Adsorption Properties Of Metal-organic Frameworks Research Articles

ZIF-8/g-C3N4 photocatalysts: enhancing CO2 reduction through improved adsorption and photocatalytic performance.

Nowadays, the widespread concern over controlling CO2 emissions and mitigating the adverse effects of greenhouse gases on global climate has attracted significant attention. In this study, g-C3N4 was synthesized by thermopolymerizing urea. Subsequently, ZIF-8 was combined with g-C3N4 using an in situ deposition method, resulting in the fabrication of ZIF-8/g-C3N4 composite photocatalysts at various molar ratios. Effective incorporation of ZIF-8 into g-C3N4 suppressed the recombination of photogenerated electrons and holes, thereby enhancing CO2 capture capacity and preserving light absorption capabilities. The ZIF-8/g-C3N4 composite demonstrates excellent photocatalytic performance for CO2 reduction, where the optimized material exhibited a CO2 adsorption capacity 1.52 times that of pure g-C3N4 and increased the conversion of CO2 to CH4 by more than sevenfold. This study harnesses the superior CO2 adsorption properties of metal-organic frameworks to develop more efficient photocatalysts, enhancing CO2 conversion efficacy and offering insights for developing efficient photocatalysts that utilize CO2.

Three-Dimensional Visualization of Adsorption Distribution in a Single Crystalline Particle of a Metal-Organic Framework.

Many unique adsorption properties of metal-organic frameworks (MOFs) have been revealed by diffraction crystallography, visualizing their vacant and guest-loaded crystal structures at the molecular scale. However, it has been challenging to see the spatial distribution of the adsorption behaviors throughout a single MOF particle in a transient equilibrium state. Here, we report three-dimensional (3D) visualization of molecular adsorption behaviors in a single crystalline particle of a MOF by in situ X-ray absorption fine structure spectroscopy combined with computed tomography for the first time. The 3D maps of water-coordinated Co sites in a 100 μm-scale MOF-74-Co crystal were obtained with 1 μm spatial resolution under several water vapor pressures. Through the visualization of the water vapor adsorption process, 3D spectroimaging revealed the mechanism and spatial heterogeneity of guest adsorption inside a single particle of a crystalline MOF.

Informative Training Data for Efficient Property Prediction in Metal-Organic Frameworks by Active Learning.

In recent data-driven approaches to material discovery, scenarios where target quantities are expensive to compute and measure are often overlooked. In such cases, it becomes imperative to construct a training set that includes the most diverse, representative, and informative samples. Here, a novel regression tree-based active learning algorithm is employed for such a purpose. It is applied to predict the band gap and adsorption properties of metal-organic frameworks (MOFs), a novel class of materials that results from the virtually infinite combinations of their building units. Simpler and low dimensional descriptors, such as those based on stoichiometric and geometric properties, are used to compute the feature space for this model owing to their ability to better represent MOFs in the low data regime. The partitions given by a regression tree constructed on the labeled part of the data set are used to select new samples to be added to the training set, thereby limiting its size while maximizing the prediction quality. Tests on the QMOF, hMOF, and dMOF data sets reveal that our method constructs small training data sets to learn regression models that predict the target properties more efficiently than existing active learning approaches, and with lower variance. Specifically, our active learning approach is highly beneficial when labels are unevenly distributed in the descriptor space and when the label distribution is imbalanced, which is often the case for real world data. The regions defined by the tree help in revealing patterns in the data, thereby offering a unique tool to efficiently analyze complex structure-property relationships in materials and accelerate materials discovery.

Sb/C composite embedded in SiOC buffer matrix via dispersion property control for novel anode material in sodium-ion batteries

Due to vast sodium reserves, sodium-ion batteries (SIBs) are more cost-efficient to produce than lithium-ion batteries. Therefore, they are actively researched as next-generation energy storage materials. Antimony is a promising anode material for SIB owing to its high theoretical capacity (660 mA h g−1) and an appropriate sodiation voltage. However, due to the rapid volume change during sodium intercalation and deintercalation, cycling stability is poor, presenting a significant obstacle to the practical application of SIBs. Alleviating the Sb volume expansion throughout the charging and discharging processes is the key to the practical implementation of Sb-based anodes. Herein, Sb/C–SiOC composites are prepared using the hydrogen bonding-based adsorption properties of metal-organic frameworks (MOFs). The final product, the Sb/C–SiOC composites, exhibited significantly improved cycle performance, such as maintaining the initial capacity after 200 cycles by the SiOC matrix acting as a conductive buffer. Additionally, the presence of MOF-derived mesoporous carbon and SiOC contributed to the improved rate performance. The hydrogen bond-based adsorption properties of the MOFs used in this study can be effectively applied to uniformly introduce a matrix or coating layer that relieves the volume expansion of high-capacity composite anodes, making it an effective strategy for developing alloy-based energy storage materials.

Gaussian approximation of dispersion potentials for efficient featurization and machine-learning predictions of metal-organic frameworks.

Energy-related descriptors in machine learning are a promising strategy to predict adsorption properties of metal-organic frameworks (MOFs) in the low-pressure regime. Interactions between hosts and guests in these systems are typically expressed as a sum of dispersion and electrostatic potentials. The energy landscape of dispersion potentials plays a crucial role in defining Henry's constants for simple probe molecules in MOFs. To incorporate more information about this energy landscape, we introduce the Gaussian-approximated Lennard-Jones (GALJ) potential, which fits pairwise Lennard-Jones potentials with multiple Gaussians by varying their heights and widths. The GALJ approach is capable of replicating information that can be obtained from the original LJ potentials and enables efficient development of Gaussian integral (GI) descriptors that account for spatial correlations in the dispersion energy environment. GI descriptors would be computationally inconvenient to compute using the usual direct evaluation of the dispersion potential energy surface. We show that these new GI descriptors lead to improvement in ML predictions of Henry's constants for a diverse set of adsorbates in MOFs compared to previous approaches to this task.

Study on Adsorption and Separation of Gas in Metal-organic Framework Materials Based on Density Functional Theory

This article reviews the recent progress on predicting the adsorption properties of metal-organic framework by using classical density functional theory and focused on the application of the classical density functional theory to the high-throughput screening, which is accelerated by fast Fourier Transform. Comparing to the conventional molecular simulations, the advantage of the accelerated classical density functional theory is the calculation speed, especially for simple small molecule systems, which makes the high-throughput screening on MOF materials feasible. However, it appears that there is a lack of efficient method to deal with the complicated molecules. How to construct a reasonable free energy functional of complicated fluid is the main challenge to state of art classical density functional theory. In a word, the improvement of CDFT theory and the combination of CDFT and molecular simulation are the two main ways for CDFT to predict gas adsorption in MOF.

Accelerating Discovery of Metal-Organic Frameworks for Methane Adsorption with Hierarchical Screening and Deep Learning.

In recent years, machine learning (ML) methods have made significant progress, and ML models have been adopted in virtually all aspects of chemistry. In this study, based on the crystal graph convolutional neural networks algorithm, an end-to-end deep learning model was developed for predicting the methane adsorption properties of metal-organic frameworks (MOFs). High-throughput grand canonical Monte Carlo calculations were carried out on the computation-ready, experimental MOF database, which contains approximately 11 000 MOFs, to construct the data set. An area under the curve of 0.930 for the test set proved the reliability of the developed deep learning model. To assess the transferability of the model, we applied it to predict the methane adsorption volume for some randomly selected covalent organic frameworks and zeolitic imidazolate framework materials. The results indicated that the model could also be suitable for other porous materials. We also applied it to the hierarchical screening of a hypothetical MOFs database (∼330 000 MOFs). Four hypothetical MOFs were demonstrated to have the highest performance in methane adsorption. A calculated maximum working capacity of 145 cm3/cm3 at 5-35 bar and 298 K indicated that the hypothetical MOF is close to the Department of Energy's 2015 target of 180 cm3/cm3. Further analyses on all screened out MOFs established correlations between some structural features with the working capacity. The successful incorporation of ML and hierarchical screening can accelerate the discovery of new materials not just for gas adsorption, but also other areas involving interactions in materials and molecules.

Metal-organic framework portable chemical sensor

Combining the chemical-specific adsorption properties of metal-organic framework (MOF) materials with the dielectric sensitivity of a novel open-ended hollow coaxial cable resonator (OE-HCCR), a mechanically-robust and portable gas sensor device (OE-HCCR-MOF) with high chemical selectivity and sensitivity is proposed and experimentally demonstrated. The operating principle of the device is based on changes in the dielectric property of the host MOF layer in response to variations in the types and concentrations of guest molecules. The changes in the dielectric property of the MOF layer modify the phase-matching condition of the microwave resonator, causing shifts in the resonance frequency of the device. By monitoring the resonance frequency shift, the adsorptions of guest molecules can be monitored in real-time and accurately quantified. In proof-of-concept demonstrations, a 200-μm layer of MOF (HKUST-1) was placed within an OE-HCCR to develop a prototype OE-HCCR-MOF sensor. The novel sensor showed high sensitivity to variations in the concentrations of carbon dioxide with good reversibility. The chemical selectivity of the prototype sensor for carbon dioxide compared to methane was also investigated.

Chemical Detection Using a Metal-Organic Framework Single Crystal Coupled to an Optical Fiber.

The quantitative detection and real-time monitoring of target chemicals in the liquid phase are made possible by combining the tailored adsorption properties of metal-organic framework (MOF) material and the precise measuring capabilities of an optical fiber (OF) Fabry-Pérot interferometer (FPI) device. As the single-crystal MOF host adsorbs target analyte guests from the environment, its dielectric properties change causing the reflection spectrum derived from the FPI device to shift. A single crystal of HKUST-1 was attached to the end-face of an OF to form the sensor OF∪MOF (∪, union). The sensor's response curve was accurately measured using low concentrations of the target analyte nitrobenzene, an explosive simulant. Additionally, the uptake rate of nitrobenzene into the MOF single crystal was characterized. The experimental results show that the sensor achieved quantitative and real-time adsorption measurements of a target analyte.

Rational design of SnO2@C nanocomposites for lithium ion batteries by utilizing adsorption properties of MOFs.

A facile synthetic strategy is developed to prepare mono-dispersed SnO2 particles within three-dimensional porous carbon frameworks by utilizing the adsorption properties of metal-organic frameworks. This composite exhibits a high reversible capacity of 900 mA h g(-1) at 100 mA g(-1) after 50 cycles, with a stable capacity retention of 880 mA h g(-1) at 100 mA g(-1) even after 200 cycles.

In-situ Gas Adsorption SC-XRD Study: Understanding Gas Uptake in a Sc-based MOF

In recent years the development of new methods of storing, trapping or separating light gases, such as CO2, CH4 and CO has become of utmost importance from an environmental and energetic point of view. Porous materials such as zeolites and porous organic polymers have long been considered good candidates for this purpose. More recently, the ample spectrum of existing metal organic frameworks (MOFs) together with their functional and mechanical properties have attracted even further interest. The porous channels found in these materials are ideal for the uptake of guests of different shapes and sizes, and with careful design they can show high selectivity. Adsorption properties of MOFs have been thoroughly studied, however obtaining in depth structural insight into the adsorption/desorption mechanism of these materials is challenging. For example, out of the hundreds of MOF structures published to date, there are less than 20 entries currently in the CSD in which the CO2 molecule can be located. Here we present our novel findings using the high-pressure gas cell at the Diamond Light Source on beamline I19, where we have studied the inclusion of CO2, CH4 and CO on the microporous scandium framework, Sc2BDC3 (BDC = benzene-1,4-dicarboxylate) and its amino-functionalised derivative, Sc2(BDC-NH2)3. Here, the different adsorption sites for CO2, CH4 and CO in both frameworks have been determined as a function of increasing gas pressure. These structures, coupled with Density Functional Theory calculations, have helped to elucidate the host-guest interactions governing the different levels of selectivity shown by both Sc2BDC3 and Sc2(BDC-NH2)3. Additionally, gas mixtures have also been studied; in particular CO2/CH4 mixtures of different compositions, explaining the selectivity of the frameworks for CO2 over other gases and showing the great potential of in situ structural experiments for investigation of the potential applications of MOFs.

Adsorption properties of the MOF-5 metal-organic framework in relation to water and benzene

Adsorption properties of synthesized metal-organic frameworks based on 1,4-dicarboxylate ligands and zinc ions were studied. It was shown that the adsorption properties of these metal-organic frameworks in relation to water and benzene are much higher than those for the known adsorbents: KT-1 and KT-2 coals, USY and ZSM-5 zeolites, and pentasil.