Machine learning using host/guest energy histograms to predict adsorption in metal-organic frameworks: Application to short alkanes and Xe/Kr mixtures.

A machine learning (ML) methodology that uses a histogram of interaction energies has been applied to predict gas adsorption in metal-organic frameworks (MOFs) using results from atomistic grand canonical Monte Carlo (GCMC) simulations as training and test data. In this work, the method is first extended to binary mixtures of spherical species, in particular, Xe and Kr. In addition, it is shown that single-component adsorption of ethane and propane can be predicted in good agreement with GCMC simulation using a histogram of the adsorption energies felt by a methyl probe in conjunction with the random forest ML method. The results for propane can be improved by including a small number of MOF textural properties as descriptors. We also discuss the most significant features, which provides physical insight into the most beneficial adsorption energy sites for a given application.

The Influence of Intrinsic Framework Flexibility on Adsorption in Nanoporous Materials.

For applications of metal-organic frameworks (MOFs) such as gas storage and separation, flexibility is often seen as a parameter that can tune material performance. In this work we aim to determine the optimal flexibility for the shape selective separation of similarly sized molecules (e.g., Xe/Kr mixtures). To obtain systematic insight into how the flexibility impacts this type of separation, we develop a simple analytical model that predicts a material's Henry regime adsorption and selectivity as a function of flexibility. We elucidate the complex dependence of selectivity on a framework's intrinsic flexibility whereby performance is either improved or reduced with increasing flexibility, depending on the material's pore size characteristics. However, the selectivity of a material with the pore size and chemistry that already maximizes selectivity in the rigid approximation is continuously diminished with increasing flexibility, demonstrating that the globally optimal separation exists within an entirely rigid pore. Molecular simulations show that our simple model predicts performance trends that are observed when screening the adsorption behavior of flexible MOFs. These flexible simulations provide better agreement with experimental adsorption data in a high-performance material that is not captured when modeling this framework as rigid, an approximation typically made in high-throughput screening studies. We conclude that, for shape selective adsorption applications, the globally optimal material will have the optimal pore size/chemistry and minimal intrinsic flexibility even though other nonoptimal materials' selectivity can actually be improved by flexibility. Equally important, we find that flexible simulations can be critical for correctly modeling adsorption in these types of systems.

Xenon Recovery at Room Temperature using Metal-Organic Frameworks.

Xenon is known to be a very efficient anesthetic gas, but its cost prohibits the wider use in medical industry and other potential applications. It has been shown that Xe recovery and recycling from anesthetic gas mixtures can significantly reduce its cost as anesthetic. The current technology uses series of adsorbent columns followed by low-temperature distillation to recover Xe; this method is expensive to use in medical facilities. Herein, we propose a much simpler and more efficient system to recover and recycle Xe from exhaled anesthetic gas mixtures at room temperature using metal-organic frameworks (MOFs). Among the MOFs tested, PCN-12 exhibits unprecedented performance with high Xe capacity and Xe/O2 , Xe/N2 and Xe/CO2 selectivity at room temperature. The in situ synchrotron measurements suggest that Xe is occupies the small pockets of PCN-12 compared to unsaturated metal centers (UMCs). Computational modeling of adsorption further supports our experimental observation of Xe binding sites in PCN-12.

Accurate Characterization of the Pore Volume in Microporous Crystalline Materials.

Pore volume is one of the main properties for the characterization of microporous crystals. It is experimentally measurable, and it can also be obtained from the refined unit cell by a number of computational techniques. In this work, we assess the accuracy and the discrepancies between the different computational methods which are commonly used for this purpose, i.e, geometric, helium, and probe center pore volumes, by studying a database of more than 5000 frameworks. We developed a new technique to fully characterize the internal void of a microporous material and to compute the probe-accessible and -occupiable pore volume. We show that, unlike the other definitions of pore volume, the occupiable pore volume can be directly related to the experimentally measured pore volumes from nitrogen isotherms.

Scanning electron microscopy image representativeness: morphological data on nanoparticles.

A sample of a nanomaterial contains a distribution of nanoparticles of various shapes and/or sizes. A scanning electron microscopy image of such a sample often captures only a fragment of the morphological variety present in the sample. In order to quantitatively analyse the sample using scanning electron microscope digital images, and, in particular, to derive numerical representations of the sample morphology, image content has to be assessed. In this work, we present a framework for extracting morphological information contained in scanning electron microscopy images using computer vision algorithms, and for converting them into numerical particle descriptors. We explore the concept of image representativeness and provide a set of protocols for selecting optimal scanning electron microscopy images as well as determining the smallest representative image set for each of the morphological features. We demonstrate the practical aspects of our methodology by investigating tricalcium phosphate, Ca3 (PO4 )2 , and calcium hydroxyphosphate, Ca5 (PO4 )3 (OH), both naturally occurring minerals with a wide range of biomedical applications.

Systematic Tuning and Multifunctionalization of Covalent Organic Polymers for Enhanced Carbon Capture.

Porous covalent polymers are attracting increasing interest in the fields of gas adsorption, gas separation, and catalysis due to their fertile synthetic polymer chemistry, large internal surface areas, and ultrahigh hydrothermal stabilities. While precisely manipulating the porosities of porous organic materials for targeted applications remains challenging, we show how a large degree of diversity can be achieved in covalent organic polymers by incorporating multiple functionalities into a single framework, as is done for crystalline porous materials. Here, we synthesized 17 novel porous covalent organic polymers (COPs) with finely tuned porosities, a wide range of Brunauer-Emmett-Teller (BET) specific surface areas of 430-3624 m(2) g(-1), and a broad range of pore volumes of 0.24-3.50 cm(3) g(-1), all achieved by tailoring the length and geometry of building blocks. Furthermore, we are the first to successfully incorporate more than three distinct functional groups into one phase for porous organic materials, which has been previously demonstrated in crystalline metal-organic frameworks (MOFs). COPs decorated with multiple functional groups in one phase can lead to enhanced properties that are not simply linear combinations of the pure component properties. For instance, in the dibromobenzene-lined frameworks, the bi- and multifunctionalized COPs exhibit selectivities for carbon dioxide over nitrogen twice as large as any of the singly functionalized COPs. These multifunctionalized frameworks also exhibit a lower parasitic energy cost for carbon capture at typical flue gas conditions than any of the singly functionalized frameworks. Despite the significant improvement, these frameworks do not yet outperform the current state-of-art technology for carbon capture. Nonetheless, the tuning strategy presented here opens up avenues for the design of novel catalysts, the synthesis of functional sensors from these materials, and the improvement in the performance of existing covalent organic polymers by multifunctionalization.

In silico prediction of MOFs with high deliverable capacity or internal surface area.

Metal-organic frameworks (MOFs) offer unprecedented atom-scale design and structural tunability, largely due to the vast number of possible organic linkers which can be utilized in their assembly. Exploration of this space of linkers allows identification of ranges of achievable material properties as well as discovery of optimal materials for a given application. Experimental exploration of the linker space has to date been quite limited due to the cost and complexity of synthesis, while high-throughput computational studies have mainly explored MOF materials based on known or readily available linkers. Here an evolutionary algorithm for de novo design of organic linkers for metal-organic frameworks is used to predict MOFs with either high methane deliverable capacity or methane accessible surface area. Known chemical reactions are applied in silico to a population of linkers to discover these MOFs. Through this design strategy, MOF candidates are found in the ten symmetric networks acs, cds, dia, hxg, lvt, nbo, pcu, rhr, sod, and tbo. The correlation between deliverable capacities and surface area is network dependent.

In silico design of porous polymer networks: high-throughput screening for methane storage materials.

Porous polymer networks (PPNs) are a class of advanced porous materials that combine the advantages of cheap and stable polymers with the high surface areas and tunable chemistry of metal-organic frameworks. They are of particular interest for gas separation or storage applications, for instance, as methane adsorbents for a vehicular natural gas tank or other portable applications. PPNs are self-assembled from distinct building units; here, we utilize commercially available chemical fragments and two experimentally known synthetic routes to design in silico a large database of synthetically realistic PPN materials. All structures from our database of 18,000 materials have been relaxed with semiempirical electronic structure methods and characterized with Grand-canonical Monte Carlo simulations for methane uptake and deliverable (working) capacity. A number of novel structure-property relationships that govern methane storage performance were identified. The relationships are translated into experimental guidelines to realize the ideal PPN structure. We found that cooperative methane-methane attractions were present in all of the best-performing materials, highlighting the importance of guest interaction in the design of optimal materials for methane storage.

On the flexibility of metal-organic frameworks.

Occasional, large amplitude flexibility in metal-organic frameworks (MOFs) is one of the most intriguing recent discoveries in chemistry and material science. Yet, there is at present no theoretical framework that permits the identification of flexible structures in the rapidly expanding universe of MOFs. Here, we propose a simple method to predict whether a MOF is flexible, based on treating it as a system of rigid elements, connected by hinges. This proposition is correct in application to MOFs based on rigid carboxylate linkers. We validate the method by correctly classifying known experimental MOFs into rigid and flexible groups. Applied to hypothetical MOFs, the method reveals an abundance of flexibility phenomena, and this seems to be at odds with the proportion of flexible structures among experimentally known MOFs. We speculate that the flexibility of a MOF may constitute an intrinsic impediment on its experimental realization. This highlights the importance of systematic prediction of large amplitude flexibility regimes in MOFs.

Optimizing nanoporous materials for gas storage.

In this work, we address the question of which thermodynamic factors determine the deliverable capacity of methane in nanoporous materials. The deliverable capacity is one of the key factors that determines the performance of a material for methane storage in automotive fuel tanks. To obtain insights into how the molecular characteristics of a material are related to the deliverable capacity, we developed several statistical thermodynamic models. The predictions of these models are compared with the classical thermodynamics approach of Bhatia and Myers [Bhatia and Myers, Langmuir, 2005, 22, 1688] and with the results of molecular simulations in which we screen the International Zeolite Association (IZA) structure database and a hypothetical zeolite database of over 100,000 structures. Both the simulations and our models do not support the rule of thumb that, for methane storage, one should aim for an optimal heat of adsorption of 18.8 kJ mol(-1). Instead, our models show that one can identify an optimal heat of adsorption, but that this optimal heat of adsorption depends on the structure of the material and can range from 8 to 23 kJ mol(-1). The different models we have developed are aimed to determine how this optimal heat of adsorption is related to the molecular structure of the material.

Communication: Remarkable electrophilicity of the oxalic acid monomer: an anion photoelectron spectroscopy and theoretical study.

Our experimental and computational results demonstrate an unusual electrophilicity of oxalic acid, the simplest dicarboxylic acid. The monomer is characterized by an adiabatic electron affinity and electron vertical detachment energy of 0.72 and 1.08 eV (±0.05 eV), respectively. The electrophilicity results primarily from the bonding carbon-carbon interaction in the singly occupied molecular orbital of the anion, but it is further enhanced by intramolecular hydrogen bonds. The well-resolved structure in the photoelectron spectrum is reproduced theoretically, based on Franck-Condon factors for the vibronic anion → neutral transitions.

Insights into Thermal Conductivity at the MOF-Polymer Interface.

Understanding the thermal conductivity in metal-organic framework (MOF)-polymer composites is crucial for optimizing their performance in applications involving heat transfer. In this work, several UiO66-polymer composites (where the polymer is either PEG, PVDF, PS, PIM-1, PP, or PMMA) are examined using molecular simulations. Our contribution highlights the interface's impact on thermal conductivity, observing an overall increasing trend attributable to the synergistic effect of MOF enhancing polymer thermal conductivity. Flexible polymers such as PEG and PVDF exhibit increased compatibility with the MOF, facilitating their integration with the MOF lattice. However, this integration leads to a moderated enhancement in thermal conductivity compared to polymers that remain separate from the MOF structure, such as PS or PP. This effect can be attributed to alterations in phonon transport pathways and shifts in interfacial interactions between the polymer and MOF. Specifically, the infiltration of the polymer like PEG and PVDF into the MOF disrupted the MOF's ordered network, introducing defects or barriers that hindered phonon propagation. In contrast, nonpolar and rigid polymers like PP, PMMA, PS, and PIM-1 exhibited greater improvements in thermal conductivity when combined with MOFs compared to the flexible polymers PVDF and PEG. Most notably, our analysis identifies a critical interface region within approximately 30-50 Å that profoundly influences thermal conductivity. The interface region, as indicated by the density profile and radius of gyration, is notably shorter but plays a pivotal role in modulating the thermal properties. The sensitivity of the system to these interface characteristics underscores the crucial role of this particular interface area in dictating the thermal conductivity. Our findings emphasize the sensitivity of thermal conductivity in polymer matrices to interface characteristics and highlight the critical role of a specific interface region in modulating thermal properties.

Prediction of mechanical and flame-retardant properties of MOF-loaded polymer composites.

We have successfully constructed a machine learning framework to predict the important properties of MOF-loaded (metal-organic framework) polymer composites. Our classification models have obtained promising predictive performance. The analysis of feature importance has revealed the relationship between MOFs and the properties of polymer composites, guiding the research focus for MOF's application in the fire safety field.

Thermogravimetric Analysis of Moisture in Natural and Thermally Treated Clay Materials.

Clays are a class of porous materials; their surfaces are naturally covered by moisture. Weak thermal treatment may be considered practical to remove the water molecules, changing the surface properties and making the micro- and/or mesoporosities accessible to interact with other molecules. Herein, a modulated thermogravimetric analysis (MTGA) study of the moisture behavior on the structures of five, both fibrous and laminar, clay minerals is reported. The effect of the thermal treatment at 150 °C, which provokes the release of weakly adsorbed water molecules, was also investigated. The activation energies for the removal of the adsorbed water (Ea) were calculated, and they were found to be higher, namely, from 160 to 190 kJ mol-1, for fibrous clay minerals compared to lamellar structures, ranging in this latter case from 80 to 100 kJ mol-1. The thermal treatment enhances the rehydration in Na-montmorillonite, stevensite, and sepiolite structures with a decrease in the energy required to remove it, while Ea increases significantly in palygorskite (from 164 to 273 kJ mol-1). As a proof of concept, the MTGA results are statistically correlated, together with a full characterization of the physico-chemical properties of the five clay minerals, with the adsorption of two molecules, i.e., aflatoxin B1 (AFB1) and ß-carotene. Herein, the amount of adsorbed molecules ranges from 12 to 97% for the former and from 22 to 35% for the latter, depending on the particular clay. The Ea was correlated with AFB1 adsorption with a Spearman score of -0.9. When the adsorbed water is forcibly removed, e.g., under vacuum conditions and high temperatures, the structure becomes the most important, decreasing the Spearman score between ß-carotene and Ea to -0.6.

Molecular dynamics simulations of gas selectivity in amorphous porous molecular solids.

Some organic cage molecules have structures with protected, internal pore volume that cannot be in-filled, irrespective of the solid-state packing mode: that is, they are intrinsically porous. Amorphous packings can give higher pore volumes than crystalline packings for these materials, but the precise nature of this additional porosity is hard to understand for disordered solids that cannot be characterized by X-ray diffraction. We describe here a computational methodology for generating structural models of amorphous porous organic cages that are consistent with experimental data. Molecular dynamics simulations rationalize the observed gas selectivity in these amorphous solids and lead to insights regarding self-diffusivities, gas diffusion trajectories, and gas hopping mechanisms. These methods might be suitable for the de novo design of new amorphous porous solids for specific applications, where "rigid host" approximations are not applicable.

In silico screening of carbon-capture materials.

One of the main bottlenecks to deploying large-scale carbon dioxide capture and storage (CCS) in power plants is the energy required to separate the CO(2) from flue gas. For example, near-term CCS technology applied to coal-fired power plants is projected to reduce the net output of the plant by some 30% and to increase the cost of electricity by 60-80%. Developing capture materials and processes that reduce the parasitic energy imposed by CCS is therefore an important area of research. We have developed a computational approach to rank adsorbents for their performance in CCS. Using this analysis, we have screened hundreds of thousands of zeolite and zeolitic imidazolate framework structures and identified many different structures that have the potential to reduce the parasitic energy of CCS by 30-40% compared with near-term technologies.

Methane storage capabilities of diamond analogues.

Methane can be an alternative fuel for vehicular usage provided that new porous materials are developed for its efficient adsorption-based storage. Herein, we search for materials for this application within the family of diamond analogues. We used density functional theory to investigate structures in which tetrahedral C atoms of diamond are separated by -CC- or -BN- groups, as well as ones involving substitution of tetrahedral C atoms with Si and Ge atoms. The adsorptive and diffusive properties of methane are studied using classical molecular simulations. Our results suggest that the all-carbon structure has the highest volumetric methane uptake of 280 VSTP/V at p = 35 bar and T = 298 K. However, it suffers from limited methane diffusion. Alternatively, the considered Si and Ge-containing analogies have fast diffusive properties but their adsorption is lower, ca. 172-179 VSTP/V, at the same conditions.

Understanding the effect of side groups in ionic liquids on carbon-capture properties: a combined experimental and theoretical effort.

Ionic liquids are an emerging class of materials with applications in a variety of fields. Steady progress has been made in the creation of ionic liquids tailored to specific applications. However, the understanding of the underlying structure-property relationships has been slower to develop. As a step in the effort to alleviate this deficiency, the influence of side groups on ionic liquid properties has been studied through an integrated approach utilizing synthesis, experimental determination of properties, and simulation techniques. To achieve this goal, a classical force field in the framework of OPLS/Amber force fields has been developed to predict ionic liquid properties accurately. Cu(I)-catalyzed click chemistry was employed to synthesize triazolium-based ionic liquids with diverse side groups. Values of densities were predicted within 3% of experimental values, whereas self-diffusion coefficients were underestimated by about an order of magnitude though the trends were in excellent agreement, the activation energy calculated in simulation correlates well with experimental values. The predicted Henry coefficient for CO(2) solubility reproduced the experimentally observed trends. This study highlights the importance of integrating experimental and computational approaches in property prediction and materials development, which is not only useful in the development of ionic liquids for CO(2) capture but has application in many technological fields.

Predicting large CO2 adsorption in aluminosilicate zeolites for postcombustion carbon dioxide capture.

Large-scale simulations of aluminosilicate zeolites were conducted to identify structures that possess large CO(2) uptake for postcombustion carbon dioxide capture. In this study, we discovered that the aluminosilicate zeolite structures with the highest CO(2) uptake values have an idealized silica lattice with a large free volume and a framework topology that maximizes the regions with nearest-neighbor framework atom distances from 3 to 4.5 Å. These predictors extend well to different Si:Al ratios and for both Na(+) and Ca(2+) cations, demonstrating their universal applicability in identifying the best-performing aluminosilicate zeolite structures.

Similarity-driven discovery of zeolite materials for adsorption-based separations.

Crystalline porous materials can be exploited in many applications. Discovery of materials with optimum adsorption properties typically involves expensive brute-force characterization of large sets of materials. An alternative approach based on similarity searching that enables discovery of materials with optimum adsorption for CO(2) and other molecules at a fraction of the cost of brute-force characterization is demonstrated.