High-performance Metal-organic Frameworks Research Articles

Data-driven discovery of novel metal organic frameworks with superior ammonia adsorption capacity

Ammonia (NH3) has been a subject of great interest due to its important roles in diverse technological applications. However, high toxicity and corrosiveness of NH3 has made it an important task to develop an efficient carrier to safely capture NH3 with high capacity. Here, we employ a machine learning (ML) model to discover high-performance metal organic frameworks (MOFs) that will work as efficient NH3 carriers. By constructing databases at two distinct conditions, adsorption and desorption, through Grand Canonical Monte Carlo (GCMC) simulations to train ML models, we identify eight novel MOFs as potentially efficient NH3 carriers through screening the large-scale MOF databases with the trained models and GCMC verification. The identified MOFs exhibit the average NH3 working capacity exceeding 1100 mg/g, and subsequent molecular dynamics simulations demonstrate mechanical stability of the predicted MOFs. Moreover, analyses of the diffusion mechanism within the proposed MOFs underscore the strong dependence of NH₃ gas diffusivity on the structural details of the materials.

Data-Driven and Machine Learning to Screen Metal-Organic Frameworks for the Efficient Separation of Methane.

With the rapid growth of the economy, people are increasingly reliant on energy sources. However, in recent years, the energy crisis has gradually intensified. As a clean energy source, methane has garnered widespread attention for its development and utilization. This study employed both large-scale computational screening and machine learning to investigate the adsorption and diffusion properties of thousands of metal-organic frameworks (MOFs) in six gas binary mixtures of CH4 (H2/CH4, N2/CH4, O2/CH4, CO2/CH4, H2S/CH4, He/CH4) for methane purification. Firstly, a univariate analysis was conducted to discuss the relationships between the performance indicators of adsorbents and their characteristic descriptors. Subsequently, four machine learning methods were utilized to predict the diffusivity/selectivity of gas, with the light gradient boosting machine (LGBM) algorithm emerging as the optimal one, yielding R2 values of 0.954 for the diffusivity and 0.931 for the selectivity. Furthermore, the LGBM algorithm was combined with the SHapley Additive exPlanation (SHAP) technique to quantitatively analyze the relative importance of each MOF descriptor, revealing that the pore limiting diameter (PLD) was the most critical structural descriptor affecting molecular diffusivity. Finally, for each system of CH4 mixture, three high-performance MOFs were identified, and the commonalities among high-performance MOFs were analyzed, leading to the proposals of three design principles involving changes only to the metal centers, organic linkers, or topological structures. Thus, this work reveals microscopic insights into the separation mechanisms of CH4 from different binary mixtures in MOFs.

Structural energy storage composites based on etching engineering Fe-doped Co MOF electrode toward high energy density supercapacitors

Metal organic frameworks (MOFs) have been used widely as cathode materials for supercapacitors because of their adjustable chemical composition, variable topological structures, and relatively simple synthesis methods. However, most MOFs are insulated and their metal organic ligand coordination bonds are not stable enough in electrolytes, which greatly limits the application of MOFs in supercapacitors. The synergistic effect between transition metal doped and chemical etching is of great significance for the preparation of high-performance MOF electrodes. This study introduces Fe into Co MOF prepared by co precipitation to optimize the electronic structure of Co, and then constructed e-Fe-doped Co MOF hierarchical structure through ammonia etching. The Co MOF achieves rapid electron exchange at the electrode electrolyte interface through this synergistic effect, improving conductivity, and increasing specific surface area, exposing more active sites, effectively enhancing the electrochemical activity of the electrode. Therefore, the e-Fe-doped Co MOF-4 h has a high specific capacity of 700 C g−1 at 1 A g−1 with a 90.08 % high-rate performance. The assembled e-Fe-doped Co MOF-4 h//N-doped WC ASC provides the energy density of 80 Wh kg−1 at a power density of 800 W kg−1 with a ∼88 % capacitance retention after 10000 cycles. These results provide ideas for utilizing synergistic effects to prepare high-energy density supercapacitors using MOF materials.

Exploring the Potential of Metal-Organic Frameworks for Cryogenic Helium-Based Gas Gap Heat Switches via High-Throughput Computational Screening.

With the advantages of a long lifetime and high reliability, gas gap heat switches (GGHSs) are attractive in many thermal management applications, especially in space-borne cryogenic systems. The performance of a GGHS is significantly affected by the adsorption characteristics of the adsorbent in the sorption pump. Compared with the commonly used adsorbent in the GGHSs (activated carbon), metal-organic frameworks (MOFs) have larger surface areas, higher pore volumes, and exceptional tunability, which motivates this study to explore their potential for application in cryogenic GGHSs. To this end, two performance metrics, the required volume of adsorbent (vsor) and total input heat (qtot), were computed for about 6000 MOFs via molecular simulations and compared with those of activated carbon. It is found that over 2300 MOFs possess a smaller vsor than activated carbon, and the smallest vsor of MOFs is about 12.7% of that of activated carbon. vsor and qtot generally change in the same direction, which implies it is possible to reduce both parameters simultaneously by choosing a suitable MOF. Structure-performance analysis reveals that 1/vsor consistently increases first and then decreases with pore limiting diameter, largest cavity diameter, available pore volume, accessible surface area, helium void fraction, and bulk density. Descriptor ranges corresponding to high-performing MOFs were identified based on Precision-Recall analysis. Notably, Zr-containing MOFs are particularly likely to have smaller vsor values than activated carbon. It is anticipated that the promising MOFs identified by this study will motivate further experimental investigations, and the insights into structure-performance relationships can serve to guide the rational design of novel MOF candidates for GGHSs.

Improving performance prediction of metal-organic framework membranes for reverse osmosis via genetic algorithm optimized artificial neural networks

The pollution of water resources and the rapid development of society are leading to the decrease of available fresh water resources in the world, which intensifies the research on seawater desalination. Metal-organic frameworks (MOFs), as promising membrane materials with diversified sizes, highly porous structures, and tunable chemical functionalities, have been extensively studied for desalination in recent years. In this paper, genetic algorithm (GA) optimized back propagation (BP) artificial neural network was first constructed, which made the prediction model of MOF membranes performances more accurate. Before the GA optimization, the structure of BP model was tuned through cross-validation and hyperparameter adjustment to achieve a better prediction performance and robustness. Then the GA was introduced attaining an enhanced R2 of 0.98 and 0.99 compared to the BP model of 0.90 and 0.97 for water permeability and salt rejection, respectively, which was also superior to the random forest, support vector machine, and multiple linear regression models. In addition, the grey relational analysis was used to screen the feature variables in order to clarify the most influential factors that affect the membrane performances. It is found that the MOF size and the thickness of polyamide layer were the key influential factors. This work will provide insights for the design of high-performance MOF based membranes and further render implications for other membrane developments.

Deep learning and big data mining for Metal–Organic frameworks with high performance for simultaneous desulfurization and carbon capture

Carbon capture and desulfurization of flue gases are crucial for the achievement of carbon neutrality and sustainable development. In this work, the “one-step” adsorption technology with high-performance metal–organic frameworks (MOFs) was proposed to simultaneously capture the SO2 and CO2. Four machine learning algorithms were used to predict the performance indicators (NCO2+SO2, SCO2+SO2/N2, and TSN) of MOFs, with Multi-Layer Perceptron Regression (MLPR) showing better performance (R2 = 0.93). To address sparse data of MOF chemical descriptors, we introduced the Deep Factorization Machines (DeepFM) model, outperforming MLPR with a higher R2 of 0.95. Then, sensitivity analysis was employed to find that the adsorption heat and porosity were the key factors for SO2 and CO2 capture performance of MOF, while the influence of open alkali metal sites also stood out. Furthermore, we established a kinetic model to batch simulate the breakthrough curves of TOP 1000 MOFs to investigate their dynamic adsorption separation performance for SO2/CO2/N2. The TOP 20 MOFs screened by the dynamic performance highly overlap with those screened by the static performance, with 76 % containing open alkali metal sites. This integrated approach of computational screening, machine learning, and dynamic analysis significantly advances the development of efficient MOF adsorbents for flue gas treatment.

Accelerating In Silico Discovery of Metal-Organic Frameworks for Ethane/Ethylene and Propane/Propylene Separation: A Synergistic Approach Integrating Molecular Simulation, Machine Learning, and Active Learning.

Cryogenic distillation, a currently employed method for C2H4/C2H6 and C3H6/C3H8 mixture separation, is energy-intensive, prompting the research toward alternative technologies, including adsorbent-based separation. In this work, we combine machine learning (ML) technique with high-throughput screening to screen ∼23,000 hypothetical metal-organic frameworks (MOFs) for paraffin (C2H6 and C3H8) selective adsorbent separation. First, structure-based prescreening was employed to remove MOFs with undesired geometric properties. Further, a random forest model built upon the multicomponent grand canonical Monte Carlo (m-GCMC) simulation data of training set MOFs was found to be the most successful in learning the relationship between MOF features and olefin/paraffin mixture separation. Using this technique, the separation performance of the remaining (test set) MOFs was predicted, and the top-performing MOFs were identified. We also employed active learning (AL) to evaluate its effectiveness in improving the prediction of olefin/paraffin selectivity. AL was discovered to be ∼29 times more efficient than the best-supervised ML model, as it was able to identify the top materials in limited training data and at a fraction of computational cost and time as compared to ML techniques. Among the top selected materials, framework chemistry was found to be the most important parameter. Nickel and copper (as a metal node) in a tfzd and hms topological arrangement respectively, were discovered to be a prevalent attribute in high-performing MOFs, further demonstrating the prominent significance of framework chemistry. Additionally, the top MOFs discovered were studied in detail and further compared to the previously reported MOFs. These MOFs show the highest selectivity for C2H4/C2H6 and C3H6/C3H8 mixture separation, as reported until date. The hierarchical strategy devised in this study will facilitate the quick screening of MOFs across multiple databases toward industrially significant separation processes by leveraging molecular simulations and AL.

Controllable defect engineering based on cobalt metal-organic framework for boosting oxygen evolution reaction

Defect engineering on metal-organic frameworks (MOFs) provides high flexibility to rationally design advanced oxygen evolution reaction (OER) catalysts with low overpotential and high stability. However, fundamental understanding the effect of defect concentration on catalytic OER activity is still quite ambiguous. Herein, the Co-MOF-Dx catalysts with regulated oxygen defects concentration are deliberately constructed via coupling one-pot solvothermal synthesis with NaBH4 chemical reduction process. Experimental findings propose that the oxygen defect concentration within Co-MOF-Dx gradually increases with raising the NaBH4 content, which could provide a flexible platform to tailor the electronic structure around active Co site and optimize adsorption/desorption capacity of oxygen intermediates. When the introduction content of NaBH4 is up to 5 mg, the resulting abundant unsaturated coordination defects could endow the Co-MOF-D5 catalyst with optimized electronic structure and more exposed active sites for improving charge transfer and adsorption/desorption capacity. It is found that the optimized Co-MOF-D5 can drive the current density of 10 mA cm−2 only at a low overpotential of 300 mV with the small Tafel slope of 53.1 mV dec−1 in alkaline medium. This work sheds light on the way for the development of high-performance MOF catalysts via modulating defect concentration.

Incorporating flexible sulfonate-imidazolium ion pair-functionalized MOF in cross-linked polyvinyl alcohol membrane for achieving superprotonic conduction

Designing high-performance metal–organic frameworks (MOFs) based proton exchange membranes for fuel cells simultaneously with a high-density proton carrier and continuous hydrogen-bonded networks remains a challenge. The present study has endeavored to construct a crystalline Cu-MOF with chemically stable based on flexible sulfonate-imidazolium ion pair ligand in which flexible sulfonic acid chains orderly anchoring on the channel wall. Moreover, the cross-linked polyvinyl alcohol (CL-PVA) was choosing as matrix to enhance the practicality of Cu-MOF synergistically in fuel cells due to its hydrophilic and hydrophobic structure distribution. The CL-PVA/Cu-MOF-X (X = 5, 10, 15, 20 wt%) composite membranes exhibit strong water affinity and thus significantly enhanced water uptake which also along with good mechanical properties. The highly hydrophilic hydroxyl groups in CL-PVA and the sulfonic acid groups in Cu-MOF form a continuous hydrogen-bonded pathway, which result to the superprotonic conduction. Among them, CL-PVA/Cu-MOF-15 shows the highest proton conductivity of 2.4 × 10-2 S cm−1 but the smallest activation energy Ea (41 kJ/mol) at 80 °C and 90 % RH, indicating more easily transportation of H+. The single-cell power density of CL-PVA/Cu-MOF-15 is up to 49.3 mW cm−2, which will open a new avenue for the practical application of proton-conducting MOFs in proton exchange membrane fuel cells.

Host–guest interactions in framework materials: Insight from modeling

The performance of metal–organic and covalent organic framework materials in sought-after applications—capture, storage, and delivery of gases and molecules, and separation of their mixtures—heavily depends on the host–guest interactions established inside the pores of these materials. Computational modeling provides information about the structures of these host–guest complexes and the strength and nature of the interactions present at a level of detail and precision that is often unobtainable from experiment. In this Review, we summarize the key simulation techniques spanning from molecular dynamics and Monte Carlo methods to correlate ab initio approaches and energy, density, and wavefunction partitioning schemes. We provide illustrative literature examples of their uses in analyzing and designing organic framework hosts. We also describe modern approaches to the high-throughput screening of thousands of existing and hypothetical metal–organic frameworks (MOFs) and covalent organic frameworks (COFs) and emerging machine learning techniques for predicting their properties and performances. Finally, we discuss the key methodological challenges on the path toward computation-driven design and reliable prediction of high-performing MOF and COF adsorbents and catalysts and suggest possible solutions and future directions in this exciting field of computational materials science.

MOFGalaxyNet: a social network analysis for predicting guest accessibility in metal–organic frameworks utilizing graph convolutional networks

Metal–organic frameworks (MOFs), are porous crystalline structures comprising of metal ions or clusters intricately linked with organic entities, displaying topological diversity and effortless chemical flexibility. These characteristics render them apt for multifarious applications such as adsorption, separation, sensing, and catalysis. Predominantly, the distinctive properties and prospective utility of MOFs are discerned post-manufacture or extrapolation from theoretically conceived models. For empirical researchers unfamiliar with hypothetical structure development, the meticulous crystal engineering of a high-performance MOF for a targeted application via a bottom-up approach resembles a gamble. For example, the precise pore limiting diameter (PLD), which determines the guest accessibility of any MOF cannot be easily inferred with mere knowledge of the metal ion and organic ligand. This limitation in bottom-up conceptual understanding of specific properties of the resultant MOF may contribute to the cautious industrial-scale adoption of MOFs.Consequently, in this study, we take a step towards circumventing this limitation by designing a new tool that predicts the guest accessibility—a MOF key performance indicator—of any given MOF from information on only the organic linkers and the metal ions. This new tool relies on clustering different MOFs in a galaxy-like social network, MOFGalaxyNet, combined with a Graphical Convolutional Network (GCN) to predict the guest accessibility of any new entry in the social network. The proposed network and GCN results provide a robust approach for screening MOFs for various host–guest interaction studies.

MOFs and COFs based pervaporation membranes for alcohols/water separation: A review

High-performance pervaporation membrane has great application and development potential in alcohols/water separation industry due to its high efficiency and economy. It is an important pathway to alleviate the fossil energy crisis and obtain various alcohols scientifically. As a new type of porous crystal material, metal-organic frameworks (MOFs) and covalent organic frameworks (COFs) with controllable pore structure and rich chemical functions are promising for constructing high-performance alcohols/water separation membranes for future manufacturing development. There are different types of MOFs and COFs based pervaporation membranes, including MOFs polycrystalline membranes, MOFs based mixed matrix membranes (MMMs), COFs continuous membranes, and COFs based MMMs. In this review, we firstly discuss the classification of membranes, describe the process mechanism and evaluation indicators of pervaporation, select decisive criterions for MOFs and COFs, summary the preparation strategies and separation performance of these four membranes, and discuss in detail the impact of operating conditions. Finally, the advantages and disadvantages of these four kinds of membranes are summarized, the challenges faced are identified, and the future development direction is pointed out, aiming to provide slight guidance for the design and development of high-performance MOFs and COFs based membranes for alcohols/water separation in the future.

Controllable Synthesis of Defective UiO-66 for Efficient Degradation and Detection of Ozone.

Metal-organic framework (MOF) structures have gained significant attention for their exceptional catalytic performance in ozone degradation, even under high humidity conditions, which is attributed to the presence of unsaturated metal sites (MOF defects). However, the correlation between MOF defects and catalytic ozone remains ambiguous, and a general approach for the controllable synthesis of high-performance MOF structures is currently lacking. Herein, different defective UiO-66 materials with cluster or ligand defects were obtained by precisely controlling small molecular acid modulators. Their catalytic performance can be analyzed in real time through the specific cataluminescence (CTL) signal of ozone at the interface. The presence of ligand defects was found to be crucial for both catalytic degradation and luminescence of ozone, and the CTL signal exhibited a positive correlation with the endogenous hydroxyl group content in the material (R2 = 0.982), while external humidity further supplemented internal water molecules within the material. Furthermore, theoretical calculations were conducted to compare the adsorption behaviors of ozone on the defective UiO-66 under dry/wet conditions, leading to the proposal of two potential reaction pathways. Subsequently, UiO-66-DA with superior catalytic performance was employed to develop a highly efficient CTL sensor capable of accurately detecting ozone (LOD = 23.3 ppb). This study held significant value in elucidating the reaction site of ozone on MOFs and achieving optimal catalytic effects through the careful selection of modulators and humidity levels.

Decorating Channel Walls in Metal-Organic Frameworks with Crown Ethers for Efficient and Selective Separation of Radioactive Strontium(II).

Nuclear accidents and the improper disposal of nuclear wastes have led to serious environmental radioactive pollutions. The rational design of adsorbents for the highly efficient separation of strontium(II) is essential in treating nuclear waste and recovering radioactive strontium resources. Metal-organic frameworks (MOFs) are potential materials for the separation of aqueous metal ions owing to their designable structure and tunable functionality. Herein, a novel 3D MOF material MOF-18Cr6, in which 1D channels are formed using 18-crown-6-ether-containing ligands as channel walls, is fabricated for strontium(II) separation. In contrast to traditional MOFs designed by grafting functional groups in the framework pores, MOF-18Cr6 possesses regular 18-crown-6-ether cavities on the channel walls, which not only can transport and intake strontium(II) via the channels, but also prevent blockage of the channels after the binding of strontium(II). Consequently, the functional sites are fully utilized to achieve a high strontium(II) removal rate of 99.73 % in simulated nuclear wastewater. This study fabricates a highly promising adsorbent for the separation of aqueous radioactive strontium(II), and more importantly, can provide a new strategy for the rational design of high-performance MOF adsorbents for separating target substances from complex aqueous environments.

Rational design of high performance metal organic framework stationary phase for gas chromatography

Metal organic frameworks (MOFs) are assembled from metal ions or clusters and organic ligands. The high tunability of these components offers a solid structural foundation for achieving efficient gas chromatography (GC) separation. This review demonstrates that the design of high performance MOFs with suitable stationarity should consider both the thermodynamic interactions provided by these MOFs and the kinetic diffusion of analytes. Thermodynamic parameters are basic indicators for describing the interactions between various analytes and the stationary phase. Thermodynamic parameters such as retention factors, McReynolds constants, enthalpy changes, and entropy changes can reflect the relative intensity of thermodynamic interactions. For example, a larger enthalpy change indicates a stronger thermodynamic interaction between the analytes and stationary phase, whereas a smaller enthalpy change indicates a weaker interaction. In addition, the degree of entropy change reflects the relative degrees of freedom of analytes in the stationary phase. A larger entropy change indicates that the analytes have fewer degrees of freedom in the stationary phase. The higher the degree of restriction, the closer the adsorption of the analytes and, thus, the longer the retention time. Thermodynamic interactions, such as metal affinity, π-π interactions, polarity, and chiral sites, can be rationally introduced into MOF structures by pre- or post-modifications depending on the target analytes. These tailored thermodynamic interactions create a favorable environment with subtle differences for efficient analyte separation. For example, MOF stationarity may require large conjugation centers to provide specific π-π interactions to separate benzenes. Chiral groups may be required in the MOF structure to provide sufficient interactions to separate chiral isomers. The kinetic diffusion rate of the analytes is another critical factor that affects the separation performance of MOFs. The diffusion coefficients of analytes in the stationary phase (Ds) can be used to evaluate their diffusion rates. The chromatographic dynamics equation illustrates that the chromatographic peak of analytes tends to be sharper and more symmetrical when the Ds is large, whereas a wider trailing peak may appear when the Ds is small. The Van Deemter equation also proves that a low Ds may lead to a high theoretical plate height and low column efficiency, whereas a high Ds may lead to a low theoretical plate height and increased column efficiency. Analyte diffusion can be significantly influenced by the pore size, shape, particle size, and packing mode of MOFs. For instance, an excessively small pore size results in increased mass transfer resistance, which affects the diffusion of analytes in the stationary phase, probably leading to serious peak trailing. Thus, a suitable pore size is required to enhance the kinetic diffusion of analytes and improve the separation performance of MOFs. Theoretically, the design of a high performance MOF stationary phase requires the creation of routes for the rapid diffusion of analytes. However, the separation ability of an MOF is determined by not only the kinetic diffusion rate of the analytes but also the thermodynamic interactions it provides. An excessively fast diffusion rate may lead to insufficient interactions between the analytes and MOFs, compromising their ability to effectively separate different analytes. The thermodynamic interactions and kinetic diffusion of analytes are synergistic and mutually essential. Therefore, this review concludes with research on the influence of both the thermodynamic interactions and kinetic diffusion of analytes on the performance of MOF stationary phases. Based on the findings of this review, we propose that high performance MOF stationary phases can be achieved by balancing the thermodynamic interactions and kinetic diffusion of analytes in these phases through the rational design of the MOF structure. We believe that this review provides useful guidelines for the design of high performance MOF stationary phases.

High-Throughput Computational Screening and Machine Learning Model for Accelerated Metal–Organic Frameworks Discovery in Toluene Vapor Adsorption

Some hazardous gases, like toluene vapor, have caused serious environmental pollution. The adsorption of toluene using metal–organic frameworks (MOFs) has been considered a useful mechanism to reduce environmental pollution. High-throughput computation using the grand canonical Monte Carlo (GCMC) approach was used to screen high-performance MOFs from the CoRE MOF database. A total of 802 MOFs are selected with a toluene uptake larger than HKUST-1 (6.34 mmol/g at 1900 Pa, 298 K) and CMOF-3b ([(L3b)Cu2]n, L3b = 4,4′,4″,4‴-(2,2′-dihydroxy-1,1′-binaphthalene-4,4′,6,6′-tetrayl)), showing the highest toluene vapor adsorption capacity (25.57 mmol/g). Approximately 80% of high-performance MOFs contain open metal sites. Further analyses of the quantitative structure–property relationships reveal that the MOF adsorption capacity for toluene could be primarily correlated with gravimetric surface area and void fraction. Moreover, using the HKUST-1 as a template, center-metal element replacement is suggested to be effective in improving toluene vapor adsorption. Finally, based on our previously proposed MOF-CGCNN algorithm, a regression model is developed to predict toluene adsorption capacity. Combined with high-throughput GCMC calculation, the machine learning model is applied to screen a larger MOF database (containing 137,953 hypothetical MOFs), which accelerates the virtual discovery of new high-performance candidate MOFs for toluene adsorption. The proposed strategy will be useful in material design or discovery for reducing toluene, thereby benefitting the environment.

Metal-organic framework-based nanomaterials as opto-electrochemical sensors for the detection of antibiotics and hormones: A review.

Increasing trace levels of antibiotics and hormones in the environment and food samples are concerning and pose a threat. Opto-electrochemical sensors have received attention due to their low cost, portability, sensitivity, analytical performance, and ease of deployment in the field as compared to conventional expensive technologies that are time-consuming and require experienced professionals. Metal-organic frameworks (MOFs) with variable porosity, active functional sites, and fluorescence capacity are attractive materials for developing opto-electrochemical sensors. Herein, the insights into the capabilities of electrochemical and luminescent MOF sensors for detection and monitoring of antibiotics and hormones from various samples are critically reviewed. The detailed sensing mechanisms and detection limits of MOF sensors are addressed. The challenges, recent advances, and future directions for the development of stable, high-performance MOFs as commercially viable next-generation opto-electrochemical sensor materials for the detection and monitoring of diverse analytes are discussed.

Rapid and Efficient Degradation of Ceftiofur Sodium by 5,6-Dimethylbenzimidazole-Modified ZIF(Fe/Co)x@CNF Nanocomposites

The exploration of transforming various metals as metal sources into metal organic frameworks (MOFs) has attracted considerable attention in recent years. This study used triethylamine and 5,6-dimethylbenzimidazole (DMBIM) to modify the iron-doped cobalt zeolite imidazole framework on carbon fiber structure. The results showed that the ZIF(Fe/Co)1.25@CNF exhibited high CEF removal efficiency that over 97% of ceftiofur sodium (CEF) was removed within 10 min, which was significantly higher than that of the common ZIF-67 materials. Compared with other ZIF-67 composites, ZIF(Fe/Co)x@CNF has a larger specific surface area, resulting in a larger contact area and more active sites during the reaction. After the introduction of DMBIM, the ZIF(Fe/Co)1.25@CNF not only easily separated from the solution but also enhanced the hydrophobicity, which provided higher catalytic stability and catalytic performance. In addition, the effects of different catalyst ratios, pollutant concentrations, solution pH, and different radicals (•OH, SO4•−, 1O2) on the activation of peroxymonosulfate (PMS) were investigated. The mechanism of degradation was elucidated by electron paramagnetic resonance experiments. This study provides a new perspective for the preparation of high-performance MOF catalysts with excellent catalytic performance and may facilitate the application of MOF materials in more practical situations.

Overcoming the Trade-Off between C2 H2 Sorption and Separation Performance by Regulating Metal-Alkyne Chemical Interaction in Metal-Organic Frameworks.

Designing porous materials for C2 H2 purification and safe storage is essential research for industrial utilization. We emphatically regulate the metal-alkyne interaction of PdII and PtII on C2 H2 sorption and C2 H2 /CO2 separation in two isostructural NbO metal-organic frameworks (MOFs), Pd/Cu-PDA and Pt/Cu-PDA. The experimental investigations and systematic theoretical calculations reveal that PdII in Pd/Cu-PDA undergoes spontaneous chemical reaction with C2 H2 , leading to irreversible structural collapse and loss of C2 H2 /CO2 sorption and separation. Contrarily, PtII in Pt/Cu-PDA shows strong di-σ bond interaction with C2 H2 to form specific π-complexation, contributing to high C2 H2 capture (28.7 cm3 g-1 at 0.01 bar and 153 cm3 g-1 at 1 bar). The reusable Pt/Cu-PDA efficiently separates C2 H2 from C2 H2 /CO2 mixtures with satisfying selectivity and C2 H2 capacity (37 min g-1 ). This research provides valuable insight into designing high-performance MOFs for gas sorption and separation.

Guest-Induced Multilevel Charge Transport Strategy for Developing Metal-Organic Frameworks to Boost Photocatalytic CO2 Reduction.

Encapsulating photogenerated charge-hopping nodes and space transport bridges within metal-organic frameworks (MOFs) is a promising method of boosting the photocatalytic performance. Herein, this work embeds electron transfer media (9,10-bis(4-pyridyl)anthracene (BPAN)) in MOF cavities to build multi-level electron transfer paths. The MOF cavities are accurately regulated to investigate the significance of the multi-level electron transfer paths in the process of CO2 photoreduction by evaluating the difference in the number of guest media. The prepared MOFs, {[Co(BPAN)(1,4-dicarboxybenzene)(H2 O)2 ]·BPAN·2H2 O} and {[Co(BPAN)2 (4,4'-biphenyldicarboxylic acid)2 (H2 O)2 ]·2BPAN·2H2 O} (denoted as BPAN-Co-1 and BPAN-Co-2), exhibit efficient visible-light-driven CO2 conversion properties. The CO photoreduction efficacy of BPAN-Co-2 (5598µmol g-1 h-1 ) is superior to that of most reported MOF-based catalysts. In addition, the enhanced CO2 photoreduction ability is supported by density functional theory (DFT). This work illustrates the feasibility of realizing charge separation characteristics in MOF catalysts at the molecular level, and provides new insight for designing high-performance MOFs for artificial photosynthesis.