Search for: All records

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 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. 

CALF-20, a Zn-triazolate-based metal-organic framework (MOF), is one of the most promising adsorbent materials for CO2 capture. However, competitive adsorption of water severely limits its performance when the relative humidity (RH) exceeds 40%, limiting the potential implementation of CALF-20 in practical settings where CO2 is saturated with moisture, such as post-combustion flue gas. In this work, three newly designed MOFs related to CALF-20, denoted as NU-220, CALF-20M-w, and CALF-20M-e that feature hydrophobic methyl-triazolate linkers are presented. Inclusion of methyl groups in the linker is proposed as a strategy to improve CO2 uptake in the presence of water. Notably, both CALF-20M-w and CALF-20M-e retain over 20% of their initial CO2 capture efficiency at 70% RH – a threshold at which CALF-20 shows negligible CO2 uptake. Grand canonical Monte Carlo (GCMC) simulations reveal that the methyl group hinders water network formation in the pores of CALF-20M-w and CALF-20M-e and enhances their CO2 selectivity over N2 in the presence of high moisture content. Moreover, calculated radial distribution functions indicate that introducing the methyl group into the triazolate linker increases the distance between water molecules and Zn coordination bonds, offering insights into the origin of the enhanced moisture stability observed for CALF-20M-w and CALF-20M-e relative to CALF-20. Overall, this straightforward design strategy has afforded more robust sorbents that can potentially meet the challenge of effectively capturing CO2 in practical industrial applications. 

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.