materials for pressure and vacuum swing adsorption. The method is based on an equilibrium process model that incorporates one-dimensional classical density functional theory (1D-DFT) and the PC-SAFT equation of state. Thereby, the presented method can efficiently screen databases of potential adsorbents and identify the best-performing materials as well as the corresponding optimized process conditions for a specific carbon capture application. We apply our method to a point-source carbon capture application at a cement plant. The results show that the process model is crucial to evaluating the performance of adsorbents instead of relying solely on material heuristics. Furthermore, we enhance our approach through multi-objective optimization and demonstrate for materials with high performance that our method is able to capture the trade-offs between two process objectives, such as specific work and purity. The presented method thus provides an efficient screening tool for adsorbents to maximize process performance.

Computational Modeling of Reticular Materials: The Past, the Present, and the Future

Reticular materials rely on a unique building concept where inorganic and organic building units are stitched together giving access to an almost limitless number of structured ordered porous materials. Given the versatility of chemical elements, underlying nets, and topologies, reticular materials provide a unique platform to design materials for timely technological applications. Reticular materials have now found their way in important societal applications, like carbon capture to address climate change, water harvesting to extract atmospheric moisture in arid environments, and clean energy applications. Combining predictions from computational materials chemistry with advanced experimental characterization and synthesis procedures unlocks a design strategy to synthesize new materials with the desired properties and functions. Within this review, the current status of modeling reticular materials is addressed and supplemented with topical examples highlighting the necessity of advanced molecular modeling to design materials for technological applications. This review is structured as a templated molecular modeling study starting from the molecular structure of a realistic material towards the prediction of properties and functions of the materials. At the end, the authors provide their perspective on the past, present of future in modeling reticular materials and formulate open challenges to inspire future model and method developments.

Hydrostable Fluorinated Metal-Organic Frameworks for CO2 Capture from a Wet Flue Gas: Multiscale Computational Screening

Metal-organic frameworks (MOFs) are promising adsorbents for CO2 capture due to readily tunable porosity and diverse functionality; however, their performance is deteriorated by the presence of H2O in a flue gas. Fluorinated MOFs (FMOFs) may impede H2O interaction with frameworks and enhance CO2 adsorption under humid conditions. In this study, a multiscale computational screening study is reported to identify the top FMOFs for CO2 capture from a wet flue gas. Initially, geometric properties as well as heats of H2O adsorption are used to shortlist FMOFs with a suitable pore size and weak H2O affinity. Then, grand-canonical Monte Carlo simulations are conducted for adsorption of a CO2/N2/H2O mixture with 60% relative humidity in 5061 FMOFs. Based on the adsorption performance, 19 FMOFs are identified as top candidates. It is revealed that the position of F atom, rather than the amount, affects CO2 adsorption; moreover, N-decorated FMOFs are preferential for selective CO2 adsorption. Finally, the hydrostability of the top FMOFs is confirmed by first-principles molecular dynamics simulations. From a microscopic level, this study provides quantitative structure-performance relationships, discovers hydrostable FMOFs with high CO2 capture performance from a wet flue gas, and would facilitate the development of new MOFs toward efficient CO2 capture under humid conditions.

Recovery of Berry Natural Products Using Pyrene-Based MOF Solid Phase Extraction

This work introduces a novel method of recovering bioactive berry natural products (BNPs) using solid phase extraction with metal-organic frameworks (MOF-SPE). Two pyrene-based MOFs with different structural topologies, Al-PyrMOF and Zr-NU-1000, were evaluated for their ability to capture and desorb BNPs, including ellagic acid, quercetin, gallic acid, and p-coumaric acid. Time-dependent BNP uptake via dispersive SPE revealed that NU-1000 outperformed Al-PyrMOF in capturing all BNPs. Our findings show NU-1000 demonstrated a higher and more consistent BNP capture profile, achieving over 90 % capture of all BNPs within 36 h, with only a 9 % variation between the most and least effectively captured BNPs. In contrast, Al-PyrMOF, displayed a staggered uptake profile, with a significant 53 % difference in capture efficiency between the most and least effectively captured BNP. However, when a BNP mixture was used at a loading concentration of 50 μg/mL, Al-PyrMOF outperformed NU-1000, capturing over 70 % of all BNPs. Al-PyrMOF also exhibited improved BNP recovery, with a minimum of two-fold greater amount recovered for all BNPs. Further testing with a BNP mixture at a concentration of 15 μg/mL demonstrated that Al-PyrMOF efficiently concentrated all BNPs, achieving a maximum extraction factor of 2.71 observed for quercetin. These findings highlight the use of Al-PyrMOF as a MOF-SPE sorbent for recovering bioactive BNPs.

A Refined Set of Universal Force Field Parameters for Some Metal Nodes in Metal-Organic Frameworks

Metal-organic frameworks (MOFs) exhibit promise as porous materials for carbon capture due to their design versatility and large pore sizes. The generic force fields (e.g., UFF and Dreiding) use one universal set of Lennard-Jones parameters for each element, while MOFs have a much richer local chemical environment than those chemical environments used to fit the UFF. When MOFs contain hard-Lewis acid metals, UFF systematically overestimates CO2 uptakes. To address this, we developed a workflow to affordably and efficiently generate reliable force fields to predict CO2 adsorption isotherms of MOFs containing metals from groups IIA (Mg, Ca, Sr, and Ba) and IIIA (Al, Ga, and In), connected to various carboxylate ligands. This method uses experimental isotherms as input. The optimal parameters are obtained by minimizing the loss function of the experimental and simulated isotherms, in which we use the Multistate Bennett Acceptance Ratio (MBAR) theory to derive the functionality relationship of loss functions in terms of force field parameters.

A Hydrolytically Stable Metal-Organic Framework for Simultaneous Desulfurization and Dehydration of Wet Flue Gas

Metal-organic frameworks (MOFs) have great prospects as adsorbents for industrial gas purification, but often suffer from issues of water stability and competitive water adsorption. Herein, we present a hydrolytically stable MOF that could selectively capture and recover trace SO2 from flue gas, and exhibits remarkable recyclability in the breakthrough experiments under wet flue-gas conditions, due to its excellent resistance to the corrosion of SO2 and the water-derived capillary forces. More strikingly, its SO2 capture efficiency is barely influenced by the increasing humidity, even if the pore filling with water is reached. Mechanistic studies demonstrate that the delicate pore structure with diverse pore dimensions and chemistry leads to different adsorption kinetics and thermodynamics as well as segregated adsorption domains of SO2 and H2O. Significantly, this non-competitive adsorption mechanism enables simultaneous desulfurization and dehydration by a single adsorbent, opening an avenue toward cost-effective and simplified processing flowcharts for flue gas purification.

Type I Porous Liquids with Super-Low Viscosities: the Construction through a Rather Simple One-Step Covalent Linkage Strategy and Its Facile Regulation by a Mixed-Ligand Strategy

Porous liquids (PLs) are a novel class of flowing liquid systems that possess accessible permanent porosity, exhibiting great prospects in gas capture and separation. Nevertheless, the further development of PLs lies in the facile synthesis and regulation of PLs with low viscosities. Herein, a novel strategy of preparing type I PLs with super-low viscosity is proposed through a simple one-step covalent linkage reaction using UiO-66-NH2 as the pore generator and monoglycidyl ether terminated polydimethylsiloxane (E-PDMS) as the sterically hindered solvent, respectively. More importantly, the idea that PLs can be regulated like advanced porous materials (APMs) is proposed and demonstrated, that is, PLs can be regulated by modulating pore generators with the mixed-ligands method. As expected, the resultant PLs not only demonstrate a promising potential in CO2 sorption as a flowing sorbent but also exhibit a superior CO2/N2 selective separation. Meanwhile, positron annihilation lifetime spectrum (PALS) and molecular dynamics (MD) simulations further verify the accessible permanent porosity and the favorable CO2 selective sorption. This work provides a simple and facile method to prepare type I PLs with super-low viscosities, shedding light on the precise regulation of PLs toward task-specific applications.

Data-Driven Screening and Discovery of Metal-Organic Frameworks as C2 Adsorbents from over 900 Experimental Isotherms

The separation of ethylene from ethane accounts for almost 100 million tons of CO2 emissions annually and 0.3% of global primary energy usage. Replacing current cryogenic distillation units with adsorption separation units, especially for the minor component of ethane, would enable significant efficiency gains. Metal-organic frameworks (MOFs) are well-suited for adsorption separation due to their high surface areas and tunable chemical properties. Exploring all possible MOFs is a daunting experimental challenge, motivating in silico screening with machine learning models. We present a database of 948 experimentally measured pure-component C2 isotherms from 192 MOFs gathered from the literature and use it to train machine learning models to predict MOF ethane and ethylene uptake across a range of temperature and pressure conditions. The models have high accuracy in interpolative tasks (mean absolute error ∼0.05 mmol/g) when trained on only 20% of available data. Performance on unseen structures was also reasonably accurate with a mean absolute error (MAE) ∼0.7 mmol/g. We apply the models to screen the CoRE MOF2019 ASR database and identify the most promising candidates. Several MOFs containing lanthanide metals were predicted to have high ethane selectivity, suggesting that this class of MOFs may merit further investigation. Feature importance analysis suggests that both optimizing MOF secondary building unit chemistry and the process conditions at which the sorbent will operate are critical for enabling ethane-selective separation. We synthesize a MOF predicted to exhibit high ethane selectivity and experimentally validate qualitative agreement with model predictions, highlighting the utility of both the data set and model in discovering unexplored C2 adsorbents.

Enhanced Carbon Dioxide Capture from Diluted Streams with Functionalized Metal-Organic Frameworks

Capturing carbon dioxide from diluted streams, such as flue gas originating from natural gas combustion, can be achieved using recyclable, humidity-resistant porous materials. Three such materials were synthesized by chemically modifying the pores of metal-organic frameworks (MOFs) with Lewis basic functional groups. These materials included aluminum 1,2,4,5-tetrakis(4-carboxylatophenyl) benzene (Al-TCPB) and two novel MOFs: Al-TCPB(OH), and Al-TCPB(NH2), both isostructural to Al-TCPB, and chemically and thermally stable. Single-component adsorption isotherms revealed significantly increased CO2 uptakes upon pore functionalization. Breakthrough experiments using a 4/96 CO2/N2 gas mixture humidified up to 75% RH at 25 °C showed that Al-TCPB(OH) displayed the highest CO2 dynamic breakthrough capacity (0.52 mmol/g) followed by that of Al-TCPB(NH2) (0.47 mmol/g) and Al-TCPB (0.26 mmol/g). All three materials demonstrated excellent recyclability over eight humid breakthrough-regeneration cycles. Solid-state nuclear magnetic resonance spectra revealed that upon CO2/H2O loading, H2O molecules do not interfere with CO2 physisorption and are localized near the Al-O(H) chain and the -NH2 functional group, whereas CO2 molecules are spatially confined in Al-TCPB(OH) and relatively mobile in Al-TCPB(NH2). Density functional theory calculations confirmed the impact of the adsorbaphore site between of two parallel ligand-forming benzene rings for CO2 capture. Our study elucidates how pore functionalization influences the fundamental adsorption properties of MOFs, underscoring their practical potential as porous sorbent materials.

The Key to MOF Membrane Fabrication and Application: the Trade-off between Crystallization and Film Formation

Metal-organic frameworks (MOFs), owing the merits of ordered and tailored channel structures in the burgeoning crystalline porous materials, have demonstrated significant promise in construction of high-performance separation membranes. However, precisely because this crystal structure with strong molecular interaction in their lattice provides robust structural integrity and resistance to chemical and thermal degradation, crystalline MOFs typically exhibit insolubility, infusibility, stiffness and brittleness, and therefore their membrane-processing properties are far inferior to the flexible amorphous polymers and hinder their subsequent storage, transportation, and utilization. Hence, focusing on film-formation and crystallization is the foundation for exploring the fabrication and application of MOF membranes. In this review, the film-forming properties of crystalline MOFs are fundamentally analyzed from their inherent characteristics and compared with those of amorphous polymers, influencing factors of polycrystalline MOF membrane formation are summarized, the trade-off relationship between crystallization and membrane formation is discussed, and the strategy solving the film formation of crystalline MOFs in recent years are systematically reviewed, in anticipation of realizing the goal of preparing crystalline membranes with optimized processability and excellent performance.

Understanding and Tuning the Effects of H2O on Catalytic CO and CO2 Hydrogenation

Catalytic COx (CO and CO2) hydrogenation to valued chemicals is one of the promising approaches to address challenges in energy, environment, and climate change. H2O is an inevitable side product in these reactions, where its existence and effect are often ignored. In fact, H2O significantly influences the catalytic active centers, reaction mechanism, and catalytic performance, preventing us from a definitive and deep understanding on the structure-performance relationship of the authentic catalysts. It is necessary, although challenging, to clarify its effect and provide practical strategies to tune the concentration and distribution of H2O to optimize its influence. In this review, we focus on how H2O in COx hydrogenation induces the structural evolution of catalysts and assists in the catalytic processes, as well as efforts to understand the underlying mechanism. We summarize and discuss some representative tuning strategies for realizing the rapid removal or local enrichment of H2O around the catalysts, along with brief techno-economic analysis and life cycle assessment. These fundamental understandings and strategies are further extended to the reactions of CO and CO2 reduction under an external field (light, electricity, and plasma). We also present suggestions and prospects for deciphering and controlling the effect of H2O in practical applications.

Gas adsorption meets geometric deep learning: points, set and match

Thanks to their unique properties such as ultra high porosity and surface area, metal-organic frameworks (MOFs) are highly regarded materials for gas adsorption applications. However, their combinatorial nature results in a vast chemical space, precluding its exploration with traditional techniques. Recently, machine learning (ML) pipelines have been established as the go-to method for large scale screening by means of predictive models. These are typically built in a descriptor-based manner, meaning that the structure must be first coarse-grained into a 1D fingerprint before it is fed to the ML algorithm. As such, the latter can not fully exploit the 3D structural information, potentially resulting in a model of lower quality. In this work, we propose a descriptor-free framework called "AIdsorb", which can directly process raw structural information for predicting gas adsorption properties. To accomplish that, the structure is first treated as a point cloud and then passed to a deep learning algorithm suitable for point cloud analysis. As a proof of concept, AIdsorb is applied for predicting CO 2 uptake in MOFs, outperforming a conventional pipeline that uses geometric descriptors as input. Additionally, to evaluate the transferability of the proposed framework to different host-guest systems, CH 4 uptake in COFs is examined. Since AIdsorb bases its roots on raw structural information, its applicability extends to all fields of material science.

Merged-nets enumeration for the systematic design of multicomponent reticular structures

Rational design of intricate multicomponent reticular structures is often hindered by the lack of suitable blueprint nets. We established the merged-net approach, proffering optimal balance between designability and complexity, as a systematic solution for the rational assembly of multicomponent structures. In this work, by methodically mapping node-net relationships among 53 basic edge-transitive nets, we conceived a signature net map to identify merging net pairs, resulting in the enumeration of 53 merged nets. We developed a practical design algorithm and proposed more than 100 multicomponent metal-organic framework platforms. The effectiveness of this approach is commended by the successful synthesis of four classes of materials, which is based on merging three-periodic nets with the four possible net periodicities. The construction of multicomponent materials based on derived nets of merged nets highlights the potential of the merged-net approach in accelerating the discovery of intricate reticular materials.

Recent Advances of Carbon Capture in Metal-Organic Frameworks: A Comprehensive Review

The excessive emission of greenhouse gases, which leads to global warming and alarms the world, has triggered a global campaign for carbon neutrality. Carbon capture and sequestration (CCS) technology has aroused wide research interest as a versatile emission mitigation technology. Metal-organic frameworks (MOFs), as a new class of high-performance adsorbents, hold great potential for CO2 capture from large point sources and ambient air due to their ultra-high specific surface area as well as pore structure. In recent years, MOFs have made great progress in the field of CO2 capture and separation, and have published a number of important results, which have greatly promoted the development of MOF materials for practical carbon capture applications. This review summarizes the most recent advanced research on MOF materials for carbon capture in various application scenarios over the past six years. The strategies for enhancing CO2 selective adsorption and separation of MOFs are described in detail, along with the development of MOF-based composites. Moreover, this review also systematically summarizes the highly concerned issues of MOF materials in practical applications of carbon capture. Finally, future research on CO2 capture by MOF materials is prospected.

Trace benzene capture by decoration of structural defects in metal-organic framework materials

Capture of trace benzene is an important and challenging task. Metal-organic framework materials are promising sorbents for a variety of gases, but their limited capacity towards benzene at low concentration remains unresolved. Here we report the adsorption of trace benzene by decorating a structural defect in MIL-125-defect with single-atom metal centres to afford MIL-125-X (X = Mn, Fe, Co, Ni, Cu, Zn; MIL-125, Ti8O8(OH)4(BDC)6 where H2BDC is 1,4-benzenedicarboxylic acid). At 298 K, MIL-125-Zn exhibits a benzene uptake of 7.63 mmol g-1 at 1.2 mbar and 5.33 mmol g-1 at 0.12 mbar, and breakthrough experiments confirm the removal of trace benzene (from 5 to <0.5 ppm) from air (up to 111,000 min g-1 of metal-organic framework), even after exposure to moisture. The binding of benzene to the defect and open Zn(II) sites at low pressure has been visualized by diffraction, scattering and spectroscopy. This work highlights the importance of fine-tuning pore chemistry for designing adsorbents for the removal of air pollutants.

Accelerating Discovery of Mechanically Stable Metal-Organic Frameworks for Vinylidene Fluoride Storage by Active Learning

Metal-organic frameworks (MOFs) are versatile nanoporous materials for a wide variety of important applications. Recently, a handful of MOFs have been explored for the storage of toxic fluorinated gases (Keasler et al. Science, 2023, 381, 1455), yet the potential of a great number of MOFs for such an environmentally sustainable application has not been thoroughly investigated. In this work, we apply active learning (AL) to accelerate the discovery of hypothetical MOFs (hMOFs) that can efficiently store a specific fluorinated gas, namely, vinylidene fluoride (VDF). First, a force field was developed for VDF and utilized to predict the working capacities (ΔN) of VDF in an initial data set of 4502 MOFs from the computation-ready experimental MOF (CoRE-MOF) database that successfully underwent featurization and grand-canonical Monte Carlo simulations. Next, the initial data set was diversified by Greedy sampling in an unexplored sample space of 119,387 hMOFs from the ab initio REPEAT charge MOF (ARC-MOF) database. A budget of 10,000 samples (i.e., <10% of total ARC-MOFs) was selected to train a random forest model. Then, ΔN in the unlabeled ARC-MOFs were predicted and top-performing ones were validated by simulations. Integrating with the stability requirement, mechanically stable ARC-MOFs were finally identified, along with high ΔN. Furthermore, by Pareto-Frontier analysis, we revealed that long linear linkers can enhance ΔN, while bulkier multiphenyl linkers or interpenetrated frameworks improve mechanical strength. From this work, we efficiently discover top-performing MOFs for VDF storage by AL and also demonstrate the importance of integrating stability to identify stable promising MOFs for a practical application.

Understanding CO adsorption in MOFs combining atomic simulations and machine learning

This study introduces a computational method integrating molecular simulations and machine learning (ML) to assess the CO adsorption capacities of synthesized and hypothetical metal-organic frameworks (MOFs) at various pressures. After extracting structural, chemical, and energy-based features of the synthesized and hypothetical MOFs (hMOFs), we conducted molecular simulations to compute CO adsorption in synthesized MOFs and used these simulation results to train ML models for predicting CO adsorption in hMOFs. Results showed that CO uptakes of synthesized MOFs and hMOFs are between 0.02-2.28 mol/kg and 0.45-3.06 mol/kg, respectively, at 1 bar, 298 K. At low pressures (0.1 and 1 bar), Henry's constant of CO is the most dominant feature, whereas structural properties such as surface area and porosity are more influential for determining the CO uptakes of MOFs at high pressure (10 bar). Structural and chemical analyses revealed that MOFs with narrow pores (4.4-7.3 Å), aromatic ring-containing linkers and carboxylic acid groups, along with metal nodes such as Co, Zn, Ni achieve high CO uptakes at 1 bar. Our approach evaluated the CO uptakes of ~ 100,000 MOFs, the most extensive and diverse set studied for CO capture thus far, as a robust alternative to computationally demanding molecular simulations and iterative experiments.

Diversity-driven, efficient exploration of a MOF design space to optimize MOF properties

Metal-organic frameworks (MOFs) promise to engender technology-enabling properties for numerous applications. However, one significant challenge in MOF development is their overwhelmingly large design space, which is intractable to fully explore even computationally. To find diverse optimal MOF designs without exploring the full design space, we develop Vendi Bayesian optimization (VBO), a new algorithm that combines traditional Bayesian optimization with the Vendi score, a recently introduced interpretable diversity measure. Both Bayesian optimization and the Vendi score require a kernel similarity function, we therefore also introduce a novel similarity function in the space of MOFs that accounts for both chemical and structural features. This new similarity metric enables VBO to find optimal MOFs with properties that may depend on both chemistry and structure. We statistically assessed VBO by its ability to optimize three NH3-adsorption dependent performance metrics that depend, to different degrees, on MOF chemistry and structure. With ten simulated campaigns done for each metric, VBO consistently outperformed random search to find high-performing designs within a 1000-MOF subset for (i) NH3 storage, (ii) NH3 removal from membrane plasma reactors, and (iii) NH3 capture from air. Then, with one campaign dedicated to finding optimal MOFs for NH3 storage in a "hybrid" ∼10 000-MOF database, we identify twelve extant and eight hypothesized MOF designs with potentially record-breaking working capacity ΔN NH3 between 300 K and 400 K at 1 bar. Specifically, the best MOF designs are predicted to (i) achieve ΔN NH3 values between 23.6 and 29.3 mmol g-1, potentially surpassing those that MOFs previously experimentally tested for NH3 adsorption would have at the proposed operation conditions, (ii) be thermally stable at the operation conditions and (iii) require only ca. 10% of the energy content in NH3 to release the stored molecule from the MOF. Finally, the analysis of the generated simulation data during the search indicates that a pore size of around 10 Å, a heat of adsorption around 33 kJ mol-1, and the presence of Ca could be part of MOF design rules that could help optimize NH3 working capacity at the proposed operation conditions.

Anomalous enhancement of humid CO2 capture by local surface bound water in polar carbon nanopores

Removal of confined space carbon dioxide (CO2) that is in low concentration and with coexisting water is necessary but challenging by physical adsorption method. To make the removal process effective, rendering the nanopore surface hydrophobic to resist water is the popular way. Instead of preventing water from occupying the nanopores, in this work, we propose to utilize the guest water for the spatially selective formation of local surface bound water and further induce the preferential CO2 capture. We observe an anomalous enhancement of CO2 capture performance under humid conditions over carbon nanopores with spatially selective polar sites. It is evidenced that the surface bound water is formed at non-CO2-selective areas of polar carbon nanopores, thus creating additional CO2 trapping sites. This work may inspire the design of environment tolerable materials for molecular separation and purification under harsh conditions.

Unveiling Temperature-Induced Structural Phase Transformations and CO2 Binding Sites in CALF-20

The increase in atmospheric carbon dioxide concentration linked to climate change has created a need for new sorbents capable of separating CO2 from exhaust gases. Recently, an easily produced metal-organic framework, CALF-20, was shown to withstand over 450,000 adsorption/desorption cycles in steam and wet acid gases. Further development and industrial application of such materials require an understanding of the observed processes. Herein, we demonstrate that conditioning as-synthesized CALF-20 single crystal transforms it into a different phase, γ-CALF-20. The transformation resulted in significant structural changes, including the binding of water molecules to Zn(II), accompanied by a reduction of 9% in the unit cell volume. Our experimental findings were supported by the energy-volume dependence of CALF-20 in the presence and absence of water molecules calculated from density functional theory. We have also monitored the sorption process of the dominant greenhouse gas, CO2, on the initial phase of CALF-20 at atomic resolution using in situ single-crystal X-ray diffraction under specific pressure. The new understanding of CALF-20 chemistry from these studies should facilitate development of novel sorbents for gas adsorption technologies.

MOFs with the Stability for Practical Gas Adsorption Applications Require New Design Rules

Metal-organic frameworks (MOFs) have been widely studied for their ability to capture and store greenhouse gases. However, most computational discovery efforts study hypothetical MOFs without consideration of their stability, limiting the practical application of novel materials. We overcome this limitation by screening hypothetical ultrastable MOFs that have predicted high thermal and activation stability, as judged by machine learning (ML) models trained on experimental measures of stability. We enhance this set by computing the bulk modulus as a measure of mechanical stability and filter 1102 mechanically robust hypothetical MOFs from a database of ultrastable MOFs (USMOF DB). Grand Canonical Monte Carlo simulations are then employed to predict the gas adsorption properties of these hypothetical MOFs, alongside a database of experimental MOFs. We identify privileged building blocks that lead MOFs in USMOF DB to show exceptional working capacities compared to the experimental MOFs. We interpret these differences by training ML models on CO2 and CH4 adsorption in these databases, showing how poor model transferability between data sets indicates that novel design rules can be derived from USMOF DB that would not have been gathered through assessment of structurally characterized MOFs. We identify geometric features and node chemistry that will enable the rational design of MOFs with enhanced gas adsorption properties in synthetically realizable MOFs.

Machine Learning Predictions of Methane Storage in MOFs: Diverse Materials, Multiple Operating Conditions, and Reverse Models

A machine learning (ML) model is developed for predicting useable methane (CH4) capacities in metal-organic frameworks (MOFs). The model applies to a wide variety of MOFs, including those with and without open metal sites, and predicts capacities for multiple pressure swing conditions. Despite its wider applicability, the model requires only 5 measurable structural features as input, yet achieves accuracies that surpass less-general models. Application of the model to a database of more than a million hypothetical MOFs identified several hundred whose capacities surpass that of the benchmark MOF, UMCM-152. Guided by the computational predictions, one of the promising candidates, UMCM-153, was synthesized and demonstrated to achieve superior volumetric capacity for CH4. Feature importance analyses reveal that pore volume and gravimetric surface area are the most important features for predicting CH4 capacity in MOFs. Finally, a reverse ML model is demonstrated. This model predicts the set of elementary MOF structural properties needed to achieve a desired CH4 capacity for a prescribed operating condition.

Uncovering the Relationship between Metal Elements and Mechanical Stability for Metal-Organic Frameworks

Assessing the mechanical robustness of metal-organic frameworks (MOFs) is crucial to enhance their applicability in various fields. Although considerable research has been conducted on the relationship between the mechanical properties of MOFs and their structural features (such as pore size, surface area, and topology), the insufficient exploration of metal elements has prevented researchers from fully understanding their mechanical behavior. To plug this knowledge gap, we constructed a database of mechanical properties for 20,342 MOFs included in the QMOF database using molecular simulations to investigate the impact of metal elements on mechanical stability. Through Shapley additive explanations (SHAP) analysis, we found that Co and Ln could enhance the structural stability of MOFs. We validated these findings using newly generated hypothetical MOFs. Notably, we adopted an interpretable machine learning technique to analyze the contribution of remarkably diverse metal elements in the 20,342 MOFs to the mechanical properties of each MOF. We anticipate that this research will serve as a valuable tool for future studies on identifying mechanically robust MOFs suitable for various industrial applications.

Accelerating Discovery of Water Stable Metal-Organic Frameworks by Machine Learning

Metal-organic frameworks (MOFs) provide an extensive design landscape for nanoporous materials that drive innovation across energy and environmental fields. However, their practical applications are often hindered by water stability challenges. In this study, a machine learning (ML) approach is proposed to accelerate the discovery of water stable MOFs and validated through experimental test. First, the largest database currently available that contains water stability information of 1133 synthesized MOFs is constructed and categorized according to experimental stability. Then, structural and chemical descriptors are applied at various fragmental levels to develop ML classifiers for predicting the water stability of MOFs. The ML classifiers achieve high prediction accuracy and excellent transferability on out-of-sample validation. Next, two MOFs are experimentally synthesized with their water stability tested to validate ML predictions. Finally, the ML classifiers are applied to discover water stable MOFs in the ab initio REPEAT charge MOF (ARC-MOF) database. Among ≈280 000 candidates, ≈130 000 (47%) MOFs are predicted to be water stable; furthermore, through multi-stability analysis, 461 (0.16%) MOFs are identified as not only water stable but also thermal and activation stable. The ML approach is anticipated to serve as a prerequisite filtering tool to streamline the exploration of water stable MOFs for important practical applications.

Precision-engineered metal-organic frameworks: fine-tuning reverse topological structure prediction and design

Digital discoveries of metal-organic frameworks (MOFs) have been significantly advanced by the reverse topological approach (RTA). The node-and-linker assembly strategy allows predictable reticulations predefined by in silico coordination templates; however, reticular equivalents lead to substantial combinatorial explosion due to the infinite design space of building units (BUs). Here, we develop a fine-tuned RTA for the structure prediction of MOFs by integrating precise topological constraints and leveraging reticular chemistry, thus transcending traditional exhaustive trial-and-error assembly. From an extensive array of chemically realistic BUs, we subsequently design a database of 94 823 precision-engineered MOFs (PE-MOFs) and further optimize their structures. The PE-MOFs are assessed for post-combustion CO2 capture in the presence of H2O and top-performing candidates are identified by integrating three stability criteria (activation, water and thermal stabilities). This study highlights the potential of synergizing PE with the RTA to enhance efficiency and precision for computational design of MOFs and beyond.

A Rechargeable "Rocking Chair" Type Zn-CO2 Battery

Rising global temperatures and critical energy shortages have spurred researches into CO2 fixation and conversion within the realm of energy storage such as Zn-CO2 batteries. However, traditional Zn-CO2 batteries employ double-compartment electrolytic cells with separate carriers for catholytes and anolytes, diverging from the "rocking chair" battery mechanism. The specific energy of these conventional batteries is constrained by the solubility of discharge reactants/products in the electrolyte. Additionally, H2O molecules tend to trigger parasitic reactions at the electrolyte/electrode interfaces, undermining the long-term stability of Zn anodes. In this report, we introduce an innovative "rocking chair" type Zn-CO2 battery that utilizes a weak-acidic zinc trifluoromethanesulfonate aqueous electrolyte compatible with both cathode and anode. This design minimizes side reactions on the Zn surface and leverages the high catalytic activity of the cathode material, allowing the battery to achieve a substantial discharge capacity of 6734 mAh g-1 and maintain performance over 65 cycles. Moreover, the successful production of pouch cells demonstrates the practical applicability of Zn-CO2 batteries. Electrode characterizations confirm superior electrochemical reversibility, facilitated by solid discharge products of ZnCO3 and C. This work advances a "rocking chair" Zn-CO2 battery with an enhanced specific energy and a reversible pathway, providing a foundation for developing high-performance metal-CO2 batteries.

MEPO-ML: a robust graph attention network model for rapid generation of partial atomic charges in metal-organic frameworks

Accurate computation of the gas adsorption properties of MOFs is usually bottlenecked by the DFT calculations required to generate partial atomic charges. Therefore, large virtual screenings of MOFs often use the QEq method which is rapid, but of limited accuracy. Recently, machine learning (ML) models have been trained to generate charges in much better agreement with DFT-derived charges compared to the QEq models. Previous ML charge models for MOFs have all used training sets with less than 3000 MOFs obtained from the CoRE MOF database, which has recently been shown to have high structural error rates. In this work, we developed a graph attention network model for predicting DFT-derived charges in MOFs where the model was developed with the ARC-MOF database that contains 279,632 MOFs and over 40 million charges. This model, which we call MEPO-ML, predicts charges with a mean absolute error of 0.025e on our test set of over 27 K MOFs. Other ML models reported in the literature were also trained using the same dataset and descriptors, and MEPO-ML was shown to give the lowest errors. The gas adsorption properties evaluated using MEPO-ML charges are found to be in significantly better agreement with the reference DFT-derived charges compared to the empirical charges, for both polar and non-polar gases. Using only a single CPU core on our benchmark computer, MEPO-ML charges can be generated in less than two seconds on average (including all computations required to apply the model) for MOFs in the test set of 27 K MOFs.

Peptide-Guided Metal-Organic Frameworks Spatial Assembly Sustain Long-Lived Charge-Separated State to Improve Photocatalytic Performance

The controlled fabrication of spatial architectures using metal-organic framework (MOF)-based particles offers opportunities for enhancing photocatalytic performances. The understanding of the contribution of assembly to a precise photocatalytic mechanism, particularly from the perspective of charge separation and extraction dynamics, still poses challenges. The present report presents a facile approach for the spatial assembly of zinc imidazolate MOF (ZIF-8), guided by β-turn peptides (SAZH). We investigated the dynamics of photoinduced carriers using transient absorption spectroscopy. The presence of a long-lived internal charge-separated state in SAZH confirms its role as an intersystem crossing state. The formation of an assembly interface facilitates efficient electron transfer from SAZH to O2, resulting in approximately 2.6 and 2 times higher concentrations of superoxide (·O2-) and hydrogen peroxide (H2O2), respectively, compared to those achieved with ZIF-8. The medical dressing fabricated from SAZH demonstrated exceptional biocompatibility and exhibited an outstanding performance in promoting wound restoration. It rapidly achieved hemostasis during the bleeding phase, followed by a nearly 100% photocatalytic killing efficiency against the infected site during the subsequent inflammatory phase. Our findings reveal a pivotal dynamic mechanism underlying the photocatalytic activity of control-assembled ZIF-8, providing valuable guidelines for the design of highly efficient MOF photocatalysts.

Polyphosphonate covalent organic frameworks

Herein, we report polyphosphonate covalent organic frameworks (COFs) constructed via P-O-P linkages. The materials are synthesized via a single-step condensation reaction of the charge-assisted hydrogen-bonded organic framework, which is constructed from phenylphosphonic acid and 5,10,15,20-tetrakis[p-phenylphosphonic acid]porphyrin and is formed by simply heating its hydrogen-bonded precursor without using chemical reagents. Above 210 °C, it becomes an amorphous microporous polymeric structure due to the oligomerization of P-O-P bonds, which could be shown by constant-time solid-state double-quantum 31P nuclear magnetic resonance experiments. The polyphosphonate COF exhibits good water and water vapor stability during the gas sorption measurements, and electrochemical stability in 0.5 M Na2SO4 electrolyte in water. The reported family of COFs fills a significant gap in the literature by providing stable microporous COFs suitable for use in water and electrolytes. Additionally, we provide a sustainable synthesis route for the COF synthesis. The narrow pores of the COF effectively capture CO2.

Binary-Cooperative Ultrathin Porous Membrane for Gas Separation

The construction of ultrathin porous membranes with stable structures is critical for achieving efficient gas separation. Inspired by the binary-cooperative structural features of bones and teeth-composed of rigid hydroxyapatite and flexible collagen, which confer excellent mechanical strength-a binary-cooperative porous membrane constructed with gel-state zeolitic imidazolate frameworks (g-ZIFs), synthesized using a metal-gel-induced strategy, is proposed. The enlarged cavity size and flexible frameworks of the g-ZIF nanoparticles significantly improve gas adsorption and diffusion, respectively. After thermal treatment, the coordination structures forming rigid segments in the g-ZIF membranes appear at the stacked g-ZIF boundaries, exhibiting a higher Young's modulus than the g-ZIF nanoparticles, denoted as the flexible segments. The g-ZIF membranes demonstrate excellent tensile and compression resistances, attributed to the effective translation of binary-cooperative effects of rigidity and flexibility into the membranes. The resulting dual-aperture structure, composed of g-ZIF nanoparticles surrounded by nanoscale apertures at the boundaries, yields a membrane with a stable CO2 permeance of 4834 GPU and CO2/CH4 selectivity of 90 within 3.0 MPa.

Mind the Gap: The Role of Mass Transfer in Shaped Nanoporous Adsorbents for Carbon Dioxide Capture

Adsorptive separations by nanoporous materials are major industrial processes. The industrial importance of solid adsorbents is only expected to grow due to the increased focus on carbon dioxide capture technology and energy-efficient separations. To evaluate the performance of an adsorbent and design a separation process, the adsorption thermodynamics and kinetics must be known. However, although diffusion kinetics determine the maximum production rate in any adsorption-based separation, this aspect has received less attention due to the challenges associated with conducting diffusion measurements. These challenges are exacerbated in the study of shaped adsorbents due to the presence of porosity at different length scales. As a result, adsorbent selection typically relies mainly on adsorption properties at equilibrium, i.e., uptake capacity, selectivity and adsorption enthalpy. In this Perspective, based on an extensive literature review on mass transfer of CO2 in nanoporous adsorbents, we discuss the importance and limitations of measuring diffusion in nanoporous materials, from the powder form to the adsorption bed, considering the nature of the process, i.e., equilibrium-based or kinetic-based separations. By highlighting the lack of and discrepancies between published diffusivity data in the context of CO2 capture, we discuss future challenges and opportunities in studying mass transfer across scales in adsorption-based separations.