Recent advances, opportunities, and challenges in high-throughput computational screening of MOFs for gas separations

Category-specific topological learning of metal-organic frameworks

Metal-organic frameworks (MOFs) are porous, crystalline materials with high surface area, adjustable porosity, and structural tunability, making them ideal for diverse applications. However, traditional experimental and computational methods have limited scalability and interpretability, hindering effective exploration of MOF structure-property relationships. To address these challenges, we introduce, for the first time, a category-specific topological learning (CSTL), which combines algebraic topology with chemical insights for robust property prediction. The model represents MOF structures as simplicial complexes and incorporates elemental categorizations to enable balanced, interpretable machine learning study. By integrating category-specific persistent homology, CSTL captures both global and local structural characteristics, rendering multi-dimensional, category-specific descriptors that support a predictive model with high accuracy and robustness across eight MOF datasets, outperforming all previous results. This alignment of topological and chemical features enhances the predictive power and interpretability of CSTL, advancing understanding of structure-property relationships of MOFs and promoting efficient material discovery.

A Facile Two-Step High-Throughput Screening Strategy of Advanced MOFs for Separating Argon from Air

Metal-organic frameworks (MOFs) based on the pressure swing adsorption (PSA) process show great promise in separating argon from air. As research burgeons, the number of MOFs has grown exponentially, rendering the experimental identification of materials with significant gas separation potential impractical. This study introduced a high-throughput screening through a two-step strategy based on structure-property relationships, which leveraged Grand Canonical Monte Carlo (GCMC) simulations, to swiftly and precisely identify high-performance MOF adsorbents capable of separating argon from air among a vast array of MOFs. Compared to traditional approaches for material development and screening, this method significantly reduced both experimental and computational resource requirements. This research pre-screened 12,020 experimental MOFs from a computationally ready experimental MOF (CoRE MOF) database down to 7328 and then selected 4083 promising candidates through structure-performance correlation. These MOFs underwent GCMC simulation assessments, showing superior adsorption performance to traditional molecular sieves. In addition, an in-depth discussion was conducted on the structural characteristics and metal atoms among the best-performing MOFs, as well as the effects of temperature, pressure, and real gas conditions on their adsorption properties. This work provides a new direction for synthesizing next-generation MOFs for efficient argon separation in labs, contributing to energy conservation and consumption reduction in the production of high-purity argon gas.

The COF Space: Materials Features, Gas Adsorption, and Separation Performances Assessed by Machine Learning

Covalent organic frameworks (COFs) are promising materials for gas adsorption; however, only a small number of COFs has been studied for a few types of gas separations to date. To unlock the full potential of the COF space, composed of 69 784 different types of materials, we studied the adsorption of five important gas molecules, CO2, CH4, H2, N2, and O2 in COFs at various pressures combining high-throughput molecular simulations and machine learning. Adsorbent performances of COFs were then explored for industrially critical separations, such as CO2/CH4, CO2/H2, CO2/N2, CH4/H2, CH4/N2, and O2/N2. The key structural and chemical properties of the most promising adsorbents were revealed. Our work offers the most extensive dataset produced for COFs in the literature composed of ∼4.3 million data points for all synthesized and hypothetical COFs' structural, chemical, and energetic features; gas adsorption properties; and selectivities to facilitate the materials discovery.

2H NMR as a Practical Tool for Following MOF Formation: A Case Study of UiO-66

Developing the mechanism for MOF formation is crucial for the rapid development of new materials. This work demonstrates that Deuterium NMR spectroscopy is the optimal inter-laboratory methodology for understanding the in situ kinetics of metal-organic framework (MOF) formation. This method is facile, affordable, and allows for the isolation and monitoring of individual reagents by using one deuterated component while the remaining components are protonated. This study utilizes 2H NMR, via the spectrometer's lock channel, to investigate the formation of UiO-66 as influenced by different modulators: acetic acid, benzoic acid, and hydrochloric acid. By monitoring the concentration of the deuterated linker and observing the chemical shift and peak width of deuterated water over time, key elements of the mechanism are unraveled. Paradoxically, conditions that cause the ligand to be consumed more slowly result in MOFs forming more quickly and with fewer defects. This phenomenon is attributed to the dissociative mechanism associated with the Zr(IV)-containing node.

Development and Application of Fe3+, Al3+, Cr3+ Dummy Atom Models for Metal-Organic Frameworks

Various metal-organic frameworks (MOFs) containing trivalent cations (such as Fe3+, Al3+, and Cr3+) have been reported and have shown great potential in applications. However, the high structural diversity and strong electronic interactions between metal centers and their ligands make the molecular dynamics simulations of MOFs challenging. In this work, we developed new dummy atom models for Fe3+, Al3+, and Cr3+ cations, which can be used in classical molecular dynamics simulations of MOFs. In our models, the correct solvation free energies and metal-ligand distances can be simultaneously reproduced. Furthermore, the usefulness and transferability of our models were validated using the commonly studied MIL-100(M) (M = Fe3+, Al3+, Cr3+) and MIL-88B(Fe3+) systems. Our developed models offer a valuable tool for simulating complex systems containing Fe3+, Al3+, and Cr3+ cations with octahedral coordination structures.

A Multi-Method Approach to Analyzing MOFs for Chemical Warfare Simulant Capture: Molecular Simulation, Machine Learning, and Molecular Fingerprints

Mustard gas (HD) is a well-known chemical warfare agent, recognized for its extreme toxicity and severe hazards. Metal-organic frameworks (MOFs), with their unique structural properties, show significant potential for HD adsorption applications. Due to the extreme hazards of HD, most experimental studies focus on its simulants, but molecular simulation research on these simulants remains limited. Simulation analyses of simulants can uncover structure-performance relationships and enable experimental validation, optimizing methods, and improving material design and performance predictions. This study integrates molecular simulations, machine learning (ML), and molecular fingerprinting (MFs) to identify MOFs with high adsorption performance for the HD simulant diethyl sulfide (DES), followed by in-depth structural analysis and comparison. First, MOFs are categorized into Top, Middle, and Bottom materials based on their adsorption efficiency. Univariate analysis, machine learning, and molecular fingerprinting are then used to identify and compare the distinguishing features and fingerprints of each category. Univariate analysis helps identify the optimal structural ranges of Top and Bottom materials, providing a reference for initial material screening. Machine learning feature importance analysis, combined with SHAP methods, identifies the key features that most significantly influence model predictions across categories, offering valuable insights for future material design. Molecular fingerprint analysis reveals critical fingerprint combinations, showing that adsorption performance is optimized when features such as metal oxides, nitrogen-containing heterocycles, six-membered rings, and C=C double bonds co-exist. The integrated analysis using HTCS, ML, and MFs provides new perspectives for designing high-performance MOFs and demonstrates significant potential for developing materials for the adsorption of CWAs and their simulants.

Infection Diagnostics Enabled by Selective Adsorption of Breath-Based Biomarkers in Zr-Based Metal-Organic Frameworks

Exhaled breath contains trace levels of volatile organic compounds (VOCs) that can reveal information about metabolic processes or pathogens in the body. These molecules can be used for medical diagnosis, but capturing and accurately measuring them is a significant challenge in chemical separations. A highly selective nanoporous sorbent can be used to capture target molecules from a breath sample and preconcentrate them for use in a detector. In this work, we present a combined predictive modeling-experimental validation study in which five Zr-based metal-organic frameworks (MOFs) were identified and tested. These MOFs display good selectivity for a variety of VOCs known to be indicators of viral infections such as influenza and COVID-19. We first used molecular simulation to identify promising MOF candidates that were subsequently synthesized and tested for recovery of a variety of VOCs (toluene, propanal, butanone, octane, acetaldehyde) at concentrations of 20 ppm in humid nitrogen. We show that MOF-818, PCN-777, and UiO-66 have particularly good selectivity for the target molecules in the presence of humidity. These three MOFs each recover around 40-60% of the targets (with the exception of acetaldehyde) at up to 95% relative humidity. MOF-818 recovers 63% of butanone and 60% of toluene at 80% relative humidity. Recovery for acetaldehyde is lower across all MOFs at high humidity, but notably, MOF-808 recovers 90% of acetaldehyde at 60% humidity.

Validating Structural Predictions of Conjugated Macromolecules in Espaloma-Enabled Reproducible Workflows

We incorporated Espaloma forcefield parameterization into MoSDeF tools for performing molecular dynamics simulations of organic molecules with HOOMD-Blue. We compared equilibrium morphologies predicted for perylene and poly-3-hexylthiophene (P3HT) with the ESP-UA forcefield in the present work against prior work using the OPLS-UA forcefield. We found that, after resolving the chemical ambiguities in molecular topologies, ESP-UA is similar to GAFF. We observed the clustering/melting phase behavior to be similar between ESP-UA and OPLS-UA, but the base energy unit of OPLS-UA was found to better connect to experimentally measured transition temperatures. Short-range ordering measured by radial distribution functions was found to be essentially identical between the two forcefields, and the long-range ordering measured by grazing incidence X-ray scattering was qualitatively similar, with ESP-UA matching experiments better than OPLS-UA. We concluded that Espaloma offers promise in the automated screening of molecules that are from more complex chemical spaces.

Metallicious: Automated Force-Field Parameterization of Covalently Bound Metals for Supramolecular Structures

Metal ions play a central, functional, and structural role in many molecular structures, from small catalysts to metal-organic frameworks (MOFs) and proteins. Computational studies of these systems typically employ classical or quantum mechanical approaches or a combination of both. Among classical models, only the covalent metal model reproduces both geometries and charge transfer effects but requires time-consuming parameterization, especially for supramolecular systems containing repetitive units. To streamline this process, we introduce metallicious, a Python tool designed for efficient force-field parameterization of supramolecular structures. Metallicious has been tested on diverse systems including supramolecular cages, knots, and MOFs. Our benchmarks demonstrate that parameters accurately reproduce the reference properties obtained from quantum calculations and crystal structures. Molecular dynamics simulations of the generated structures consistently yield stable simulations in explicit solvent, in contrast to similar simulations performed with nonbonded and cationic dummy models. Overall, metallicious facilitates the atomistic modeling of supramolecular systems, key for understanding their dynamic properties and host-guest interactions. The tool is freely available on GitHub (https://github.com/duartegroup/metallicious).

Metal-Organic Frameworks through the Lens of Artificial Intelligence: A Comprehensive Review

Metal-organic frameworks (MOFs) are a class of hybrid porous materials that have gained prominence as a noteworthy material with varied applications. Currently, MOFs are in extensive use, particularly in the realms of energy and catalysis. The synthesis of these materials poses considerable challenges, and their computational analysis is notably intricate due to their complex structure and versatile applications in the field of material science. Density functional theory (DFT) has helped researchers in understanding reactions and mechanisms, but it is costly and time-consuming and requires bigger systems to perform these calculations. Machine learning (ML) techniques were adopted in order to overcome these problems by implementing ML in material data sets for synthesis, structure, and property predictions of MOFs. These predictions are fast, efficient, and accurate and do not require heavy computing. In this review, we discuss ML models used in MOF and their incorporation with artificial intelligence (AI) in structure and property predictions. The advantage of AI in this field would accelerate research, particularly in synthesizing novel MOFs with multiple properties and applications oriented with minimum information.

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.

Study on regeneration mechanism of composite adsorbent by Mg-MOF-74-based modified biochar

In this paper, composite adsorbent was prepared from biochar and Mg-MOF-74 by in-situ growth method to investigate regeneration mechanism. The effects of O2 and temperature on regeneration characteristics were investigated by CO2 adsorption properties and characterization techniques, and the optimal regeneration conditions were determined. Regeneration mechanism of adsorbent was revealed by adsorption kinetics and elemental valence analysis. The related wave function parameters were calculated based on DFT to reveal the repair mechanism of the failed oxidation sites from the microscopic level. The mechanism of CO2 adsorption by the repaired oxidation sites was explored based on the regenerated adsorption configuration. It was found that the regeneration performance of the adsorbent exhibited a trend of increasing and then decreasing with the increase of O2 concentration and temperature, and the optimal regeneration conditions were determined to be 5 % O2 concentration and 200 °C. At optimal regeneration conditions, a synergistic interaction between O2 and poly-metals was generated to enhance the adsorbent polarity. O2 also reacted with the adsorbent in a redox reaction to produce new oxygen-containing functional groups and cause pore expansion, the mass transfer and diffusion was enhanced. The oxidation site adsorbed O2 to undergo electron rearrangement and release the adsorbed CO2. Due to the nature of common orbital hybridization between metals, the metals underwent conjugation and synergistic effects with O2 to form tetrahedral co-coordination structures with lower energies. The electron density and electric field effects of the system were enhanced. The former enhanced interaction with CO2 to form carbonate. The latter increased the activity of the neighboring N atom, which in turn generated a stable ring structure with carbonate, and CO2 adsorption was enhanced.

pyMSER─An Open-Source Library for Automatic Equilibration Detection in Molecular Simulations

Automated molecular simulations are used extensively for predicting material properties. Typically, these simulations exhibit two regimes: a dynamic equilibration part, followed by a steady state. For extracting observable properties, the simulations must first reach a steady state so that thermodynamic averages can be taken. However, as equilibration depends on simulation conditions, predicting the optimal number of simulation steps a priori is impossible. Here, we demonstrate the application of the Marginal Standard Error Rule (MSER) for automatically identifying the optimal truncation point in Grand Canonical Monte Carlo (GCMC) simulations. This novel automatic procedure determines the point at which a steady state is reached, ensuring that figures of merit are extracted in an objective, accurate, and reproducible fashion. In the case of GCMC simulations of gas adsorption in metal-organic frameworks, we find that this methodology reduces the computational cost by up to 90%. As MSER statistics are independent of the simulation method that creates the data, this library is, in principle, applicable to any time series analysis in which equilibration truncation is required. The open-source Python implementation of our method, pyMSER, is publicly available for reuse and validation at https://github.com/IBM/pymser.

Controllable Design of Polyamide Composite Membrane Separation Layer Structures via Metal-Organic Frameworks: A Review

Optimizing the structure of the polyamide (PA) layer to improve the separation performance of PA thin-film composite (TFC) membranes has always been a hot topic in the field of membrane preparation. As novel crystalline materials with high porosity, multi-functional groups, and good compatibility with membrane substrate, metal-organic frameworks (MOFs) have been introduced in the past decade for the modification of the PA structure in order to break through the separation trade-off between permeability and selectivity. This review begins by summarizing the recent progress in the control of MOF-based thin-film nanocomposite (TFN) membrane structures. The review also covers different strategies used for preparing TFN membranes. Additionally, it discusses the mechanisms behind how these strategies regulate the structure and properties of PA. Finally, the design of a competent MOF material that is suitable to reach the requirements for the fabrication of TFN membranes is also discussed. The aim of this paper is to provide key insights into the precise control of TFN-PA structures based on MOFs.

Computational and Machine Learning Methods for CO2 Capture Using Metal-Organic Frameworks

Machine learning (ML) using data sets of atomic and molecular force fields (FFs) has made significant progress and provided benefits in the fields of chemistry and material science. This work examines the interactions between chemistry and materials computational science at the atomic and molecular scales for metal-organic framework (MOF) adsorbent development toward carbon dioxide (CO2) capture. Herein, a connection will be drawn between atomic forces predicted by ML algorithms and the structures of MOFs for CO2 adsorption. Our study also takes into account the successes of atomic computational screening in the field of materials science, especially quantum ML, and its relationship to ML algorithms that clarify advancements in the area of CO2 adsorption by MOFs. Additionally, we reviewed the processes for supplying data to ML algorithms for algorithm training, including text mining from scientific articles, and MOF's formula processing linked to the chemical properties of MOFs. To create ML algorithms for future research, we recommend that the digitization of scientific records can help efficiently synthesize advanced MOFs. Finally, a future vision for developing pioneer MOF synthesis routes for CO2 capture is presented in this review article.

Large-Scale Computational Screening-Aided Development of High-Performance Adsorbent for Simultaneous Capture of Aromatic Volatile Organic Compounds

The development of an efficient adsorbent for the simultaneous capture of large amounts of benzene, toluene, ethylbenzene, and xylene isomers (BTEX) is an important and challenging issue. Here, through a stepwise screening of 10,142 metal-organic framework (MOF) structures from the computation-ready, experimental (CoRE) MOF database, 65 MOFs are proposed as promising adsorbent candidates for BTEX capture by considering the structures with accessible pore sizes for BTEX adsorption, sufficient hydrophobicity, high benzene selectivity (>0.2), and large total BTEX uptake (>3 mmol/g). Among the top-performing MOFs in terms of the BTEXmatrix (total BTEX uptake × benzene selectivity), EGUELUY01 was synthesized, and it exhibited large uptakes (≈5 mmol/g) for all BTEX components at concentrations of 1200-1500 ppm, which are superior to the BTEX uptake of the benchmark adsorbent, activated carbon. Moreover, some structure-property relationships required for BTEX adsorbents are provided through the obtained large-scale simulation data and machine learning analysis. The determined relationships will be useful for the future development of efficient BTEX adsorbents.

Highly stable mesoporous Co/Ni mixed metal-organic framework [Co/Ni(μ3-tp)2(μ2-pyz)2] for Co (II) heavy metal ions (HMIs) remediation

A bimetallic cobalt/nickel-based metal-organic framework (MOF), [Co/Ni(μ3-tp)2(μ2-pyz)2], denoted as Co/Ni-MOF, has been successfully prepared by a hydrothermal method. The MOF was prepared by incorporating mixed O- and N- donor ligands, specifically terephthalic acid (tp) and pyrazine (pyz). The Mesoporous Co/Ni-MOF was comprehensively characterized using various analytical methods such as XRD, BET, FT-IR, TGA (23 % char yields), SEM, and EDS analyses. The synthesized mesoporous Co/Ni-MOF was then used to absorb Co (II) from aquatic areas efficiently. Several critical parameters, such as the beginning Co (II) concentration (25-150 mg/L), the effect of pH (2-10), the duration of time (5-30 min), and the amount of adsorbent (0.003-0.02 g), were systematically investigated. Remarkably, the Mesoporous Co/Ni MOF displayed a significant adsorption capacity of 372.66 mg g-1 in the optimum conditions, including pH = 6, amount of adsorbent = 0.003 g, duration of time = 25 min, and beginning Co (II) concentration = 150 mg/L. Adsorption data from the experimental studies of the mesoporous Co/Ni MOF are matched based on the non-linear pseudo-first-order (PSO) kinetic model (R2 = 0.9999), and a chemical process is suggested for chemisorption. Furthermore, the adsorption isotherms of Co (II) heavy metal ions (HMIs) are an excellent fit with the non-linear Temkin, indicating that they explain the sorbent/sorbate interactions concerning the heat of adsorption. It is evident from the thermodynamic parameters that adsorption is a spontaneous and favorable exothermic process. These results highlight the promising adsorption performance and potential applications of the mesoporous Co/Ni-MOF as an effective adsorbent for Co (II) elimination from aquatic areas. Four-cycle regeneration studies were the most effective for the Co (II) under study.

A holistic platform for accelerating sorbent-based carbon capture

Reducing carbon dioxide (CO2) emissions urgently requires the large-scale deployment of carbon-capture technologies. These technologies must separate CO2 from various sources and deliver it to different sinks1,2. The quest for optimal solutions for specific source-sink pairs is a complex, multi-objective challenge involving multiple stakeholders and depends on social, economic and regional contexts. Currently, research follows a sequential approach: chemists focus on materials design3 and engineers on optimizing processes4,5, which are then operated at a scale that impacts the economy and the environment. Assessing these impacts, such as the greenhouse gas emissions over the plant's lifetime, is typically one of the final steps6. Here we introduce the PrISMa (Process-Informed design of tailor-made Sorbent Materials) platform, which integrates materials, process design, techno-economics and life-cycle assessment. We compare more than 60 case studies capturing CO2 from various sources in 5 global regions using different technologies. The platform simultaneously informs various stakeholders about the cost-effectiveness of technologies, process configurations and locations, reveals the molecular characteristics of the top-performing sorbents, and provides insights on environmental impacts, co-benefits and trade-offs. By uniting stakeholders at an early research stage, PrISMa accelerates carbon-capture technology development during this critical period as we aim for a net-zero world.

Computational Simulations of Metal-Organic Frameworks to Enhance Adsorption Applications

Metal-organic frameworks (MOFs), renowned for their exceptional porosity and crystalline structure, stand at the forefront of gas adsorption and separation applications. Shortly after their discovery through experimental synthesis, computational simulations quickly become an important method in broadening the use of MOFs by offering deep insights into their structural, functional, and performance properties. This review specifically addresses the pivotal role of molecular simulations in enlarging the molecular understanding of MOFs and enhancing their applications, particularly for gas adsorption. After reviewing the historical development and implementation of molecular simulation methods in the field of MOFs, high-throughput computational screening (HTCS) studies used to unlock the potential of MOFs in CO2 capture, CH4 storage, H2 storage, and water harvesting are visited and recent advancements in these adsorption applications are highlighted. The transformative impact of integrating artificial intelligence with HTCS on the prediction of MOFs' performance and directing the experimental efforts on promising materials is addressed. An outlook on current opportunities and challenges in the field to accelerate the adsorption applications of MOFs is finally provided.

PACMAN: A Robust Partial Atomic Charge Predicter for Nanoporous Materials Based on Crystal Graph Convolution Networks

We report a fast and easy method (PACMAN) to assign partial atomic charges on metal-organic framework (MOF) and covalent-organic framework (COF) crystal structures based on graph convolution networks (GCNs) trained on >1.8 million high-fidelity partial atomic charge data obtained from the Quantum Metal-Organic Framework (QMOF) database. The developed model shows outstanding performance, achieving a mean absolute error (MAE) of 0.0055 e (test set performance) while maintaining consistency with DDEC6, Bader, and CM5 charges across diverse chemistry and topologies of MOFs and COFs. We find that the new method accurately assigns partial atomic charges for ion-containing nanoporous materials, which has not been possible in previous machine learning (ML) models. Grand canonical Monte Carlo (GCMC) simulation results for CO2 and N2 uptakes and the Widom particle insertion calculation for Henry's law constant of water results based on PACMAN and the original DDEC6 charges show excellent agreements compared to other ML models reported in the literature. The runtime analysis of the new method demonstrates that the partial atomic charges of MOF and COF structures with up to 500 atoms can be obtained in less than 10 s. An easy-to-use web interface has been developed to facilitate the adoption of the developed model.

Effect of Co-Adsorbed Guest Adsorbates on the Separation of Ethylene/Ethane Mixtures on Metal-Organic Frameworks with Open Metal Sites

Direct determination of the equilibrium adsorption and spectroscopic observation of adsorbent-adsorbate interaction is crucial to evaluate the olefin/paraffin separation performance of porous adsorbents. However, the experimental characterization of competitive adsorption of various adsorbates at atomic-molecular level in the purification of multicomponent gas mixtures is challenging and rarely conducted. Herein, solid-state NMR spectroscopy is employed to examine the effect of co-adsorbed guest adsorbates on the separation of ethylene/ethane mixtures on Mg-MOF-74, Zn-MOF-74 and UTSA-74. 1H MAS NMR facilitates the determination of equilibrium uptake and adsorption selectivity of ethylene/ethane in ternary mixtures. The co-adsorption of H2O and CO2 significantly leads to the degradation of ethylene uptake and ethylene/ethane selectivity. The detailed host-guest and guest-guest interactions are unraveled by 2D 1H-1H spin diffusion homo-nuclear correlation and static 25Mg NMR experiments. The experimental results verify H2O coordinated on open metal sites can supply a new adsorption site for ethylene and ethane. The effects of guest adsorbates on the adsorption capacity and adsorption selectivity of ethylene/ethane mixtures are in the following order: H2O>CO2>O2. This work provides a direct approach for exploring the equilibrium adsorption and detailed separation mechanism of multicomponent gas mixtures using MOFs adsorbents.

Coarse-grained force field for ZIF-8: A study on adsorption, diffusion, and structural properties

Metal-organic frameworks (MOFs) are revolutionizing a spectrum of industries, from groundbreaking gas storage solutions to transformative biological system applications. The intricate architecture of these materials necessitates the use of advanced computational techniques for a comprehensive understanding of their molecular structure and prediction of their physical properties. Coarse-grained (CG) simulations shine a spotlight on the often-neglected influences of defects, pressure effects, and spatial disorders on the performance of MOFs. These simulations are not just beneficial but indispensable for high-demand applications, such as mixed matrix membranes and intricate biological system interfaces. In this work, we propose an optimized CG force field tailored for ZIF-8. Our work provides a deep dive into sorption isotherms and diffusion coefficients of small molecules. We demonstrate the structural dynamics of ZIF-8, particularly how it responds to pressurization, which affects its crystal structure and leads to local changes in aperture size and area. Emphasizing the game-changing potential of CG simulations, we explore the characteristics of amorphization in ZIF-8. Through computational exploration, we aim to bridge the knowledge gap, enhancing the potential applications of nanoporous materials for various applications.

The Open DAC 2023 Dataset and Challenges for Sorbent Discovery in Direct Air Capture

Direct air capture (DAC) of CO2 with porous adsorbents such as metal-organic frameworks (MOFs) has the potential to aid large-scale decarbonization. Previous screening of MOFs for DAC relied on empirical force fields and ignored adsorbed H2O and MOF deformation. We performed quantum chemistry calculations overcoming these restrictions for thousands of MOFs. The resulting data enable efficient descriptions using machine learning.

Efficient Modeling of Water Adsorption in MOFs Using Interpolated Transition Matrix Monte Carlo

With the specter of accelerating climate change, securing access to potable water has become a critical global challenge. Atmospheric water harvesting (AWH) through metal-organic frameworks (MOFs) emerges as one of the promising solutions. The standard numerical methods applied for rapid and efficient screening for optimal sorbents face significant limitations in the case of water adsorption (slow convergence and inability to overcome high energy barriers). To address these challenges, we employed grand canonical transition matrix Monte Carlo (GC-TMMC) methodology and proposed an efficient interpolation scheme that significantly reduces the number of required simulations while maintaining accuracy of the results. Through the example of water adsorption in three MOFs: MOF-303, MOF-LA2-1, and NU-1000, we show that the extrapolation of the free energy landscape allows for prediction of the adsorption properties over a continuous range of pressure and temperature. This innovative and versatile method provides rich thermodynamic information, enabling rapid, large-scale computational screening of sorbents for adsorption, applicable for a variety of sorbents and gases. As the presented methodology holds strong applicative potential, we provide alongside this paper a modified version of the RASPA2 code with a ghost swap move implementation and a Python library designed to minimize the user's input for analyzing data derived from the TMMC simulations.

Rapid and Accurate Screening of the COF Space for Natural Gas Purification: COFInformatics

In this work, we introduced COFInformatics, a computational approach merging molecular simulations and machine learning (ML) algorithms, to evaluate all synthesized and hypothetical covalent organic frameworks (COFs) for the CO2/CH4 mixture separation under four different adsorption-based processes: pressure swing adsorption (PSA), vacuum swing adsorption (VSA), temperature swing adsorption (TSA), and pressure-temperature swing adsorption (PTSA). We first extracted structural, chemical, energy-based, and graph-based molecular fingerprint features of every single COF structure in the very large COF space, consisting of nearly 70,000 materials, and then performed grand canonical Monte Carlo simulations to calculate the CO2/CH4 mixture adsorption properties of 7540 COFs. These features and simulation results were used to develop ML models that accurately and rapidly predict CO2/CH4 mixture adsorption and separation properties of all 68,614 COFs. The most efficient separation process and the best adsorbent candidates among the entire COF spectrum were identified and analyzed in detail to reveal the most important molecular features that lead to high-performance adsorbents. Our results showed that (i) many hypoCOFs outperform synthesized COFs by achieving higher CO2/CH4 selectivities; (ii) the top COF adsorbents consist of narrow pores and linkers comprising aromatic, triazine, and halogen groups; and (iii) PTSA is the most efficient process to use COF adsorbents for natural gas purification. We believe that COFInformatics promises to expedite the evaluation of COF adsorbents for CO2/CH4 separation, thereby circumventing the extensive, time- and resource-intensive molecular simulations.

Binder-Free MOF-Based and MOF-Derived Nanoarrays for Flexible Electrochemical Energy Storage: Progress and Perspectives

The fast development of Internet of Things and the rapid advent of next-generation versatile wearable electronics require cost-effective and highly-efficient electroactive materials for flexible electrochemical energy storage devices. Among various electroactive materials, binder-free nanostructured arrays have attracted widespread attention. Featured with growing on a conductive and flexible substrate without using inactive and insulating binders, binder-free 3D nanoarray electrodes facilitate fast electron/ion transportation and rapid reaction kinetics with more exposed active sites, maintain structure integrity of electrodes even under bending or twisted conditions, readily release generated joule heat during charge/discharge cycles and achieve enhanced gravimetric capacity of the whole device. Binder-free metal-organic framework (MOF) nanoarrays and/or MOF-derived nanoarrays with high surface area and unique porous structure have emerged with great potential in energy storage field and been extensively exploited in recent years. In this review, common substrates used for binder-free nanoarrays are compared and discussed. Various MOF-based and MOF-derived nanoarrays, including metal oxides, sulfides, selenides, nitrides, phosphides and nitrogen-doped carbons, are surveyed and their electrochemical performance along with their applications in flexible energy storage are analyzed and overviewed. In addition, key technical issues and outlooks on future development of MOF-based and MOF-derived nanoarrays toward flexible energy storage are also offered.

High-throughput computational screening of MOF adsorbents for efficient propane capture from air and natural gas mixtures

In this study, we used a high-throughput computational screening approach to examine the potential of metal-organic frameworks (MOFs) for capturing propane (C3H8) from different gas mixtures. We focused on Quantum MOF (QMOF) database composed of both synthesized and hypothetical MOFs and performed Grand Canonical Monte Carlo (GCMC) simulations to compute C3H8/N2/O2/Ar and C3H8/C2H6/CH4 mixture adsorption properties of MOFs. The separation of C3H8 from air mixture and the simultaneous separation of C3H8 and C2H6 from CH4 were studied for six different adsorption-based processes at various temperatures and pressures, including vacuum-swing adsorption (VSA), pressure-swing adsorption (PSA), vacuum-temperature swing adsorption (VTSA), and pressure-temperature swing adsorption (PTSA). The results of molecular simulations were used to evaluate the MOF adsorbents and the type of separation processes based on selectivity, working capacity, adsorbent performance score, and regenerability. Our results showed that VTSA is the most effective process since many MOFs offer high regenerability (>90%) combined with high C3H8 selectivity (>7 × 103) and high C2H6 + C3H8 selectivity (>100) for C3H8 capture from air and natural gas mixtures, respectively. Analysis of the top MOFs revealed that materials with narrow pores (<10 Å) and low porosities (<0.7), having aromatic ring linkers, alumina or zinc metal nodes, typically exhibit a superior C3H8 separation performance. The top MOFs were shown to outperform commercial zeolite, MFI for C3H8 capture from air, and several well-known MOFs for C3H8 capture from natural gas stream. These results will direct the experimental efforts to the most efficient C3H8 capture processes by providing key molecular insights into selecting the most useful adsorbents.

Non-CO2 greenhouse gas separation using advanced porous materials

Global warming has become a growing concern over decades, prompting numerous research endeavours to reduce the carbon dioxide (CO2) emission, the major greenhouse gas (GHG). However, the contribution of other non-CO2 GHGs including methane (CH4), nitrous oxide (N2O), fluorocarbons, perfluorinated gases, etc. should not be overlooked, due to their high global warming potential and environmental hazards. In order to reduce the emission of non-CO2 GHGs, advanced separation technologies with high efficiency and low energy consumption such as adsorptive separation or membrane separation are highly desirable. Advanced porous materials (APMs) including metal-organic frameworks (MOFs), covalent organic frameworks (COFs), hydrogen-bonded organic frameworks (HOFs), porous organic polymers (POPs), etc. have been developed to boost the adsorptive and membrane separation, due to their tunable pore structure and surface functionality. This review summarizes the progress of APM adsorbents and membranes for non-CO2 GHG separation. The material design and fabrication strategies, along with the molecular-level separation mechanisms are discussed. Besides, the state-of-the-art separation performance and challenges of various APM materials towards each type of non-CO2 GHG are analyzed, offering insightful guidance for future research. Moreover, practical industrial challenges and opportunities from the aspect of engineering are also discussed, to facilitate the industrial implementation of APMs for non-CO2 GHG separation.

Reproductive and whole-body toxicity of Ag-doped and -undoped ZIF-8 nanoparticles and the building blocks: An Artemia-based comparative bioassay

The present research assessed, for the first time, toxicity of ZIF-8 (1 mg/L) and the building blocks (0.1 mg/L Zn2+ and 0.4 mg/L 2-methylimidazole (2-MIm)), besides that of AgNPs@ZIF-8 (0.25, 0.5, and 1 mg/L) and AgNO3 (0.1 mg/L) to aquatic organisms. Two consecutive generations (F0 & F1) of Artemia salina were exposed to these chemicals. All of the chemical treatments considerably caused mortality in F0, especially AgNPs@ZIF-8 and AgNO3, whereas F1 displayed notable tolerance and survived comparable to the control group, except in the case of AgNO3 treatment. Similarly, growth indices (weight, mainly in ZIF-8, Zn2+, and 2-MIm; length, in Ag-doped ZIF-8 and AgNO3) were significantly retarded in F0 and especially F1 of all treatments, and 2-MIm caused the greatest length retardation in F0. AgNPs@ZIF-8 (0.5 and 1 mg/L), 2-MIm, and AgNO3 postponed the ovary emergence in about 40%-60% of the exposed F0, and ZIF-8 delayed this phenomenon in some individuals of F0 and F1 up to 6 days. This temporal disturbance was also observed in time to first brood of almost all experimental F0 and F1 groups, with being over 80% of F1 exposed to ZIF-8, 2-MIm, and Zn2+, as well as about 50% of F0 treated with 2-MIm, and Zn2+. The highest neonate number was recorded for F0 and F1 exposed to AgNO3 and Zn2+, while ZIF-8 and, importantly, 2-MIm decreased the reproductivity to the lowest levels in both generations. Generally, the reproductive frequency was significantly decreased in all F0 and F1 treatments, especially 2-MIm, ZIF-8, AgNPs@ZIF-8 (0.25 & 1 mg/L). This study highlighted the neglected importance of 2-MIm in assessing overall toxicity of ZIF-8, and even other organic ligands of MOFs, and also filled a gap in the literature by investigating the potential effect of additives such as AgNPs on the toxicity of ZIF-8 and other MOFs.

Accelerated Discovery of Metal-Organic Frameworks for CO2 Capture by Artificial Intelligence

The existence of a very large number of porous materials is a great opportunity to develop innovative technologies for carbon dioxide (CO2) capture to address the climate change problem. On the other hand, identifying the most promising adsorbent and membrane candidates using iterative experimental testing and brute-force computer simulations is very challenging due to the enormous number and variety of porous materials. Artificial intelligence (AI) has recently been integrated into molecular modeling of porous materials, specifically metal-organic frameworks (MOFs), to accelerate the design and discovery of high-performing adsorbents and membranes for CO2 adsorption and separation. In this perspective, we highlight the pioneering works in which AI, molecular simulations, and experiments have been combined to produce exceptional MOFs and MOF-based composites that outperform traditional porous materials in CO2 capture. We outline the future directions by discussing the current opportunities and challenges in the field of harnessing experiments, theory, and AI for accelerated discovery of porous materials for CO2 capture.

Hierarchical Computational Screening of Quantum Metal-Organic Framework Database to Identify Metal-Organic Frameworks for Volatile Organic-Compound Capture from Air

The design and discovery of novel porous materials that can efficiently capture volatile organic compounds (VOCs) from air are critical to address one of the most important challenges of our world, air pollution. In this work, we studied a recently introduced metal-organic framework (MOF) database, namely, quantum MOF (QMOF) database, to unlock the potential of both experimentally synthesized and hypothetically generated structures for adsorption-based n-butane (C4H10) capture from air. Configurational Bias Monte Carlo (CBMC) simulations were used to study the adsorption of a quaternary gas mixture of N2, O2, Ar, and C4H10 in QMOFs for two different processes, pressure swing adsorption (PSA) and vacuum-swing adsorption (VSA). Several adsorbent performance evaluation metrics, such as C4H10 selectivity, working capacity, the adsorbent performance score, and percent regenerability, were used to identify the best adsorbent candidates, which were then further studied by molecular simulations for C4H10 capture from a more realistic seven-component air mixture consisting of N2, O2, Ar, C4H10, C3H8, C3H6, and C2H6. Results showed that the top five QMOFs have C4H10 selectivities between 6.3 × 103 and 9 × 103 (3.8 × 103 and 5 × 103) at 1 bar (10 bar). Detailed analysis of the structure-performance relations showed that low/mediocre porosity (0.4-0.6) and narrow pore sizes (6-9 Å) of QMOFs lead to high C4H10 selectivities. Radial distribution function analyses of the top materials revealed that C4H10 molecules tend to confine close to the organic parts of MOFs. Our results provided the first information in the literature about the VOC capture potential of a large variety and number of MOFs, which will be useful to direct the experimental efforts to the most promising adsorbent materials for C4H10 capture from air.

Well-constructed a water stable Cu-BTC@TpPa-1 binary composite with excellent capture ability toward malachite green

Adsorptive removal of dyes (e.g., malachite green (MG)) from wastewater using commercially available adsorbents is not significantly efficient. Metal-organic frameworks (MOFs) such as Cu-BTC is considered as an excellent adsorbent in adsorption-separation filed. However, the water instability of Cu-BTC restricts its potential utilization in dye wastewater purification. In this paper, we have developed a novel metal/covalent-organic frameworks (Cu-BTC@TpPa-1) binary composite by solvothermal method. This composite serves as a multifunctional platform for the effective removal of MG from water. This Cu-BTC@TpPa-1 obviously keeps structural integrity soaked in water for 7 days. And its heat resistant performance can achieve 360 °C because of the TpPa-1 protection, which is outdistance to that of Cu-BTC. The adsorbed capacity of MG over Cu-BTC@TpPa-1 is exceptionally high, with an uptake of up to 64.12 mg/g, which is superior compared to previous adsorbents, highlighting its superior adsorption capabilities. The adsorptive performance was controlled by the associative effects of Cu-BTC and TpPa-1 with an association effect of π-complexation and electrostatic attraction. The Cu-BTC@TpPa-1 might be a prospective adsorbent for MG capture from industrial wastewater.

Understanding the Heat Transfer Performance of Zeolitic Imidazolate Frameworks upon Gas Adsorption by Molecular Dynamics Simulations

Metal-organic frameworks (MOFs) with ultrahigh specific surface area and porosity have emerged as promising nanoporous materials for gas separation, storage, and adsorption-driven thermal energy conversion systems such as adsorption heat pumps. However, an inadequate understanding of the thermal transport of MOFs with adsorbed gases hampers the thermal management of such systems in practical applications. In this work, an in-depth investigation on the mechanistic heat transfer performance of three topological zeolitic imidazolate frameworks (ZIFs) upon hydrogen, methane, and ethanol adsorption was carried out by molecular dynamics simulations. It is revealed that the trade-off between the additional heat transfer pathway and phonon scattering resulting from adsorbed gases determines the thermal conductivity of ZIFs. It is found that the increased thermal conductivity with the increased number of adsorbed gases is correlated with the overlap energy between the vibrational density of states of gases and Zn atoms, suggesting the additional heat transfer pathways formed between gas molecules and frameworks. Moreover, the gas spatial distribution and diffusion also impose remarkable impacts on the heat transfer performance. Both the homogeneous gas distribution and the fast gas diffusion are conducive to form effective heat transfer pathways, leading to enhanced thermal conductivity. This study provides molecular insight into the mechanism of the improved thermal conductivity of ZIFs upon gas adsorption, which may pave the way for effective thermal management in MOF-related applications.

Advancing CH4/H2 separation with covalent organic frameworks by combining molecular simulations and machine learning

A high-throughput computational screening approach combined with machine learning (ML) was introduced to unlock the potential of both synthesized and hypothetical COFs (hypoCOFs) for adsorption-based CH₄/H₂ separation. We studied 597 synthesized COFs for adsorption of a CH₄/H₂ mixture using Grand Canonical Monte Carlo (GCMC) simulations under pressure-swing adsorption (PSA) and vacuum-swing adsorption (VSA) conditions. Based on the simulation results, the CH₄/H₂ selectivities, CH₄ working capacities, adsorbent performance scores, and regenerabilities of the synthesized COFs were assessed and the structural properties of the top-performing COFs were identified. The hypoCOF database composed of 69 840 materials was then filtered to identify 7737 hypothetical materials having similar structural properties to the top synthesized COFs. These hypothetical COFs were then examined for CH₄/H₂ separation using molecular simulations and the results showed that the top hypoCOFs have CH₄ selectivities and working capacities in the ranges of 21.9-28.7 (64.7-128.6) and 5.8-7.6 (1.3-3.1) mol kg^-1 under PSA (VSA) conditions, respectively, outperforming the synthesized COFs and metal-organic frameworks (MOFs). ML models were then developed based on the hypoCOF simulation results to accurately predict the CH₄/H₂ mixture adsorption properties of all remaining hypothetical materials when their structural and chemical properties are fed into the models. These models accurately assessed the CH₄/H₂ mixture separation performances of any hypoCOF within seconds without performing computationally demanding molecular simulations. The computational approach that we have proposed in this study will provide an accurate and efficient assessment of COF materials for CH₄/H₂ separation and significantly accelerate the experimental efforts towards the design and discovery of new high-performing COF adsorbents.

An accuracy improved ratiometric SERS sensor for rhodamine 6G in chili powder using a metal-organic framework support

Internal standard molecule 4-mercaptobenzoic acid (4-MBA) embedded Au core-Ag shell nanorods (Au-MBA@Ag NRs) were prepared by a seed-mediated growth method, then loaded on octahedral MIL-88B-NH₂ to obtain a novel ratiometric SERS substrate of Au-MBA@Ag NRs/PSS/MIL-88B-NH₂ (AMAPM) for detecting rhodamine 6G (R6G) in chili powder. The porous structure and excellent adsorption ability of MIL-88B-NH₂, allowed for increased loading of Au-MBA@Ag NRs, thereby shortening the distance between adsorbed R6G and the "hot spot" resulting from local surface plasmon resonance (LSPR) of Au-MBA@Ag NRs. Based on the SERS characteristic peak ratio of R6G to 4-MBA, the ratiometric SERS substrate displayed improved accuracy and excellent performance for R6G detection, with a wide linear range of 5-320 nM and a low detection limit of 2.29 nM as well as fine stability, reproducibility and specificity. The proposed ratiometric SERS substrate offered a simple, fast and sensitive sensing strategy for R6G detection in chili powder, which demonstrated potential applications in food safety and the analysis of trace analytes in complex matrices.

CuO-decorated MOF derived ZnO polyhedral nanostructures for exceptional H2S gas detection

Considering that H₂S is a hazardous gas that poses a significant risk to people's lives, research into H₂S gas sensors has garnered a lot of interest. This work reports a CuO/ZnO multifaceted nanostructures(NS) created by heat treating Cu²⁺/ZIF-8 impregnation precursors, and their microstructure and gas sensing characteristics were examined using various characterization techniques (XRD, XPS, SEM, TEM, and BET). The as-prepared hollow CuO/ZnO multifunctional nanostructures had a high gas response value (425@50 ppm H₂S gas), quick response and recovery times (57/191s @20 ppm), a low limit of detection (1.6@500 ppb H₂S), good humidity resistance and highly selective towards H₂S gas. The hollow CuO/ZnO multifaceted nanostructures possessed enhanced gas sensing capabilities which may be related to their porous hollow nanostructures, the manufactured p-CuO/n-ZnO heterojunctions, and the spillover effect between CuO and H₂S.

In Silico Screening of Metal-Organic Frameworks and Zeolites for He/N2 Separation

In silico screening of 10,143 metal-organic frameworks (MOFs) and 218 all-silica zeolites for adsorption-based and membrane-based He and N₂ separation was performed. As a result of geometry-based prescreening, structures having zero accessible surface area (ASA) and pore limiting diameter (PLD) less than 3.75 Å were eliminated. So, both gases can be adsorbed and pass-through MOF and zeolite pores. The Grand canonical Monte Carlo (GCMC) and equilibrium molecular dynamics (EMD) methods were used to estimate the Henry's constants and self-diffusion coefficients at infinite dilution conditions, as well as the adsorption capacity of an equimolar mixture of helium and nitrogen at various pressures. Based on the obtained results, adsorption, diffusion and membrane selectivities as well as membrane permeabilities were calculated. The separation potential of zeolites and MOFs was evaluated in the vacuum and pressure swing adsorption processes. In the case of membrane-based separation, we focused on the screening of nitrogen-selective membranes. MOFs were demonstrated to be more efficient than zeolites for both adsorption-based and membrane-based separation. The analysis of structure-performance relationships for using these materials for adsorption-based and membrane-based separation of He and N₂ made it possible to determine the ranges of structural parameters, such as pore-limiting diameter, largest cavity diameter, surface area, porosity, accessible surface area and pore volume corresponding to the most promising MOFs for each separation model discussed in this study. The top 10 most promising MOFs were determined for membrane-based, vacuum swing adsorption and pressure swing adsorption separation methods. The effect of the electrostatic interaction between the quadrupole moment of nitrogen molecules and MOF atoms on the main adsorption and diffusion characteristics was studied. The obtained results can be used as a guide for selection of frameworks for He/N₂ separation.

Molecular dynamics simulation of adsorption and separation of xylene isomers by Cu-HKUST-1

Metal-organic frameworks (MOFs) are widely used in the adsorption separation of various gases. A fundamental understanding of the effective separation of xylene isomers helps improve aromatic products' separation efficiency and reduce industrial separation costs. Grand Canonical Monte Carlo (GCMC) simulations combined with Molecular Science is widely used to predict gas adsorption and diffusion in single crystals with metal-organic frameworks. We performed a GCMC + MD combined approach to study xylene isomers' adsorption and separation in Cu-HKUST-1 to predict the permeability and selectivity of the ternary gas mixture in the MOF with the adsorption and diffusion usage data. Most current studies take into account the computational cost and difficulty. Most recent research models are limited to the adsorption of a single or specific molecule, such as hydrogen, methane, carbon dioxide, etc. For this reason, we report an attempt to study the adsorption separation of aromatic gases (p-xylene/o-xylene/m-xylene) based on Cu-HKUST-1 single-crystal materials based on some previous research methods with an appropriate increase in computational cost. To predict the adsorption selectivity and permeability of the ternary mixture of xylene isomers on the MOF surface, the model simulation calculates key parameters of gas adsorption, including gas adsorption volume (N), the heat of adsorption (Q st), Henry coefficient (K), and diffusion coefficient (D).

Synthesis of mesoporous-structured MIL-68(Al)/MCM-41-NH2 for methyl orange adsorption: Optimization and Selectivity

Here, we report a novel amino-modified mesoporous-structured aluminum-based metal-organic framework adsorbent, MIL-68(Al)/MCM-41-NH₂, for dye sewage treatment. The introduction of molecular sieves overcomes the inherent defects of microporous MOFs in contaminant transfer and provides more active sites to enhance adsorption efficiency. Compared with using organic amino ligands directly, this strategy is ten times cheaper. The composite was well characterized and analyzed in terms of morphology, structure and chemical composition. Batch experiments were carried out to study the influences of essential factors on the process, such as pH and temperature. In addition, their interactions and the optimum conditions were examined using response surface methodology (RSM). The adsorption kinetics, isotherms and thermodynamics were systematically elucidated. In detail, the adsorption process conforms to pseudo-second-order kinetics and follows the Sips and Freundlich isothermal models. Moreover, the maximum adsorption capacity Q_s of methyl orange (MO) was 477 mg g^-1. It could be concluded that the process was spontaneous, exothermic, and entropy-reducing. Several binary dye systems have been designed for selective adsorption research. Our material has an affinity for anionic pigments. The adsorption mechanisms were discussed in depth. The electrostatic interaction might be the dominant effect. Meanwhile, hydrogen bonding, π-π stacking, and pore filling might be important driving forces. The excellent thermal stability and recyclability of the adsorbent are readily noticed. After five reuse cycles, the composite still possesses a removal efficiency of 90% for MO. Overall, the efficient and low-cost composite can be regarded as a promising adsorbent for the selective adsorption of anionic dyes from wastewater.

Polyvinyl alcohol/starch-based film incorporated with grape skin anthocyanins and metal-organic framework crystals for colorimetric monitoring of pork freshness

An intelligent film for the visual monitoring of pork freshness was developed using degradable polyvinyl alcohol (PVA)/starch (PS) to immobilize the chromogenic agent of anthocyanins and the volatile amine collector of metal-organic frameworks (MOFs). The indicative property of grape skin anthocyanins (GSAs) was verified using the UV-vis spectra, corresponding to multi-color changing in a pH range of 2-12. Interestingly, the introduction of MIL-101 crystals in the PS/GSAs film significantly increased the specific surface area (approximately 10 times) of the film, the superior volatile amine enrichment capability of MIL-101 enabling the film to detect freshness with a high degree of sensitivity. Moreover, the as-prepared film exhibited good antibacterial properties attributed to MIL-101, which help maintain the freshness of the pork. Owing to these advantages, the PS-GSAs/MIL-101 film was tested to real-timely monitor pork freshness in package, the results were further confirmed basis the total volatile basic nitrogen values.

Computational investigation of multifunctional MOFs for adsorption and membrane-based separation of CF4/CH4, CH4/H2, CH4/N2, and N2/H2 mixtures

The ease of functionalization of metal-organic frameworks (MOFs) can unlock unprecedented opportunities for gas adsorption and separation applications as the functional groups can impart favorable/unfavorable regions/interactions for the desired/undesired adsorbates. In this study, the effects of the presence of multiple functional groups in MOFs on their CF₄/CH₄, CH₄/H₂, CH₄/N₂, and N₂/H₂ separation performances were computationally investigated combining grand canonical Monte Carlo (GCMC) and molecular dynamics (MD) simulations. The most promising adsorbents showing the best combinations of selectivity, working capacity, and regenerability were identified for each gas separation. 15, 13, and 16 out of the top 20 MOFs identified for the CH₄/H₂, CH₄/N₂, and N₂/H₂ adsorption-based separation, respectively, were found to have -OCH₃ groups as one of the functional groups. The biggest improvements in CF₄/CH₄, CH₄/H₂, CH₄/N₂, and N₂/H₂ selectivities were found to be induced by the presence of -OCH₃-OCH₃ groups in MOFs. For CH₄/H₂ separation, MOFs with two and three functionalized linkers were the best adsorbent candidates while for N₂/H₂ separation, all the top 20 materials involve two functional groups. Membrane performances of the MOFs were also studied for CH₄/H₂ and CH₄/N₂ separation and the results showed that MOFs having -F-NH₂ and -F-OCH₃ functional groups present the highest separation performances considering both the membrane selectivity and permeability.

Electrostatic potential-derived charge: a universal OER performance descriptor for MOFs

Metal-organic frameworks (MOFs) provide opportunities for the design of high-efficiency catalysts attributed to their high compositional and structural tunability. Meanwhile, the huge number of MOFs poses a great challenge to experimental-intensive development of high-performance functional applications. By taking the computationally feasible and structurally representative trigonal prismatic secondary building units (SBUs) of MOFs as the entry point, we introduce a descriptor-based approach for designing high-performance MOFs for the oxygen evolution reaction (OER). The electrostatic potential-derived charge (ESPC) is identified as a robust and universal OER performance descriptor of MOFs, showing a distinct linear relationship with the onset potentials of OER elemental steps. Importantly, we establish an ESPC-based physical pattern of active site-intermediate binding strength, which interprets the rationality of ESPC as an OER performance descriptor. We further reveal that the SBUs with Ni/Cu as active site atoms while Mn/Fe/Co/Ni as spectator atoms have excellent OER activity through the variation pattern of ESPC along with metal composition. The universal correlation between ESPC and OER activity provides a rational rule for designing high-performance MOF-based OER electrocatalysts and can be easily extended to design functional MOFs for a rich variety of catalytic applications.