Free-standing homochiral 2D monolayers by exfoliation of molecular crystals.

Two-dimensional materials with monolayer thickness and extreme aspect ratios are sought for their high surface areas and unusual physicochemical properties1. Liquid exfoliation is a straightforward and scalable means of accessing such materials2, but has been restricted to sheets maintained by strong covalent, coordination or ionic interactions3-10. The exfoliation of molecular crystals, in which repeat units are held together by weak non-covalent bonding, could generate a greatly expanded range of two-dimensional crystalline materials with diverse surfaces and structural features. However, at first sight, these weak forces would seem incapable of supporting such intrinsically fragile morphologies. Against this expectation, we show here that crystals composed of discrete supramolecular coordination complexes can be exfoliated by sonication to give free-standing monolayers approximately 2.3 nanometres thick with aspect ratios up to approximately 2,500:1, sustained purely by apolar intermolecular interactions. These nanosheets are characterized by atomic force microscopy and high-resolution transmission electron microscopy, confirming their crystallinity. The monolayers possess complex chiral surfaces derived partly from individual supramolecular coordination complex components but also from interactions with neighbours. In this respect, they represent a distinct type of material in which molecular components are all equally exposed to their environment, as if in solution, yet with properties arising from cooperation between molecules, because of crystallinity. This unusual nature is reflected in the molecular recognition properties of the materials, which bind carbohydrates with strongly enhanced enantiodiscrimination relative to individual molecules or bulk three-dimensional crystals.

Nanoscopic Insights into the Collapse of Metal-Organic Frameworks during Solvent Evacuation: Molecular Simulation Investigation.

Many metal-organic frameworks (MOFs) undergo structural collapse upon solvent evacuation during activation, which is attributed to the capillary force generated by the solvent. However, little effort has been devoted to unveiling the nature of such a force. Herein, we employ molecular dynamics (MD) simulations to investigate the evacuation of different solvents in two MOFs (MOF-5 and UMCM-9). The contractive stress induced by solvent evacuation is quantified and unraveled to positively correlate with the surface tension of the solvent. Moreover, the mechanical strength (or amorphization) of the MOF is calculated using reactive MD simulations. By comparing the contractive stress with the amorphization stress, for the first time, we predict the likelihood of collapse of MOFs during activation by different solvents, which agrees well with the experiments. The methodology developed provides nanoscopic insights into the activation process; it can assist in avoiding structural collapse by judiciously selecting a proper solvent for activation or by modifying a framework.

Chiral Covalent Organic Cages: Structural Isomerism and Enantioselective Catalysis.

Covalent organic cages are a prominent class of discrete porous architectures; however, their structural isomerism remains relatively unexplored. Here, we demonstrate the structural isomerism of chiral covalent organic cages that renders distinct enantioselective catalytic properties. Imine condensations of tetra-topic 5,10-di(3,5-diformylphenyl)-5,10-dihydrophenazine and ditopic 1,2-cyclohexanediamine produce two chiral [4 + 8] organic cage isomers with totally different topologies and geometries that depend on the orientations of four tetraaldehyde units with respect to each other. One isomer (PN-1) has an unprecedented Johnson-type J26 structure, whereas another (PN-2) adopts a tetragonal prismatic structure. After the reduction of the imine linkages, the cages are transformed into two amine bond-linked isomers PN-1R and PN-2R. After binding to Ni(II) ions, both can serve as efficient catalysts for asymmetric Michael additions, whereas PN-2R affords obviously higher enantioselectivity and reactivity than PN-1R presumably because of its large cavity and open windows that can concentrate reactants for the reactions. Density-functional theory (DFT) calculations further confirm that the enantioselective catalytic performance varies depending on the isomer.

Electrostatic Repulsion Facilitated Ion Transport in Covalent-Organic Framework Membranes.

Covalent-organic framework (COF) membranes are increasingly used for many potential applications including ion separation, fuel cells, and ion batteries. It is of central importance to fundamentally and quantitatively understand ion transport in COF membranes. In this study, a series of COF membranes is designed with different densities and arrangements of functional groups and subsequently utilize molecular simulation to provide microscopic insights into ion transport in these membranes. The membrane with a single-sided layer exhibits the highest chloride ion (Cl-) conductivity of 77.2 mS cm-1 at 30 °C. Replacing the single-sided layer with a double-sided layer or changing layer arrangement leads to a decrease in Cl- conductivity up to 33% or 53%, respectively. It is revealed that the electrostatic repulsion between ions serves as a driving force to facilitate ion transport and the positions of functional groups determine the direction of electrostatic repulsion. Furthermore, the ordered pores generate concentrated ions and allow rapid ion transport. This study offers bottom-up inspiration on the design of new COF membranes with moderate density and proper arrangement of functional groups to achieve high ion conductivity.

Accelerating Discovery of Water Stable Metal-Organic Frameworks by Machine Learning.

Metal-organic frameworks (MOFs) provide an extensive design landscape for nanoporous materials that drive innovation across energy and environmental fields. However, their practical applications are often hindered by water stability challenges. In this study, a machine learning (ML) approach is proposed to accelerate the discovery of water stable MOFs and validated through experimental test. First, the largest database currently available that contains water stability information of 1133 synthesized MOFs is constructed and categorized according to experimental stability. Then, structural and chemical descriptors are applied at various fragmental levels to develop ML classifiers for predicting the water stability of MOFs. The ML classifiers achieve high prediction accuracy and excellent transferability on out-of-sample validation. Next, two MOFs are experimentally synthesized with their water stability tested to validate ML predictions. Finally, the ML classifiers are applied to discover water stable MOFs in the ab initio REPEAT charge MOF (ARC-MOF) database. Among ≈280 000 candidates, ≈130 000 (47%) MOFs are predicted to be water stable; furthermore, through multi-stability analysis, 461 (0.16%) MOFs are identified as not only water stable but also thermal and activation stable. The ML approach is anticipated to serve as a prerequisite filtering tool to streamline the exploration of water stable MOFs for important practical applications.

Classifying and Predicting the Thermal Expansion Properties of Metal-Organic Frameworks: A Data-Driven Approach.

Metal-organic frameworks (MOFs) are versatile materials for a wide variety of potential applications. Tunable thermal expansion properties promote the application of MOFs in thermally sensitive composite materials; however, they are currently available only in a handful of structures. Herein, we report the first data set for thermal expansion properties of 33,131 diverse MOFs generated from molecular simulations and subsequently develop machine learning (ML) models to (1) classify different thermal expansion behaviors and (2) predict volumetric thermal expansion coefficients (αV). The random forest model trained on hybrid descriptors combining geometric, chemical, and topological features exhibits the best performance among different ML models. Based on feature importance analysis, linker chemistry and topological arrangement are revealed to have a dominant impact on thermal expansion. Furthermore, we identify common building blocks in MOFs with exceptional thermal expansion properties. This data-driven study is the first of its kind, not only constructing a useful data set to facilitate future studies on this important topic but also providing design guidelines for advancing new MOFs with desired thermal expansion properties.

Exploiting Metal-Organic Frameworks for Vinylidene Fluoride Adsorption: From Force Field Development, Computational Screening to Machine Learning.

Metal-organic frameworks (MOFs) represent a distinctive class of nanoporous materials with considerable potential across a wide range of applications. Recently, a handful of MOFs has been explored for the storage of environmentally hazardous fluorinated gases (Keasler et al. Science 2023, 381, 1455), yet the potential of over 100,000 MOFs for this specific application has not been thoroughly investigated, particularly due to the absence of an established force field. In this study, we develop an accurate force field for nonaversive hydrofluorocarbon vinylidene fluoride (VDF) and conduct high-throughput computational screening to identify top-performing MOFs with high VDF adsorption capacities. Quantitative structure-property relationships are analyzed via machine learning models on the combinations of geometric, chemical, and topological features, followed by feature importance analysis to probe the effects of these features on VDF adsorption. Finally, from detailed structural analysis via radial distribution functions and spatial densities, we elucidate the significance of different interaction modes between VDF and metal nodes in top-performing MOFs. By synergizing force-field development, computational screening, and machine learning, our findings provide microscopic insights into VDF adsorption in MOFs that will advance the development of new nanoporous materials for high-performance VDF storage or capture.

Computational design of metal hydrides on a defective metal-organic framework HKUST-1 for ethylene dimerization.

Catalytic ethylene dimerization to 1-butene is a crucial reaction in the chemical industry, as 1-butene is used for the production of most common plastics (e.g., polyethylene). With well-defined tuneable structures and unsaturated active sites, defective metal-organic frameworks have recently emerged as potential catalysts for ethylene dimerization. Herein, we computationally design a series of metal hydrides on defective HKUST-1 namely H-M-DHKUST-1 (M: Co, Ni, Cu, Ru, Rh and Pd), and subsequently assess their catalytic activity for ethylene dimerization by density functional theory calculations. Due to the antiferromagnetic behavior of dimeric metal-based clusters, we comprehensively investigate all possible multiplicity states on H-M-DHKUST-1 and observe multiplicity crossing. The ground-state reaction barriers for four elementary steps (initiation, C-C coupling, ß-hydride elimination and 1-butene desorption) are rationalized and C-C coupling is revealed to be the rate-determining step on H-Co-, H-Ni-, H-Ru-, H-Rh- and H-Pd-DHKUST-1. The energy barrier for ß-hydride elimination is found to be the lowest on H-Ru- and H-Rh-DHKUST-1, attributed to the weak stability of agostic arrangement; however, the energy barrier for 1-butene desorption is the highest on H-Rh-DHKUST-1. Among the designed H-M-DHKUST-1, Co- and Ni-based ones are predicted to exhibit the best overall catalytic performance. The mechanistic insights from this study may facilitate the development of new MOFs toward efficient ethylene dimerization and other industrially important reactions.

Manipulation of Cationic Group Density in Covalent Organic Framework Membranes for Efficient Anion Transport.

Anion exchange membranes with high anion conductivity are highly desired for electrochemical applications. Increasing ion exchange capacity is a straightforward approach to enhancing anion conductivity but faces a challenge in dimensional stability. Herein, we report the design and preparation of three kinds of isoreticular covalent organic framework (COF) membranes bearing tunable quaternary ammonium group densities as anion conductors. Therein, the cationic groups are integrated into the backbones by flexible ether-bonded alkyl side chains. The highly quaternary ammonium-group-functionalized building units endow COF membranes with abundant cationic groups homogeneously distributed in the ordered channels. The flexible side chains alleviate electrostatic repulsion and steric hindrance caused by large cationic groups, ensuring a tight interlayer stacking and multiple interactions. As a result, our COF membranes achieve a high ion exchange capacity and exceptional dimensional stability simultaneously. Furthermore, the effect of the ionic group density on the ion conductivity in rigid COF channels is systematically explored. Experiments and simulations reveal that the ionic group concentration and side chain mobility jointly determine the ion transport behavior, resulting in the abnormal phenomenon that the anion conductivity is not positively correlated to the ionic group density. The optimal COF membrane achieves the ever-reported highest hydroxide ion conductivity over 300 mS cm-1 at 80 °C and 100% RH. This study offers insightful guidelines on the rational design and preparation of high-performance anion conductors.

Designing Nanofluidic Diode from a Hybrid-Bilayer Covalent Organic Framework: Molecular Simulation Investigation.

Nanofluidic diodes are potentially useful in many important applications such as sensing, electronics, and energy conversion. However, the manufacturing of controllable nanopores for nanofluidic diodes is technically challenging. Herein, a nanofluidic diode is designed from a highly programmatic covalent organic framework (COF). Through molecular simulation, remarkable diode behavior is observed in a hybrid-bilayer COF but not in its constituent single-layer COFs. The rectification effect of ion current in the hybrid-bilayer COF is attributed to an asymmetric electrostatic potential across the COF nanopore. Furthermore, a synergistic effect of counterion is unraveled in the hybrid-bilayer COF, and the presence of counterion is found to reduce the entry barrier and facilitate ion transport. The performance of the hybrid-bilayer COF as a nanofluidic diode is comprehensively investigated by varying salt concentration, layer number, interlayer spacing, and slipping. This proof-of-concept simulation study demonstrates the feasibility of the hybrid-bilayer COF as a nanofluidic diode and the finding may stimulate the development of new nanofluidic platforms.

Fast water transport and molecular sieving through ultrathin ordered conjugated-polymer-framework membranes.

The development of membranes that block solutes while allowing rapid water transport is of great importance. The microstructure of the membrane needs to be rationally designed at the molecular level to achieve precise molecular sieving and high water flux simultaneously. We report the design and fabrication of ultrathin, ordered conjugated-polymer-framework (CPF) films with thicknesses down to 1 nm via chemical vapour deposition and their performance as separation membranes. Our CPF membranes inherently have regular rhombic sub-nanometre (10.3 × 3.7 Å) channels, unlike membranes made of carbon nanotubes or graphene, whose separation performance depends on the alignment or stacking of materials. The optimized membrane exhibited a high water/NaCl selectivity of ∼6,900 and water permeance of ∼112 mol m-2 h-1 bar-1, and salt rejection >99.5% in high-salinity mixed-ion separations driven by osmotic pressure. Molecular dynamics simulations revealed that water molecules quickly and collectively pass through the membrane by forming a continuous three-dimensional network within the hydrophobic channels. The advent of ordered CPF provides a route towards developing carbon-based membranes for precise molecular separation.

Leveraging Machine Learning for Metal-Organic Frameworks: A Perspective.

Metal-organic frameworks (MOFs) have attracted tremendous interest because of their tunable structures, functionalities, and physiochemical properties. The nearly infinite combinations of metal nodes and organic linkers have led to the synthesis of over 100,000 experimental MOFs and the construction of millions of hypothetical counterparts. It is intractable to identify the best candidates in the immense chemical space of MOFs for applications via conventional trial-to-error experiments or brute-force simulations. Over the past several years, machine learning (ML) has substantially transformed the way of MOF discovery, design, and synthesis. Driven by the abundant data from experiments or simulations, ML can not only efficiently and accurately predict MOF properties but also quantitatively derive structure-property relationships for rational design and screening. In this Perspective, we summarize recent achievements in leveraging ML for MOFs from the aspects of data acquisition, featurization, model training, and applications. Then, current challenges and new opportunities are discussed for the future exploration of ML to accelerate the development of new MOFs in this vibrant field.

Machine Learning-Assisted Design of Thin-Film Composite Membranes for Solvent Recovery.

Organic solvents are extensively utilized in industries as raw materials, reaction media, and cleaning agents. It is crucial to efficiently recover solvents for environmental protection and sustainable manufacturing. Recently, organic solvent nanofiltration (OSN) has emerged as an energy-efficient membrane technology for solvent recovery; however, current OSN membranes are largely fabricated by trial-and-error methods. In this study, for the first time, we develop a machine learning (ML) approach to design new thin-film composite membranes for solvent recovery. The monomers used in interfacial polymerization, along with membrane, solvent and solute properties, are featurized to train ML models via gradient boosting regression. The ML models demonstrate high accuracy in predicting OSN performance including solvent permeance and solute rejection. Subsequently, 167 new membranes are designed from 40 monomers and their OSN performance is predicted by the ML models for common solvents (methanol, acetone, dimethylformamide, and n-hexane). New top-performing membranes are identified with methanol permeance superior to that of existing membranes. Particularly, nitrogen-containing heterocyclic monomers are found to enhance microporosity and contribute to higher permeance. Finally, one new membrane is experimentally synthesized and tested to validate the ML predictions. Based on the chemical structures of monomers, the ML approach developed here provides a bottom-up strategy toward the rational design of new membranes for high-performance solvent recovery and many other technologically important applications.

Crystalline C-C and C═C Bond-Linked Chiral Covalent Organic Frameworks.

While crystalline covalent organic frameworks (COFs) linked by C-C bonds are highly desired in synthetic chemistry, it remains a formidable challenge to synthesize. Efforts to generate C-C single bonds in COFs via de novo synthesis usually afford amorphous structures rather than crystalline phases. We demonstrate here that C-C single bond-based COFs can be prepared by direct reduction of C═C bond-linked frameworks via crystal-to-crystal transformation. By Knoevenagel polycondensation of chiral tetrabenzaldehyde of dibinaphthyl-22-crown-6 with 1,4-phenylenediacetonitrile or 4,4'-biphenyldiacetonitrile, two olefin-linked chiral COFs with 2D layered tetragonal structure are prepared. Reduction of olefin linkages of the as-prepared CCOFs produces two C-C single bond linked frameworks, which retains high crystallinity and porosity as well as high chemical stability in both strong acids and bases. The quantitative reduction is confirmed by Fourier transform infrared and cross-polarization magic angle spinning 13C NMR spectroscopy. Compared to the pristine structures, the reduced CCOFs display blue-shifted emission with enhanced quantum yields and fluorescence lifetimes, while the parent CCOFs exhibit higher enantioselectivity than the reduced analogs when be used as fluorescent sensors to detect chiral amino alcohols via supramolecular interactions with the built-in crown ether moieties. This work provides an attractive strategy for making chemically stable functionalized COFs with new linkages that are otherwise hard to produce.

Highly Stable Zr(IV)-Based Metal-Organic Frameworks for Chiral Separation in Reversed-Phase Liquid Chromatography.

Separation of racemic mixtures is of great importance and interest in chemistry and pharmacology. Porous materials including metal-organic frameworks (MOFs) have been widely explored as chiral stationary phases (CSPs) in chiral resolution. However, it remains a challenge to develop new CSPs for reversed-phase high-performance liquid chromatography (RP-HPLC), which is the most popular chromatographic mode and accounts for over 90% of all separations. Here we demonstrated for the first time that highly stable Zr-based MOFs can be efficient CSPs for RP-HPLC. By elaborately designing and synthesizing three tetracarboxylate ligands of enantiopure 1,1'-biphenyl-20-crown-6, we prepared three chiral porous Zr(IV)-MOFs with the framework formula [Zr6O4(OH)8(H2O)4(L)2]. They share the same flu topological structure but channels of different sizes and display excellent tolerance to water, acid, and base. Chiral crown ether moieties are periodically aligned within the framework channels, allowing for stereoselective recognition of guest molecules via supramolecular interactions. Under acidic aqueous eluent conditions, the Zr-MOF-packed HPLC columns provide high resolution, selectivity, and durability for the separation of a variety of model racemates, including unprotected and protected amino acids and N-containing drugs, which are comparable to or even superior to several commercial chiral columns for HPLC separation. DFT calculations suggest that the Zr-MOF provides a confined microenvironment for chiral crown ethers that dictates the separation selectivity.

Concluding remarks: Cooperative phenomena in framework materials.

The Faraday Discussion on Cooperative Phenomena in Framework Materials took place online on 13-16 October 2020. At this unique meeting, there were impressive presentations and stimulating discussions on the current state and future direction of cooperative phenomena in framework materials, particularly flexible metal-organic frameworks or porous coordination polymers. This article aims to highlight the presentations and achievements at the meeting, and also discuss personal perspectives on the fundamental challenges for future exploration in this vibrant field.

Catalytic amino acid production from biomass-derived intermediates.

Amino acids are the building blocks for protein biosynthesis and find use in myriad industrial applications including in food for humans, in animal feed, and as precursors for bio-based plastics, among others. However, the development of efficient chemical methods to convert abundant and renewable feedstocks into amino acids has been largely unsuccessful to date. To that end, here we report a heterogeneous catalyst that directly transforms lignocellulosic biomass-derived α-hydroxyl acids into α-amino acids, including alanine, leucine, valine, aspartic acid, and phenylalanine in high yields. The reaction follows a dehydrogenation-reductive amination pathway, with dehydrogenation as the rate-determining step. Ruthenium nanoparticles supported on carbon nanotubes (Ru/CNT) exhibit exceptional efficiency compared with catalysts based on other metals, due to the unique, reversible enhancement effect of NH3 on Ru in dehydrogenation. Based on the catalytic system, a two-step chemical process was designed to convert glucose into alanine in 43% yield, comparable with the well-established microbial cultivation process, and therefore, the present strategy enables a route for the production of amino acids from renewable feedstocks. Moreover, a conceptual process design employing membrane distillation to facilitate product purification is proposed and validated. Overall, this study offers a rapid and potentially more efficient chemical method to produce amino acids from woody biomass components.

Highly Selective CO2 Conversion to Methanol in a Bifunctional Zeolite Catalytic Membrane Reactor.

The hydrogenation of sequestrated CO2 to methanol can reduce CO2 emission and establish a sustainable carbon circuit. However, the transformation of CO2 into methanol is challenging because of the thermodynamic equilibrium limitation and the deactivation of catalysts by water. In the present work, different reactor types have been evaluated for CO2 catalytic hydrogenation to methanol. Best results have been obtained in a bifunctional catalytic membrane reactor (CMR) based on a zeolite LTA membrane and a catalytic Cu-ZnO-Al2 O3 -ZrO2 layer on top. Due to the in situ and rapid removal of the produced water from the catalytic layer through the hydrophilic zeolite LTA membrane, it is effective to break the thermodynamic equilibrium limitation, thus significantly increasing the CO2 conversion (36.1 %) and methanol selectivity (100 %). Further, the catalyst deactivation by the produced water can be effectively inhibited, thus maintaining a high long-term activity of the CMR.

Confinement-Driven Enantioselectivity in 3D Porous Chiral Covalent Organic Frameworks.

3D covalent organic frameworks (COFs) with well-defined porous channels are shown to be capable of inducing chiral molecular catalysts from non-enantioselective to highly enantioselective in catalyzing organic transformations. By condensations of a tetrahedral tetraamine and two linear dialdehydes derived from enantiopure 1,1'-binaphthol (BINOL), two chiral 3D COFs with a 9-fold or 11-fold interpenetrated diamondoid framework are prepared. Enhanced Brønsted acidity was observed for the chiral BINOL units that are uniformly distributed within the tubular channels compared to the non-immobilized acids. This facilitates the Brønsted acid catalysis of cyclocondensation of aldehydes and anthranilamides to produce 2,3-dihydroquinazolinones. DFT calculations show the COF catalyst provides preferential secondary interactions between the substrate and framework to induce enantioselectivities that are not achievable in homogeneous systems.

Self-Assembly of Highly Stable Zirconium(IV) Coordination Cages with Aggregation Induced Emission Molecular Rotors for Live-Cell Imaging.

The self-assembly of highly stable zirconium(IV)-based coordination cages with aggregation induced emission (AIE) molecular rotors for in vitro bio-imaging is reported. The two coordination cages, NUS-100 and NUS-101, are assembled from the highly stable trinuclear zirconium vertices and two flexible carboxyl-decorated tetraphenylethylene (TPE) spacers. Extensive experimental and theoretical results show that the emissive intensity of the coordination cages can be controlled by restricting the dynamics of AIE-active molecular rotors though multiple external stimuli. Because the two coordination cages have excellent chemical stability in aqueous solutions (pH stability: 2-10) and impressive AIE characteristics contributed by the molecular rotors, they can be employed as novel biological fluorescent probes for in vitro live-cell imaging.