Large-Scale Structural Refinement and Screening of Zirconium Metal-Organic Frameworks for H2S/CH4 Separation

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.

Identification of folic acid and sulfaquinoxaline using a heterometallic Zn-Eu MOF as a sensor

A d-f heterometallic MOF using the 2,2'-bipyridine-4,4'-dicarboxylic acid ligand (H₂LZ) was obtained by solvothermal synthesis, namely [EuZn(LZ)₂(HCOO)(H₂O)₃]_n (1). The structure analysis shows that compound 1 comprises heterometallic Zn²⁺ and Eu³⁺ ions, which are connected by LZ^2- and HCOO^- anions to form a three-dimensional framework. MOF 1 exhibited high stability of fluorescence intensity in the scope of pH 2-11 in an aqueous solution. Furthermore, MOF 1 served as an excellent selective sensing material for the detection of folic acid in the presence of some imitating materials of the human body and discerned sulfaquinoxaline in sulfonamide drugs with high sensitivity, selectivity, and reusability. Moreover, we designed and manufactured a sensor paper based on MOF 1 as a portable device for the visual detection of folic acid and sulfaquinoxaline. More crucially, this is the first example in which luminescent MOF is used to identify sulfaquinoxaline molecules in an aqueous solution. In addition, the luminescence sensing mechanisms of MOF 1 for the detection of the above analytes were explored in detail.

Mapping the Porous and Chemical Structure-Function Relationships of Trace CH3I Capture by Metal-Organic Frameworks using Machine Learning

Large-scale computational screening has become an indispensable tool for functional materials discovery. It, however, remains a challenge to adequately interrogate the large amount of data generated by a screening study. Here, we computationally screened 1087 metal-organic frameworks (MOFs), from the CoRE MOF 2014 database, for capturing trace amounts (300 ppmv) of methyl iodide (CH₃I); as a primary representative of organic iodides, CH₃¹²⁹I is one of the most difficult radioactive contaminants to separate. Furthermore, we demonstrate a simple and general approach for mapping and interrogating the high-dimensional structure-function data obtained by high-throughput screening; this involves learning two-dimensional embeddings of the high-dimensional data by applying unsupervised learning to encoded structural and chemical features of MOFs. The resulting various porous and chemical structure-function maps are human-interpretable, revealing not only top-performing MOFs but also complex structure-function correlations that are hidden when inspecting individual MOF features. These maps also alleviate the need of laborious visual inspection of a large number of MOFs by clustering similar MOFs, per the encoding features, into defined regions on the map. We also show that these structure-function maps are amenable to supervised classification of the performances of MOFs for trace CH₃I capture. We further show that the machine-learning models trained on the 1087 CoRE MOFs can be used to predict an unseen set of 250 MOFs randomly selected from a different MOF database, achieving high prediction accuracies.

Metal-organic framework-based materials for the abatement of air pollution and decontamination of wastewater

Developing new and efficient technologies for environmental remediation is becoming significant due to the increase in global concerns such as climate change, severe epidemics, and energy crises. Air pollution, primarily due to increased levels of H₂S, SO_x, NH₃, NO_x, CO, volatile organic compounds (VOC), and particulate matter (PM) in the atmosphere, has a significant impact on public health, and exhaust gases harm the natural sulfur, nitrogen, and carbon cycles. Similarly, wastewater discharged to the environment with metal ions, herbicides, pharmaceuticals, personal care products, dyes, and aromatics/organic compounds is a risk for health since it may lead to an outbreak of waterborne pathogens and increase the exposure to endocrine-disrupting agents. Therefore, developing new and efficient air and water quality management systems is critical. Metal-organic frameworks (MOFs) are novel materials for which the main application areas include gas storage and separation, water harvesting from the atmosphere, chemical sensing, power storage, drug delivery, and food preservation. Due to their versatile structural motifs that can be modified during synthesis, MOFs also have a great promise for green applications including air and water pollution remediation. The motivation to use MOFs for environmental applications prompted the modification of their structures via the addition of metal and functional groups, as well as the creation of heterostructures by mixing MOFs with other nanomaterials, to effectively remove hazardous contaminants from wastewater and the atmosphere. In this review, we focus on the state-of-the-art environmental applications of MOFs, particularly for water treatment and air pollution, by highlighting the groundbreaking studies in which MOFs have been used as adsorbents, membranes, and photocatalysts for the abatement of air and water pollution. We finally address the opportunities and challenges for the environmental applications of MOFs.

Zr-MOFs for CF4/CH4, CH4/H2, and CH4/N2 separation: towards the goal of discovering stable and effective adsorbents

Zirconium metal-organic frameworks (MOFs) can be promising adsorbents for various applications as they are highly stable in different chemical environments. In this work, a collection of Zr-MOFs comprised of more than 100 materials is screened for CF₄/CH₄, CH₄/H₂, and CH₄/N₂ separations using atomistic-level simulations. The top three MOFs for the CF₄/CH₄ separation are identified as PCN-700-BPDC-TPDC, LIFM-90, and BUT-67 exhibiting CF₄/CH₄ adsorption selectivities of 4.8, 4.6, and 4.7, CF₄ working capacities of 2.0, 2.0, and 2.1 mol kg^-1, and regenerabilities of 85.1, 84.2, and 75.7%, respectively. For the CH₄/H₂ separation, MOF-812, BUT-67, and BUT-66 are determined to be the top performing MOFs demonstrating CH₄/H₂ selectivities of 61.6, 36.7, and 46.2, CH₄ working capacities of 3.0, 4.1, and 3.4 mol kg^-1, and CH₄ regenerabilities of 70.7, 82.7, and 74.7%, respectively. Regarding the CH₄/N₂ separation, BUT-67, Zr-AbBA, and PCN-702 achieving CH₄/N₂ selectivities of 4.5, 3.4, and 3.8, CH₄ working capacities of 3.6, 3.9, and 3.5 mol kg^-1, and CH₄ regenerabilities of 81.1, 84.0, and 84.5%, in successive order, show the best overall separation performances. To further elucidate the adsorption in top performing adsorbents, the adsorption sites in these materials are analyzed using radial distribution functions and adsorbate density profiles. Finally, the water affinities of Zr-MOFs are explored to comment on their practical use in real gas separation applications. Our findings may inspire future studies probing the adsorption/separation mechanisms and performances of Zr-MOFs for different gases.

The synergistic effect of phosphomolybdic acid on rhodium-based metal–organic frameworks for the efficient selective photocatalytic reduction of CO2 to CO

PMA@NH₂-MIL-68(Rh) with a mangosteen spherical structure were synthesized by a hydrothermal method and used for the highly efficient selective photocatalytic reduction of CO₂ to CO.

Screening of metal-organic frameworks for water adsorption heat transformation using structure-property relationships

It is of great importance to correlate the water adsorption performance of MOFs to their physicochemical features in order to design and prepare MOFs for applications in adsorption heat transformation. In this work, both data analysis from existing studies and Grand Canonical Monte Carlo molecular simulation investigations were carried out. The results indicated that the highest water adsorption capacity was determined by the pore volume of MOF adsorbents, while there was a linear correlation interrelationship between isosteric heats of adsorption and the water adsorption performance at a low relative pressure. More detailed analysis showed that the charge distribution framework and pore size of MOFs contributed together to the hydrophilicity. Electrostatic interaction between water molecules and the framework atoms played a key role at low relative water pressure. A quantitative structure-property relationship model that can correlate the hydrophilicity of MOFs to their pore size and atomic partial charge was established. Along with some qualitative considerations, the screening methodology is proposed and is used to screen proper MOFs in the CoRE database. Seven MOFs were detected, and four of them were synthesized to validate the screening principle. The results indicated that these four MOFs possessed outstanding water adsorption performance and could be considered as promising candidates in applications for adsorption heating and cooling.