A Collection of More than 900 Gas Mixture Adsorption Experiments in Porous Materials from Literature Meta-Analysis

Quantifying the Uncertainty of Force Field Selection on Adsorption Predictions in MOFs

Comparisons between simulated and experimental adsorption isotherms in MOFs are fraught with challenges. On the experimental side, there is significant variation between isotherms measured on the same system, with a significant percentage (∼20%) of published data being considered outliers. On the simulation side, force fields are often chosen "off-the-shelf" with little or no validation. The effect of this choice on the reliability of simulated adsorption predictions has not yet been rigorously quantified. In this work, we fill this gap by systematically quantifying the uncertainty arising from force field selection on adsorption isotherm predictions. We choose methane adsorption, where electrostatic interactions are negligible, to independently study the effect of the framework Lennard-Jones parameters on a series of prototypical materials that represent the most widely studied MOF "families". Using this information, we compute an adsorption "consensus isotherm" from simulations, including a quantification of uncertainty, and compare it against a manually curated set of experimental data from the literature. By considering many experimental isotherms measured by different groups and eliminating outliers in the data using statistical analysis, we conduct a rigorous comparison that avoids the pitfalls of the standard approach of comparing simulation predictions to a single experimental data set. Our results show that (1) the uncertainty in simulated isotherms can be as large as 15% and (2) standard force fields can provide reliable predictions for some systems but can fail dramatically for others, highlighting systematic shortcomings in those models. Based on this, we offer recommendations for future simulation studies of adsorption, including high-throughput computational screening of MOFs.

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.

Adsorption of Natural Gas in Metal-Organic Frameworks: Selectivity, Cyclability, and Comparison to Methane Adsorption

Evaluation of metal-organic frameworks (MOFs) for adsorbed natural gas (ANG) technology employs pure methane as a surrogate for natural gas (NG). This approximation is problematic, as it ignores the impact of other heavier hydrocarbons present in NG, such as ethane and propane, which generally have more favorable adsorption interactions with MOFs compared to methane. Herein, using quantitative Raman spectroscopic analysis and Monte Carlo calculations, we demonstrate the adsorption selectivity of high-performing MOFs, such as MOF-5, MOF-177, and SNU-70, for a methane and ethane mixture (95:5) that mimics the composition of NG. The impact of selectivity on the storage and deliverable capacities of these adsorbents during successive cycles of adsorption and desorption, simulating the filling and emptying of an ANG tank, is also demonstrated. The study reveals a gradual reduction in the storage performance of MOFs, particularly with smaller pore volumes, due to ethane accumulation over long-term cycling, until a steady state is reached with substantially degraded storage performance.

Single-Component Adsorption Equilibria of CO2, CH4, Water, and Acetone on Tapered Porous Carbon Molecular Sieves

Engineered carbon molecular sieves (CMSs) with tapered pores, high surface area, and high total pore volume were investigated for their CO2, CH4, water, and acetone adsorption properties at 288.15, 298.15, 308.15 K, and pressures of <1 bar. The results were compared with BPL carbon. The samples exhibited higher adsorption capacity for CO2 compared to BPL carbon, with Carboxen 1005 being the highest due to the presence of ultramicropores (pores smaller than 0.8 nm). Similar observations were made for CH4 except at 288.15 K. Although the CMSs exhibited higher hydrophobicity than BPL carbon, the latter had the highest acetone uptake for all investigated temperatures due to its higher oxygen content, which facilitates stronger interactions with polar VOC molecules. Heats of adsorption were calculated using the Clausius-Clapeyron equation after fitting the isotherms with the dual-site Langmuir-Freundlich model, and results largely corroborated the order of adsorption capacities of CO2, CH4, and water on the carbon materials.

The Challenge of Water Competition in Physical Adsorption of CO2 by Porous Solids for Carbon Capture Applications - A Short Perspective

With ever-increasing efforts to design sorbent materials to capture carbon dioxide from flue gas and air, this perspective article is provided based on nearly a decade of collaboration across science, engineering, and industry partners. A key point learned is that a holistic view of the carbon capture problem is critical. While researchers can be inclined to value their own fields and associated metrics, often, key parameters are those that enable synergy between materials and processes. While the role of water in the chemisorption of CO2 is well-studied, in this perspective, it is hoped to highlight the often-overlooked but critical role of water in assessing the potential of a physical adsorbent for CO2 capture. This is a challenge that requires interdisciplinarity. As such, this document is written for a general audience rather than experts in any specific discipline.

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.

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.

Adaptive Pore Opening to Form Tailored Adsorption Sites in a Cooperatively Flexible Framework Enables Record Inverse Propane/Propylene Separation

A proposed low-energy alternative to the separation of alkanes from alkenes by energy-intensive cryogenic distillation is separation by porous adsorbents. Unfortunately, most adsorbents preferentially take up the desired, high-value major component alkene, requiring frequent regeneration. Adsorbents with inverse selectivity for the minor component alkane would enable the direct production of purified, reagent-grade alkene, greatly reducing global energy consumption. However, such materials are exceedingly rare, especially for propane/propylene separation. Here, we report that through adaptive and spontaneous pore size and shape adaptation to optimize an ensemble of weak noncovalent interactions, the structurally responsive metal-organic framework CdIF-13 (sod-Cd(benzimidazolate)2) exhibits inverse selectivity for propane over propylene with record-setting separation performance under industrially relevant temperature, pressure, and mixture conditions. Powder synchrotron X-ray diffraction measurements combined with first-principles calculations yield atomic-scale insight and reveal the induced fit mechanism of adsorbate-specific pore adaptation and ensemble interactions between ligands and adsorbates. Dynamic column breakthrough measurements confirm that CdIF-13 displays selectivity under mixed-component conditions of varying ratios, with a record measured selectivity factor of α ≈ 3 at 95:5 propylene:propane at 298 K and 1 bar. When sequenced with a low-cost rigid adsorbent, we demonstrated the direct purification of propylene under ambient conditions. This combined atomic-level structural characterization and performance testing firmly establishes how cooperatively flexible materials can be capable of unprecedented separation factors.

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.

A Robust Framework for Generating Adsorption Isotherms to Screen Materials for Carbon Capture

To rank the performance of materials for a given carbon capture process, we rely on pure component isotherms from which we predict the mixture isotherms. For screening a large number of materials, we also increasingly rely on isotherms predicted from molecular simulations. In particular, for such screening studies, it is important that the procedures to generate the data are accurate, reliable, and robust. In this work, we develop an efficient and automated workflow for a meticulous sampling of pure component isotherms. The workflow was tested on a set of metal-organic frameworks (MOFs) and proved to be reliable given different guest molecules. We show that the coupling of our workflow with the Clausius-Clapeyron relation saves CPU time, yet enables us to accurately predict pure component isotherms at the temperatures of interest, starting from a reference isotherm at a given temperature. We also show that one can accurately predict the CO₂ and N₂ mixture isotherms using ideal adsorbed solution theory (IAST). In particular, we show that IAST is a more reliable numerical tool to predict binary adsorption uptakes for a range of pressures, temperatures, and compositions, as it does not rely on the fitting of experimental data, which typically needs to be done with analytical models such as dual-site Langmuir (DSL). This makes IAST a more suitable and general technique to bridge the gap between adsorption (raw) data and process modeling. To demonstrate this point, we show that the ranking of materials, for a standard three-step temperature swing adsorption (TSA) process, can be significantly different depending on the thermodynamic method used to predict binary adsorption data. We show that, for the design of processes that capture CO₂ from low concentration (0.4%) streams, the commonly used methodology to predict mixture isotherms incorrectly assigns up to 33% of the materials as top-performing.

Surprising Use of the Business Innovation Bass Diffusion Model To Accurately Describe Adsorption Isotherm Types I, III, and V

Using adsorption isotherm data to determine heats of adsorption or predict mixture adsorption using the ideal adsorbed solution theory (IAST) relies on accurate fits of the data with continuous, mathematical models. Here, we derive an empirical two-parameter model to fit isotherm data of IUPAC types I, III, and V in a descriptive way based on the Bass model for innovation diffusion. We report 31 isotherm fits to existing literature data covering all six types of isotherms, various adsorbents, such as carbons, zeolites, and metal-organic frameworks (MOFs), as well as different adsorbing gases (water, carbon dioxide, methane, and nitrogen). We find several cases, especially for flexible MOFs, where previously reported isotherm models reached their limits and either failed to fit the data or could not sufficiently be fitted due to stepped type V isotherms. Moreover, in two instances, models specifically developed for distinct systems are fitted with a higher R² value compared to the models in the original reports. Using these fits, it is demonstrated how the new Bingel-Walton isotherm can be used to qualitatively assess the hydrophilic or hydrophobic behavior of porous materials from the relative magnitude of the two fitting parameters. The model can also be employed to find matching heats of adsorption values for systems with isotherm steps using one, continuous fit instead of partial, stepwise fits or interpolation. Additionally, using our single, continuous fit to model stepped isotherms in IAST mixture adsorption predictions leads to good agreement with the results from the osmotic framework adsorbed solution theory that was specifically developed for these systems using a stepwise, approximate fitting, which is yet far more complex. Our new isotherm equation accomplishes all of these tasks with only two fitted parameters, providing a simple, accurate method for modeling a variety of adsorption behavior.

Exemplar Mixtures for Studying Complex Mixture Effects in Practical Chemical Separations

Materials and processes for chemical separations must be used in complex environments to have an impact in many practical settings. Despite these complexities, much research on chemical separations has focused on idealized chemical mixtures. In this paper, we suggest that research communities for specific chemical separations should develop well-defined exemplar mixtures to bridge the gap between fundamental studies and practical applications and we provide a hierarchical framework of chemical mixtures for this purpose. We illustrate this hierarchy with examples, including CO₂ capture, capture of uranium from seawater, and separations of mixtures from electrocatalytic CO₂ reactions, among others. We conclude with four recommendations for the research community to accelerate the development of innovative separations strategies for pressing real-world challenges.

Material Evolution with Nanotechnology, Nanoarchitectonics, and Materials Informatics: What will be the Next Paradigm Shift in Nanoporous Materials?

Materials science and chemistry have played a central and significant role in advancing society. With the shift toward sustainable living, it is anticipated that the development of functional materials will continue to be vital for sustaining life on our planet. In the recent decades, rapid progress has been made in materials science and chemistry owing to the advances in experimental, analytical, and computational methods, thereby producing several novel and useful materials. However, most problems in material development are highly complex. Here, the best strategy for the development of functional materials via the implementation of three key concepts is discussed: nanotechnology as a game changer, nanoarchitectonics as an integrator, and materials informatics as a super-accelerator. Discussions from conceptual viewpoints and example recent developments, chiefly focused on nanoporous materials, are presented. It is anticipated that coupling these three strategies together will open advanced routes for the swift design and exploratory search of functional materials truly useful for solving real-world problems. These novel strategies will result in the evolution of nanoporous functional materials.

Carbon dioxide capture with zeotype materials

This review describes the application of zeotype materials for the capture of CO2 in different scenarios, the critical parameters defining the adsorption performances, and the challenges of zeolitic adsorbents for CO2 capture.

Performance-Based Screening of Porous Materials for Carbon Capture

Computational screening methods have changed the way new materials and processes are discovered and designed. For adsorption-based gas separations and carbon capture, recent efforts have been directed toward the development of multiscale and performance-based screening workflows where we can go from the atomistic structure of an adsorbent to its equilibrium and transport properties at different scales, and eventually to its separation performance at the process level. The objective of this work is to review the current status of this new approach, discuss its potential and impact on the field of materials screening, and highlight the challenges that limit its application. We compile and introduce all the elements required for the development, implementation, and operation of multiscale workflows, hence providing a useful practical guide and a comprehensive source of reference to the scientific communities who work in this area. Our review includes information about available materials databases, state-of-the-art molecular simulation and process modeling tools, and a complete catalogue of data and parameters that are required at each stage of the multiscale screening. We thoroughly discuss the challenges associated with data availability, consistency of the models, and reproducibility of the data and, finally, propose new directions for the future of the field.

Water Bridges Substitute for Defects in Amine-Functionalized UiO-66, Boosting CO2 Adsorption

The binary adsorption of CO₂ and water on an amine-functionalized UiO-66 metal-organic framework (MOF) was studied experimentally and computationally. Grand canonical Monte Carlo simulations were used to investigate three additional UiO-66 MOFs with different functionalized linkers. Each MOF was studied in a defect-free form as well as two additional forms with precise linker defects. Binary adsorption isotherms are presented for CO₂ at specific water loadings. While water loading in defect-free MOFs reduces the CO₂ uptake, the defects slightly boost the CO₂ uptake at low water loadings. It was found that water bridges form between the metal oxide cores, replacing the missing linkers. Effectively, this creates smaller pores that are more welcoming of CO₂ adsorption. Experimental measurement of the binary isotherms for UiO-66-NH₂ shows a behavior that is consistent with this enhancement.

Alkyl decorated metal-organic frameworks for selective trapping of ethane from ethylene above ambient pressures

The trapping of paraffins is beneficial compared to selective olefin adsorption for adsorptive olefin purification from a process engineering point of view. Here we demonstrate the use of a series of Zn2(X-bdc)2(dabco) (where X-bdc2- is bdc2- = 1,4-benzenedicarboxylate with substituting groups X, DM-bdc2- = 2,5-dimethyl-1,4-benzenedicarboxylate or TM-bdc2- = 2,3,5,6-tetramethyl-1,4-benzenedicarboxylate and dabco = diazabicyclo[2.2.2.]octane) metal-organic frameworks (MOFs) for the adsorptive removal of ethane from ethylene streams. The best performing material from this series is Zn2(TM-bdc)2(dabco) (DMOF-TM), which shows a high ethane uptake of 5.31 mmol g-1 at 110 kPa, with a good IAST selectivity of 1.88 towards ethane over ethylene. Through breakthrough measurements a high productivity of 13.1 L kg-1 per breakthrough is revealed with good reproducibility over five consecutive cycles. Molecular simulations show that the methyl groups of DMOF-TM are forming a van der Waals trap with the methylene groups from dabco, snuggly fitting the ethane. Further, rarely used high pressure coadsorption measurements, in pressure regimes that most scientific studies on hydrocarbon separation on MOFs ignore, reveal an increase in ethane capacity and selectivity for binary mixtures with increased pressures. The coadsorption measurements reveal good selectivity of 1.96 at 1000 kPa, which is verified also through IAST calculations up to 3000 kPa. This study overall showcases the opportunities that pore engineering by alkyl group incorporation and pressure increase offer to improve hydrocarbon separation in reticular materials.

An upper bound visualization of design trade-offs in adsorbent materials for gas separations: alkene/alkane adsorbents

The last 20 years has seen an explosion in the number of publications investigating porous solids for gas adsorption and separation. The combination of external drivers such as anthropogenic climate change and industrial efficiency has been coupled with discovery of new materials such as synthetic zeolites, metal-organic frameworks, covalent organic frameworks, and non-porous adsorbents. Numerous reviews catalogue these materials and their properties. However, the field lacks a unifying resource to visually compare and analyse materials properties with regard to their utility as a scientific advance and potential for industrial use. In the related field of membrane science, the 'Robeson upper bound' empirically describes the trade-off between gas permeability and selectivity and has become a ubiquitous tool for comparing membrane materials. In this article, we propose upper and lower bounds that empirically correlate the trade-offs encountered when designing adsorbent materials for gas separation, specifically: capacity, selectivity, and heat of adsorption. We apply bound visualizations to adsorbents studied for light alkene/alkane separations and highlight their use in identifying candidate materials for examination within process models and for guiding insights to the most effective materials design strategies. Furthermore, we note the limitations of upper and lower bound visualizations and provide links to a database resource for researchers to produce and download bound visualization plots. We anticipate that introducing bound visualizations to the field of adsorbents for gas separations will allow researchers to provide context for the importance of new materials discoveries, understand trade-offs in adsorbent design, and connect process engineers with candidate materials.

Deep learning combined with IAST to screen thermodynamically feasible MOFs for adsorption-based separation of multiple binary mixtures

The structures of metal-organic frameworks (MOFs) can be tuned to reproducibly create adsorption properties that enable the use of these materials in fixed-adsorption beds for non-thermal separations. However, with millions of possible MOF structures, the challenge is to find the MOF with the best adsorption properties to separate a given mixture. Thus, computational, rather than experimental, screening is necessary to identify promising MOF structures that merit further examination, a process traditionally done using molecular simulation. However, even molecular simulation can become intractable when screening an expansive MOF database for their separation properties at more than a few composition, temperature, and pressure combinations. Here, we illustrate progress toward an alternative computational framework that can efficiently identify the highest-performing MOFs for separating various gas mixtures at a variety of conditions and at a fraction of the computational cost of molecular simulation. This framework uses a "multipurpose" multilayer perceptron (MLP) model that can predict single component adsorption of various small adsorbates, which, upon coupling with ideal adsorbed solution theory (IAST), can predict binary adsorption for mixtures such as Xe/Kr, CH₄/CH₆, N₂/CH₄, and Ar/Kr at multiple compositions and pressures. For this MLP+IAST framework to work with sufficient accuracy, we found it critical for the MLP to make accurate predictions at low pressures (0.01-0.1 bar). After training a model with this capability, we found that MOFs in the 95th and 90th percentiles of separation performance determined from MLP+IAST calculations were 65% and 87%, respectively, the same as MOFs in the simulation-predicted 95th percentile across several mixtures at diverse conditions (on average). After validating our MLP+IAST framework, we used a clustering algorithm to identify "privileged" MOFs that are high performing for multiple separations at multiple conditions. As an example, we focused on MOFs that were high performing for the industrially relevant separations 80/20 Xe/Kr at 1 bar and 80/20 N₂/CH₄ at 5 bars. Finally, we used the MOF free energies (calculated on our entire database) to identify privileged MOFs that were also likely synthetically accessible, at least from a thermodynamic perspective.

A Universal Standard Archive File for Adsorption Data

New advanced adsorbents are a crucial driver for the development of energy and environmental applications. Tremendous potential is provided by machine learning and data mining techniques, as these approaches can identify the most appropriate adsorbent for a particular application. However, the current scientific reporting of adsorption isotherms in graphs and figures is not adequate to reproduce original experimentally measured data. This report proposes the specification of a new standard adsorption information file (AIF) inspired by the ubiquitous crystallographic information file (CIF) and based on the self-defining text archive and retrieval (STAR) procedure, also used to represent biological nuclear magnetic resonance experiments (NMR-STAR). The AIF is a flexible and easily extended free-format archive file that is readily human and machine readable and is simple to edit using a basic text editor or parse for database curation. This format represents the first steps toward an open adsorption data format as a basis for a decentralized adsorption data library. An open format facilitates the electronic transmission of adsorption data between laboratories, journals, and larger databases, which is key in the effort to increase open science in the field of porous materials in the future.