Metal-organic frameworks invert molecular reactivity: Lewis acidic phosphonium zwitterions catalyze the Aldol-Tishchenko reaction.

The influence of metal–organic frameworks (MOFs) as additives is herein described for the reaction of n-alkyl aldehydes in the presence of methylvinylketone and triphenylphosphine. In the absence of a MOF, the expected Morita–Baylis–Hillman product, a β-hydroxy enone, is observed. In the presence of MOFs with UMCM-1 and MOF-5 topologies, the reaction is selective to Aldol-Tishchenko products, the 1 and 3 n-alkylesters of 2-alkyl-1,3-diols, which is unprecedented in organocatalysis. The (3-oxo-2-butenyl)triphenylphosphonium zwitterion, a commonly known nucleophile, is identified as the catalytic active species. This zwitterion favors nucleophilic character in solution, whereas once confined within the framework, it becomes an electrophile yielding Aldol-Tishchenko selectivity. Computational investigations reveal a structural change in the phosphonium moiety induced by the steric confinement of the framework that makes it accessible and an electrophile

Biporous Metal-Organic Framework with Tunable CO2/CH4 Separation Performance Facilitated by Intrinsic Flexibility.

In this work, we report the synthesis of SION-8, a novel metal-organic framework (MOF) based on Ca(II) and a tetracarboxylate ligand TBAPy4- endowed with two chemically distinct types of pores characterized by their hydrophobic and hydrophilic properties. By altering the activation conditions, we gained access to two bulk materials: the fully activated SION-8F and the partially activated SION-8P with exclusively the hydrophobic pores activated. SION-8P shows high affinity for both CO2 ( Qst = 28.4 kJ/mol) and CH4 ( Qst = 21.4 kJ/mol), while upon full activation, the difference in affinity for CO2 ( Qst = 23.4 kJ/mol) and CH4 ( Qst = 16.0 kJ/mol) is more pronounced. The intrinsic flexibility of both materials results in complex adsorption behavior and greater adsorption of gas molecules than if the materials were rigid. Their CO2/CH4 separation performance was tested in fixed-bed breakthrough experiments using binary gas mixtures of different compositions and rationalized in terms of molecular interactions. SION-8F showed a 40-160% increase (depending on the temperature and the gas mixture composition probed) of the CO2/CH4 dynamic breakthrough selectivity compared to SION-8P, demonstrating the possibility to rationally tune the separation performance of a single MOF by manipulating the stepwise activation made possible by the MOF's biporous nature

Origin of the strong interaction between polar molecules and copper(II) paddle-wheels in metal organic frameworks

The copper paddle-wheel is the building unit of many metal organic frameworks. Because of the ability of the copper cations to attract polar molecules, copper paddle-wheels are promising for carbon dioxide adsorption and separation. They have therefore been studied extensively, both experimentally and computationally. In this work we investigate the copper–CO2 interaction in HKUST-1 and in two different cluster models of HKUST-1: monocopper Cu(formate)2 and dicopper Cu2(formate)4. We show that density functional theory methods severely underestimate the interaction energy between copper paddle-wheels and CO2, even including corrections for the dispersion forces. In contrast, a multireference wave function followed by perturbation theory to second order using the CASPT2 method correctly describes this interaction. The restricted open-shell Møller–Plesset 2 method (ROS-MP2, equivalent to (2,2) CASPT2) was also found to be adequate in describing the system and used to develop a novel force field. Our parametrization is able to predict the experimental CO2 adsorption isotherms in HKUST-1, and it is shown to be transferable to other copper paddle-wheel systems

Accurate Characterization of the Pore Volume in Microporous Crystalline Materials

Pore volume is one of the main properties for the characterization of microporous crystals. It is experimentally measurable, and it can also be obtained from the refined unit cell by a number of computational techniques. In this work, we assess the accuracy and the discrepancies between the different computational methods which are commonly used for this purpose, i.e, geometric, helium, and probe center pore volumes, by studying a database of more than 5000 frameworks. We developed a new technique to fully characterize the internal void of a microporous material and to compute the probe-accessible and-occupiable pore volume. We show that, unlike the other definitions of pore volume, the occupiable pore volume can be directly related to the experimentally measured pore volumes from nitrogen isotherms

Photocatalytic Hydrogen Generation from a Visible-Light-Responsive Metal–Organic Framework System: Stability versus Activity of Molybdenum Sulfide Cocatalysts

We report the use of two earth abundant molybdenum sulfide-based cocatalysts, Mo(3)S(13)(2-)clusters and 1T-MoS2 nanoparticles (NPs), in combination with the visible-light active metal-organic framework (MOF) MIL-125-NH2 for the photocatalytic generation of hydrogen (H-2) from water splitting. Upon irradiation (lambda >= 420 nm), the best-performing mixtures of Mo3S132-/MIL-125-NH2 and 1T-MoS2/MIL-125-NH2 exhibit high catalytic activity, producing H-2 with evolution rates of 2094 and 1454 mu mol h(-1) g(MOF)(-1) and apparent quantum yields of 11.0 and 5.8% at 450 nm, respectively, which are among the highest values reported to date for visible-light-driven photocatalysis with MOFs. The high performance of Mo3S132- can be attributed to the good contact between these clusters and the MOF and the large number of catalytically active sites, while the high activity of 1T-MoS2 NPs is due to their high electrical conductivity leading to fast electron transfer processes. Recycling experiments revealed that although the Mo3S132-/MIL-125-NH2 slowly loses its activity, the 1T-MoS2/MIL-125-NH2 retains its activity for at least 72 h. This work indicates that earth-abundant compounds can be stable and highly catalytically active for photocatalytic water splitting, and should be considered as promising cocatalysts with new MOFs besides the traditional noble metal NPs

Metal-organic frameworks as kinetic modulators for branched selectivity in hydroformylation.

Finding heterogeneous catalysts that are superior to homogeneous ones for selective catalytic transformations is a major challenge in catalysis. Here, we show how micropores in metal-organic frameworks (MOFs) push homogeneous catalytic reactions into kinetic regimes inaccessible under standard conditions. Such property allows branched selectivity up to 90% in the Co-catalysed hydroformylation of olefins without directing groups, not achievable with existing catalysts. This finding has a big potential in the production of aldehydes for the fine chemical industry. Monte Carlo and density functional theory simulations combined with kinetic models show that the micropores of MOFs with UMCM-1 and MOF-74 topologies increase the olefins density beyond neat conditions while partially preventing the adsorption of syngas leading to high branched selectivity. The easy experimental protocol and the chemical and structural flexibility of MOFs will attract the interest of the fine chemical industries towards the design of heterogeneous processes with exceptional selectivity

Evaluating Charge Equilibration Methods To Generate Electrostatic Fields in Nanoporous Materials

Charge equilibration (Qeq) methods can estimate the electrostatic potential of molecules and periodic frameworks by assigning point charges to each atom, using only a small fraction of the resources needed to compute density functional (DFT)-derived charges. This makes possible, for example, the computational screening of thousands of microporous structures to assess their performance for the adsorption of polar molecules. Recently, different variants of the original Qeq scheme were proposed to improve the quality of the computed point charges. One focus of this research was to improve the gas adsorption predictions in metal-organic frameworks (MOFs), for which many different structures are available. In this work, we review the evolution of the method from the original Qeq scheme, understanding the role of the different modifications on the final output. We evaluated the result of combining different protocols and set of parameters, by comparing the Qeq charges with high quality DFT-derived DDEC charges for 2338 MOF structures. We focused on the systematic errors that are attributable to specific atom types to quantify the final precision that one can expect from Qeq methods in the context of gas adsorption where the electrostatic potential plays a significant role, namely, CO2 and H2S adsorption. In conclusion, both the type of algorithm and the input parameters have a large impact on the resulting charges, and we draw some guidelines to help the user to choose the proper combination of the two for obtaining a meaningful set of charges. We show that, considering this set of MOFs, the accuracy of the original Qeq scheme is often still comparable with the most recent variants, even if it clearly fails in the presence of certain atom types, such as alkali metals

Big-Data Science in Porous Materials: Materials Genomics and Machine Learning

By combining metal nodes with organic linkers we can potentially synthesize millions of possible metal organic frameworks (MOFs). At present, we have libraries of over ten thousand synthesized materials and millions of in-silico predicted materials. The fact that we have so many materials opens many exciting avenues to tailor make a material that is optimal for a given application. However, from an experimental and computational point of view we simply have too many materials to screen using brute-force techniques. In this review, we show that having so many materials allows us to use big-data methods as a powerful technique to study these materials and to discover complex correlations. The first part of the review gives an introduction to the principles of big-data science. We emphasize the importance of data collection, methods to augment small data sets, how to select appropriate training sets. An important part of this review are the different approaches that are used to represent these materials in feature space. The review also includes a general overview of the different ML techniques, but as most applications in porous materials use supervised ML our review is focused on the different approaches for supervised ML. In particular, we review the different method to optimize the ML process and how to quantify the performance of the different methods. In the second part, we review how the different approaches of ML have been applied to porous materials. In particular, we discuss applications in the field of gas storage and separation, the stability of these materials, their electronic properties, and their synthesis. The range of topics illustrates the large variety of topics that can be studied with big-data science. Given the increasing interest of the scientific community in ML, we expect this list to rapidly expand in the coming years.Comment: Editorial changes (typos fixed, minor adjustments to figures