Influence of Pressure and Temperature on the Flexible Behavior of Iron-Based MIL-53 with the CO2 Host: A Comprehensive Experimental and DFT Study

This research focuses on developing MIL-53-type compounds with Fe obtained with ligands derived from PET waste, followed by the controlled addition of hydrofluoric acid (HF). Incorporating HF into the MOF structure induced substantial changes in the material textural properties, resulting in a significant change in CO2 adsorption. Furthermore, a distinctive structural alteration (breathing effect) was observed in the CO2 isotherms at different temperatures; these structural changes have not been observed by X-ray diffraction (XRD) because this characterization has been performed at room temperature, whereas the adsorption experiments were conducted at 260, 273, and 303 K and different pressures. Subsequently, DFT studies were performed to investigate the CO2-filling mechanisms and elucidate the material respiration effect. This approach offers promising opportunities for sustainable materials with improved gas adsorption properties.

Probing Structural Transformations and Degradation Mechanisms by Direct Observation in SIFSIX-3-Ni for Direct Air Capture

Despite global efforts to reduce carbon dioxide (CO2) emissions, continued industrialization threatens to exacerbate climate change. This work investigates methods to capture CO2, with a focus on the SIFSIX-3-Ni metal-organic framework (MOF) as a direct air capture (DAC) sorbent. SIFSIX-3-Ni exhibits promising CO2 adsorption properties but suffers from degradation processes under accelerated aging, which are akin to column regeneration conditions. Herein, we have grown the largest SIFSIX-3-Ni single crystals to date, facilitating single crystal X-ray diffraction analyses that enabled direct observation of the H2O and CO2 dynamics through adsorption and desorption. In addition, a novel space group (I4/mcm) for the SIFSIX-3-Ni is identified, which provided insights into structural transitions within the framework and elucidated water's role in degrading CO2 uptake performance as the material ages. In situ X-ray scattering methods revealed long-range and local structural transformations associated with CO2 adsorption in the framework pores as well as a temperature-dependent desorption mechanism. Pair distribution function analysis revealed a partial decomposition to form nonporous single-layer nanosheets of edge-sharing nickel oxide octahedra upon aging. The formation of these nanosheets is irreversible and reduces the amount of active material for the CO2 sorption. These findings provide crucial insights for the development of efficient and stable DAC sorbents, effectively reducing greenhouse gases, and suggest avenues for enhancing MOF stability under practical DAC conditions.

CO2 conversion in a dielectric barrier discharge plasma by argon dilution over MgO/HKUST-1 catalyst using response surface methodology

Metal-organic frameworks (MOFs) as carbon dioxide adsorption in combination with metal oxides have shown catalyst application in CO2 conversion. Herein, the MgO/HKUST-1 catalyst is synthesized to direct conversion of CO2 upon dilution by argon in a cylindrical dielectric barrier discharge (DBD) reactor. A water-cooling circulation adjusts the reactor temperature, and aluminum powder is used as a high-voltage electrode. The effect of the discharge power, feed flow rate, CO2 fraction, and their interaction in plasma and plasma catalyst method on CO2 conversion (R1), effective CO2 conversion (R2), and energy efficiency (R3) is evaluated by central composite design (CCD) based on response surface methodology. The Analysis of Variance (ANOVA) results demonstrate that the quadratic regression model describes CO2 conversion and effective CO2 conversion, and the reduced cubic model describes energy efficiency. The results indicate that the method (plasma, plasma catalyst) and discharge power on R1 and R2 have a considerable effect. Also, the method and CO2 fraction on R3 have the greatest impact, respectively. In the plasma and plasma catalyst method maximum CO2 conversion is 12.3% and 20.5% at a feed flow rate of 80 ml/min, CO2 fraction of 50%, and discharge power of 74 W.

CNTs/Fe-BTC Composite Materials for the CO2-Photocatalytic Reduction to Clean Fuels: Batch and Continuous System

CNTs/Fe-BTC composite materials were synthesized with the one-step solvothermal method. MWCNTs and SWCNTs were incorporated in situ during synthesis. The composite materials were characterized by different analytical techniques and used in the CO₂-photocatalytic reduction to value-added products and clean fuels. In the incorporation of CNTs into Fe-BTC, better physical-chemical and optical properties were observed compared to Fe-BTC pristine. SEM images showed that CNTs were incorporated into the porous structure of Fe-BTC, indicating the synergy between them. Fe-BTC pristine showed to be selective to ethanol and methanol; although, it was more selective to ethanol. However, the incorporation of small amounts of CNTs into Fe-BTC not only showed higher production rates but changes in the selectivity compared with the Fe-BTC pristine were also observed. It is important to mention that the incorporation of CNTs into MOF Fe-BTC allowed for increasing the mobility of electrons, decreasing the recombination of charge carriers (electron/hole), and increasing the photocatalytic activity. In both reaction systems (batch and continuous), composite materials showed to be selective towards methanol and ethanol; however, in the continuous system, lower production rates were observed due to the decrease in the residence time compared to the batch system. Therefore, these composite materials are very promising systems to convert CO₂ to clean fuels that could replace fossil fuels soon.

Unveiling Unexpected Modulator-CO2 Dynamics within a Zirconium Metal-Organic Framework

Carbon capture, storage, and utilization (CCSU) represents an opportunity to mitigate carbon emissions that drive global anthropogenic climate change. Promising materials for CCSU through gas adsorption have been developed by leveraging the porosity, stability, and tunability of extended crystalline coordination polymers called metal-organic frameworks (MOFs). While the development of these frameworks has yielded highly effective CO₂ sorbents, an in-depth understanding of the properties of MOF pores that lead to the most efficient uptake during sorption would benefit the rational design of more efficient CCSU materials. Though previous investigations of gas-pore interactions often assumed that the internal pore environment was static, discovery of more dynamic behavior represents an opportunity for precise sorbent engineering. Herein, we report a multifaceted in situ analysis following the adsorption of CO₂ in MOF-808 variants with different capping agents (formate, acetate, and trifluoroacetate: FA, AA, and TFA, respectively). In situ diffuse reflectance infrared Fourier transform spectroscopy (DRIFTS) analysis paired with multivariate analysis tools and in situ powder X-ray diffraction revealed unexpected CO₂ interactions at the node associated with dynamic behavior of node-capping modulators in the pores of MOF-808, which had previously been assumed to be static. MOF-808-TFA displays two binding modes, resulting in higher binding affinity for CO₂. Computational analyses further support these dynamic observations. The beneficial role of these structural dynamics could play an essential role in building a deeper understanding of CO₂ binding in MOFs.

CRAFTED: An exploratory database of simulated adsorption isotherms of metal-organic frameworks

Grand Canonical Monte Carlo is an important method for performing molecular-level simulations and assisting the study and development of nanoporous materials for gas capture applications. These simulations are based on the use of force fields and partial charges to model the interaction between the adsorbent molecules and the solid framework. The choice of the force field parameters and partial charges can significantly impact the results obtained, however, there are very few databases available to support a comprehensive impact evaluation. Here, we present a database of simulations of CO₂ and N₂ adsorption isotherms on 690 metal-organic frameworks taken from the CoRE MOF 2014 database. We performed simulations with two force fields (UFF and DREIDING), six partial charge schemes (no charges, Qeq, EQeq, MPNN, PACMOF, and DDEC), and three temperatures (273, 298, 323 K). The resulting isotherms compose the Charge-dependent, Reproducible, Accessible, Forcefield-dependent, and Temperature-dependent Exploratory Database (CRAFTED) of adsorption isotherms.

A robust and porous titanium metal-organic framework for gas adsorption, CO2 capture and conversion

A robust and porous titanium metal-organic framework (Ti-MOF; LCU-402) has been hydrothermally synthesized through combining a tetranuclear Ti₂Ca₂(μ₃-O)₂(μ₂-H₂O)_1.3(H₂O)₄(O₂C-)₈ cluster and a tritopic 1,3,5-benzene(tris)benzoic (BTB) ligand. LCU-402 shows remarkable stability and permanent porosity for CO₂, CH₄, C₂H₂, C₂H₄, and C₂H₆ gas adsorption. Moreover, LCU-402 as a heterogeneous catalyst can smoothly convert CO₂ under a simulated flue atmosphere into organic carbonate molecules by cycloaddition reactions of CO₂ and epoxides, indicating that LCU-402 might be a promising catalyst candidate in practical applications. We are confident that the identification of a persistent titanium-oxo building unit would accelerate the development of new porous Ti-MOF materials.

Cooperative CO2 adsorption mechanism in a perfluorinated CeIV-based metal organic framework

Adsorbents able to uptake large amounts of gases within a narrow range of pressure, i.e., phase-change adsorbents, are emerging as highly interesting systems to achieve excellent gas separation performances with little energy input for regeneration. A recently discovered phase-change metal-organic framework (MOF) adsorbent is F4_MIL-140A(Ce), based on Ce^IV and tetrafluoroterephthalate. This MOF displays a non-hysteretic step-shaped CO₂ adsorption isotherm, reaching saturation in conditions of temperature and pressure compatible with real life application in post-combustion carbon capture, biogas upgrading and acetylene purification. Such peculiar behaviour is responsible for the exceptional CO₂/N₂ selectivity and reverse CO₂/C₂H₂ selectivity of F4_MIL-140A(Ce). Here, we combine data obtained from a wide pool of characterisation techniques - namely gas sorption analysis, in situ infrared spectroscopy, in situ powder X-ray diffraction, in situ X-ray absorption spectroscopy, multinuclear solid state nuclear magnetic resonance spectroscopy and adsorption microcalorimetry - with periodic density functional theory simulations to provide evidence for the existence of a unique cooperative CO₂ adsorption mechanism in F4_MIL-140A(Ce). Such mechanism involves the concerted rotation of perfluorinated aromatic rings when a threshold partial pressure of CO₂ is reached, opening the gate towards an adsorption site where CO₂ interacts with both open metal sites and the fluorine atoms of the linker.

Interpretable Graph Transformer Network for Predicting Adsorption Isotherms of Metal-Organic Frameworks

Predicting interactions between metal-organic frameworks (MOFs) and their adsorbates based on structures is critical to design high-performance porous materials. Many gas uptake prediction models have been proposed, but adsorption isotherm prediction is still challenging for most existing models. Here, we report a deep learning approach (MOFNet) that can predict adsorption isotherms for MOFs based on hierarchical representation and pressure adaptive mechanism. We elaborately design a hierarchical representation to encode the MOF structures. We adopt a graph transformer network to capture atomic-level information, which can help learn chemical features required under low-pressure conditions. A pressure adaptive mechanism is employed to interpolate and extrapolate the given limited data points by transfer learning, which can predict adsorption isotherms on a wider pressure range by only one model. We demonstrate that our predictor outperformed other traditional machine learning as well as graph neural network models on the challenging benchmarks and also achieves high performance on the real-world experimental observed adsorption isotherms. Finally, we interpret the models to discover and present potential structure-property relationships using the self-attention mechanism in the network. The proof-of-concept applications, such as disordered MOF predictions and missing data imputation of gas adsorption isotherms, showcase the generality and usability of our model to improve MOF material design.