In silico screening of carbon-capture materials

Avoiding pitfalls in molecular simulation of vapor sorption: Example of propane and isobutane in metal-organic frameworks for adsorption cooling applications

This study introduces recommendations for conducting molecular simulations of vapor adsorption, with an emphasis on enhancing the accuracy, reproducibility, and comparability of results. The first aspect we address is consistency in the implementation of some details of typical molecular models, including tail corrections and cutoff distances, due to their significant influence on generated data. We highlight the importance of explicitly calculating the saturation pressures at relevant temperatures using methods such as Gibbs ensemble Monte Carlo simulations and illustrate some pitfalls in extrapolating saturation pressures using this method. For grand canonical Monte Carlo (GCMC) simulations, the input fugacity is usually calculated using an equation of state, which often requires the critical parameters of the fluid. We show the importance of using critical parameters derived from the simulation with the same model to ensure internal consistency between the simulated explicit adsorbate phase and the implicit bulk phase in GCMC. We show the advantages of presenting isotherms on a relative pressure scale to facilitate easier comparison among models and with experiment. Extending these guidelines to a practical case study, we evaluate the performance of various isoreticular metal-organic frameworks (MOFs) in adsorption cooling applications. This includes examining the advantages of using propane and isobutane as working fluids and identifying MOFs with a superior performance.

Screening Cu-Zeolites for Methane Activation Using Curriculum-Based Training

Machine learning (ML), when used synergistically with atomistic simulations, has recently emerged as a powerful tool for accelerated catalyst discovery. However, the application of these techniques has been limited by the lack of interpretable and transferable ML models. In this work, we propose a curriculum-based training (CBT) philosophy to systematically develop reactive machine learning potentials (rMLPs) for high-throughput screening of zeolite catalysts. Our CBT approach combines several different types of calculations to gradually teach the ML model about the relevant regions of the reactive potential energy surface. The resulting rMLPs are accurate, transferable, and interpretable. We further demonstrate the effectiveness of this approach by exhaustively screening thousands of [CuOCu]2+ sites across hundreds of Cu-zeolites for the industrially relevant methane activation reaction. Specifically, this large-scale analysis of the entire International Zeolite Association (IZA) database identifies a set of previously unexplored zeolites (i.e., MEI, ATN, EWO, and CAS) that show the highest ensemble-averaged rates for [CuOCu]2+-catalyzed methane activation. We believe that this CBT philosophy can be generally applied to other zeolite-catalyzed reactions and, subsequently, to other types of heterogeneous catalysts. Thus, this represents an important step toward overcoming the long-standing barriers within the computational heterogeneous catalysis community.

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.

Engineering Enzymes for Environmental Sustainability

The development and implementation of sustainable catalytic technologies is key to delivering our net-zero targets. Here we review how engineered enzymes, with a focus on those developed using directed evolution, can be deployed to improve the sustainability of numerous processes and help to conserve our environment. Efficient and robust biocatalysts have been engineered to capture carbon dioxide (CO2) and have been embedded into new efficient metabolic CO2 fixation pathways. Enzymes have been refined for bioremediation, enhancing their ability to degrade toxic and harmful pollutants. Biocatalytic recycling is gaining momentum, with engineered cutinases and PETases developed for the depolymerization of the abundant plastic, polyethylene terephthalate (PET). Finally, biocatalytic approaches for accessing petroleum-based feedstocks and chemicals are expanding, using optimized enzymes to convert plant biomass into biofuels or other high value products. Through these examples, we hope to illustrate how enzyme engineering and biocatalysis can contribute to the development of cleaner and more efficient chemical industry.

Engineering Enzymes for Environmental Sustainability

The development and implementation of sustainable catalytic technologies is key to delivering our net-zero targets. Here we review how engineered enzymes, with a focus on those developed using directed evolution, can be deployed to improve the sustainability of numerous processes and help to conserve our environment. Efficient and robust biocatalysts have been engineered to capture carbon dioxide (CO2 ) and have been embedded into new efficient metabolic CO2 fixation pathways. Enzymes have been refined for bioremediation, enhancing their ability to degrade toxic and harmful pollutants. Biocatalytic recycling is gaining momentum, with engineered cutinases and PETases developed for the depolymerization of the abundant plastic, polyethylene terephthalate (PET). Finally, biocatalytic approaches for accessing petroleum-based feedstocks and chemicals are expanding, using optimized enzymes to convert plant biomass into biofuels or other high value products. Through these examples, we hope to illustrate how enzyme engineering and biocatalysis can contribute to the development of cleaner and more efficient chemical industry.

An ethynyl-modified interpenetrated metal-organic framework for highly efficient selective gas adsorption

An ethynyl-modified interpenetrated MOF material with lvt topology, [Cu2(BTEB)(NMF)2]·NMF·8H2O (compound 1, H4BTEB = 4,4',4'',4'''-(benzene-1,2,4,5-tetrayltetrakis(ethyne-2,1-diyl))tetrabenzoic acid, NMF = N-Methylformamide), was successfully synthesized by using an alkynyl-functionalized H4BTEB organic ligand under solvothermal conditions. Structural analysis shows that compound 1, consisting of a tetradentate carboxylic acid ligand and classical [Cu2(CO2)4] paddle-wheel structure building units, has a rare 4-connected lvt topology with dual interpenetrating structure, which can improve the framework stability, as well as the gas adsorption capacity and selectivity due to the restricted pore channel. According to the study of gas adsorption performance, compound 1 with a larger surface area, boasts a superior adsorption capacity for small gas molecules. Also, ideal adsorption solution theory (IAST) computational simulation shows that compound 1 has good gas adsorption selectivity for C3H8/CH4, indicating its potential application in gas separation.

DFT-Quality Adsorption Simulations in Metal-Organic Frameworks Enabled by Machine Learning Potentials

Nanoporous materials such as metal-organic frameworks (MOFs) have been extensively studied for their potential for adsorption and separation applications. In this respect, grand canonical Monte Carlo (GCMC) simulations have become a well-established tool for computational screenings of the adsorption properties of large sets of MOFs. However, their reliance on empirical force field potentials has limited the accuracy with which this tool can be applied to MOFs with challenging chemical environments such as open-metal sites. On the other hand, density-functional theory (DFT) is too computationally demanding to be routinely employed in GCMC simulations due to the excessive number of required function evaluations. Therefore, we propose in this paper a protocol for training machine learning potentials (MLPs) on a limited set of DFT intermolecular interaction energies (and forces) of CO2 in ZIF-8 and the open-metal site containing Mg-MOF-74, and use the MLPs to derive adsorption isotherms from first principles. We make use of the equivariant NequIP model which has demonstrated excellent data efficiency, and as such an error on the interaction energies below 0.2 kJ mol-1 per adsorbate in ZIF-8 was attained. Its use in GCMC simulations results in highly accurate adsorption isotherms and heats of adsorption. For Mg-MOF-74, a large dependence of the obtained results on the used dispersion correction was observed, where PBE-MBD performs the best. Lastly, to test the transferability of the MLP trained on ZIF-8, it was applied to ZIF-3, ZIF-4, and ZIF-6, which resulted in large deviations in the predicted adsorption isotherms and heats of adsorption. Only when explicitly training on data for all ZIFs, accurate adsorption properties were obtained. As the proposed methodology is widely applicable to guest adsorption in nanoporous materials, it opens up the possibility for training general-purpose MLPs to perform highly accurate investigations of guest adsorption.

How Can (or Why Should) Process Engineering Aid the Screening and Discovery of Solid Sorbents for CO2 Capture?

ConspectusAdsorption using solid sorbents is emerging as a serious contender to amine-based liquid absorption for postcombustion CO2 capture. In the last 20+ years, significant efforts have been invested in developing adsorption processes for CO2 capture. In particular, significant efforts have been invested in developing new adsorbents for this application. These efforts have led to the generation of hundreds of thousands of (hypothetical and real) adsorbents, e.g., zeolites and metal-organic frameworks (MOFs). Identifying the right adsorbent for CO2 capture remains a challenging task. Most studies are focused on identifying adsorbents based on certain adsorption metrics. Recent studies have demonstrated that the performance of an adsorbent is intimately linked to the process in which it is deployed. Any meaningful screening should thus consider the complexity of the process. However, simulation and optimization of adsorption processes are computationally intensive, as they constitute the simultaneous propagation of heat and mass transfer fronts; the process is cyclic, and there are no straightforward design tools, thereby making large-scale process-informed screening of sorbents prohibitive.This Account discusses four papers that develop computational methods to incorporate process-based evaluation for both bottom-up (chemistry to engineering) screening problems and top-down (engineering to chemistry) inverse problems. We discuss the development of the machine-assisted adsorption process learning and emulation (MAPLE) framework, a surrogate model based on deep artificial neural networks (ANNs) that can predict process-level performance by considering both process and material inputs. The framework, which has been experimentally validated, allows for reliable, process-informed screening of large adsorbent databases. We then discuss how process engineering tools can be used beyond adsorbent screening, i.e., to estimate the practically achievable performance and cost limits of pressure vacuum swing adsorption (PVSA) processes should the ideal bespoke adsorbent be made. These studies show what conditions stand-alone PVSA processes are attractive and when they should not be considered. Finally, recent developments in physics-informed neural networks (PINNS) enable the rapid solution of complex partial differential equations, providing tools to potentially identify optimal cycle configurations. Ultimately, we provide areas where further developments are required and emphasize the need for strong collaborations between chemists and chemical engineers to move rapidly from discovery to field trials, as we do not have much time to fulfill commitments to net-zero targets.

A Robust Squarate-Cobalt Metal-Organic Framework for CO2/N2 Separation

The separation of CO₂ from the industrial post-combustion flue gas is of great importance to reduce the increasingly serious greenhouse effect, yet highly challenging due to the extremely high stability, low cost, and high separation performance requirements for adsorbents under the practical operating conditions. Herein, we report a robust squarate-cobalt metal-organic framework (MOF), FJUT-3, featuring an ultra-small 1D square channel decorated with -OH groups, for CO₂/N₂ separation. Remarkably, FJUT-3 not only has excellent stability under harsh chemical conditions but also presents low-cost property for scale-up synthesis. Moreover, FJUT-3 shows excellent CO₂ separation performance under various humid and temperature conditions confirmed by the transient breakthrough experiments, thus enabling FJUT-3 with adequate potentials for industrial CO₂ capture and removal. The distinct CO₂ adsorption mechanism is well elucidated by theoretical calculations, in which the hierarchical C···O_CO2, C-O···C_CO2, and O-H···O_CO2 interactions play a vital synergistic role in the selective CO₂ adsorption process.

Research progress and perspectives on carbon capture, utilization, and storage (CCUS) technologies in China and the USA: a bibliometric analysis

Carbon dioxide capture, utilization, and storage (CCUS) technology is an emerging technology with large-scale emission reduction potential and an essential component of the global response to climate change to achieve net-zero goals. As the two most important countries in global climate governance, it is necessary to review and examine the current status and trends of research in the field of CCUS in China and the USA. This paper uses bibliometric tools to review and analyze peer-reviewed articles in the Web of Science from both countries during 2000-2022. The results show a significant increase in research interest among scholars from both countries. The number of publications in the CCUS field in China and the USA was 1196 and 1302, respectively, showing an increasing trend. China and the USA have become the most influential countries in CCUS. And the USA has a more significant academic influence on a global scale. Furthermore, the research hotspots in the field of CCUS are diverse and differentiated. That is, China and the USA pay attention to different research hotspots or have different focuses in different periods. This paper also finds that new capture materials and technology development, geological storage monitoring and early warning, CO₂ utilization and new energy development, sustainable business models, incentive policies and measures, and public awareness are critical directions for future research in the field of CCUS, to provide a comprehensive review and comparison of CCUS technology development in China and the USA. It helps to gain insight into the research differences and linkages between the two countries in the field of CCUS and identify the research gaps between them. And place some consensus that policymakers can use.

Implementation of a Core-Shell Design Approach for Constructing MOFs for CO2 Capture

Adsorption-based capture of CO₂ from flue gas and from air requires materials that have a high affinity for CO₂ and can resist water molecules that competitively bind to adsorption sites. Here, we present a core-shell metal-organic framework (MOF) design strategy where the core MOF is designed to selectively adsorb CO₂, and the shell MOF is designed to block H₂O diffusion into the core. To implement and test this strategy, we used the zirconium (Zr)-based UiO MOF platform because of its relative structural rigidity and chemical stability. Previously reported computational screening results were used to select optimal core and shell MOF compositions from a basis set of possible building blocks, and the target core-shell MOFs were prepared. Their compositions and structures were characterized using scanning electron microscopy, transmission electron microscopy, and powder X-ray diffraction. Multigas (CO₂, N₂, and H₂O) sorption data were collected both for the core-shell MOFs and for the core and shell MOFs individually. These data were compared to determine whether the core-shell MOF architecture improved the CO₂ capture performance under humid conditions. The combination of experimental and computational results demonstrated that adding a shell layer with high CO₂/H₂O diffusion selectivity can significantly reduce the effect of water on CO₂ uptake.

An Ecosystem for Digital Reticular Chemistry

The vastness of the materials design space makes it impractical to explore using traditional brute-force methods, particularly in reticular chemistry. However, machine learning has shown promise in expediting and guiding materials design. Despite numerous successful applications of machine learning to reticular materials, progress in the field has stagnated, possibly because digital chemistry is more an art than a science and its limited accessibility to inexperienced researchers. To address this issue, we present mofdscribe, a software ecosystem tailored to novice and seasoned digital chemists that streamlines the ideation, modeling, and publication process. Though optimized for reticular chemistry, our tools are versatile and can be used in nonreticular materials research. We believe that mofdscribe will enable a more reliable, efficient, and comparable field of digital chemistry.

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.

Unveiling Hidden Zeolitic Imidazolate Frameworks Guided by Intuition-Based Geometrical Factors

Herein, synthesizable candidate topologies to form zeolitic imidazolate frameworks (ZIFs) are efficiently identified from over 2 000 000 hypothetical structures in zeolite databases, using structural descriptors extracted from known ZIFs. A combination of intuition-based structural descriptors, such as ring patterns, node numbers, and TOT bridging angles (T = tetrahedral metal nodes in zeolites and ZIFs), is used as data filters to eliminate topologies infeasible for ZIF formation. Carefully chosen structural descriptors facilitate the prediction of plausible ZIF topologies. To investigate potential applications as porous ZIFs, this work performs hydrogen adsorption screening and suggested notable target ZIFs. The collection of new plausible ZIFs, derived from the combined descriptors, will be a structural blueprint for synthetic chemists.

Zeolites in Adsorption Processes: State of the Art and Future Prospects

Zeolites have been widely used as catalysts, ion exchangers, and adsorbents since their industrial breakthrough in the 1950s and continue to be state-of the-art adsorbents in many separation processes. Furthermore, their properties make them materials of choice for developing and emerging separation applications. The aim of this review is to put into context the relevance of zeolites and their use and prospects in adsorption technology. It has been divided into three different sections, i.e., zeolites, adsorption on nanoporous materials, and chemical separations by zeolites. In the first section, zeolites are explained in terms of their structure, composition, preparation, and properties, and a brief review of their applications is given. In the second section, the fundamentals of adsorption science are presented, with special attention to its industrial application and our case of interest, which is adsorption on zeolites. Finally, the state-of-the-art relevant separations related to chemical and energy production, in which zeolites have a practical or potential applicability, are presented. The replacement of some of the current separation methods by optimized adsorption processes using zeolites could mean an improvement in terms of sustainability and energy savings. Different separation mechanisms and the underlying adsorption properties that make zeolites interesting for these applications are discussed.

Ranking the synthesizability of hypothetical zeolites with the sorting hat

Zeolites are nanoporous alumino-silicate frameworks widely used as catalysts and adsorbents. Even though millions of siliceous networks can be generated by computer-aided searches, no new hypothetical framework has yet been synthesized. The needle-in-a-haystack problem of finding promising candidates among large databases of predicted structures has intrigued materials scientists for decades; yet, most work to date on the zeolite problem has been limited to intuitive structural descriptors. Here, we tackle this problem through a rigorous data science scheme-the "Zeolite Sorting Hat"-that exploits interatomic correlations to discriminate between real and hypothetical zeolites and to partition real zeolites into compositional classes that guide synthetic strategies for a given hypothetical framework. We find that, regardless of the structural descriptor used by the Zeolite Sorting Hat, there remain hypothetical frameworks that are incorrectly classified as real ones, suggesting that they might be good candidates for synthesis. We seek to minimize the number of such misclassified frameworks by using as complete a structural descriptor as possible, thus focusing on truly viable synthetic targets, while discovering structural features that distinguish real and hypothetical frameworks as an output of the Zeolite Sorting Hat. Further ranking of the candidates can be achieved based on thermodynamic stability and/or their suitability for the desired applications. Based on this workflow, we propose three hypothetical frameworks differing in their molar volume range as the top targets for synthesis, each with a composition suggested by the Zeolite Sorting Hat. Finally, we analyze the behavior of the Zeolite Sorting Hat with a hierarchy of structural descriptors including intuitive descriptors reported in previous studies, finding that intuitive descriptors produce significantly more misclassified hypothetical frameworks, and that more rigorous interatomic correlations point to second-neighbor Si-O distances around 3.2-3.4 Å as the key discriminatory factor.

Quantitative Structure-Property Relationship Analysis for the Prediction of Propylene Adsorption Capacity in Pure Silicon Zeolites at Various Pressure Levels

This work is devoted to the development of quantitative structure-property relationship (QSPR) models using various regression analyses to predict propylene (C₃H₆) adsorption capacity at various pressures in zeolites from a topologically diverse International Zeolite Association database. Based on univariate and multilinear regression analysis, the accessible volume and largest cavity diameter are the most crucial factors determining C₃H₆ uptake at high and low pressures, respectively. An artificial neural network (ANN) model with five structural descriptors is sufficient to predict C₃H₆ uptake at high pressures. For combined pressures, the prediction of an ANN model with pore size distribution is pleasing. The isosteric heat of adsorption (Q _st) has a significant impact on the improvement of the prediction of low-pressure gas adsorption, which finely classifies zeolites into high or low C₃H₆ adsorbers. The conjunction of high-throughput screening and QSPR models contributes to being able to prescreen the database rapidly and accurately for top performers and perform further detailed and time-consuming computational-intensive molecular simulations on these candidates for other gas adsorption applications.

A Machine Learning-Aided Equilibrium Model of VTSA Processes for Sorbents Screening Applied to CO2 Capture from Diluted Sources

The large design space of the sorbents’ structure and the associated capability of tailoring properties to match process requirements make adsorption-based technologies suitable candidates for improved CO₂ capture processes. This is particularly of interest in novel, diluted, and ultradiluted separations as direct CO₂ removal from the atmosphere. Here, we present an equilibrium model of vacuum temperature swing adsorption cycles that is suitable for large throughput sorbent screening, e.g., for direct air capture applications. The accuracy and prediction capabilities of the equilibrium model are improved by incorporating feed-forward neural networks, which are trained with data from rate-based models. This allows one, for example, to include the process productivity, a key performance indicator typically obtained in rate-based models. We show that the equilibrium model reproduces well the results of a sophisticated rate-based model in terms of both temperature and composition profiles for a fixed cycle as well as in terms of process optimization and sorbent comparison. Moreover, we apply the proposed equilibrium model to screen and identify promising sorbents from the large NIST/ARPA-E database; we do this for three different (ultra)diluted separation processes: direct air capture, y_CO2 = 0.1%, and y_CO2 = 1.0%. In all cases, the tool allows for a quick identification of the most promising sorbents and the computation of the associated performance indicators. Also, in this case, outcomes are very well in line with the 1D model results. The equilibrium model is available in the GitHub repository https://github.com/UU-ER/SorbentsScreening0D.

HSIL-Based Synthesis of Ultracrystalline K,Na-JBW, a Zeolite Exhibiting Exceptional Framework Ordering and Flexibility

A reproducible synthesis strategy for ultracrystalline K,Na-aluminosilicate JBW zeolite is reported. The synthesis uses a Na-based hydrated silicate ionic liquid (HSIL) as a silicon source and gibbsite as the aluminum source. ²⁷Al and ²³Na NMR spectra exhibit crystalline second-order quadrupole patterns in the hydrated as well as dehydrated states and distinct resonances for different T-sites demonstrating an exceptional degree of order of the elements of the JBW framework, observed for the first time in a zeolite. Detailed structural analysis via NMR crystallography, combining powder X-ray diffraction and solid-state NMR of all elements (²⁷Al, ²⁹Si, ²³Na, ³⁹K, and ¹H), reveals remarkable de- and rehydration behavior of the JBW framework, transforming from its as-made hydrated structure via a modified anhydrous state into a different rehydrated symmetry while showing astonishing flexibility for a semicondensed aluminosilicate. Its crystallinity, exceptional degree of ordering of the T atoms and sodium cations, and the fully documented structure qualify this defect-free K,Na-aluminosilicate JBW zeolite as a suitable model system for developing NMR modeling methods.

Computational screening methodology identifies effective solvents for CO2 capture

Carbon capture and storage technologies are projected to increasingly contribute to cleaner energy transitions by significantly reducing CO₂ emissions from fossil fuel-driven power and industrial plants. The industry standard technology for CO₂ capture is chemical absorption with aqueous alkanolamines, which are often being mixed with an activator, piperazine, to increase the overall CO₂ absorption rate. Inefficiency of the process due to the parasitic energy required for thermal regeneration of the solvent drives the search for new tertiary amines with better kinetics. Improving the efficiency of experimental screening using computational tools is challenging due to the complex nature of chemical absorption. We have developed a novel computational approach that combines kinetic experiments, molecular simulations and machine learning for the in silico screening of hundreds of prospective candidates and identify a class of tertiary amines that absorbs CO₂ faster than a typical commercial solvent when mixed with piperazine, which was confirmed experimentally.

Converting CO2 into heterocyclic compounds under accelerated performance through Fe3O4 grafted ionic liquid catalysts

Solid supported catalysts such as amines has high demand for chemical fixation of CO2 into commodity chemicals. Here, we demonstrate an accelerated platform for 1,8-diazabicyclo[5.4.0]undec-7-ene (DBU)-ionic liquids (ILs) catalyzed CO2...

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.

Mechanistic insights into the CO2 capture and reduction on K-promoted Cu/Al2O3 by spatiotemporal operando methodologies

Integrated CO2 capture and conversion processes bring the promise of drastic abatement of CO2 emission together with its valorisation to chemical building blocks such as CH4 and CO.

Recent Progress and Future Prospects on All-Organic Polymer Dielectrics for Energy Storage Capacitors

With the development of advanced electronic devices and electric power systems, polymer-based dielectric film capacitors with high energy storage capability have become particularly important. Compared with polymer nanocomposites with widespread attention, all-organic polymers are fundamental and have been proven to be more effective choices in the process of scalable, continuous, and large-scale industrial production, leading to many dielectric and energy storage applications. In the past decade, efforts have intensified in this field with great progress in newly discovered dielectric polymers, fundamental production technologies, and extension toward emerging computational strategies. This review summarizes the recent progress in the field of energy storage based on conventional as well as heat-resistant all-organic polymer materials with the focus on strategies to enhance the dielectric properties and energy storage performances. The key parameters of all-organic polymers, such as dielectric constant, dielectric loss, breakdown strength, energy density, and charge-discharge efficiency, have been thoroughly studied. In addition, the applications of computer-aided calculation including density functional theory, machine learning, and materials genome in rational design and performance prediction of polymer dielectrics are reviewed in detail. Based on a comprehensive understanding of recent developments, guidelines and prospects for the future development of all-organic polymer materials with dielectric and energy storage applications are proposed.

Monte carlo simulations and experiments of all-silica zeolite LTA assembly combining structure directing agents that match cage sizes

We investigated the influence of organic structure-directing agents (OSDAs) on the formation rates of all-silica zeolite LTA using both simulations and experiments, to shed light on the crystallization process. We compared syntheses using one OSDA with a diameter close to the size of the large cavity in LTA, and two OSDAs of diameters matching the sizes of both the small and large LTA cavities. Reaction-ensemble Monte Carlo (RxMC) simulations predict a speed up of LTA formation using two OSDAs matching the LTA pore sizes; this qualitative result is confirmed by experimental studies of crystallization kinetics, which find a speedup in all-silica LTA crystallization of a factor of 3. Analyses of simulated rings and their Si-O-Si angular energies during RxMC crystallizations show that all ring sizes in the faster crystallization exhibit lower angular energies, on average, than in the slower crystallization, explaining the origin of the speedup through packing effects.

Toward comprehensive exploration of the physisorption space in porous pseudomaterials using an iterative mutation search algorithm

We describe an updated algorithm for efficiently exploring structure-property spaces relating to physisorption of gases in porous materials. This algorithm uses previously described "pseudomaterials," which are crystals of randomly arranged and parameterized Lennard-Jones spheres, and combines it with a new iterative mutation exploration method. This algorithm is significantly more efficient at sampling the structure-property space than previously reported methods. For the sake of benchmarking to prior work, we apply this method to exploring methane adsorption at 35 bars (298 K) and void fraction as the main structure-property combination. We demonstrate the effect and importance of the changes that were required to increase efficiency over prior methods. The most important changes were (1) using "discrete" mutations less often, (2) decreasing degrees of freedom, and (3) removing biasing from mutations on bounded parameters.

ResponZIF Structures: Zeolitic Imidazolate Frameworks as Stimuli-Responsive Materials

Zeolitic imidazolate frameworks (ZIFs) have long been recognized as a prominent subset of the metal-organic framework (MOF) family, in part because of their ease of synthesis and good thermal and chemical stability, alongside attractive properties for diverse potential applications. Prototypical ZIFs like ZIF-8 have become embodiments of the significant promise held by porous coordination polymers as next-generation designer materials. At the same time, their intriguing property of experiencing significant structural changes upon the application of external stimuli such as temperature, mechanical pressure, guest adsorption, or electromagnetic fields, among others, has placed this family of MOFs squarely under the umbrella of stimuli-responsive materials. In this review, we provide an overview of the current understanding of the triggered structural and electronic responses observed in ZIFs (linker and bond dynamics, crystalline and amorphous phase changes, luminescence, etc.). We then describe the state-of-the-art experimental and computational methodology capable of shedding light on these complex phenomena, followed by a comprehensive summary of the stimuli-responsive nature of four prototypical ZIFs: ZIF-8, ZIF-7, ZIF-4, and ZIF-zni. We further expose the relevant challenges for the characterization and fundamental understanding of responsive ZIFs, including how to take advantage of their flexible properties for new application avenues.

Conversion of CO2 into Formic Acid on Transition Metal-Porphyrin-like Graphene: First Principles Calculations

Recently, transition metal (TM)-porphyrin-like graphene has been predicted to be a promising material for CO₂ capturing under favorable conditions. Such materials can capture CO₂ at 300 K and release it at 450 K. However, the captured CO₂ gas is mostly stored in oceans. With the aid of first principles calculations, we herein propose a method in which the captured CO₂ is converted into an environmentally friendly product, formic acid. Addition of H₂ to CO₂ molecules adsorbed on Sc- and Ti-porphyrin-like graphene was found to catalyze this conversion. We also performed nudged elastic band calculations and thermodynamic analysis using the first-order Polanyi-Wigner equation and equilibrium statistical mechanics to investigate the chemical reactions involved in this conversion. In addition, we performed Bader charge analysis to obtain insights into the mechanism of charge transfer and adsorption throughout the conversion. Our study presents a novel method in which the captured CO₂ is treated by converting it into an environmentally friendly product. Since this method does not require CO₂ storage, it is expected to be an effective strategy to manage the rising CO₂ level in the environment.

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.

CO2 Capture at Medium to High Temperature Using Solid Oxide-Based Sorbents: Fundamental Aspects, Mechanistic Insights, and Recent Advances

Carbon dioxide capture and mitigation form a key part of the technological response to combat climate change and reduce CO₂ emissions. Solid materials capable of reversibly absorbing CO₂ have been the focus of intense research for the past two decades, with promising stability and low energy costs to implement and operate compared to the more widely used liquid amines. In this review, we explore the fundamental aspects underpinning solid CO₂ sorbents based on alkali and alkaline earth metal oxides operating at medium to high temperature: how their structure, chemical composition, and morphology impact their performance and long-term use. Various optimization strategies are outlined to improve upon the most promising materials, and we combine recent advances across disparate scientific disciplines, including materials discovery, synthesis, and in situ characterization, to present a coherent understanding of the mechanisms of CO₂ absorption both at surfaces and within solid materials.

Porous materials for carbon dioxide separations

Global investment in counteracting climate change has galvanized increasing interest in carbon capture and sequestration (CCS) as a versatile emissions mitigation technology. As decarbonization efforts accelerate, CCS can target the emissions of large point-source emitters, such as coal- or natural gas-fired power plants, while also supporting the production of renewable or low-carbon fuels. Furthermore, CCS can enable decarbonization of difficult-to-abate industrial processes and can support net CO₂ removal from the atmosphere through bioenergy coupled with CCS or direct air capture. Here we review the development of porous materials as next-generation sorbents for CO₂ capture applications. We focus on stream- and sector-specific challenges while highlighting case studies within the context of the rapidly shifting energy landscape. We conclude with a discussion of key needs from the materials community to expand deployment of carbon capture technologies.

Deciphering the Weak CO2···Framework Interactions in Microporous MOFs Functionalized with Strong Adsorption Sites-A Ubiquitous Observation

Carbon capture from industrial effluents such as flue gas or natural gas mixture (cf. landfill gas), the primary sources of CO₂ emission, greatly aids in balancing the environmental carbon cycle. In this context, the most energy-efficient physisorptive CO₂ separation process can benefit immensely from improved porous sorbents. Metal organic frameworks (MOFs), especially the ultramicroporous MOFs, built from readily available small and rigid ligands, are highly promising because of their high selectivity (CO₂/N₂) and easy scalability. Here, we report two new ultramicroporous Co-adeninato isophthalate MOFs. They concomitantly carry basic functional groups (-NH₂) and Lewis acidic sites (coordinatively unsaturated Co centers). They show good CO₂ capacity (3.3 mmol/g at 303 K and 1 bar) along with high CO₂/N₂ (∼600 at 313 K and 1 bar and ∼340 at 303 K and 1 bar) selectivity, working capacity, and smooth diffusion kinetics (D_c = 7.5 × 10^-9 m² s^-1). The MOFs exhibit good CO₂/N₂ kinetic separation under both dry and wet conditions with a smooth breakthrough profile. Despite their well-defined CO₂ adsorption sites, these MOFs exhibit only a moderately strong interaction with CO₂ as evidenced from their HOA values. This counterintuitive observation is ubiquitous among many MOFs adorned with strong CO₂ adsorption sites. To gain insights, we have identified the binding sites for CO₂ using simulation and MD studies. The radial distribution function analysis reveals that despite the amine and bare-metal sites, the pore size and the pore structure determine the positions for the CO₂ molecules. The most favorable sites become the confined spaces lined by aromatic rings. A plausible explanation for the lack of strong adsorption in these MOFs is premised from these collective studies, which could aid in the future design of superior CO₂ sorbents.

Computational Screening of Trillions of Metal-Organic Frameworks for High-Performance Methane Storage

In the past decade, there has been an increasing number of computational screening works to facilitate finding optimal materials for a variety of different applications. Unfortunately, most of these screening studies are limited to their initial set of materials and result in a brute-force type of screening approach. In this work, we present a systematic strategy that can find metal-organic frameworks (MOFs) with the desired properties from an extremely diverse and large set of over 100 trillion possible MOFs using machine learning and evolutionary algorithm. It is demonstrated that our algorithm can discover 964 MOFs with methane working capacity over 200 cm³ cm^-3 and 96 MOFs with methane working capacity over the current world record of 208 cm³ cm^-3. We believe that this methodology can take advantage of the modular nature of MOFs and can readily be extended to other important applications as well.

Strongly Bound Excitons in Metal-Organic Framework MOF-5: A Many-Body Perturbation Theory Study

During the past years, one of the most iconic metal-organic frameworks (MOFs), MOF-5, has been characterized as a semiconductor by theory and experiments. Here we employ the GW many-body perturbation theory in conjunction with the Bethe-Salpeter equation to compute the electronic structure and optical properties of this MOF. The GW calculations show that MOF-5 is a wide-band-gap insulator with a fundamental gap of ∼8 eV. The strong excitonic effects, arising from highly localized states and low screening, result in an optical gap of 4.5 eV and in an optical absorption spectrum in excellent agreement with experiments. The origin of the incorrect conclusion reported by past studies and the implication of this result are also discussed.