Investigating the Effect of Trace Levels of Manganese Ions During Solvothermal Synthesis of Massey University Framework-16 on CO2 Uptake Capacity.

The effects of impurities on reaction precursors for metal-organic framework (MOF) synthesis have not been studied in extensive detail. The impact of these impurities can be an important factor while considering scale-up of these materials. In this work, we study the apparently positive impact of the presence of manganese ions for the synthesis of a Co-based MOF, Massey University Framework-16 (MUF-16). The presence of a trace amount of manganese in the reaction mixture led to consistently high CO2 uptake across multiple batches. Characterization including X-ray diffraction, scanning electron microscopy, Fourier transform infrared-attenuated total reflectance, ultraviolet-visible spectroscopy, thermogravimetric analysis, X-ray photoelectron spectroscopy, and extended X-ray absorption fine structure spectroscopy led us to hypothesize that the differences in CO2 adsorption among materials with differing synthesis routes arise from variations in the local environment around the cobalt metal center. Aided by density functional theory calculations, we speculate that manganese ions get inserted into the structure during crystallization and act as catalysts for ligand substitution, improving the possibility for octahedral coordination of cobalt with the ligand, thus leading to Co-based pristine structures with higher CO2 uptakes.

Assessing Impacts of Atmospheric Conditions on Efficiency and Siting of Large-Scale Direct Air Capture Facilities.

The cost and efficiency of direct air capture (DAC) of carbon dioxide (CO2) will be decisive in determining whether this technology can play a large role in decarbonization. To probe the role of meteorological conditions on DAC we examine, at 1 × 1° resolution for the continental United States (U.S.), the impacts of temperature, humidity, atmospheric pressure, and CO2 concentration for a representative amine-based adsorption process. Spatial and temporal variations in atmospheric pressure and CO2 concentration lead to strong variations in the CO2 available in ambient air across the U.S. The specific DAC process that we examine is described by a process model that accounts for both temperature and humidity. A process that assumes the same operating choices at all locations in the continental U.S. shows strong variations in performance, with the most influential variables being the H2O gas phase volume fraction and temperature, both of which are negatively correlated with DAC productivity for the specific process that we consider. The process also shows a moderate positive correlation of ambient CO2 with productivity and recovery. We show that optimizing the DAC process at seven representative locations to reflect temporal and spatial variations in ambient conditions significantly improves the process performance and, more importantly, would lead to different choices in the sites for the best performance than models based on a single set of process conditions. Our work provides a framework for assessing spatial variations in DAC performance that could be applied to any DAC process and indicates that these variations will have important implications in optimizing and siting DAC facilities.

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.

Probing Structural Defects in MOFs Using Water Stability.

Defects in the crystal structures of metal-organic frameworks (MOFs), whether present intrinsically or introduced via so-called defect engineering, can play strong roles in the properties of MOFs for various applications. Unfortunately, direct experimental detection and characterization of defects in MOFs are very challenging. We show that in many cases, the differences between experimentally observed and computationally predicted water stabilities of MOFs can be used to deduce information on the presence of point defects in real materials. Most computational studies of MOFs consider these materials to be defect-free, and in many cases, the resulting structures are predicted to be hydrophobic. Systematic experimental studies, however, have shown that many MOFs are hydrophilic. We show that the existence of chemically plausible point defects can often account for this discrepancy and use this observation in combination with detailed molecular simulations to assess the impact of local defects and flexibility in a variety of MOFs for which defects had not been considered previously.

Machine Learning Models for Predicting Molecular Diffusion in Metal-Organic Frameworks Accounting for the Impact of Framework Flexibility.

Molecular diffusion in MOFs plays an important role in determining whether equilibrium can be reached in adsorption-based chemical separations and is a key driving force in membrane-based separations. Molecular dynamics (MD) simulations have shown that in some cases inclusion of framework flexibility in MOF changes predicted molecular diffusivities by orders of magnitude relative to more efficient MD simulations using rigid structures. Despite this, all previous efforts to predict molecular diffusion in MOFs in a high-throughput way have relied on MD data from rigid structures. We use a diverse data set of MD simulations in flexible and rigid MOFs to develop a classification model that reliably predicts whether framework flexibility has a strong impact on molecular diffusion in a given MOF/molecule pair. We then combine this approach with previous high-throughput MD simulations to develop a reliable model that efficiently predicts molecular diffusivities in cases in which framework flexibility can be neglected. The use of this approach is illustrated by making predictions of molecular diffusivities in ∼70,000 MOF/molecule pairs for molecules relevant to gas separations.

Structural and Adsorption Properties of ZIF-8-7 Hybrid Materials Synthesized by Acid Gas-Assisted and De Novo Routes.

The tuning of micropore environments in zeolitic imidazolate frameworks (ZIFs) by mixed-linker synthesis has the potential for enabling new molecular separation properties. However, de novo synthesis of mixed-linker (hybrid) ZIFs is often challenging due to the disparate chemical properties of the different linkers. Here, we elucidate the structure and properties of an unconventional ZIF-8-7 hybrid material synthesized via a controlled-acid-gas-assisted degradation and reconstruction (solvent-assisted crystal redemption, SACRed) strategy. Selective insertion of benzimidazole (ZIF-7 linker) into ZIF-8 using SACRed is used as a facile method to generate a ZIF-8-7 hybrid material that is otherwise difficult to synthesize by de novo methods. Detailed crystal structure and textural characterizations clarify the significant differences in the microstructure of the SACRed-derived ZIF-8-7 hybrid material relative to a de novo synthesized hybrid of the same overall linker composition as well as the parent ZIF-8 material. Unary and binary adsorption measurements reveal the tunability of adsorption characteristics as well as the prevalence of nonideal cooperative mixture adsorption effects that lead to large deviations from predictions made with ideal adsorbed solution theory.

Does Mixed Linker-Induced Surface Heterogeneity Impact the Accuracy of IAST Predictions in UiO-66-NH2?

To move toward more energy-efficient adsorption-based processes, there is a need for accurate multicomponent data under realistic conditions. While the Ideal Adsorbed Solution Theory (IAST) has been established as the preferred prediction method due to its simplicity, limitations and inaccuracies for less ideal adsorption systems have been reported. Here, we use amine-functionalized derivatives of the UiO-66 structure to change the extent of homogeneity of the internal surface toward the adsorption of the two probe molecules carbon dioxide and ethylene. Although it might seem plausible that more functional groups lead to more heterogeneity and, thus, less accurate predictions by IAST, we find a mixed-linker system with increased heterogeneity in terms of added adsorption sites where IAST predictions and experimental loadings agree exceptionally well. We show that incorporating uncertainty analysis into predictions with IAST is important for assessing the accuracy of these predictions. Energetic investigations combined with Grand Canonical Monte Carlo simulations reveal almost homogeneous carbon dioxide but heterogeneous ethylene adsorption in the mixed-linker material, resulting in local, almost pure phases of the individual components.

Efficient Exploration of Adsorption Space for Separations in Metal-Organic Frameworks Combining the Use of Molecular Simulations, Machine Learning, and Ideal Adsorbed Solution Theory.

Adsorption-based separations using metal-organic frameworks (MOFs) are promising candidates for replacing common energy-intensive separation processes. The so-called adsorption space formed by the combination of billions of possible molecules and thousands of reported MOFs is vast. It is very challenging to comprehensively evaluate the performance of MOFs for chemical separation through experiments. Molecular simulations and machine learning (ML) have been widely applied to make predictions for adsorption-based separations. Previous ML approaches to these issues were typically limited to smaller molecules and often had poor accuracy in the dilute limit. To enable exploration of a wider adsorption space, we carefully selected a diverse set of 45 molecules and 335 MOFs and generated single-component isotherms of 15,075 MOF-molecule pairs by grand canonical Monte Carlo. Using this database, we successfully developed accurate (r2 > 0.9) machine learning models predicting adsorption isotherms of diverse molecules in large libraries of MOFs. With this approach, we can efficiently make predictions of large collections of MOFs for arbitrary mixture separations. By combining molecular simulation data and ML predictions with Ideal Adsorbed Solution Theory, we tested the ability of these approaches to make predictions of adsorption selectivity and loading for challenging near-azeotropic mixtures.

Quantum Chemical Simulations of CO2 and N2 Capture in Reline, a Prototypical Deep Eutectic Solvent.

Deep eutectic solvents such as reline are an emerging class of low-cost, environmentally friendly solvents with tunable properties that are potentially applicable for the capture and separation of CO2. Experimental measurements showed that a reline-based membrane contactor can capture and separate CO2 via physisorption through a dissolution process with 96.7% purity from a mixed gas containing CO2 and N2 (50:50% molar ratio). We examine the nature of the interaction of CO2 and N2 with reline employing quantum chemical methods. We focus on explaining the mechanism by which CO2 and N2 bind to reline and the reason for the high selectivity for absorption of CO2 compared to N2. We analyze the dynamics, energetics, and binding motifs for CO2 and N2 in reline employing density functional theory, density functional tight binding, and ab initio molecular dynamics. We also investigate the effect of reline on the vibrational spectra of CO2 and reline. Our simulations indicate that the selective capture of CO2 from the mixture of CO2 and N2 is due to the interplay between attractive electrostatic and charge polarization forces with opposing entropic effects, which shift the energetic balance and make the N2 absorption unfavorable in reline.

Hierarchical ZIF-8 Materials via Acid Gas-Induced Defect Sites: Synthesis, Characterization, and Functional Properties.

Microporous metal-organic frameworks (MOFs) have been widely studied for molecular separation and catalysis. The uniform micropores of MOFs (<2 nm) can introduce diffusion limitations and render the interiors of the crystal inaccessible to target molecules. The introduction of hierarchical porosity (interconnected micro and mesopores) can enhance intra-crystalline diffusion while maintaining the separation/catalytic selectivity. Conventional hierarchical MOF synthesis involves complex strategies such as elongated linkers, soft templating, and sacrificial templates. Here, we demonstrate a more general approach using our controlled acid gas-enabled degradation and reconstruction (Solvent-Assisted Crystal Redemption) strategy. Selective linker labilization of ZIF-8 is shown to generate a hierarchical pore structure with mesoporous cages (∼50 nm) while maintaining microporosity. Detailed structural and spectroscopic characterization of the controlled degradation, linker insertion, and subsequent linker thermolysis is presented to show the clustering of acid gas-induced defects and the generation of mesopores. These findings indicate the generality of controlled degradation and reconstruction as a means for linker insertion in a wider variety of MOFs and creating hierarchical porosity. Enhanced molecular diffusion and catalytic activity in the hierarchical ZIF-8 are demonstrated by the adsorption kinetics of 1-butanol and a Knoevenagel condensation reaction.

Quantitative Simulations of Siloxane Adsorption in Metal-Organic Frameworks.

We present a transferable force field (FF) for simulating the bulk properties of linear and cyclic siloxanes and the adsorption of these species in metal-organic frameworks (MOFs). Unlike previous FFs for siloxanes, our FF accurately reproduces the vapor-liquid equilibria of each species in the bulk phase. The quality of our FF combined with the Universal Force Field using standard Lorentz-Berthelot combining rules for MOF atoms was assessed in a wide range of MOFs without open metal sites, showing good agreement with dispersion-corrected density functional theory calculations. Predictions with this FF show good agreement with the limited experimental data for siloxane adsorption in MOFs that is available. As an example of using the FF to predict adsorption properties in MOFs, we present simulations examining entropy effects in binary linear and cyclic siloxane mixture coadsorption in the large-pore MOF with structure code FOTNIN.

Efficient Generation of Large Collections of Metal-Organic Framework Structures Containing Well-Defined Point Defects.

High-throughput molecular simulations of metal-organic frameworks (MOFs) are a useful complement to experiments to identify candidates for chemical separation and storage. All previous efforts of this kind have used simulations in which MOFs are approximated as defect-free. We introduce a tool to readily generate missing-linker defects in MOFs and demonstrate this tool with a collection of 507 defective MOFs. We introduce the concept of the maximum possible defect concentration; at higher defect concentrations, deviations from the defect-free crystal structure would be readily evident experimentally. We studied the impact of defects on molecular adsorption as a function of defect concentrations. Defects have a slightly negative or negligible influence on adsorption at low pressures for ethene, ethane, and CO2 but a strong positive influence for methanol due to hydrogen bonding with defects. Defective structures tend to have loadings slightly higher than those of defect-free structures for all adsorbates at elevated pressures.

Two-Dimensional Energy Histograms as Features for Machine Learning to Predict Adsorption in Diverse Nanoporous Materials.

A major obstacle for machine learning (ML) in chemical science is the lack of physically informed feature representations that provide both accurate prediction and easy interpretability of the ML model. In this work, we describe adsorption systems using novel two-dimensional energy histogram (2D-EH) features, which are obtained from the probe-adsorbent energies and energy gradients at grid points located throughout the adsorbent. The 2D-EH features encode both energetic and structural information of the material and lead to highly accurate ML models (coefficient of determination R2 ∼ 0.94-0.99) for predicting single-component adsorption capacity in metal-organic frameworks (MOFs). We consider the adsorption of spherical molecules (Kr and Xe), linear alkanes with a wide range of aspect ratios (ethane, propane, n-butane, and n-hexane), and a branched alkane (2,2-dimethylbutane) over a wide range of temperatures and pressures. The interpretable 2D-EH features enable the ML model to learn the basic physics of adsorption in pores from the training data. We show that these MOF-data-trained ML models are transferrable to different families of amorphous nanoporous materials. We also identify several adsorption systems where capillary condensation occurs, and ML predictions are more challenging. Nevertheless, our 2D-EH features still outperform structural features including those derived from persistent homology. The novel 2D-EH features may help accelerate the discovery and design of advanced nanoporous materials using ML for gas storage and separation in the future.

Adapting UFF4MOF for Heterometallic Rare-Earth Metal-Organic Frameworks.

Heterometallic metal-organic frameworks based on rare-earth metals (RE-MOFs) have potential in a number of applications where energy transfer between nearby metal atoms is required. This observation implies that it is important to understand the level of local mixing that is achieved between metals of different types during synthesis of RE-MOFs. Density functional theory calculations can give quantitative information on the relative energy of different configurations of RE-MOFs, but these calculations cannot be applied to the full range of medium- and long-range orderings that are possible in heterometallic materials. This limitation can be overcome using force field (FF)-based calculations if appropriate FFs are available. We show that an existing generic FF for MOFs, UFF4MOF, does not accurately predict energies of mixing in heterometallic Nd/Yb MOFs and introduce a modified FF to address this shortcoming. The resulting FF is used to explore metal orderings in large simulation volumes for a Nd/Yb MOF, illustrating the complexities that can arise in the structure of heterometallic RE-MOFs.

Trends in Siting of Metals in Heterometallic Nd-Yb Metal-Organic Frameworks and Molecular Crystals.

Several studies suggest that metal ordering within metal-organic frameworks (MOFs) is important for understanding how MOFs behave in relevant applications; however, these siting trends can be difficult to determine experimentally. To garner insight into the energetic driving forces that may lead to nonrandom ordering within heterometallic MOFs, we employ density functional theory (DFT) calculations on several bimetallic metal-organic crystals composed of Nd and Yb metal atoms. We also investigate the metal siting trends for a newly synthesized MOF. Our DFT-based energy of mixing results suggest that Nd will likely occupy sites with greater access to electronegative atoms and that local homometallic domains within a mixed-metal Nd-Yb system are favored. We also explore the use of less computationally extensive methods such as classical force fields and cluster expansion models to understand their feasibility for large system sizes. This study highlights the impact of metal ordering on the energetic stability of heterometallic MOFs and crystal structures.

Unblocking a rigid purine MOF for kinetic separation of xylenes.

The separation of xylene isomers still remains an industrially challenging task. Here, porous purine-based metal-organic frameworks (MOFs) have been synthesized and studied for their potential in xylene separations. In particular, Zn(purine)I showed excellent para-xylene/ortho-xylene separation capability with a diffusion selectivity of 6 and high equilibrium adsorption selectivity as indicated by coadsorption experiments. This high selectivity is attributed to the shape and size of the channel aperture within the rigid framework of Zn(purine)I.

Metal-Organic Framework Integrating Ionic Framework and Bimetallic Coupling Effect for Highly Efficient Oxygen Evolution Reaction.

Metal-organic frameworks (MOFs) are recognized as promising electrocatalysts for the oxygen evolution reaction (OER) because of their permanent porosity and rich architectural diversity; however, ionic MOFs enabling fast ions exchange during OER are rarely explored. Here, an ionic MOF (Ni-btz) constructed with an azolate ligand is selected, and continuous 3D bimetallic MOF (NiFe-btz) films deriving from high-degree intergrowth of microsized MOFs particles are fabricated. The as-prepared NiFe-btz/NF-OH electrode exhibits excellent OER performance with a low overpotential of 239 mV at 10 mA cm-2 under alkaline condition. The OER charge transfer process and bimetallic coupling effect in ionic NiFe-btz are probed by density functional theory calculations and confirmed via X-ray photoelectron spectroscopy and in situ Raman measurements. The partial density of states of NiFe-btz indicates that the main contribution for electron density around the Fermi level is from Cl ions clarifying the profitable impact of ionic MOF framework. This work systematically demonstrates the relationship of electronic structure and OER activity in ionic, bimetallic MOFs and expands the scope of 3D MOF films for efficient OER.

Curated Collection of More than 20,000 Experimentally Reported One-Dimensional Metal-Organic Frameworks.

A collection of more than 20,000 experimentally derived crystal structures for metal-organic frameworks (MOFs) that do not have two- or three-dimensional covalently bonded networks has been developed from the materials available at the Cambridge Crystallographic Data Centre. Of these 20,000 1D MOFs, more than 12,000 structures have been verified to be solvent-free and in exact agreement with the stoichiometry of the synthesized materials. More than 10% of the complete data set comprise materials including two or more distinct metals. The band gaps of more than 12,000 1D MOFs have been computed at the density functional theory-generalized gradient approximation level, finding more than 2000 materials that have a zero band gap. Molecular simulations of CH4 adsorption in a small number of 1D MOFs indicated that adsorbate-induced deformation plays a significant role in determining adsorption isotherms in these materials. As a result, methods that have been used previously for high-throughput predictions of molecular adsorption in 3D MOFs are not suitable for 1D MOFs.

Kinetic Model of Acid Gas Induced Defect Propagation in Zeolitic Imidazolate Frameworks.

Understanding the degradation of nanoporous materials under exposure to common acid gas contaminants (e.g., SO2, CO2, NO2, and H2S) is essential to elongate their lifetime and thus enable their practical applications in separations and catalysis. Previous theoretical investigations have focused on the formation of isolated point defects, which are insufficient to provide direct insights into the long-term evolution of the bulk properties of materials such as zeolitic imidazolate frameworks (ZIFs) under sustained acid gas exposure. To bridge this divide in both length and time scales, we developed a first-principles lattice-based kinetic model to simulate the defect propagation and bulk material breakdown in ZIFs. This model closely reproduces the experimentally measured macroscopic evolution of the time-dependent bulk materials proprieties and also yields important new insights regarding the autocatalytic nature of ZIF degradation and the spatial distribution of defects. Our results suggest new experimental directions to identify nascent defect clusters in degraded ZIFs and avenues to mitigate degradation under challenging conditions of acid gas exposure.

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.