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CO₂capture from post-combustion flue gas originating from coal or natural gas power plants, or even from the ambient atmosphere, is a promising strategy to reduce the atmospheric CO₂concentration and achieve global decarbonization goals. However, the co-existence of water vapor in these sources presents a significant challenge, as water often competes with CO₂for adsorption sites, thereby diminishing the performance of adsorbent materials. Selectively capturing CO₂in the presence of moisture is a key goal, as there is a growing demand for materials capable of selectively adsorbing CO₂under humid conditions. Among these, metal–organic frameworks (MOFs), a class of porous, highly tunable materials, have attracted extensive interest for gas capture, storage, and separation applications. The numerous combinations of secondary building units and organic linkers offer abundant opportunities for designing systems with enhanced CO₂selectivity. Interestingly, some recent studies have demonstrated that interactions between water and CO₂within the confined pore space of MOFs can enhance CO₂uptake, flipping the traditionally detrimental role of moisture into a beneficial one. These findings introduce a new paradigm: water-enhanced CO₂capture in MOFs. In this review, we summarize these recent discoveries, highlighting examples of MOFs that exhibit enhanced CO₂adsorption under humid conditions compared to dry conditions. We discuss the underlying mechanisms, design strategies, and structural features that enable this behavior. Finally, we offer a brief perspective on future directions for MOF development in the context of water-enhanced CO₂capture. 

Downsizing noble metal catalysts is essential for improving atomic efficiency in sustainable energy applications. Typically, strategies focus on anchoring atomically scaled catalysts onto heteroatom-rich substrates, but these interactions can unintentionally alter the electronic structure of the catalyst, complicating the hydrogen evolution reaction (HER) mechanism. This study focuses on elucidating the interfacial mechanism of HER using structurally well-defined platinum single-atom (Pt SA) electrocatalysts. Unlike chemically reduced SAs, electrochemically deposited Pt SA catalysts do not rely on strong support interactions. As a result, these isolated Pt atoms can potentially achieve the theoretical maximum hydrogen production efficiency. This work introduces electrocatalysts composed solely of true SA sites, clarifying previous ambiguities surrounding the concept of SA electrocatalysis. 

Metal-organic frameworks (MOFs) have been examined extensively for CO2 capture, and the influence of water co-adsorption on these processes is particularly relevant, as CO2 capture generally occurs in humid gas streams. To investi-gate CO2/H2O co-adsorption, binary adsorption isotherms of CO2 and H2O were measured on MOF-808-TFA (TFA = trifluoro-acetic acid). When water was pre-adsorbed on MOF-808-TFA, and a subsequent CO2 adsorption isotherm was measured, the CO2 adsorption was slightly reduced, as expected. However, when CO2 was adsorbed first and then an H2O adsorption iso-therm was measured, no significant H2O adsorption capacity was observed. The near complete loss of water adsorption ca-pacity was observed even when only a trace amount of CO2 was pre-adsorbed. The results show that unexpected, non-state function adsorption equilibria can result from dynamic MOF behaviors and defect sites, which may lead to counterintuitive adsorption data compared to traditional materials. 

In this study, the adsorption mechanism of water in the metal–organic framework NU-1000 was investigated using molecular simulations. The simulations predict a significant impact of small changes in terminal aquo ligand orientation on the shape and pressure of the condensation step in the water adsorption isotherm. The analysis revealed that the rotational mobility of aquo ligands, often neglected in computational studies, can shift the condensation step by up to 20% in the relative humidity scale. By examining adsorption modes and interaction sites, it was demonstrated that configurational changes in the Zr6O8 node affect water adsorption significantly and can change the nature of the interactions from hydrophobic to hydrophilic. We propose a robust approach to account for these changes in simulations, achieving good agreement with experimental results. This work underscores the necessity of considering local, molecular flexibility in water adsorption simulations to avoid mischaracterization of MOFs’ water adsorption properties. 

Many metal-organic frameworks (MOFs) are known to show complex structural flexibility such as breathing, swelling, and linker rotations, and understanding the impact of these structural changes on their guest adsorption properties is important in developing MOFs for practical applications. In this study, we used a multi-scale computational approach to provide a molecular-level understanding of how flexibility affects water adsorption in the MOF, NbOFFIVE-1-Ni. This material has narrow pores and good hydrothermal stability, which make it attractive for CO2 capture. We utilized density functional theory (DFT) calculations and grand canonical Monte Carlo (GCMC) simulations to study the impact of NbOFFIVE-1-Ni structural flexibility on its water adsorption at different humidity conditions. Studying the water adsorption in different configurations of NbOFFIVE-1-Ni demonstrated that DFT optimization in the presence of adsorbed water molecules and rotating the linkers are useful strategies to mimic its structural flexibility. Our results illustrate the significance of taking structural flexibility into account when designing MOFs for water adsorption and other relevant applications. 

Enzymatic cascades play a crucial role in energy conversion and chemical transformations in living organisms. Due to enzymes’ high selectivity in catalysis and eco-friendly nature, operating enzymatic cascades in cell-free systems has the potential to improve existing chemical transformations. However, applications involving enzymes are often limited by their poor stability in cell-free environments, and the impact of immobilization on the kinetics of enzymatic cascades remains relatively underexplored, largely due to challenges in determining the support structure and controlling the enzyme immobilization process. In this work, we developed NU-1510-Cr, a chromium-based mesoporous metal–organic framework (MOF) with the mtn topology to encapsulate an enzyme cascade that oxidizes ethanol to acetaldehyde and subsequently to acetic acid. The crystalline hierarchical pores provide spatial control of the enzymes within the host, where the impact of the spatial organization on the cascaded reaction kinetics was assessed. The findings offer valuable insights for those aiming to immobilize and compartmentalize biocatalytic or chemocatalytic cascade systems and develop efficient microscale and nanoscale bioreactors. 

Abstract Data-driven materials design often encounters challenges where systems possess qualitative (categorical) information. Specifically, representing Metal-organic frameworks (MOFs) through different building blocks poses a challenge for designers to incorporate qualitative information into design optimization, and leads to a combinatorial challenge, with large number of MOFs that could be explored. In this work, we integrated Latent Variable Gaussian Process (LVGP) and Multi-Objective Batch-Bayesian Optimization (MOBBO) to identify top-performing MOFs adaptively, autonomously, and efficiently. We showcased that our method (i) requires no specific physical descriptors and only uses building blocks that construct the MOFs for global optimization through qualitative representations, (ii) is application and property independent, and (iii) provides an interpretable model of building blocks with physical justification. By searching only ~1% of the design space, LVGP-MOBBO identified all MOFs on the Pareto front and 97% of the 50 top-performing designs for the CO₂working capacity and CO₂/N₂selectivity properties. 

Although technologically promising, the reduction of carbon dioxide (CO₂) to produce carbon monoxide (CO) remains economically challenging owing to the lack of an inexpensive, active, highly selective, and stable catalyst. We show that nanocrystalline cubic molybdenum carbide (α-Mo₂C), prepared through a facile and scalable route, offers 100% selectivity for CO₂reduction to CO while maintaining its initial equilibrium conversion at high space velocity after more than 500 hours of exposure to harsh reaction conditions at 600°C. The combination of operando and postreaction characterization of the catalyst revealed that its high activity, selectivity, and stability are attributable to crystallographic phase purity, weak CO-Mo₂C interactions, and interstitial oxygen atoms, respectively. Mechanistic studies and density functional theory (DFT) calculations provided evidence that the reaction proceeds through an H₂-aided redox mechanism. 

Abstract This review spotlights the role of atomic‐level modeling in research on metal‐organic frameworks (MOFs), especially the key methodologies of density functional theory (DFT), Monte Carlo (MC) simulations, and molecular dynamics (MD) simulations. The discussion focuses on how periodic and cluster‐based DFT calculations can provide novel insights into MOF properties, with a focus on predicting structural transformations, understanding thermodynamic properties and catalysis, and providing information or properties that are fed into classical simulations such as force field parameters or partial charges. Classical simulation methods, highlighting force field selection, databases of MOFs for high‐throughput screening, and the synergistic nature of MC and MD simulations, are described. By predicting equilibrium thermodynamic and dynamic properties, these methods offer a wide perspective on MOF behavior and mechanisms. Additionally, the incorporation of machine learning (ML) techniques into quantum and classical simulations is discussed. These methods can enhance accuracy, expedite simulation setup, reduce computational costs, as well as predict key parameters, optimize geometries, and estimate MOF stability. By charting the growth and promise of computational research in the MOF field, the aim is to provide insights and recommendations to facilitate the incorporation of computational modeling more broadly into MOF research.