Computational Predictions and Experimental Validation of Alkane Oxidative Dehydrogenation by Fe2M MOF Nodes

Progress and challenges in nitrous oxide decomposition and valorization

Nitrous oxide (N2O) decomposition is increasingly acknowledged as a viable strategy for mitigating greenhouse gas emissions and addressing ozone depletion, aligning significantly with the UN's sustainable development goals (SDGs) and carbon neutrality objectives. To enhance efficiency in treatment and explore potential valorization, recent developments have introduced novel N2O reduction catalysts and pathways. Despite these advancements, a comprehensive and comparative review is absent. In this review, we undertake a thorough evaluation of N2O treatment technologies from a holistic perspective. First, we summarize and update the recent progress in thermal decomposition, direct catalytic decomposition (deN2O), and selective catalytic reduction of N2O. The scope extends to the catalytic activity of emerging catalysts, including nanostructured materials and single-atom catalysts. Furthermore, we present a detailed account of the mechanisms and applications of room-temperature techniques characterized by low energy consumption and sustainable merits, including photocatalytic and electrocatalytic N2O reduction. This article also underscores the extensive and effective utilization of N2O resources in chemical synthesis scenarios, providing potential avenues for future resource reuse. This review provides an accessible theoretical foundation and a panoramic vision for practical N2O emission controls.

MOF/graphene oxide based composites in smart supercapacitors: a comprehensive review on the electrochemical evaluation and material development for advanced energy storage devices

The surge in interest surrounding energy storage solutions, driven by the demand for electric vehicles and the global energy crisis, has spotlighted the effectiveness of carbon-based supercapacitors in meeting high-power requirements. Concurrently, metal-organic frameworks (MOFs) have gained attention as a template for their integration with graphene oxide (GO) in composite materials which have emerged as a promising avenue for developing high-power supercapacitors, elevating smart supercapacitor efficiency, cyclic stability, and durability, providing crucial insights for overcoming contemporary energy storage obstacles. The identified combination leverages the strengths of both materials, showcasing significant potential for advancing energy storage technologies in a sustainable and efficient manner. In this research, an in-depth review has been presented, in which properties, rationale and integration of MOF/GO composites have been critically examined. Various fabrication techniques have been thoroughly analyzed, emphasizing the specific attributes of MOFs, such as high surface area and modifiable porosity, in tandem with the conductive and stabilizing features of graphene oxide. Electrochemical characterizations and physicochemical mechanisms underlying MOF/GO composites have been examined, emphasizing their synergistic interaction, leading to superior electrical conductivity, mechanical robustness, and energy storage capacity. The article concludes by identifying future research directions, emphasizing sustainable production, material optimization, and integration strategies to address the persistent challenges in the field of energy storage. In essence, this research article aims to offer a concise and insightful resource for researchers engaged in overcoming the pressing energy storage issues of our time through the exploration of MOF/GO composites in smart supercapacitors.

Improving gas adsorption modeling for MOFs by local calibration of Hubbard U parameters

While computational screening with density functional theory (DFT) is frequently employed for the screening of metal-organic frameworks (MOFs) for gas separation and storage, commonly applied generalized gradient approximations (GGAs) exhibit self-interaction errors, which hinder the predictions of adsorption energies. We investigate the Hubbard U parameter to augment DFT calculations for full periodic MOFs, targeting a more precise modeling of gas molecule-MOF interactions, specifically for N2, CO2, and O2. We introduce a calibration scheme for the U parameter, which is tailored for each MOF, by leveraging higher-level calculations on the secondary building unit (SBU) of the MOF. When applied to the full periodic MOF, the U parameter calibrated against hybrid HSE06 calculations of SBUs successfully reproduces hybrid-quality calculations of the adsorption energy of the periodic MOF. The mean absolute deviation of adsorption energies reduces from 0.13 eV for a standard GGA treatment to 0.06 eV with the calibrated U, demonstrating the utility of the calibration procedure when applied to the full MOF structure. Furthermore, attempting to use coupled cluster singles and doubles with perturbative triples calculations of isolated SBUs for this calibration procedure shows varying degrees of success in predicting the experimental heat of adsorption. It improves accuracy for N2 adsorption for cases of overbinding, whereas its impact on CO2 is minimal, and ambiguities in spin state assignment hinder consistent improvements of O2 adsorption. Our findings emphasize the limitations of cluster models and advocate the use of full periodic MOF systems with a calibrated U parameter, providing a more comprehensive understanding of gas adsorption in MOFs.

Fe-MOF Catalytic Nanoarchitectonic toward Electrochemical Ammonia Production

Electrochemical reduction of nitrate into ammonia has lately been identified as one among the promising solutions to address the challenges triggered by the growing global energy demand. Exploring newer electrocatalyst materials is vital to make this process effective and feasible. Recently, metal-organic framework (MOF)-based catalysts are being well investigated for electrocatalytic ammonia synthesis, accounting for their enhanced structural and compositional integrity during catalytic reduction reactions. In this study, we investigate the ability of the PCN-250-Fe3 MOF toward ammonia production in its pristine and activated forms. The activated MOF catalyst delivered a faradaic efficiency of about 90% at -1 V vs RHE and a yield rate of 2.5 × 10-4 mol cm-2 h-1, while the pristine catalyst delivered a 60% faradaic efficiency at the same potential. Theoretical studies further provide insights into the nitrate reduction reaction mechanism catalyzed by the PCN-250-Fe3 MOF catalyst. In short, simpler and cost-effective strategies such as pretreatment of electrocatalysts have an upper hand in aggravating the intrinsic material properties, for catalytic applications, when compared to conventional material modification approaches.

Computational Discovery of Stable Metal-Organic Frameworks for Methane-to-Methanol Catalysis

The challenge of direct partial oxidation of methane to methanol has motivated the targeted search of metal-organic frameworks (MOFs) as a promising class of materials for this transformation because of their site-isolated metals with tunable ligand environments. Thousands of MOFs have been synthesized, yet relatively few have been screened for their promise in methane conversion. We developed a high-throughput virtual screening workflow that identifies MOFs from a diverse space of experimental MOFs that have not been studied for catalysis, yet are thermally stable, synthesizable, and have promising unsaturated metal sites for C-H activation via a terminal metal-oxo species. We carried out density functional theory calculations of the radical rebound mechanism for methane-to-methanol conversion on models of the secondary building units (SBUs) from 87 selected MOFs. While we showed that oxo formation favorability decreases with increasing 3d filling, consistent with prior work, previously observed scaling relations between oxo formation and hydrogen atom transfer (HAT) are disrupted by the greater diversity in our MOF set. Accordingly, we focused on Mn MOFs, which favor oxo intermediates without disfavoring HAT or leading to high methanol release energies─a key feature for methane hydroxylation activity. We identified three Mn MOFs comprising unsaturated Mn centers bound to weak-field carboxylate ligands in planar or bent geometries with promising methane-to-methanol kinetics and thermodynamics. The energetic spans of these MOFs are indicative of promising turnover frequencies for methane to methanol that warrant further experimental catalytic studies.

Modulating the Energetics of C-H Bond Activation in Methane by Utilizing Metalated Porphyrinic Metal-Organic Frameworks

In recent years, much effort has been directed toward utilizing metal-organic frameworks (MOFs) for activating C-H bonds of light alkanes. The energy demanding steps involved in the catalytic pathway are the formation of metal-oxo species and the subsequent cleavage of the C-H bonds of alkanes. With the intention of exploring the tunability of the activation barriers involved in the catalytic pathway of methane hydroxylation, we have employed density functional theory to model metalated porphyrinic MOFs (MOF-525(M)). We find that the heavier congeners down a particular group have high exothermic oxo-formation enthalpies ΔH_O and hence are associated with low N₂O activation barriers. Independent analyses of activation barriers and structure-activity relationship leads to the conclusion that MOF-525(Ru) and MOF-525(Ir) can act as an effective catalysts for methane hydroxylation. Hence, ΔH_O has been found to act as a guide, in the first place, in choosing the optimum catalyst for methane hydroxylation from a large set of available systems.

Conceptual and Practical Aspects of Metal-Organic Frameworks for Solid-Gas Reactions

The presence of site-isolated and well-defined metal sites has enabled the use of metal-organic frameworks (MOFs) as catalysts that can be rationally modulated. Because MOFs can be addressed and manipulated through molecular synthetic pathways, they are chemically similar to molecular catalysts. They are, nevertheless, solid-state materials and therefore can be thought of as privileged solid molecular catalysts that excel in applications involving gas-phase reactions. This contrasts with homogeneous catalysts, which are overwhelmingly used in the solution phase. Herein, we review theories dictating gas phase reactivity within porous solids and discuss key catalytic gas-solid reactions. We further treat theoretical aspects of diffusion within confined pores, the enrichment of adsorbates, the types of solvation spheres that a MOF might impart on adsorbates, definitions of acidity/basicity in the absence of solvent, the stabilization of reactive intermediates, and the generation and characterization of defect sites. The key catalytic reactions we discuss broadly include reductive reactions (olefin hydrogenation, semihydrogenation, and selective catalytic reduction), oxidative reactions (oxygenation of hydrocarbons, oxidative dehydrogenation, and carbon monoxide oxidation), and C-C bond forming reactions (olefin dimerization/polymerization, isomerization, and carbonylation reactions).

Structure and Site Evolution of Framework Ni Species in MIL-127 MOFs for Propylene Oligomerization Catalysis

A mixed-valence oxotrimer metal-organic framework (MOF), Ni-MIL-127, with a fully coordinated nickel atom and two iron atoms in the inorganic node, generates a missing linker defect upon thermal treatment in helium (>473 K) to engender an open coordination site on nickel which catalyzes propylene oligomerization devoid of any cocatalysts or initiators. This catalyst is stable for ∼20 h on stream at 500 kPa and 473 K, unprecedented for this chemistry. The number of missing linkers on synthesized and activated Ni-MIL-127 MOFs is quantified using temperature-programmed oxidation, ¹H nuclear magnetic resonance spectroscopy, and X-ray absorption spectroscopy to be ∼0.7 missing linkers per nickel; thus, a majority of Ni species in the MOF framework catalyze propylene oligomerization. In situ NO titrations under reaction conditions enumerate ∼62% of the nickel atoms as catalytically relevant to validate the defect density upon thermal treatment. Propylene oligomerization rates on Ni-MIL-127 measured at steady state have activation energies of 55-67 kJ mol^-1 from 448 to 493 K and are first-order in propylene pressures from 5 to 550 kPa. Density functional theory calculations on cluster models of Ni-MIL-127 are employed to validate the plausibility of the missing linker defect and the Cossee-Arlman mechanism for propylene oligomerization through comparisons between apparent activation energies from steady-state kinetics and computation. This study illustrates how MOF precatalysts engender defective Ni species which exhibit reactivity and stability characteristics that are distinct and can be engineered to improve catalytic activity for olefin oligomerization.

Spectroscopic and reactive characterization of mixed-metal Fe–Cr trimer nodes in metal–organic framework MIL-100

Metal–organic framework MIL-100 featuring heterometallic Fe and Cr M3O nodes were synthesized; mixing of the metals within the nodes is evidenced using a combination of in situ IR spectroscopy, NO titrations, and CO oxidation kinetics.

New Strategies for Direct Methane-to-Methanol Conversion from Active Learning Exploration of 16 Million Catalysts

Despite decades of effort, no earth-abundant homogeneous catalysts have been discovered that can selectively oxidize methane to methanol. We exploit active learning to simultaneously optimize methane activation and methanol release calculated with machine learning-accelerated density functional theory in a space of 16 M candidate catalysts including novel macrocycles. By constructing macrocycles from fragments inspired by synthesized compounds, we ensure synthetic realism in our computational search. Our large-scale search reveals that low-spin Fe(II) compounds paired with strong-field (e.g., P or S-coordinating) ligands have among the best energetic tradeoffs between hydrogen atom transfer (HAT) and methanol release. This observation contrasts with prior efforts that have focused on high-spin Fe(II) with weak-field ligands. By decoupling equatorial and axial ligand effects, we determine that negatively charged axial ligands are critical for more rapid release of methanol and that higher-valency metals [i.e., M(III) vs M(II)] are likely to be rate-limited by slow methanol release. With full characterization of barrier heights, we confirm that optimizing for HAT does not lead to large oxo formation barriers. Energetic span analysis reveals designs for an intermediate-spin Mn(II) catalyst and a low-spin Fe(II) catalyst that are predicted to have good turnover frequencies. Our active learning approach to optimize two distinct reaction energies with efficient global optimization is expected to be beneficial for the search of large catalyst spaces where no prior designs have been identified and where linear scaling relationships between reaction energies or barriers may be limited or unknown.

Exploring mechanistic routes for light alkane oxidation with an iron-triazolate metal-organic framework

In this work, we computationally explore the formation and subsequent reactivity of various iron-oxo species in the iron-triazolate framework Fe₂(μ-OH)₂(bbta) (H₂bbta = 1H,5H-benzo(1,2-d:4,5-d')bistriazole) for the catalytic activation of strong C-H bonds. With the direct conversion of methane to methanol as the probe reaction of interest, we use density functional theory (DFT) calculations to evaluate multiple mechanistic pathways in the presence of either N₂O or H₂O₂ oxidants. These calculations reveal that a wide range of transition metal-oxo sites - both terminal and bridging - are plausible in this family of metal-organic frameworks, making it a unique platform for comparing the electronic structure and reactivity of different proposed active site motifs. Based on the DFT calculations, we predict that Fe₂(μ-OH)₂(bbta) would exhibit a relatively low barrier for N₂O activation and energetically favorable formation of an [Fe(O)]²⁺ species that is capable of oxidizing C-H bonds. In contrast, the use of H₂O₂ as the oxidant is predicted to yield an assortment of bridging iron-oxo sites that are less reactive. We also find that abstracting oxo ligands can exhibit a complex mixture of both positive and negative spin density, which may have broader implications for relating the degree of radical character to catalytic activity. In general, we consider the coordinatively unsaturated iron sites to be promising for oxidation catalysis, and we provide several recommendations on how to further tune the catalytic properties of this family of metal-triazolate frameworks.

MOFSimplify, machine learning models with extracted stability data of three thousand metal-organic frameworks

We report a workflow and the output of a natural language processing (NLP)-based procedure to mine the extant metal–organic framework (MOF) literature describing structurally characterized MOFs and their solvent removal and thermal stabilities. We obtain over 2,000 solvent removal stability measures from text mining and 3,000 thermal decomposition temperatures from thermogravimetric analysis data. We assess the validity of our NLP methods and the accuracy of our extracted data by comparing to a hand-labeled subset. Machine learning (ML, i.e. artificial neural network) models trained on this data using graph- and pore-geometry-based representations enable prediction of stability on new MOFs with quantified uncertainty. Our web interface, MOFSimplify, provides users access to our curated data and enables them to harness that data for predictions on new MOFs. MOFSimplify also encourages community feedback on existing data and on ML model predictions for community-based active learning for improved MOF stability models.

Measurement(s)	thermal decomposition
Technology Type(s)	thermogravimetry

Light alkane oxidation over well-defined active sites in metal–organic framework materials

We review structure–catalytic property relationships for MOF materials used in the direct oxidation of light alkanes, focusing specifically on the elucidation of active site structures and probes for reaction mechanisms.

Using Machine Learning and Data Mining to Leverage Community Knowledge for the Engineering of Stable Metal-Organic Frameworks

Although the tailored metal active sites and porous architectures of MOFs hold great promise for engineering challenges ranging from gas separations to catalysis, a lack of understanding of how to improve their stability limits their use in practice. To overcome this limitation, we extract thousands of published reports of the key aspects of MOF stability necessary for their practical application: the ability to withstand high temperatures without degrading and the capacity to be activated by removal of solvent molecules. From nearly 4000 manuscripts, we use natural language processing and image analysis to obtain over 2000 solvent-removal stability measures and 3000 thermal degradation temperatures. We analyze the relationships between stability properties and the chemical and geometric structures in this set to identify limits of prior heuristics derived from smaller sets of MOFs. By training predictive machine learning (ML, i.e., Gaussian process and artificial neural network) models to encode the structure-property relationships with graph- and pore-structure-based representations, we are able to make predictions of stability orders of magnitude faster than conventional physics-based modeling or experiment. Interpretation of important features in ML models provides insights that we use to identify strategies to engineer increased stability into typically unstable 3d-transition-metal-containing MOFs that are frequently targeted for catalytic applications. We expect our approach to accelerate the time to discovery of stable, practical MOF materials for a wide range of applications.

Deterministic role of structural flexibility on catalytic activity of conductive 2D layered metal-organic frameworks

A combined quantum mechanics and classical molecular dynamics approach is used to unravel the effects of structural deformations and heterogeneity on catalytic activity of 2D π-stacked layered metal-organic frameworks. Theory predicts that the flexible nature of these materials creates a complex array of catalytically active sites for oxidative dehydrogenation of propane. Using an ensemble approach and oxygen bond formation energy, as an excellent probe, we investigate the catalytic activity down to the single active site level.

The state of the field: from inception to commercialization of metal–organic frameworks

We provide a brief overview of the state of the MOF field from their inception to their synthesis, potential applications, and finally, to their commercialization.

Synthetic and computational assessment of a chiral metal-organic framework catalyst for predictive asymmetric transformation

Understanding and controlling molecular recognition mechanisms at a chiral solid interface is a continuously addressed challenge in heterogeneous catalysis. Here, the molecular recognition of a chiral peptide-functionalized metal–organic framework (MOF) catalyst towards a pro-chiral substrate is evaluated experimentally and in silico. The MIL-101 metal–organic framework is used as a macroligand for hosting a Noyori-type chiral ruthenium molecular catalyst, namely (benzene)Ru@MIL-101-NH-Gly-Pro. Its catalytic perfomance toward the asymmetric transfer hydrogenation (ATH) of acetophenone into R- and S-phenylethanol are assessed. The excellent match between the experimentally obtained enantiomeric excesses and the computational outcomes provides a robust atomic-level rationale for the observed product selectivities. The unprecedented role of the MOF in confining the molecular Ru-catalyst and in determining the access of the prochiral substrate to the active site is revealed in terms of highly face-specific host–guest interactions. The predicted surface-specific face differentiation of the prochiral substrate is experimentally corroborated since a three-fold increase in enantiomeric excess is obtained with the heterogeneous MOF-based catalyst when compared to its homogeneous molecular counterpart.

Understanding and controlling molecular recognition mechanisms at a chiral solid interface has been addressed in metal–organic framework catalysts for the asymmetric transfer hydrogenation reaction.

Electronic Structure Modeling of Metal-Organic Frameworks

Owing to their molecular building blocks, yet highly crystalline nature, metal-organic frameworks (MOFs) sit at the interface between molecule and material. Their diverse structures and compositions enable them to be useful materials as catalysts in heterogeneous reactions, electrical conductors in energy storage and transfer applications, chromophores in photoenabled chemical transformations, and beyond. In all cases, density functional theory (DFT) and higher-level methods for electronic structure determination provide valuable quantitative information about the electronic properties that underpin the functions of these frameworks. However, there are only two general modeling approaches in conventional electronic structure software packages: those that treat materials as extended, periodic solids, and those that treat materials as discrete molecules. Each approach has features and benefits; both have been widely employed to understand the emergent chemistry that arises from the formation of the metal-organic interface. This Review canvases these approaches to date, with emphasis placed on the application of electronic structure theory to explore reactivity and electron transfer using periodic, molecular, and embedded models. This includes (i) computational chemistry considerations such as how functional, k-grid, and other model variables are selected to enable insights into MOF properties, (ii) extended solid models that treat MOFs as materials rather than molecules, (iii) the mechanics of cluster extraction and subsequent chemistry enabled by these molecular models, (iv) catalytic studies using both solids and clusters thereof, and (v) embedded, mixed-method approaches, which simulate a fraction of the material using one level of theory and the remainder of the material using another dissimilar theoretical implementation.

Exploring the Tunability of Trimetallic MOF Nodes for Partial Oxidation of Methane to Methanol

Density functional theory is used to study the tunability of trigonal prismatic SBUs found in metal-organic frameworks (MOFs) such as MIL-100, MIL-101, and PCN-250/MIL-127 of chemical composition M³⁺₂M²⁺(μ₃-O)(RCOO)₆ for the partial oxidation of methane to methanol. We performed a combinatorial screening by varying the composition of the trimetallic node (M₁³⁺)₂(M₂²⁺) (where M₁ and M₂ = V, Cr, Mn, Fe, Co, and Ni) and calculated the reaction pathway on both M₁ and M₂ sites. The systematic replacement of metals in the trimetallic cluster allowed us to study the influence of spectator atoms on the catalytic activity of a specific metal site in the cluster toward the N₂O activation and C-H bond activation steps of the reaction. In the screening, we identified the top-performing node compositions with predicted barriers lower than those already reported for experimentally tested MOFs with trigonal prismatic SBUs. This work demonstrates the opportunity to tune the catalytic activity of MOFs for redox reactions by changing their metal node composition.

Density-functional theory models of Fe(iv)O reactivity in metal-organic frameworks: self-interaction error, spin delocalisation and the role of hybrid exchange

We study the reactivity of Fe(iv)O moieties supported by a metal-organic framework (MOF-74) in the oxidation reaction of methane to methanol using all-electron, periodic density-functional theory calculations. We compare results concerning the electronic properties and reactivity obtained using two hybrid (B3LYP and sc-BLYP) and two standard generalised gradient corrected (PBE and BLYP) semi-local density functional approximations. The semi-local functionals are unable to reproduce the expected reaction profiles and yield a qualitatively incorrect representation of the reactivity. Non-local hybrid functionals provide a substantially more reliable description and predict relatively modest (ca. 60 kJ mol^-1) reaction energy barriers for the H-atom abstraction reaction from CH₄ molecules. We examine the origin of these differences and we highlight potential means to overcome the limitations of standard semi-local functionals in reactivity calculations in solid-state systems.